EQC Quality Assessment: "Satellite fire burned area completeness for seasonal climatology maps"
Dataset: satellite-fire-burned-area [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Satellite fire burned area completeness for seasonal climatology maps > Quality assessment statement
---
These are the key outcomes of this assessment

* The dataset accurately reflects the burned area values observed during significant fire events, such as the summer of 2003 in Portugal, with a high level of precision. The burned area recorded (more than 3500 km² in Centro PT and Alentejo regions) is consintent with the Portuguese national statistics and findings by Lourenço and Luciano (2018). This consistency suggests that the dataset accurately represents extreme fire events.

* The results of this notebook shows that the spatial resolution is sufficient to detect regional differences in burned area, and the temporal resolution is adequate to highlight annual and seasonal variations, ensuring a comprehensive understanding of fire patterns over time.

* The stability of the dataset is evidenced by its consistency with other credible sources, such as Grünig et al., 2023, which supports the findings of significant burned areas in key regions during specific years. The dataset reliably identifies both extreme fire events and regions with consistently low burned areas, such as La Rioja and Murcia.

attachment:b0838746-6694-43a3-8244-486eb1346215.png
---
height: 500px
---
The figure shows the regional climatology of the burned area over the period 2001 to 2019. The averaged burned area, and corresponding spatial pattern, can serve as a baseline for wildfire prevention and monitoring.
```

## 📋 Methodology

[](code-section-1)

[](code-section-2)

[](code-section-3)

[](code-section-4)

- [](code-section-4.1)

- [](code-section-4.2)

- [](code-section-4.3)

[](code-section-5)

## 📈 Analysis and results

(code-section-1)=
### 1. Define the AoI, search and download FIRE dataset
Before we begin, we must prepare our environment. This includes installing the Application Programming Interface (API) of the CDS and importing the various python libraries that we will need.

#### Install CDS API
To install the CDS API, run the following command. We use an exclamation mark to pass the command to the shell (not to the Python interpreter).
If you already have the CDS API installed, you can skip or comment this step.

#### Import all the libraries/packages

We will be working with data in NetCDF format. To best handle this data we will use libraries for working with multidimensional arrays, in particular Xarray. We will also need libraries for plotting and viewing data, in this case we will use Matplotlib and Cartopy.

#### Data overview

To search for data, visit the CDS website: http://cds.climate.copernicus.eu Here you can search for 'Satellite observations' data using the search bar. The data we need for this tutorial is the Fire burned area from 2001 to present derived from satellite observations. This data provides global (at grid scale) and continents (at pixel scale) information of total burned area (BA), following the Global Climate Observing System (GCOS) convention. The dataset is provided with a high spatial resolution, through the analysis of reflectance changes from medium resolution sensors (Terra MODIS, Sentinel-3 OLCI) supported by thermal information.

The BA is identified by the date of the first detection of the burned signal in the case of the pixel product, and by the total BA per grid cell in the case of the grid product. Information such as land cover class, confidence level or standard error are also provided.

The temporal resolution varies per version, from 15 days (at grid scale) to monthly (at pixel scale), and its vertical resolution corresponds to the surface (single level).

Different versions are available, offering the first global BA time series at 250 m resolution (FireCCI v5.0cds and FireCCI v5.1cds developed as part of the Fire ECV Climate Change Initiative Project (Fire CCI)). This algorithm was adapted to Sentinel-3 OLCI data to create the C3S v1.0 burned area product, extending the BA database to the present.

Having selected the correct dataset, we now need to specify what product type, variables, temporal and geographic coverage we are interested in. In this Use case, we will retrieve information of the grid ESA-CCI version. These can all be selected in the “Download data” tab from the CDS. In this tab a form appears in which we will select the following parameters to download, for example:

- Origin: ESA-CCI
- Sensor: MODIS
- Variable: Grid
- Version: 5.1.1cds
- Region: all
- Year: 2001 to 2019
- Month: all
- Nominal day: 01
- Format: Zip file (.zip)


EQC Quality Assessment: "Satellite fire burned area completeness for seasonal climatology maps"
Dataset: satellite-fire-burned-area [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Satellite fire burned area completeness for seasonal climatology maps > Quality assessment statement
---
- Version: 5.1.1cds
- Region: all
- Year: 2001 to 2019
- Month: all
- Nominal day: 01
- Format: Zip file (.zip)

At the end of the download form, select `Show API request`. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook.

Having copied the API request to a Jupyter Notebook cell, running it will retrieve and download the data you requested into your local directory. However, before you run it, the terms and conditions of this particular dataset need to have been accepted directly at the CDS website. The option to view and accept these conditions is given at the end of the download form, just above the `Show API request` option. In addition, it is also useful to define the time period and AoI parameters and edit the request accordingly, as exemplified in the cells below.

Region of interest
Shapefile with regions

Define request

Download and regionalize
Reindex using year/month (shift months + 1)
Convert units from m2 to km2

(code-section-2)=
### 2. Inspect and view data

Now that we have downloaded the data, we can inspect it. We have requested the data in NetCDF format. This is a commonly used format for array-oriented scientific data. To read and process this data we will make use of the Xarray library. Xarray is an open source project and Python package that makes working with labelled multi-dimensional arrays simple and efficient. We will read the data from our NetCDF file into an xarray.Dataset.

Let's inspect the years to see which have the highest and the lowest values of burned area.

Plot BA time series

From these statistics and the corresponding maps, we can conclude that 2003 and 2018 have the highest and the lowest sum burned area values (respectively). We can also see some hotspot regions in the centre of Portugal in August.

(code-section-3)=
### 3. Define the unit of analysis: Nomenclature of territorial units for statistics, level 2 (NUTS 2)

The [NUTS](https://ec.europa.eu/eurostat/web/nuts) are a hierarchical system divided into 3 levels. NUTS 1 correspond to major socio-economic regions, NUTS 2 correspond to basic regions for the application of regional policies, and NUTS 3 correspond to small regions for specific diagnoses. Additionally, a NUTS 0 level, usually co-incident with national boundaries is also available. The NUTS legislation is periodically amended; therefore multiple years are available for download.

In this user question, it is used the most updated version of the NUTS corresponding to version of 2021. In this study case, NUTS 2 will be used, providing the information regarding the main regions/parcels of the Iberian Peninsula.

Note: The sum of burned area and the climatology are calculated per each NUTS 2 region. Generated maps, bar charts and plots will take some minutes to process all the months, years and regions.

Merging BA with NUTS 2 geodataframe

(code-section-4)=
### 4. Annual Summaries and Summer Climatology Analysis of the Burned Area (JJA of 2001-2019)

The step below produces the Annual Summaries of BA by representing the Bar Charts and the Maps of the Sum of the Burned Area per NUTS 2, per month/year. In this case, we are analysing the Summer (JJA) months, which is when wildfires tend to be more frequent, in the Iberian Peninsula Region.

In addition, the Summer Climatology for BA aims to provide an overview of fire activity over a long-term period, such as multiple years or decades.

According to the European Environment Agency (EEA (2021) Europe's changing climate hazards — an index-based interactive EEA report, Report no. 15/2021, EEA. DOI: 10.2800/458052) methodology, the following steps are conducted:
- the sum of Burned Area is calculated for each year within the defined period;
- the resulting timeseries is average across the number of years.

This average BA represents the typical magnitude of burned areas during the summer period (June, July and August, from 2001 to 2019).

The Annual Summaries and Summer Climatology are represented in several ways, such as maps, bar charts and timeseries plots.

(code-section-4.1)=
#### 4.1. Annual Summaries - Maps and Bar Charts of total Burned Area for the summer season, per NUTS regions per year

Ploting maps and bar charts of annual BA by NUTS 2 region, considering the Summer season


EQC Quality Assessment: "Satellite fire burned area completeness for seasonal climatology maps"
Dataset: satellite-fire-burned-area [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Satellite fire burned area completeness for seasonal climatology maps > Quality assessment statement
---
. Annual Summaries - Maps and Bar Charts of total Burned Area for the summer season, per NUTS regions per year

Ploting maps and bar charts of annual BA by NUTS 2 region, considering the Summer season

From the above set of plots, we can already distinguish certain years and areas more prone to extreme wildfire events. To better analyse its temporal pattern, let's plot the annual sum as a time series plot - this plot is equivalent to the first one in this notebook, but now we have added the NUTS 2 regions to facilitate our understanding of which regions have greater BA and in which years.

(code-section-4.2)=
#### 4.2. Annual Summaries - Timeseries of total Burned Area for the summer season, per NUTS regions per year

Ploting annual timeseries of BA by NUTS 2 regions, considering the Summer season

Having looked into this time series, we can clearly see that the highest BA values are reached in certain regions, such as 'Centro (PT)', 'Alentejo' or 'Norte', in Portugal, or 'Andalucía' and 'Extremadura', in Spain. However, we can also see that there is no indication of an increase in BA, in recent years. Now let's plot the time series per region to see this in more detail. We will also use a colour code for month to highlight which are characterized by higher BA values.

List of regions with higher BA
Ploting annual timeseries of BA by NUTS 2 regions and months, considering the Summer season
print(region)

From these plots, we can now confirm that the month of August is when more BA is detected in the product, which agrees with the known climate seasonality of the region. We will now compute the climatology per season to see typical spatial patterns of the fire BA, together with a bar chart with climate BA values per NUTS 2.

(code-section-4.3)=
#### 4.3. Seasonal Climatology (2001-2019) - Map and Bar Chart of the average total Burned Area for the summer season, per NUTS regions

With this final plot we can see what the average BA is, and the corresponding spatial pattern, to serve as a baseline for wildfire prevention and monitoring.

(code-section-5)=
### 5. Main takeaways

- In particular, during the summer of 2003 the highest value of burned area is observed in Portugal (more specifically, in Centro PT and Alentejo regions), accounting for more than 3500 km², which agrees with known extreme values of burned area occurring in that year (as per the Portuguese national statistics on burned area, available at www.pordata.pt). This results also are consisten with, [Lourenço and Luciano (2018)](https://doi.org/10.4000/MEDITERRANEE.9958), which recorded a burned area for Portugal with values similar with this analysis (more that 4000 km² of burned area); hence, the results presented in this study show that the dataset depicts, in a very clear way, the events described.

- Considering the Annual Summary analysis for the NUTS 2 regions, it can be concluded that Centro PT, Alentejo, Norte, Andalucía and Galicia are the regions that registered the highest values of burned area (this statement it is supported by the study of [Grünig et. al., 2023](https://doi.org/10.1111/GCB.16547), which found a largest average maximum fire size recorded for Portugal and highest average maximum burn severity for Spain), during the Summer months (JJA), from 2001 to 2019 - 2233 km²(2003, in Centro PT, Portugal), 1326 km² (2003, in Alentejo, Portugal), 1301 km² (2005, in Norte, Portugal), 840 km² (2004, in Andalucía, Spain), and 1117 km² (2006, in Galicia, Spain).

- By contrast, several regions show consistently absence or very low values of burned area for the same time period - e.g., La Rioja only had a total of 9 km² of burned area occuring in only 4 out of the 19 years; equivalent pattern can be described for the Region of Murcia, which only had a total of 11 km² of burned area occurring in only 5 out of the 19 years.

## ℹ If you want to know more

### Key resources

Some key resources and further reading were linked throughout this assessment.

The CDS catalogue entry for the data used were:

* Fire burned area from 2001 to present derived from satellite observations:

https://cds.climate.copernicus.eu/datasets/satellite-fire-burned-area?tab=overview


EQC Quality Assessment: "Satellite fire burned area completeness for seasonal climatology maps"
Dataset: satellite-fire-burned-area [CDS]
Aspect: climate-and-weather-extremes_q01  |  Category: Satellite_ECVs
Section: Satellite fire burned area completeness for seasonal climatology maps > Quality assessment statement
---
catalogue entry for the data used were:

* Fire burned area from 2001 to present derived from satellite observations:

https://cds.climate.copernicus.eu/datasets/satellite-fire-burned-area?tab=overview

EEA FIRE Climatology Methodology:
* https://www.eea.europa.eu/publications/europes-changing-climate-hazards-1

FIRE dataset Product User Guide (PUG):
* https://dast.copernicus-climate.eu/documents/satellite-fire-burned-area/D3.3.14-v1.0_PUGS_CDR_BA-FireCCI_MODIS_v5.1cds_PRODUCTS_v1.0.1.pdf

Eurostat NUTS (Nomenclature of territorial units for statistics) regions and definition link:
* https://ec.europa.eu/eurostat/web/nuts

Code library used
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`, prepared by [B-Open](https://www.bopen.eu/)

### References

[[1]](https://doi.org/10.1007/s00704-023-04427-y) Bento, Virgílio A., Ana Russo, Inês Vieira, and Célia M. Gouveia. 2023. “Identification of Forest Vulnerability to Droughts in the Iberian Peninsula.” Theoretical and Applied Climatology 152 (1–2): 559–79.

[[2]](https://doi.org/10.4000/MEDITERRANEE.9958) Lourenço, and Luciano. 2018. “Forest Fires in Continental PortugalResult of Profound Alterations in Society and Territorial Consequences”. Méditerranée (Online): 130 | 2018.

[[3]](https://doi.org/10.1111/GCB.16547) Grünig, Marc, Rupert Seidl, and Cornelius Senf. 2023. “Increasing Aridity Causes Larger and More Severe Forest Fires across Europe.” Global Change Biology 29 (6): 1648–59.


EQC Quality Assessment: "Satellite fire burned area trends assessment for climate monitoring"
Dataset: satellite-fire-burned-area [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite fire burned area trends assessment for climate monitoring > Quality assessment question
---
* **How well can we disclose what are the trends and quality of the total burned area over the Iberian Peninsula?**

In this Use Case we will access the Fire burned area from 2001 to present derived from satellite observations (henceforth, FIRE) data from the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S) and analyse the spatial patterns of the FIRE reliability over a given Area of Interest (AoI), considering the Nomenclature of Territorial Units for Statistics, at the level 2, which are used for decision monitoring and decision-making purposes (see section 3).


EQC Quality Assessment: "Satellite fire burned area trends assessment for climate monitoring"
Dataset: satellite-fire-burned-area [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite fire burned area trends assessment for climate monitoring > Quality assessment statement
---
These are the key outcomes of this assessment

* The dataset shows strong spatial completeness by covering key NUTS 2 regions and temporal completeness over an 18-year period. However, this study focuses only on the summer; hence, significant fire events outside of this season are not accounted for.

* The spatial resolution is sufficient to detect temporal and regional variations. For example, the dataset accurately reflects the burned area values observed during significant fire events, such as the summer of 2003 in Portugal. The burned area recorded (more than 200 km² in Centro PT region) is consistent with the Portuguese national statistics and findings by [[2]](https://doi.org/10.4000/MEDITERRANEE.9958) and [[10]](https://doi.org/10.3390/cli12090143).

* Furthermore, the trend analysis indicates the absence of significant trends across the studied areas. These results are consistent those described in the literature, by [[8]](https://doi.org/10.1038/s41598-019-50281-2), where the authors showed non-significant changes of the Burned Area in Portugal (June to October, from 1980 to 2017).

* These results are also consistent across different product versions, since a good global agreement has been described between FireCCI51, MCD64A1 c6 and GFED4 (all based on MODIS information), particularly starting in 2003 when both MODIS Terra and Aqua data were available. ([[9]](https://climate.esa.int/en/projects/fire/key-documents/); page 45)

## 📋 Methodology

**[](code-section-1)**

**[](code-section-2)**

* **[](code-section-2.1)**

* **[](code-section-2.2)**

**[](code-section-3)**

## 📈 Analysis and results

(code-section-1)=
### 1. Data overview, download and NUTS regions definition

#### Import all the libraries/packages

We will be working with data in NetCDF format. To best handle this data we will use libraries for working with multidimensional arrays, in particular Xarray. We will also need libraries for plotting and viewing data, in this case we will use Matplotlib and Cartopy.

#### Data overview

To search for data, visit the CDS website: http://cds.climate.copernicus.eu Here you can search for 'Satellite observations' data using the search bar. The data we need for this tutorial is the Fire burned area from 2001 to present derived from satellite observations. This data provides global (at grid scale) and continents (at pixel scale) information of total burned area (BA), following the Global Climate Observing System (GCOS) convention. The dataset is provided with a high spatial resolution, through the analysis of reflectance changes from medium resolution sensors (Terra MODIS, Sentinel-3 OLCI) supported by thermal information.

The BA is identified by the date of the first detection of the burned signal in the case of the pixel product, and by the total BA per grid cell in the case of the grid product. Information such as land cover class, confidence level or standard error are also provided.

The BA products can be on pixel scale and grid scale, based on resolution and coverage. At the grid scale, data is provided globally with a horizontal resolution of 0.25° latitude by 0.25° longitude, offering a coarser spatial resolution that is suitable for broad, global analyses.

This grid product data is available every 15 days in version 5.0cds and monthly in versions 5.1.1cds and 1.1.

Conversely, at the pixel scale, data is more detailed, covering continents with a finer horizontal resolution of 250m or 300m, and is aggregated on a monthly basis. This pixel product provides higher spatial resolution suitable for more localized, detailed studies.

Different versions are available, offering the first global BA time series at 250 m resolution (FireCCI v5.0cds and FireCCI v5.1cds developed as part of the Fire ECV Climate Change Initiative Project (Fire CCI)). This algorithm was adapted to Sentinel-3 OLCI data to create the C3S v1.1 burned area product, extending the BA database to the present.

Having selected the correct dataset, we now need to specify what product type, variables, temporal and geographic coverage we are interested in. In this Use case, we will retrieve information of the grid ESA-CCI version. These can all be selected in the `Download data` tab from the CDS. In this tab a form appears in which we will select the following parameters to download, for example:


EQC Quality Assessment: "Satellite fire burned area trends assessment for climate monitoring"
Dataset: satellite-fire-burned-area [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite fire burned area trends assessment for climate monitoring > Quality assessment statement
---
we will retrieve information of the grid ESA-CCI version. These can all be selected in the `Download data` tab from the CDS. In this tab a form appears in which we will select the following parameters to download, for example:

- Origin: ESA-CCI
- Sensor: MODIS
- Variable: Grid
- Version: 5.1.1cds
- Region: all
- Year: 2001 to 2019
- Month: all
- Nominal day: 01
- Format: Zip file (.zip)

At the end of the download form, select `Show API request`. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook-

Having copied the API request to a Jupyter Notebook cell, running it will retrieve and download the data you requested into your local directory. However, before you run it, the terms and conditions of this particular dataset need to have been accepted directly at the CDS website. The option to view and accept these conditions is given at the end of the download form, just above the `Show API request` option. In addition, it is also useful to define the time period and AoI parameters and edit the request accordingly, as exemplified in the cells below.

Region of interest
Shapefile with regions

Define request

Download and regionalize
Reindex using year/month (shift months + 1)
Convert units from m2 to km2

```text
100%|██████████| 19/19 [00:02<00:00,  7.97it/s]
```

#### Define the unit of analysis: Nomenclature of territorial units for statistics, level 2 (NUTS 2)

The [NUTS](https://ec.europa.eu/eurostat/web/nuts) are a hierarchical system divided into 3 levels. NUTS 1 correspond to major socio-economic regions, NUTS 2 correspond to basic regions for the application of regional policies, and NUTS 3 correspond to small regions for specific diagnoses. Additionally, a NUTS 0 level, usually co-incident with national boundaries is also available. The NUTS legislation is periodically amended; therefore multiple years are available for download.

In this user question, NUTS 2 will be used, providing the information regarding the main regions/parcels of the Iberian Peninsula.

Note: The sum of burned area and the climatology are calculated per each NUTS 2 region. Generated maps, bar charts and plots will take some minutes to process all the months, years and regions.

Merging BA with NUTS 2 geodataframe

(code-section-2)=
### 2. Trend Analysis and Slope Calculation
#### Mann-Kendall Test and Theil-Senn slope

The Theil-Sen (1968) trend analysis, a non-parametric method, calculates the median slope of all data point pairs. It's a robust technique for trend identification, especially in the presence of outliers and noise. The Theil-Sen estimator offers several advantages, such as simplicity, resistance to extreme values, and statistical validity. This makes it a preferred choice for trend analysis in various applications
The Sen slope estimator is found to be a powerful tool to develop the linear relationships. Sen’s slope has the advantage over the slope of regression, in the sense that gross data series errors and outliers do not have much effect.

The Mann-Kendall test (Kendall, 1975; Mann, 1945) is a non-parametric test that determines whether there is a statistically significant upward or downward trend (monotonic trend) over time. Kendall's Tau (τ) is a measure of the strength and direction of monotonic trends in time series data. It is often used in environmental studies, hydrology, climatology, and various other fields to assess trends in data where the relationship may not be linear and where non-parametric methods are preferred and is often referred to as Kendall's Tau or Kendall's Rank Correlation Coefficient.

The τ value can be either positive or negative. It quantifies the strength and direction of the trend in the data.

* A positive tau (τ > 0) indicates an increasing or upward trend.
* A negative tau (τ < 0) indicates a decreasing or downward trend.
* A tau value close to zero (|τ| ≈ 0) suggests no significant trend or a random pattern in the data.

In addition to τ value, the Mann-Kendall test provides a p-value that indicates whether the observed trend is statistically significant. A low p-value (typically less than a chosen significance level, e.g., 0.05) suggests that the trend is statistically significant.

These methods were therefore adopted below to estimate trends and their statistical significance.

(code-section-2.1)=
### 2.1. Annual Trend Analysis and Slope Calculation for specific hotspot regions


EQC Quality Assessment: "Satellite fire burned area trends assessment for climate monitoring"
Dataset: satellite-fire-burned-area [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite fire burned area trends assessment for climate monitoring > Quality assessment statement
---
) suggests that the trend is statistically significant.

These methods were therefore adopted below to estimate trends and their statistical significance.

(code-section-2.1)=
### 2.1. Annual Trend Analysis and Slope Calculation for specific hotspot regions

To illustrate the burned area trends, time series of the total burned area over four regions are shown (Galicia (ES), Andalucía (ES), Norte (PT) and Centro (PT)). For each time series, the Theil-Sen trend line is plotted and the values of its slope and intercept are provided, together with τ and the p-value, both returned by the Mann-Kendall test

Computation and definition of Mean-Kendall and Theil-Sen Statistics

Function for ploting trends

The results show no significant trends, and a very high interannual variability. In general, these results largely agree with those of [[6]](https://doi.org/10.3390/FIRE4040074), although they used different periods, performed a global analysis and used a more sophisticated trend analysis scheme. They showed that the CCI datasets exhibit much less significant trends when compared to longer term datasets (such as those based in AVHRR observations), mainly due to the short time span of the timeseries.

(code-section-2.2)=
### 2.2. Yearly Trend Maps by NUTS regions.

Next, a regional view of these results is provided in map form, where the trends and their significance were calculated for each NUTS region.

Create a proxy artist for the legend handle
Turn off the axis
Show the legend with the proxy artist

Regarding this map, it can be stated that the same conclusions apply to all NUTS regions over the Iberian Peninsula, as none of them exhibits significant burned area trends over the considered period, likely because the trends may not be robust due to the short time span of the time series. As discussed in sections 3.2.1 and 3.2.2 of [[6]](https://doi.org/10.3390/FIRE4040074), longer time series are crucial for detecting significant trends in burned area, as shorter periods are often subject to greater variability and noise.

This aligns with findings from [[5]](https://doi.org/10.1088/2515-7620/AB25D2), which highlight that satellite datasets with limited temporal coverage can lead to inconsistencies and non-significant trends due to year-to-year fluctuations and sensor changes. Therefore, to obtain reliable and significant trends, it is recommended to utilize longer datasets that span several decades.

(code-section-3)=
### 3. Main takeaways

- The trend analysis across the studied NUTS 2 regions (Galicia, Andalucía, Norte, and Centro) for the summer months (June, July, August) from 2001 to 2019 revealed no significant trends in the burned area. This lack of significance suggests that the observed variations in burned area are largely due to interannual variability rather than a consistent temporal trend.

- The results align with findings from previous studies (e.g., [[6]](https://doi.org/10.3390/FIRE4040074)) that emphasize the importance of longer time series for trend detection. The short 18-year span of the dataset used in this analysis, particularly focused on the summer months, likely contributes to the absence of significant trends. Longer datasets would reduce the influence of year-to-year variability and provide more robust trend analysis.

- The absence of significant trends in the Iberian Peninsula's burned area is consistent with other research, such as [[8]](https://doi.org/10.1038/s41598-019-50281-2), which also found non-significant changes in burned areas in Portugal over a similar period (1980-2017). This reinforces the notion that short-term datasets are insufficient for capturing long-term trends in fire activity.

- Despite the lack of significant trends, the analysis demonstrates the reliability of the satellite-based dataset in capturing regional variations in burned area, particularly during significant fire events (e.g., the 2003 fires in Portugal). However, the non-significant trends suggest that more comprehensive datasets with broader temporal coverage are necessary for definitive conclusions.

- To achieve a more accurate and significant understanding of trends in burned areas, future studies should utilize longer datasets that span several decades. This would help mitigate the impact of short-term fluctuations and provide a clearer picture of fire activity trends over time.

## ℹ If you want to know more

### Key resources

Some key resources and further reading were linked throughout this assessment.

The CDS catalogue entry for the data used were:

* Fire burned area from 2001 to present derived from satellite observations:

https://cds.climate.copernicus.eu/datasets/satellite-fire-burned-area?tab=overview

EEA FIRE Climatology Methodology:
* https://www.eea.europa.eu/publications/europes-changing-climate-hazards-1


EQC Quality Assessment: "Satellite fire burned area trends assessment for climate monitoring"
Dataset: satellite-fire-burned-area [CDS]
Aspect: trend-assessment_q02  |  Category: Satellite_ECVs
Section: Satellite fire burned area trends assessment for climate monitoring > Quality assessment statement
---
ernicus.eu/datasets/satellite-fire-burned-area?tab=overview

EEA FIRE Climatology Methodology:
* https://www.eea.europa.eu/publications/europes-changing-climate-hazards-1

FIRE dataset Product User Guide (PUG):
* https://dast.copernicus-climate.eu/documents/satellite-fire-burned-area/D3.3.14-v1.0_PUGS_CDR_BA-FireCCI_MODIS_v5.1cds_PRODUCTS_v1.0.1.pdf

Eurostat NUTS (Nomenclature of territorial units for statistics) regions and definition link:
* https://ec.europa.eu/eurostat/web/nuts

Code library used
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`, prepared by [B-Open](https://www.bopen.eu/)

### References

[[1]](https://doi.org/10.1007/s00704-023-04427-y) Bento, Virgílio A., Ana Russo, Inês Vieira, and Célia M. Gouveia. 2023. “Identification of Forest Vulnerability to Droughts in the Iberian Peninsula.” Theoretical and Applied Climatology 152 (1–2): 559–79.

[[2]](https://doi.org/10.4000/MEDITERRANEE.9958) Lourenço, and Luciano. 2018. “Forest Fires in Continental PortugalResult of Profound Alterations in Society and Territorial Consequences”. no. 130 (September).

[[3]](https://doi.org/10.1111/GCB.16547) Grünig, Marc, Rupert Seidl, and Cornelius Senf. 2023. “Increasing Aridity Causes Larger and More Severe Forest Fires across Europe.” Global Change Biology 29 (6): 1648–59.

[[4]](https://doi.org/10.26682/csjuod.2020.23.2.41) Aswad, Fawaz & Yousif, Ali & Ibrahim, Sayran. (2020). "Trend Analysis Using Mann-Kendall and Sen’s Slope Estimator Test for Annual and Monthly Rainfall for Sinjar District, Iraq."

[[5]](https://doi.org/10.1088/2515-7620/AB25D2) Forkel, Matthias, Wouter Dorigo, Gitta Lasslop, Emilio Chuvieco, Stijn Hantson, Angelika Heil, Irene Teubner, Kirsten Thonicke, and Sandy P. Harrison. 2019. “Recent Global and Regional Trends in Burned Area and Their Compensating Environmental Controls.” Environmental Research Communications 1 (5): 051005.

[[6]](https://doi.org/10.3390/FIRE4040074) Otón, Gonzalo, José Miguel C. Pereira, João M.N. Silva, and Emilio Chuvieco. 2021. “Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018).” Fire 2021, Vol. 4, Page 74 4 (4): 74.

[[7]](https://doi.org/10.1007/s10668-020-00842-7) Rasul, Azad, Gaylan R.Faqe Ibrahim, Hasan M. Hameed, and Kevin Tansey. 2021. “A Trend of Increasing Burned Areas in Iraq from 2001 to 2019.” Environment, Development and Sustainability 23 (4): 5739–55.

[[8]](https://doi.org/10.1038/s41598-019-50281-2) Turco, M., Jerez, S., Augusto, S. et al. Climate drivers of the 2017 devastating fires in Portugal. Sci Rep 9, 13886 (2019).

[[9]](https://climate.esa.int/en/projects/fire/key-documents/) Stroppiana D., Sali M., Boschetti M., Busetto L., Ranghetti L., Franquesa M., Lizundia-Loiola J., Pettinari M.L. (2022) ESA CCI ECV Fire Disturbance: D4.1 Product Validation and Intercomparison Report, version 2.1. Available at: https://climate.esa.int/en/projects/fire/key-documents/

[[10]](https://doi.org/10.3390/cli12090143) DaCamara, C.C. The Signature of Climate in Annual Burned Area in Portugal. Climate 2024, 12, 143. https://doi.org/10.3390/cli12090143


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Quality assessment question(s)
---
* **Does version 3 of the C3S LAI dataset (derived from SPOT satellite imagery) provide sufficient temporal and spatial completeness to capture the impact of the 2009–2010 drought on vegetation in Yunnan Province?**
* **How does the choice of relevant quality flags affect product spatial and temporal completeness?**

The Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) dataset, provided by the Climate Data Store (C3S), offers 10-daily gridded observations from 1981 to the present. It includes effective LAI values derived from multiple satellite sensors across different product versions. Effective LAI is defined as half the total surface area of photosynthetically active plant elements per unit of horizontal ground area. It relates to true LAI through a canopy-dependent structure factor. As a key biophysical parameter, LAI is widely used to assess vegetation status, monitor ecosystem dynamics, and inform environmental and agricultural decision-making.

This quality assessment uses version 3 of the LAI product, selected because it covers the study period from 2007 to 2013. During this time, data were obtained from the System Pour l'Observation de la Terre (SPOT) satellite, Vegetation (VGT) sensor, with a spatial resolution of 1 km and a 10-day temporal resolution. The dataset is evaluated over Yunnan Province, China, with particular attention to the 2009-2010 drought.

The objectives of this assessment are: first, to assess whether the product's spatial and temporal completeness is sufficient to capture drought-related changes in LAI, which serves as a proxy for vegetation health; and second, to evaluate how different choices of quality flags influence the analysis.


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Quality assessment statement
---
These are the key outcomes of this assessment

* Version 3 of the C3S LAI dataset provides sufficient temporal and spatial completeness to detect the impact of the 2009–2010 drought on vegetation in Yunnan Province when an appropriate quality-filtering strategy is applied.
* Applying a relaxed quality filter (bitmask 0x9C1) significantly increases LAI data availability during the wet season in Yunnan, maintaining over 84% completeness. However, this comes at the cost of increased uncertainty, as it includes pixels affected by albedo-related issues.
* The conservative filter (bitmask 0xFC1), which excludes all flagged data, severely limits completeness during wet months, particularly from June to August, and especially in densely vegetated areas such as evergreen and mixed forests. However, this filter can be applied during the dry season with high completeness, while maintaining higher standards of data reliability.
* While the relaxed and conservative filters yield comparable aggregated anomalies during dry-season conditions, particularly during the peak of the drought (e.g. March 2010), greater caution is warranted during wet, high-LAI periods, where retrieval uncertainties increase and filtering choices have a stronger influence on the aggregated signal.
* The standardized LAI anomaly in Yunnan’s Tropic of Cancer zone was predominantly negative in March 2010, with extensive negative values also observed in the adjacent months, indicating that this period represents the strongest drought-related impact on LAI. This pattern is mostly consistent with the observations reported by Zhang et al. (2024) [[1]](https://doi.org/10.1038/s41598-024-58068-w).
```


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Methodology
---
The LAI variable from version 3 of the C3S product was downloaded for the period 2007-2013 and clipped to Yunnan Province, China. Two different quality filters were applied to the data: bitmask 0xFC1 (hereafter referred to as the conservative filter) and bitmask 0x9C1 (hereafter referred to as the relaxed filter). For each month in the study period, the percentage of completeness (the fraction of pixels that are not missing) was calculated under both filters for comparison. A date exhibiting a large difference in completeness between filters was selected, and the corresponding LAI map was visually compared with a land cover map to identify whether specific land cover types or zones were disproportionately affected by missing data. Monthly mean LAI over the entire province was then calculated and plotted for each year to assess whether the 2009-2010 drought is evident as lower LAI relative to other years. This analysis was performed for both filters to allow comparison. Finally, standardized LAI anomalies were calculated for Yunnan's Tropic of Cancer zone, and the results were compared with those reported by Zhang et al. (2024) [[1]](https://doi.org/10.1038/s41598-024-58068-w).

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](section-1)**
* Download LAI data from the SPOT satellite (VGT sensor) for the period 2007-2013.

**[](section-2)**
* Apply two distinct quality-masking approaches to filter the data.
* Plot the percentage of valid LAI values over time for each approach.
* Map LAI across Yunnan to compare the results from the two quality-masking methods.
* Analyse the outcomes in relation to the dataset Documentation.

**[](section-3)**
* Calculate the spatial and monthly mean LAI, and plot the values for each year over the months of the year. Compare the results for the relaxed and the conservative filters.
* Calculate the spatial and monthly standard deviation of LAI, and plot the values for the year 2010. Compare the results for the relaxed and the conservative filters.
* Map LAI anomalies in Yunnan's Tropic of Cancer zone for the drought period from August 2009 to October 2010, relative to the reference years 2007-2008 and 2012-2013.


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Analysis and results > 1. Request and download data > Download and transform
---
In this step, the dataset was spatially subsetted using a shapefile of Yunnan, an inland province in southwestern China. The shapefile was accessed from the GADM Database of Global Administrative Areas (version 4.1) via the UC Davis GeoData repository [[2]](https://gadm.org).

To filter out unreliable data, two distinct quality-masking strategies were applied:

* **Conservative:** Retains only the highest-quality retrievals by applying bitmask 0xFC1. This mask excludes data flagged with any of the following bits: 0, 6, 7, 8, 9, 10, and 11, corresponding to missing input data, untrusted retrievals, unusable observations, inconsistent inputs, high albedo uncertainty (both snow and non-snow), and unreliable uncertainty estimates.
* **Relaxed filter:** Permits retrievals with high albedo uncertainty (considered still usable), using bitmask 0x9C1. This mask excludes a smaller subset of flags: bits 0, 6, 7, 8, and 11, while allowing data flagged only for high albedo uncertainty in non-snow and snow conditions (bits 9 and 10).

These bitmasks are defined in Table 2, with flag definitions detailed in Table 7 of the [Product User Guide and Specification PUGS](https://dast.copernicus-climate.eu/documents/satellite-lai-fapar/D3.3.9-v3.0_PUGS_CDR_LAI_FAPAR_MULTI_SENSOR_v3.0_PRODUCTS_v1.0.1.pdf).

```text
100%|██████████| 84/84 [00:32<00:00,  2.58it/s]
100%|██████████| 84/84 [00:33<00:00,  2.51it/s]
```

(section-2)=


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Analysis and results > 2. Completeness analysis > Filter data
---
For each time step, the percentage of valid LAI data points within the Yunnan region is computed, and the results are plotted over time. This operation is repeated using both the conservative and relaxed filters.

The goal of this comparison is to assess how different treatments of uncertainty flags affect data completeness over Yunnan, particularly during periods of increased retrieval uncertainty such as the rainy season.

*Figure 1. Percentage of valid LAI values within the Yunnan region using the conservative mask (0xFC1) and the relaxed mask (0x9C1). Percentages are shown over time, with points color-coded by month. Note that the y-axis scale differs between the two panels.*

Using the conservative filter, the percentage of valid LAI data in Yunnan drops significantly between June and August, reaching levels below 20%. Adjacent months such as May and September also show reduced data completeness (see *Figure 1*, top pannel). This pattern corresponds to Yunnan’s marked wet and dry seasons [[3]](https://doi.org/10.1016/j.chnaes.2013.09.004), where the rainy season leads to a higher frequency of missing observations and albedo-related uncertainties, mainly due to cloud cover and wet canopy conditions.

In contrast, when applying the relaxed filter, the percentage of valid LAI data remains consistently above 84% throughout the period studied (2007–2013) (see *Figure 1*, bottom panel). This improvement is due to the relaxed mask allowing pixels flagged only for high albedo uncertainty in both non-snow and snow conditions (bits 9 and 10). In this context, bit 9 often relates to reflectance disturbances caused by clouds or wet vegetation, which are prevalent in Yunnan during the rainy season. As shown in Figure 15 of the [PQAR](https://dast.copernicus-climate.eu/documents/satellite-lai-fapar/D2.3.9-v3.0_PQAR_CDR_LAI_FAPAR_MULTI_SENSOR_v3.0_PRODUCTS_v1.1.pdf), the study area is frequently affected by the activation of bit 9, especially during wet months. It can also be observed that the dry months at the end of the year (November–December) exhibit higher completeness for the relaxed filter, but the difference between wet and dry seasons is significantly less pronounced than under the conservative filter.

It is also instructive to investigate the spatial pattern of missing LAI values. *Figure 2* shows LAI maps for August 10 from 2007 to 2013, comparing the conservative and relaxed filters. August 10 was chosen to exemplify differences in data completeness depending on the filter used beacuse it falls within the wet season and has low completeness under the conservative filter, as shown in *Figure 1*. The results highlight a strong contrast between the two approaches: the conservative filter produces highly incomplete maps across much of Yunnan in all years, whereas the relaxed filter provides nearly complete spatial coverage.

In some years, the conservative filter shows higher completeness in eastern Yunnan, where cropland, woody savannas, and grasslands are more prevalent than elsewhere in the province. Missing data under the conservative filter occur throughout Yunnan, particularly in high-LAI regions such as the mixed and evergreen forests in the west and southwest (see *Figure 3*).

1. Build list of dates (August 10 of each year)
2. Create figure and axes
Titles and formatting
Shared colorbar
3. Update function for animation
Ensure latitude runs south → north
Update array content
4. Create animation

1. Updated color list:
0–0.2: white
0.2–0.4: light yellow → 0.6: yellow → 0.8: deep yellow-green
0.8–2.0: greens (dense vegetation)

*Figure 2. Animated comparison of LAI maps on August 10 from 2007 to 2013 under the conservative (left) and relaxed (right) filters.*

![land_cover.png](attachment:b1a763fc-0793-47fb-86e7-5c22b44c66d7.png)

*Figure 3. Land cover map of Yunnan based on MODIS Land Surface Type data product -MCD12Q1. Figure reproduced from Kim et al. (2017) [[4]](https://doi.org/10.1007/s11629-016-3971-x).*

(section-3)=


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Analysis and results > 3. Drought effect on LAI > Spatial and monthly mean
---
Yunnan Province is positioned at the intersection of two major monsoon systems, the Indian monsoon and the East Asian monsoon, as well as the Qinghai-Tibet Plateau [[3]](https://doi.org/10.1016/j.chnaes.2013.09.004). These monsoons bring heavy rainfall during a distinct wet season, but due to the region’s varied topography, precipitation is unevenly distributed, often leading to water shortages during the dry season. Natural vegetation and wetlands help buffer this imbalance by storing and gradually releasing water. However, ongoing droughts have revealed the degradation of these ecosystems. Climate change is expected to intensify both the frequency and severity of droughts across Southeast Asia, including Yunnan [[4]](https://doi.org/10.1007/s11629-016-3971-x).

Yunnan has historically faced frequent droughts, with severe events occurring every two to three years between 1950 and 2014. The drought from autumn 2009 to spring 2010 was the most extreme since meteorological records began in 1959. It caused widespread ecological and agricultural disruption—drying up rivers and reservoirs, triggering large-scale crop failures, leading to the death of rare plant species, and altering water quality in plateau lakes [[1]](https://doi.org/10.1038/s41598-024-58068-w).

Drought impacts on vegetation—such as reduced leaf growth, early senescence, and plant mortality—can be detected at regional scales using satellite-derived Leaf Area Index (LAI) data [[4]](https://doi.org/10.1007/s11629-016-3971-x). In the specific case of Yunnan, commonly used products include GLASS LAI (derived from MODIS surface reflectance) [[4]](https://doi.org/10.1007/s11629-016-3971-x) and MODIS LAI [[1]](https://doi.org/10.1038/s41598-024-58068-w), both of which are valuable for monitoring vegetation responses to drought.

The spatial and monthly mean LAI over Yunnan was calculated for each year from 2007 to 2013 to assess whether values in 2009 and 2010 were lower than in other years. These means were derived using both the relaxed and conservative filters to test whether the results support the earlier observations from *Figures 2* and *3*, namely, that the conservative filter tends to filter out high-LAI areas during the wet season, thereby lowering mean values compared with the relaxed filter.

*Figure 4. Monthly mean LAI from 2007–2013 under conservative (left) and relaxed (right) filters, plotted by calendar month with consistent year-wise color coding.*

Under the relaxed filter (right panel of *Figure 4*), the values for 2009 appear to fall within the typical range for that time of year compared with other non-drought years. In contrast, many months in 2010 show visibly lower values than the corresponding months in other years. This pattern may reflect the delayed impact of the drought, with vegetation stress becoming more pronounced over time. Moreover, because the spatial mean represents an average across a large and ecologically diverse region, localized drought effects in 2009 may have been masked in the overall mean.

As expected, the conservative filter produces mean LAI values similar to those from the relaxed filter in months with high data completeness (November to April). However, in months with low completeness, the conservative filter yields noticeably lower mean LAI values. In 2010, mean LAI values are still reduced relative to other years, though the effect is observed in fewer months than when using the relaxed filter.

The monthly mean LAI for 2010 was computed by aggregating dekadal LAI retrievals spatially over the study region and temporally to monthly resolution. The associated retrieval uncertainty (LAI_ERR), provided as one standard deviation (1σ) under a Gaussian error assumption, was averaged over the same spatial and temporal domains and displayed as ±1σ (~68%) and ±2σ (~95%) bands around the monthly mean LAI (see *Figure 5*).

It is important to note that, because the uncertainty bands are derived from the mean of LAI_ERR values, they represent the typical retrieval uncertainty of individual observations within each month, rather than the formal uncertainty of the aggregated monthly mean LAI. In other words, the bands describe the magnitude of per-pixel retrieval error and should not be interpreted as confidence intervals of the spatially averaged mean.


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Analysis and results > 3. Drought effect on LAI > Spatial and monthly mean
---
within each month, rather than the formal uncertainty of the aggregated monthly mean LAI. In other words, the bands describe the magnitude of per-pixel retrieval error and should not be interpreted as confidence intervals of the spatially averaged mean.

The uncertainty envelopes are relatively wide compared to the magnitude of the mean LAI values. This indicates that retrieval uncertainty is of comparable magnitude to the signal itself, particularly under low-LAI conditions. During the dry months, both filtering strategies yield similar uncertainty levels, suggesting comparable retrieval performance. In contrast, during the wet season (June–August), the relaxed filter exhibits substantially wider uncertainty bands, with moderately elevated uncertainty in May and September. This pattern suggests that under dense canopy conditions, where radiative transfer inversion becomes less constrained, the relaxed filtering approach retains more retrievals with higher associated uncertainty. The wider bands therefore reflect increased retrieval imprecision rather than increased spatial variability.

Build legend entries for this filter
----- Custom legend -----

*Figure 5. Monthly and spatial mean of LAI and ±1σ (~68%) and ±2σ (~95%) (bands) over Yunnan province in 2010 under conservative (top) and relaxed (bottom) filters.*

Because the retrieval uncertainty (LAI_ERR) was found to be large relative to the magnitude of the monthly mean LAI, a simple arithmetic mean would assign equal weight to observations with very different reliability. To account for this heteroscedastic uncertainty, we therefore computed inverse-variance weighted monthly and spatial means for 2010. Let $L_{f,t,x}$ denote LAI for filter $f$, dekadal time step $t$, and pixel $x$, and let $\sigma_{f,t,x}$ denote the corresponding LAI_ERR (1σ retrieval uncertainty). For all finite observations with $\sigma_{f,t,x} > 0$, we define the weight as

$$
w_{f,t,x} = \frac{1}{\sigma_{f,t,x}^{2}},
$$

and set the weight to zero otherwise. Monthly means were first computed per pixel using a weighted average over all dekadal observations within month $m$,

$$
\mu_{f,m,x} =
\frac{\sum_{t \in m} w_{f,t,x} \, L_{f,t,x}}
{\sum_{t \in m} w_{f,t,x}}.
$$

Assuming independent Gaussian errors and that LAI_ERR represents the standard deviation of each observation, the propagated uncertainty of this weighted monthly mean is

$$
\sigma_{f,m,x}^{(\mathrm{month})}
=\sqrt{\frac{1}{\sum_{t \in m} w_{f,t,x}}}.
$$

To obtain a single regional value per month, these monthly pixel means were then aggregated spatially using inverse-variance weights based on their propagated uncertainty,

$$
W_{f,m,x}
=\frac{1}{\left(\sigma_{f,m,x}^{(\mathrm{month})}\right)^2},
$$

yielding the spatially weighted monthly mean

$$
\mu_{f,m}
=\frac{\sum_{x} W_{f,m,x} \, \mu_{f,m,x}}
{\sum_{x} W_{f,m,x}}.
$$

This approach reduces the influence of observations with large retrieval uncertainty and yields a minimum-variance linear estimator of the monthly regional mean under the assumptions of independent Gaussian errors.

monthly per-pixel
spatial per-month

*Figure 6. Monthly inverse-variance weighted spatial mean LAI for 2010.*

--- Inputs ---
Simple (your existing monthly mean LAI from monthly_mean)
Weighted (from your inverse-variance workflow; already computed earlier)
If you have `mu` already, use it; otherwise uncomment the next line:
mu = monthly_spatial_weighted.compute()
--- Plot: 2 columns ---
Left: simple mean
Right: weighted mean

*Figure 7. Monthly spatial mean LAI for 2010 computed using (left) simple arithmetic averaging and (right) inverse-variance weighting. Lines represent conservative and relaxed filters.*

In *Figure 6*, the propagated uncertainty band (±1σ) are not visibly distinguishable from the mean curves. This indicates that the formal uncertainty of the spatially aggregated monthly mean LAI is very small relative to the magnitude of the mean itself. Under the assumption of independent Gaussian errors, the inverse-variance weighting leads to a strong reduction of uncertainty when aggregating across a large number of observations. Consequently, the standard deviation of the regional mean becomes negligible at the scale of the figure.

*Figure 7* compares the simple (unweighted) and inverse-variance weighted monthly means. From May to October, the relaxed filter yields consistently higher LAI values than the conservative filter, with the largest differences occurring during the wet season. When inverse-variance weighting is applied, the monthly mean LAI decreases for both filtering schemes across all months. This indicates that observations with higher retrieval uncertainty tend to be associated with higher LAI values and are down-weighted in the weighted aggregation.


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Analysis and results > 3. Drought effect on LAI > Spatial and monthly mean
---
When inverse-variance weighting is applied, the monthly mean LAI decreases for both filtering schemes across all months. This indicates that observations with higher retrieval uncertainty tend to be associated with higher LAI values and are down-weighted in the weighted aggregation.

The effect is most pronounced during the wet months, particularly in September, where the discrepancy between simple and weighted means is largest. This suggests that retrieval uncertainty increases under dense canopy conditions and that the relaxed filtering scheme retains a greater proportion of high-uncertainty observations during peak vegetation periods. As a result, inverse-variance weighting exerts a stronger downward adjustment in these months, reducing the influence of less reliable retrievals. During the dry months (March and April), when mean LAI values are lowest, the inverse-variance weighted means remain lower than the simple means, but the differences are substantially smaller than those observed during the wet season.

Overall, the comparison demonstrates that accounting for heteroscedastic retrieval uncertainty meaningfully alters the magnitude of the seasonal LAI cycle, particularly during periods of high vegetation density, while leaving the general seasonal pattern intact.


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Analysis and results > 3. Drought effect on LAI > Standardised LAI anomaly
---
Zhang et al. (2024) [[1]](https://doi.org/10.1038/s41598-024-58068-w) investigated the effects of drought on vegetation in the Tropic of Cancer zone of Yunnan. To achieve this, they used various vegetation health indicators, including the LAI, which was obtained from the MOD15A2H product (layer name: LAI_500m). They calculated the monthly LAI anomaly for the drought period from August 2009 to October 2010. The reference periods 2007–2008 and 2012–2013 were chosen as the baseline to compute the LAI anomalies because they experienced minimal drought events and are adjacent to the drought years under investigation (2009–2010). This proximity helps control for long-term changes, such as those driven by human activity, that might otherwise skew the analysis [[1]](https://doi.org/10.1038/s41598-024-58068-w). For example, northwestern Yunnan was a key region in the Grain-for-Green Programme, which may have significantly influenced vegetation dynamics [[5]](https://doi.org/10.1016/j.scitotenv.2016.09.026).

In this assessment, we applied the same approach as Zhang et al. (2024) to the C3S LAI product with the relaxed filter, using the same reference period (2007–2008 and 2012–2013) to compute the standardised LAI anomaly:

$\text{LAI anomaly} = \frac{\text{LAI}_t - \overline{\text{LAI}}}{\delta_{\text{LAI}}}$

where:

$\text{LAI}_t$ = LAI 2010 for a specific month for that pixel

$\overline{\text{LAI}}$ = Mean LAI 2007-2008 and 2012-2013 for that same month for that pixel

$\delta_{\text{LAI}}$ = Standard deviation LAI 2007-2008 and 2012-2013 for that same month for that pixel

Extract LAI data
--- Step 1. Clip to geographic range (Tropic of Cancer region in Yunnan) ---
--- Step 2. Define normal period (2007–2008 and 2012–2013) ---
--- Step 3. Compute monthly climatology (mean + std) ---
--- Step 4. Select target years (2009 and 2010) ---
--- Step 5. Compute monthly means for each year ---
--- Step 6. Calculate standardized monthly anomaly (SMA) ---
Avoid inf/-inf

--- Custom colormap ---

--- Subset anomalies to Aug 2009 – Oct 2010 ---
Time labels for subplot titles
--- Set up 3×4 subplot grid ---
--- Plot each month ---
Set to the Tropic of Cancer rectangle
--- Date label under each subplot ---
--- Gridlines ---
--- Longitude ticks only for top row ---
--- Latitude ticks for left column only ---
--- Shared colorbar at bottom ---
--- Main title ---

*Figure 6. Monthly standardized LAI anomalies during the drought period from August 2009 to October 2010, calculated using the C3S LAI product (relaxed quality filter).*

![Zhang2024.jpg](attachment:21b489ba-8fcd-4b82-80eb-f14234020195.jpg)

*Figure 7. Monthly standardized LAI anomalies during the drought period from August 2009 to October 2010, based on the MOD15A2H product. Source: Zhang et al. (2024) [[1]](https://doi.org/10.1038/s41598-024-58068-w)*

According to the Standardized Precipitation Evapotranspiration Index (SPEI) calculated by Zhang et al. (2024) [[1]](https://doi.org/10.1038/s41598-024-58068-w), the drought fully developed across the study area between October 2009 and February 2010. Because LAI has a lagged response to drought, the results obtained with the C3S product (*Figure 6*) are consistent: March 2010 shows the largest areas with negative anomalies, with adjacent months also displaying widespread negative values. These findings align with Zhang et al. (2024) [[1]](https://doi.org/10.1038/s41598-024-58068-w), who reported that the period of greatest vegetation stress due to the cumulative effects of drought occurred between March and May 2010, corresponding to spring.


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > Analysis and results > 3. Drought effect on LAI > Standardised LAI anomaly
---
https://doi.org/10.1038/s41598-024-58068-w), who reported that the period of greatest vegetation stress due to the cumulative effects of drought occurred between March and May 2010, corresponding to spring.

Some differences can be observed between the *Figure 6* (C3S) and *Figure 7* (MODIS). C3S LAI anomalies then to show more extremes (values <-2 or 2>) than the anomamlies calculated using MODIS. For example, it can be noted that the anomalies in March 2010 using the C3S were even more negative than the ones using MODIS. Also, the C3S data show positive anomalies in the western part of the study area during October and November 2009, as well as January and May 2010—patterns that are absent in the MODIS results. Another difference is that, according to MODIS, vegetation LAI had largely returned to normal conditions by October 2010, whereas the C3S data still indicate scattered pixels with negative anomalies.

Although LAI responds more slowly to drought because it reflects cumulative canopy biomass rather than immediate physiological stress, it tends to recover relatively quickly once water availability improves, since canopy structure is often not severely damaged in the short term. In contrast, gross primary productivity (GPP) responds directly to water stress and requires more time to recover, as plants must restore physiological functions such as stomatal conductance and photosynthetic activity. This makes GPP a more sensitive indicator of drought impacts and a better tool for identifying the regions most affected [[1]](https://doi.org/10.1038/s41598-024-58068-w). Nevertheless, LAI remains valuable for assessing longer-term structural changes in vegetation and for linking drought effects to phenological dynamics and canopy development.


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > ℹ If you want to know more
---
* Zhu, H. & Tan, Y. (2022). Flora and Vegetation of Yunnan, Southwestern China: Diversity, Origin and Evolution. Diversity, 14(5), 340. [](https://doi.org/10.3390/d14050340)
* Qian, L.-S., Chen, J.-H., Deng, T., & Sun, H. (2020). Plant diversity in Yunnan: Current status and future directions. Plant Diversity, 42(4), 281–291. [](https://doi.org/10.1016/j.pld.2020.07.006)
* Lan, T., & Yan, X. (2024). Analysis of drought characteristics and causes in Yunnan Province in the last 60 years (1961–2020). Journal of Hydrometeorology, 25, 177–190. [](https://doi.org/10.1175/JHM-D-23-0092.1)


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > ℹ If you want to know more > Key resources
---
Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)

Documentation:
* [Product User Guide and Specification (PUGS) - Multi-sensor CDR LAI and fAPAR v3.0](https://dast.copernicus-climate.eu/documents/satellite-lai-fapar/D3.3.9-v3.0_PUGS_CDR_LAI_FAPAR_MULTI_SENSOR_v3.0_PRODUCTS_v1.0.1.pdf)
* [Algorithm Theoretical Basis Document (ATBD) - Multi-sensor CDR LAI and fAPAR v3.0](https://dast.copernicus-climate.eu/documents/satellite-lai-fapar/D1.4.4-v3.0_ATBD_CDR_LAI_FAPAR_MULTI_SENSOR_v3.0_PRODUCTS_v1.0.1.pdf)
* [Product Quality Assurance Document (PQAD) - Multi-sensor LAI and fAPAR v3.0](https://dast.copernicus-climate.eu/documents/satellite-lai-fapar/D2.2.9-v3.0_PQAD_CDR_LAI_FAPAR_MULTI_SENSOR_v3.0_PRODUCTS_v1.1.pdf)
* [Product Quality Assessment Report (PQAR) - Multi-sensor LAI and fAPAR v3.0](https://dast.copernicus-climate.eu/documents/satellite-lai-fapar/D2.3.9-v3.0_PQAR_CDR_LAI_FAPAR_MULTI_SENSOR_v3.0_PRODUCTS_v1.1.pdf)


EQC Quality Assessment: "Impact of Drought on the Leaf Area Index in Yunnan Province, China"
Dataset: satellite-lai-fapar [CDS]
Aspect: extremes-detection_q02  |  Category: Satellite_ECVs
Section: Impact of Drought on the Leaf Area Index in Yunnan Province, China > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1038/s41598-024-58068-w) Zhang, Y., Gu, T., He, S., Cheng, F., Wang, J., Ye, H., Zhang, Y., Su, H., & Li, Q. (2024). Extreme drought along the tropic of cancer (Yunnan section) and its impact on vegetation. Sci Rep 14, 7508.

[[2]](https://gadm.org) GADM. (2023). GADM database of global administrative areas (version 4.1). Data accessed via UC Davis GeoData repository.

[[3]](https://doi.org/10.1016/j.chnaes.2013.09.004) Yu, W., Shao, M., Ren, M., Zhou, H., Jiang, Z., & Li, D. (2013). Analysis on spatial and temporal characteristics of drought in Yunnan Province. Acta Ecologica Sinica, 33(6), 317–324.

[[4]](https://doi.org/10.1007/s11629-016-3971-x) Kim, K., Wang, M.-c., Ranjitkar, S., Liu, S.-h., Xu, J.-c., & Zomer, R. J. (2017). Using leaf area index (LAI) to assess vegetation response to drought in Yunnan province of China. J. Mt. Sci. 14, 1863–1872.

[[5]](https://doi.org/10.1016/j.scitotenv.2016.09.026) Wang, J., Peng, J., Zhao, M., Liu, Y., & Chen, Y. (2017). Significant trade-off for the impact of Grain-for-Green Programme on ecosystem services in North-western Yunnan, China. Science of The Total Environment, 574, 57–64.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Quality assessment question
---
* **Is the dataset suitable for the analysis of afforestation/deforestation trends in the Iberian Peninsula?**

Land Cover data is an invaluable resource for a wide range of fields, from climate change research to forest management. Land Cover products that provide historical timelines enable scientists, policymakers, and planners to understand and analyse the transformation of land cover over recent decades ([EUROSTAT,2022](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Forests,_forestry_and_logging); [New EU Forest Strategy for 2030](https://commission.europa.eu/document/cf3294e1-8358-4c93-8de4-3e1503b95201_en)).

This notebook will access the ***Land cover classification gridded maps from 1992 to present derived from satellite observations*** (henceforth, LC) data from the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S), and analyse the spatial patterns of a specific LC type over a given Area of Interest (AoI) and time.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Quality assessment statement
---
These are the key outcomes of this assessment

The dataset maintains strong temporal continuity, with annual updates ensuring a smooth and reliable representation of forest land cover changes over time. While breakpoints were identified, they generally did not indicate major disruptions, reinforcing the dataset’s stability for long-term trend analysis.

The presence of breakpoints does not necessarily indicate abrupt landscape shifts but rather highlights the sensitivity of detection methods to gradual changes. This suggests that while breakpoints can help refine analysis, their impact on overall trends remains limited, emphasising the dataset’s resilience to minor variations.

For the specific land cover type analysed, the dataset exhibits a consistent ability to capture underlying trends. The similarity in results across segmented and total trends suggests that the data structure is well-calibrated, minimising distortions that could arise from classification inconsistencies or methodological biases.

```

![Forest_Map_Series.png](attachment:Forest_Map_Series.png)


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Methodology
---
**This Use Case comprises the following steps:**

**[](code-section-1)**

**[](code-section-2)**

**[](code-section-3)**

**[](code-section-4)**

**[](code-section-5)**


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data
---
Before we begin we must prepare our environment. This includes installing the Application Programming Interface (API) of the CDS, and importing the various python libraries that we will need.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data > Install CDS API
---
To install the CDS API, run the following command. We use an exclamation mark to pass the command to the shell (not to the Python interpreter).
If you already have the CDS API installed, you can skip or comment this step.

```text
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: cdsapi in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (0.7.7)
Requirement already satisfied: ecmwf-datastores-client>=0.4.0 in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from cdsapi) (0.4.1)
Requirement already satisfied: requests>=2.5.0 in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from cdsapi) (2.32.5)
Requirement already satisfied: tqdm in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from cdsapi) (4.67.1)
Requirement already satisfied: attrs in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from ecmwf-datastores-client>=0.4.0->cdsapi) (25.4.0)
Requirement already satisfied: multiurl>=0.3.7 in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from ecmwf-datastores-client>=0.4.0->cdsapi) (0.3.7)
Requirement already satisfied: typing-extensions in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from ecmwf-datastores-client>=0.4.0->cdsapi) (4.15.0)
Requirement already satisfied: pytz in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from multiurl>=0.3.7->ecmwf-datastores-client>=0.4.0->cdsapi) (2025.2)
Requirement already satisfied: python-dateutil in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from multiurl>=0.3.7->ecmwf-datastores-client>=0.4.0->cdsapi) (2.9.0.post0)
Requirement already satisfied: charset_normalizer<4,>=2 in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from requests>=2.5.0->cdsapi) (3.4.4)
Requirement already satisfied: idna<4,>=2.5 in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from requests>=2.5.0->cdsapi) (3.11)
Requirement already satisfied: urllib3<3,>=1.21.1 in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from requests>=2.5.0->cdsapi) (2.6.0)
Requirement already satisfied: certifi>=2017.4.17 in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from requests>=2.5.0->cdsapi) (2025.11.12)
Requirement already satisfied: six>=1.5 in /data/common/miniforge3/envs/wp5/lib/python3.12/site-packages (from python-dateutil->multiurl>=0.3.7->ecmwf-datastores-client>=0.4.0->cdsapi) (1.17.0)
```

##### Import all the libraries/packages

We will be working with data in NetCDF format. To best handle this type of data we will use libraries for working with multidimensional arrays, in particular Xarray.
We will also need libraries for plotting and viewing data.

Import Standard Libraries
Import Numerical & Statistical Libraries
import ruptures as rpt
Import Geospatial Libraries
Import Visualization Libraries
Import External Tools
Set Matplotlib Style
Set the CDSAPI location

##### Data Overview


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data > Install CDS API
---
Libraries
Import Numerical & Statistical Libraries
import ruptures as rpt
Import Geospatial Libraries
Import Visualization Libraries
Import External Tools
Set Matplotlib Style
Set the CDSAPI location

##### Data Overview

To search for data, visit the CDS website: http://cds.climate.copernicus.eu Here you can search for 'Satellite observations' using the search bar. The data we need for this tutorial is the ***Land cover classification gridded maps from 1992 to present derived from satellite observations***. This catalogue entry provides global Land Cover Classification (LCC) maps with a very high spatial resolution, with a L4 processing level, on an annual basis with a one-year delay, following the [Global Climate Observing System (GCOS) convention requirements](https://library.wmo.int/records/item/58111-the-2022-gcos-ecvs-requirements-gcos-245). LULC maps correspond to a global classification scheme, encompassing 22 classes.

The dataset consists of 2 versions (v2.0.7 produced by the European Space Agency (ESA) Climate Change Initiative (CCI) and v2.1.1 produced by Copernicus Climate Change Service (C3S)).

Data specifications for this use case:
* **Years:** 1992 to 2022
* **Version:** v2.0.7 before 1992 and v2.1.1 after 2016
* **Format:** Zip files

At the end of the download form, select “**Show API request**”. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook. Having copied the API request, running it will retrieve and download the data you requested into your local directory. However, before you run it, the **terms and conditions** of this particular dataset need to have been accepted directly at the CDS website. The option to view and accept these conditions is given at the end of the download form, just above the “**Show API request**” option. In addition, it is also useful to define the time period and AoI parameters and edit the request accordingly, as exemplified in the cells below.

Years to download
List of requests to retrieve data

Download the dataset

```text
100%|██████████| 32/32 [00:00<00:00, 88.34it/s]
/data/common/miniforge3/envs/wp5/lib/python3.12/site-packages/earthkit/data/readers/netcdf/fieldlist.py:202: FutureWarning: In a future version of xarray the default value for data_vars will change from data_vars='all' to data_vars=None. This is likely to lead to different results when multiple datasets have matching variables with overlapping values. To opt in to new defaults and get rid of these warnings now use `set_options(use_new_combine_kwarg_defaults=True) or set data_vars explicitly.
  return xr.open_mfdataset(
/data/wp5/.tmp/ipykernel_11804/1703755849.py:7: DeprecationWarning: dropping variables using `drop` is deprecated; use drop_vars.
  ds = ds.assign_coords(year=ds["time"].dt.year).swap_dims(time="year").drop("time")
```

Inspect the database


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 1. Define the AoI, search and download LC data > Install CDS API
---
_vars.
  ds = ds.assign_coords(year=ds["time"].dt.year).swap_dims(time="year").drop("time")
```

Inspect the database

```text
<xarray.Dataset> Size: 7GB
Dimensions:              (year: 31, latitude: 3600, longitude: 5040, bounds: 2)
Coordinates:
  * year                 (year) int64 248B 1992 1993 1994 ... 2020 2021 2022
  * latitude             (latitude) float64 29kB 45.0 45.0 44.99 ... 35.0 35.0
  * longitude            (longitude) float64 40kB -9.999 -9.996 ... 3.996 3.999
    lat_bounds           (latitude, bounds, longitude) float64 290MB dask.array<chunksize=(1200, 1, 1680), meta=np.ndarray>
    lon_bounds           (longitude, bounds) float64 81kB dask.array<chunksize=(5040, 2), meta=np.ndarray>
    time_bounds          (year, bounds, longitude) datetime64[ns] 2MB dask.array<chunksize=(1, 2, 5040), meta=np.ndarray>
Dimensions without coordinates: bounds
Data variables:
    lccs_class           (year, latitude, longitude) uint8 562MB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    processed_flag       (year, latitude, longitude) float32 2GB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    current_pixel_state  (year, latitude, longitude) float32 2GB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    observation_count    (year, latitude, longitude) uint16 1GB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    change_count         (year, latitude, longitude) uint8 562MB dask.array<chunksize=(1, 1800, 2520), meta=np.ndarray>
    crs                  (year, longitude) int32 625kB dask.array<chunksize=(1, 5040), meta=np.ndarray>
Attributes: (12/38)
    id:                         ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-v2.0.7cds
    title:                      Land Cover Map of ESA CCI brokered by CDS
    summary:                    This dataset characterizes the land cover of ...
    type:                       ESACCI-LC-L4-LCCS-Map-300m-P1Y
    project:                    Climate Change Initiative - European Space Ag...
    references:                 http://www.esa-landcover-cci.org/
    ...                         ...
    geospatial_lon_max:         180
    spatial_resolution:         300m
    geospatial_lat_units:       degrees_north
    geospatial_lat_resolution:  0.002778
    geospatial_lon_units:       degrees_east
    geospatial_lon_resolution:  0.002778
```

(code-section-2)=


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Compute Land Cover classes area coverage for each NUTS 2 region
---
To identify changes in LC patterns, by regions, NUTS 2 will be used, providing the information regarding the main regions of Iberian Peninsula.

The NUTS are a hierarchical system divided into 3 levels (https://ec.europa.eu/eurostat/web/nuts). NUTS 1 correspond to major socio-economic regions, NUTS 2 correspond to basic regions for the application of regional policies, and NUTS 3 correspond to small regions for specific diagnoses. Additionally a NUTS 0 level, usually co-incident with national boundaries is also available. The NUTS legislation is periodically amended; therefore multiple years are available for download.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Mask regions
---
First, we need to establish the geometry of the NUTS region (level 2) in order to make the corresponding statistics.

Configure Dask
Define CRS and bounding box for Iberian Peninsula
Load and filter GeoDataFrame
Ensure dataset CRS is set
Ensure dataset coordinates overlap with the filtered regions
Subset dataset to valid ranges
Check subset dataset dimensions
Create the regionmask
Create a 2D mask


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Compute cell area
---
Then, we can calculate the area of each pixel taking into consideration the curvature of the earth (i.e., weighted by the cosine of Latitude).

Scaling factor for conversion (constant longitude resolution)
Use the latitude values directly from the dataset
Calculate the difference between consecutive latitude values
Convert latitude differences to kilometres
Compute the grid cell area for each latitude
Assign attributes to the grid cell area
Add the grid cell area as a coordinate to the dataset


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Select Forest Classes and Prepare Dataset
---
To analyse forest cover, we selected all land cover (LC) classes corresponding to forest types from the LC dataset. The classes are described in the [(C3S, LC - Product User Guide and Specification (2024)](https://dast.copernicus-climate.eu/documents/satellite-land-cover/WP2-FDDP-LC-2021-2022-SENTINEL3-300m-v2.1.1_PUGS_v1.1_final.pdf).

The following class codes were included:

- **50** – Tree cover, broadleaved, evergreen
- **60** – Tree cover, broadleaved, deciduous, closed
- **61** – Tree cover, broadleaved, deciduous, open
- **62** – Tree cover, broadleaved, deciduous
- **70** – Tree cover, needleleaved, evergreen
- **71** – Tree cover, needleleaved, evergreen, closed
- **72** – Tree cover, needleleaved, evergreen, open
- **80** – Tree cover, needleleaved, deciduous
- **81** – Tree cover, needleleaved, deciduous, closed
- **82** – Tree cover, needleleaved, deciduous, open
- **90** – Tree cover, mixed leaf type
- **100** – Mosaic tree and shrub / herbaceous cover
- **160** – Tree cover, flooded, fresh or brackish water
- **170** – Tree cover, flooded, saline water

All these classes were aggregated to represent **forest cover** in the analysis.

Define forest classes
Create a mask for forested areas
Masked forest area using cell area
Stack latitude and longitude into a single dimension
Create a stacked mask and align dimensions
Attach the stacked mask to the dataset
Group by regions and compute forested area for each year


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Compute forest area per region and year
---
Ensure valid region indexing and naming
Save growth data by year
Add results to the list
Convert results to a DataFrame
Add geometry to results_df
Ensure results_df is a GeoDataFrame


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Map forest percentage coverage over-time by NUTS regions in the AoI
---
Reproject the GeoDataFrame to a projected CRS (EPSG:3035 is a good choice for Europe)
Calculate the total area of each region (in square kilometers)
Ensure 'Year' is converted to string if needed
Calculate Forest Percentage
Pivot the data to have years as columns
Extract years for plotting
Normalize color scale across all maps based on percentage coverage
Plot settings
Plot each year in a separate subplot
Hide unused subplots
Step 8: Adjust layout
Allocate space for the color bar
Create a single ScalarMappable object for the common colorbar


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Map Analysis
---
- Over the 28-year period, all regions maintain a consistent area of forest.

- Regions such as Principado de Asturias, Cantabria, País Vasco, Galicia, and Centro (PT) consistently exhibit the highest levels of forest coverage.

- Northern Spain (Galicia, Asturias, Cantabria, País Vasco) is often affected by negative fluctuations, particularly in 1998–2000 and 2003–2004. The classification models may have struggled with complex topography and mixed vegetation types [(Zhao et al., 2023)](https://doi.org/10.3390/rs15092285).

- Southern Portugal & Spain (Alentejo, Algarve, Andalucía), unlike northern Spain, experiences more positive fluctuations in area, particularly in 1998-2004.

- From 2006–2007 onward the maps show more stable estimates.


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Potential Drivers and Methodological Considerations in Afforestation/Deforestation Trends in the Iberian Peninsula (1992–2022)
---
Forest dynamics in the Iberian Peninsula over the last three decades have been shaped by a combination of **natural events**, **human activities**, and **policy interventions**. Before analysing trends in a land use and land cover (LULC) time series, it is crucial to account for abrupt shifts that may distort trend estimates. These shifts, often referred to as breakpoints, can result from:

**Natural Events**

- Wildfires:  The Iberian Peninsula experiences frequent and intense wildfires, particularly in **Galicia, Catalonia, and central Portugal**. Major fire seasons occurred in **2003, 2005, and 2017**, causing the destruction of vast areas of forest [(Mato et al., 2014)](https://api.semanticscholar.org/CorpusID:265869582). Wildfires have become a key driver of land cover change, especially in fire-prone regions of Portugal and Spain [(Silva et al., 2011)](https://doi.org/10.1016/j.landurbplan.2011.03.001).

- Droughts:  Recurrent droughts have exacerbated forest degradation, weakening tree resilience and delaying post-fire recovery [(Gouveia et al., 2012)](https://nhess.copernicus.org/articles/12/3123/2012/). The severe droughts of **2005 and 2017** significantly reduced vegetation cover, affecting Mediterranean pine and oak forests [(Vidal-Macua et al., 2017)](https://doi.org/10.1016/j.foreco.2017.10.011).

**Human Activities**

- Farmland Abandonment: Rural depopulation since the 1990s has driven widespread farmland abandonment, leading to natural forest regeneration in regions like **northern Portugal and Galicia** [(Palmero-Iniesta et al., 2021)](https://api.semanticscholar.org/CorpusID:238829360).

- Logging and Eucalyptus Plantations:  Logging activities and the expansion of eucalyptus plantations in **Galicia and central Portugal** have caused periodic forest loss and regrowth [(Silva et al., 2011)](https://doi.org/10.1016/j.landurbplan.2011.03.001).

**Policy and Economic Influences**

- Afforestation and Reforestation: EU-funded afforestation programs under the **Common Agricultural Policy (CAP)** promoted the planting of trees on abandoned farmland, particularly in **Castilla y León, Extremadura, and parts of Portugal**, leading to abrupt increases in forest cover [(Sevillano et al., 2018)](https://doi.org/10.1016/j.landusepol.2018.06.054).

- Natura 2000 Network and Conservation Policies: The **Natura 2000 network** has contributed to forest conservation efforts, particularly in regions like **Sierra de Guadarrama (Madrid)** and **Peneda-Gerês National Park (Portugal)** [(Regos et al., 2015)](https://doi.org/10.1016/j.jag.2014.11.010).

- Economic Downturn (2008–2013):  The economic crisis reduced logging temporarily alleviating deforestation pressures (Mateus & Fernandes, 2014).

**Methodology**

- Certain shifts in forest land cover may be related to data collection and processing rather than actual forest changes. For instance, transitions between different satellite sensors can introduce artificial breaks in the time series [(Chelali et al., 2019)](https://doi.org/10.1109/JURSE.2019.8808967).

- **The LC dataset is generated from a multi-sensor surface reflectance time series derived from several satellite missions. As sensor technology evolved, different instruments were used to produce consistent global composites.**

<div style="display: flex; justify-content: center;">


EQC Quality Assessment: "Satellite Land Cover trend assessment for Spatial Planning and Land Management"
Dataset: satellite-land-cover [CDS]
Aspect: completeness_q03  |  Category: Satellite_ECVs
Section: Satellite Land Cover trend assessment for Spatial Planning and Land Management > Analysis and results > 2. Inspect and view data for the defined AoI (Iberian Peninsula) > Potential Drivers and Methodological Considerations in Afforestation/Deforestation Trends in the Iberian Peninsula (1992–2022)
---
ensor surface reflectance time series derived from several satellite missions. As sensor technology evolved, different instruments were used to produce consistent global composites.**

| Surface Reflectance (SR) Input | Reference Period | Satellite Sensor / Mission | Main Characteristics |
|---|---|---|---|
| AVHRR SR composites | 1992–1999 | AVHRR-2 (NOAA-11, NOAA-14) | ~1 km spatial resolution, visible and near-infrared observations |
| SPOT-VGT SR composites | 1999–2013 | SPOT-4 / SPOT-5 VEGETATION | 1 km spatial resolution, 4 spectral bands (blue, red, NIR, SWIR) |
| MERIS SR composites | 2003–2012 | Envisat MERIS | ~300 m spatial resolution, 15 spectral bands in visible and near-infrared |
| PROBA-V SR composites | 2013–2019 | PROBA-V | ~300 m spatial resolution, 4 spectral bands (blue, red, NIR, SWIR) |
| Sentinel-3 SR composites | 2020 | Sentinel-3 OLCI | ~300 m spatial resolution, multispectral observations |
| Sentinel-3 SR composites | 2021–2022 | Sentinel-3 OLCI + SLSTR | Optical and thermal observations supporting land-cover mapping |

Source: See Figure 1-3 in C3S, LC - Product User Guide and Specification (2024)

- In addition to optical surface reflectance observations, auxiliary datasets are used to improve land-cover mapping. In particular, **Envisat ASAR Wide Swath Mode (WSM)** radar observations (2005–2012) are incorporated as ancillary information [(C3S - LC Target Requirements and Gap Analysis Document, 2024)](https://dast.copernicus-climate.eu/documents/satellite-land-cover/WP3-TR-GAD-2023_LC_v1.1_final.pdf).

- While the dataset relies on multiple satellite sensors, it is not generated through simple sensor-to-sensor transitions. Instead, the global land-cover time series is built around a baseline land-cover map derived from MERIS observations (2003–2012). Earlier periods (1992–2003) are reconstructed through back-dating using AVHRR and SPOT-VGT data, while later years are produced through incremental updates using SPOT-VGT, PROBA-V, and Sentinel-3 observations. During several periods, multiple sensors are used simultaneously, with different roles (e.g., temporal consistency versus spatial refinement).

| Global LC database | Reference period | Satellite data source |
|---|---|---|
| Baseline 10-year global LC map | 2003–2012 | • MERIS FR/RR global SR composites between 2003 and 2012 |
| Global annual LC maps | 1992–1999 | • Baseline 10-year global LC map
 • AVHRR global SR composites between 1992 and 1999 for back-dating the baseline |
|  | 1999–2013 | • Baseline 10-year global LC map
 • SPOT-VGT global SR composites between 1999 and 2013 for up- and back-dating the baseline
 • MERIS FR global SR composites between 2003 and 2012 to delineate the identified changes at 300 m spatial resolution
 • PROBA-V global SR composites at 300 m for year 2013 to delineate the identified changes at 300 m spatial resolution |
|  | 2014–2019 | • PROBA-V global SR composites at 1 km for years 2014 to 2019 for updating the baseline
 • PROBA-V time series at 300 m for 2014 to 2019 to delineate the identified changes at the LC map spatial resolution |
|  | 2020–2022 | • S3 global SR composites at 1 km for years 2020 to 2022 for updating the baseline
 • S3 time series at 300 m for 2020 to 2022 to delineate the identified changes at the LC map spatial resolution |

Source: See Table 1-3 in C3S, LC - Product User Guide and Specification (2024)

(code-section-3)=