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metadata/dimension_definitions(8).md

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+# Dimension definitions
+
+This document defines the dimensions and array orientations used in the
+released DERE dataset.
+
+## Temporal prediction setup
+
+The complete ED target sequence spans 29 calendar years from 1992 to 2020.
+
+The ED target values from December 1992 are used once as the initial state.
+Together with the monthly input features from the following 28 years, this
+initial state is used to predict the ED target variables for every month of
+the following 28 years.
+
+The model performs prediction over 336 monthly time steps:
+
+```text
+28 years × 12 months = 336 monthly prediction steps
+```
+
+The temporal relationship is:
+
+```text
+Initial state:
+    ed_simulation_y from the final month of the first year
+
+Inputs:
+    ed_simulation_x from every month of the following 28 years
+
+Prediction targets:
+    ed_simulation_y from every month of the following 28 years
+```
+
+## Core dimensions
+
+| Dimension | Meaning |
+|---|---|
+| `sample` | A sampled global grid cell or an in-situ site matched to the corresponding simulation grid cell |
+| `full_year` | Year axis of the complete ED target sequence, corresponding to 1992–2020; length 29 |
+| `prediction_year` | Year axis of the prediction period, corresponding to 1993–2020; length 28 |
+| `month` | Calendar month within a year; length 12 |
+| `feature` | Model-input variable; length 136 |
+| `simulation_target` | ED simulation output variable; length 10 |
+| `observed_target` | In-situ carbon-flux variable; length 3 |
+| `age_class` | Forest initial-age class used for age-specific ED simulation outputs, ED simulation PFT arrays, and LiDAR-derived age weights; length 18 |
+| `network` | In-situ dataset identifier: `above` (ABoVE), `ameriflux` (AmeriFlux), `fluxnet` (FLUXNET), `icos-ww` (ICOS-WW), or `multiple` (sites occurring in more than one network, separated to keep the network-specific subsets non-overlapping) |
+| `split` | Dataset partition. The released data contain training and testing splits. |
+
+The 18 representative forest ages are:
+
+```text
+[1, 10, 20, 30, 41, 50, 60, 70, 90,
+ 110, 140, 190, 240, 290, 340, 390, 440, 490]
+```
+
+## GlobalMask arrays
+
+The GlobalMask dataset is divided into two non-overlapping subsets:
+
+- `training split`: 3373 global grid-cell samples used for model training.
+- `testing split`: 852 held-out global grid-cell samples used for final model
+  evaluation.
+
+The two splits contain the same variables and use the same dimension
+definitions. They differ only in the number of samples.
+
+### `ed_simulation_x`
+
+The array contains the monthly ED input features for the 28-year prediction
+period.
+
+Dimensions:
+
+```text
+[sample, prediction_year, month, feature]
+```
+
+Released shapes:
+
+```text
+training split: [3373, 28, 12, 136]
+testing split:  [852, 28, 12, 136]
+```
+
+### `ed_simulation_y`
+
+The array contains the complete 29-year age-specific ED simulation target
+sequence.
+
+Dimensions:
+
+```text
+[sample, age_class, full_year, month, simulation_target]
+```
+
+Released shapes:
+
+```text
+training split: [3373, 18, 29, 12, 10]
+testing split:  [852, 18, 29, 12, 10]
+```
+
+The initial target state is derived from the final month of the first year:
+
+```python
+initial_y = ed_simulation_y[:, :, 0, -1, :]
+```
+
+Derived dimensions:
+
+```text
+[sample, age_class, simulation_target]
+```
+
+The prediction target contains every month of the following 28 years:
+
+```python
+target_y = ed_simulation_y[:, :, 1:, :, :]
+```
+
+Derived dimensions:
+
+```text
+[sample, age_class, prediction_year, month, simulation_target]
+```
+
+The model relationship is:
+
+```text
+initial_y from the final month of year 1
++
+ed_simulation_x from all 336 months of the following 28 years
+→
+target_y for all 336 months of the following 28 years
+```
+
+### `ed_simulation_pft_bl`, `ed_simulation_pft_nl`, and `ed_simulation_pft_gs`
+
+The arrays contain the annual age-specific ED simulation PFT fractions for
+broadleaf, needleleaf, and grass-and-shrub vegetation.
+
+Dimensions:
+
+```text
+[sample, age_class, full_year]
+```
+
+Released shapes:
+
+```text
+training split: [3373, 18, 29]
+testing split:  [852, 18, 29]
+```
+
+BL, NL, and GS are stored as separate arrays, so `pft_type` is not an explicit
+dimension.
+
+### `lidar_age_weight_fraction`
+
+The array contains the LiDAR-derived fraction associated with each of the
+18 forest age classes.
+
+Dimensions:
+
+```text
+[sample, age_class]
+```
+
+Released shapes:
+
+```text
+training split: [3373, 18]
+testing split:  [852, 18]
+```
+
+### `esa_cci_bl_fraction`, `esa_cci_nl_fraction`, and `esa_cci_gs_fraction`
+
+The arrays contain the annual ESA CCI broadleaf, needleleaf, and grass-and-shrub
+vegetation PFT fractions.
+
+Dimensions:
+
+```text
+[sample, full_year]
+```
+
+Released shapes:
+
+```text
+training split: [3373, 29]
+testing split:  [852, 29]
+```
+
+The three PFT groups are stored as separate arrays. There is no monthly
+dimension in these arrays.
+
+## InSituMatched arrays
+
+The number of matched in-situ sites depends on the network subset and data
+split. Therefore, the symbolic `sample` dimension is used below instead of a
+fixed sample count.
+
+### `ed_simulation_x`
+
+The array contains the same 28-year monthly ED input sequence used in the
+GlobalMask dataset, extracted at the matched in-situ locations.
+
+Dimensions:
+
+```text
+[sample, prediction_year, month, feature]
+```
+
+Shape:
+
+```text
+[sample, 28, 12, 136]
+```
+
+### `ed_simulation_y`
+
+The array contains the complete 29-year age-specific ED simulation target
+sequence at the matched in-situ locations.
+
+Dimensions:
+
+```text
+[sample, age_class, full_year, month, simulation_target]
+```
+
+Shape:
+
+```text
+[sample, 18, 29, 12, 10]
+```
+
+The initial state and prediction target are derived in the same way as for
+GlobalMask:
+
+```python
+initial_y = ed_simulation_y[:, :, 0, -1, :]
+target_y = ed_simulation_y[:, :, 1:, :, :]
+```
+
+Derived shapes:
+
+```text
+initial_y:
+    [sample, 18, 10]
+
+target_y:
+    [sample, 18, 28, 12, 10]
+```
+
+### `observed_y`
+
+The array contains the in-situ carbon-flux observations.
+
+Dimensions:
+
+```text
+[sample, full_year, month, observed_target]
+```
+
+Shape:
+
+```text
+[sample, 29, 12, 3]
+```
+
+The three observed target variables are:
+
+```text
+GPP
+RECO
+NEE
+```
+
+The first year is retained for temporal alignment with the complete ED target
+sequence. For model evaluation, predictions are compared with the available
+in-situ observations over the following 28-year prediction period. Missing
+observation time steps are excluded from evaluation.
+
+### `lidar_age_weight_fraction`
+
+The array contains one LiDAR-derived fraction for each of the 18 age classes.
+
+Dimensions:
+
+```text
+[sample, age_class]
+```
+
+Shape:
+
+```text
+[sample, 18]
+```
+
+### `esa_cci_bl_fraction`, `esa_cci_nl_fraction`, and `esa_cci_gs_fraction`
+
+The arrays contain the annual ESA CCI broadleaf, needleleaf, and grass-and-shrub
+vegetation PFT fractions at the matched in-situ locations.
+
+Dimensions:
+
+```text
+[sample, full_year]
+```
+
+Shape:
+
+```text
+[sample, 29]
+```
+
+The three PFT groups are stored as separate arrays. There is no monthly
+dimension in these arrays.
+
+## Conceptual relationship to the paper
+
+In the paper:
+
+- `x_(s,t)` denotes physical and environmental conditions at location `s` and
+  time `t`.
+- `c_k` denotes an initial forest-age state.
+- `(y^P_(s,t))_k` denotes the ED simulation output corresponding to initial age
+  state `c_k`.
+- `y_(s,t)` denotes the in-situ carbon-flux observation.
+- `z_(s,t)` denotes the aggregated satellite PFT observation.
+- `alpha_k` denotes the weight associated with an initial forest-age state.
+
+In the released prediction setup, the first-year final-month ED target values
+provide the one-time initial target state. The monthly input features from the
+following 28 years are then used to predict the monthly ED target values over
+the same 28-year period.
+
+## Released-array summary
+
+All released arrays use sample-first orientation whenever a `sample`
+dimension is present.
+
+### GlobalMask
+
+```text
+ed_simulation_x:
+    [sample, prediction_year, month, feature]
+
+ed_simulation_y:
+    [sample, age_class, full_year, month, simulation_target]
+
+ed_simulation_pft_bl:
+    [sample, age_class, full_year]
+
+ed_simulation_pft_nl:
+    [sample, age_class, full_year]
+
+ed_simulation_pft_gs:
+    [sample, age_class, full_year]
+
+lidar_age_weight_fraction:
+    [sample, age_class]
+
+esa_cci_bl_fraction:
+    [sample, full_year]
+
+esa_cci_nl_fraction:
+    [sample, full_year]
+
+esa_cci_gs_fraction:
+    [sample, full_year]
+```
+
+### InSituMatched
+
+```text
+ed_simulation_x:
+    [sample, prediction_year, month, feature]
+
+ed_simulation_y:
+    [sample, age_class, full_year, month, simulation_target]
+
+lidar_age_weight_fraction:
+    [sample, age_class]
+
+esa_cci_bl_fraction:
+    [sample, full_year]
+
+esa_cci_nl_fraction:
+    [sample, full_year]
+
+esa_cci_gs_fraction:
+    [sample, full_year]
+
+observed_y:
+    [sample, full_year, month, observed_target]
+```
+
+The following arrays are derived from `ed_simulation_y`:
+
+```text
+initial_y:
+    [sample, age_class, simulation_target]
+
+target_y:
+    [sample, age_class, prediction_year, month, simulation_target]
+```

metadata/feature_names.csv

@@ -1,22 +1,22 @@
-index_start,index_end,name,unit,description
-0,0,ta_m,K,Monthly averaged air temperature
-1,1,pr,mm,Total monthly precipitation
-2,2,tsl1,K,Monthly averaged soil temp. at 0.0988 m
-3,3,tsl2,K,Monthly averaged soil temp. at 0.1952 m
-4,4,tsl3,K,Monthly averaged soil temp. at 0.3859 m
-5,5,tsl4,K,Monthly averaged soil temp. at 0.7626 m
-6,6,tsl5,K,Monthly averaged soil temp. at 1.5071 m
-7,7,tsl6,K,Monthly averaged soil temp. at 10 m
-8,31,co2,ppm,Monthly average of hourly CO? ambient
-32,32,dst,1,Disturbance rate
-33,56,hus,1,Monthly average of hourly air specific humidity
-57,80,ta_h,K,Monthly average of hourly air temperature
-81,104,rsds,W m?�,Monthly average of hourly DSR
-105,128,sfcWind,m s?�,Monthly average of hourly wind speed
-129,129,k_sat,mm yr?�,Saturated hydraulic conductivity
-130,130,s_theta,m� m?�,Saturated water content in MVG
-131,131,r_theta,m� m?�,Residual water content in MVG
-132,132,L,1,Parameter L in MVG
-133,133,n,1,Parameter n in MVG
-134,134,m,1,Parameter m in MVG
-135,135,sd,mm,Soil depth to bedrock
+index_start,index_end,name,unit,description
+0,0,ta_m,K,Monthly averaged air temperature
+1,1,pr,mm,Total monthly precipitation
+2,2,tsl1,K,Monthly averaged soil temp. at 0.0988 m
+3,3,tsl2,K,Monthly averaged soil temp. at 0.1952 m
+4,4,tsl3,K,Monthly averaged soil temp. at 0.3859 m
+5,5,tsl4,K,Monthly averaged soil temp. at 0.7626 m
+6,6,tsl5,K,Monthly averaged soil temp. at 1.5071 m
+7,7,tsl6,K,Monthly averaged soil temp. at 10 m
+8,31,co2,ppm,Monthly average of hourly CO₂ ambient
+32,32,dst,1,Disturbance rate
+33,56,hus,1,Monthly average of hourly air specific humidity
+57,80,ta_h,K,Monthly average of hourly air temperature
+81,104,rsds,W m⁻²,Monthly average of hourly DSR
+105,128,sfcWind,m s⁻¹,Monthly average of hourly wind speed
+129,129,k_sat,mm yr⁻¹,Saturated hydraulic conductivity
+130,130,s_theta,m³ m⁻³,Saturated water content in MVG
+131,131,r_theta,m³ m⁻³,Residual water content in MVG
+132,132,L,1,Parameter L in MVG
+133,133,n,1,Parameter n in MVG
+134,134,m,1,Parameter m in MVG
+135,135,sd,mm,Soil depth to bedrock

metadata/insitu_site_metadata.csv

@@ -1,317 +1,317 @@
-network,split,site_id,latitude,longitude
-above,train,0,68.3541488,19.050333
-above,train,1,68.74617,-133.50171
-above,train,2,55.87962,-98.48081
-above,train,3,53.91634,-104.69203
-above,train,4,53.15,-104.1
-above,train,5,64.182029,19.556539
-above,train,6,55.91667,-98.96444
-above,train,7,67.05,62.933333
-above,train,8,61.3079,-121.2992
-above,train,9,67.997239,24.209178
-above,train,10,70.4696,-157.4089
-above,train,11,64.8689,-111.5748
-above,train,12,65.12367,-147.48756
-above,train,13,65.11983333,-147.5123528
-above,train,14,65.396775,-149.1214944
-above,train,15,53.62889,-106.19779
-above,train,16,64.11277777,19.45694444
-above,train,17,68.61304,161.34143
-above,train,18,62.7833,30.9333
-above,train,19,54.47,-113.32
-above,train,20,60.125,17.91805556
-above,train,21,60.016,133.824
-above,train,22,54.25392,-105.8775
-above,train,23,60.9,68.7
-above,train,24,60.8008,89.3507
-above,train,25,52.29027778,-75.25416667
-above,train,26,54.95384,-112.46698
-above,train,27,56.4615278,32.9220833
-above,train,28,53.98717,-105.11779
-above,train,29,60.08649722,17.47950278
-above,train,30,56.63583,-99.94833
-above,train,31,69.5056,-148.225
-above,train,32,63.153,-123.252
-above,train,33,67.7549,29.690137
-above,train,34,67.36238641,26.63859066
-above,train,35,60.64183,23.95952
-above,train,36,49.6925,-74.34206
-above,train,37,70.82913889,147.4942778
-above,train,38,64.1382,-51.3784
-above,train,39,63.8784,-149.2536
-above,train,40,72.3738231,126.4957919
-above,train,41,48.27333333,106.8508333
-above,train,42,60.99825,16.21727778
-above,train,43,68.4865,-155.7503
-above,train,44,48.2167,-82.1556
-above,train,45,61.84741,24.29477
-above,train,46,63.79025,-68.56005
-above,train,47,70.35,-148.55
-ameriflux,train,0,38.0992,-121.4993
-ameriflux,train,1,38.2006,-122.0264
-ameriflux,train,2,35.089,-111.762
-ameriflux,train,3,42.5378,-72.1715
-ameriflux,train,4,27.3836,-81.9509
-ameriflux,train,5,44.1467,-89.5002
-ameriflux,train,6,43.0645,-116.7486
-ameriflux,train,7,33.3482,-79.2322
-ameriflux,train,8,45.7624,-122.3303
-ameriflux,train,9,39.0603,-78.0716
-ameriflux,train,10,46.7697,-100.9154
-ameriflux,train,11,40.1776,-112.4524
-ameriflux,train,12,43.6405,-80.4123
-ameriflux,train,13,64.8618,-163.7002
-ameriflux,train,14,27.8446,-109.2977
-ameriflux,train,15,40.4619,-103.0293
-ameriflux,train,16,65.1198,-147.429
-ameriflux,train,17,-16.498,-56.412
-ameriflux,train,18,63.8811,-145.7514
-ameriflux,train,19,65.3968,-148.9348
-ameriflux,train,20,39.2298,-92.1167
-ameriflux,train,21,34.4385,-106.2377
-ameriflux,train,22,31.8173,-110.8508
-ameriflux,train,23,46.7815,-117.0821
-ameriflux,train,24,33.4012,-97.57
-ameriflux,train,25,35.799,-76.656
-ameriflux,train,26,49.8673,-125.3336
-ameriflux,train,27,38.8929,-78.1395
-ameriflux,train,28,-3.8344,-73.319
-ameriflux,train,29,47.1617,-99.1066
-ameriflux,train,30,32.5849,-106.6032
-ameriflux,train,31,38.09,-109.39
-ameriflux,train,32,39.0561,-95.1907
-ameriflux,train,33,44.9535,-110.5391
-ameriflux,train,34,28.125,-81.4362
-ameriflux,train,35,41.3795,-82.5125
-ameriflux,train,36,-7.9682,-38.3842
-ameriflux,train,37,31.5659,-110.1344
-ameriflux,train,38,28.7084,-80.7427
-ameriflux,train,39,68.6058,-149.311
-ameriflux,train,40,43.0896,-89.4158
-ameriflux,train,41,52.7008,-83.955
-ameriflux,train,42,38.7441,-92.2
-ameriflux,train,43,38.8901,-76.56
-ameriflux,train,44,39.0824,-96.5603
-ameriflux,train,45,34.3349,-106.7442
-ameriflux,train,46,44.7143,-93.0898
-ameriflux,train,47,40.8608,-77.8488
-ameriflux,train,48,49.1293,-122.9849
-ameriflux,train,49,39.0882,-75.4372
-ameriflux,train,50,65.154,-147.5026
-ameriflux,train,51,34.4255,-105.8615
-ameriflux,train,52,38.9488,-91.9945
-ameriflux,train,53,45.2091,-68.747
-ameriflux,train,54,35.8884,-106.5321
-ameriflux,train,55,43.3448,-89.7117
-ameriflux,train,56,32.5417,-87.8039
-ameriflux,train,57,40.2759,-105.5459
-ameriflux,train,58,55.1119,-122.8414
-ameriflux,train,59,29.6893,-81.9934
-ameriflux,train,60,36.8193,-97.8198
-ameriflux,train,61,58.6658,-93.83
-ameriflux,train,62,64.6963,-148.3235
-ameriflux,train,63,46.2339,-89.5373
-ameriflux,train,64,44.3167,-79.9333
-ameriflux,train,65,50.1774,-97.8686
-ameriflux,train,66,39.2167,-86.5406
-fluxnet,train,0,55.6905,12.1918
-fluxnet,train,1,-30.1913,120.6541
-fluxnet,train,2,-28.2395,-56.1886
-fluxnet,train,3,41.3966,-106.8024
-fluxnet,train,4,46.0827,-89.9792
-fluxnet,train,5,38.1087,-121.6531
-fluxnet,train,6,47.1167,11.3175
-fluxnet,train,7,-15.2588,132.3706
-fluxnet,train,8,44.7171,-0.7693
-fluxnet,train,9,41.8494,13.5881
-fluxnet,train,10,-34.0021,140.5891
-fluxnet,train,11,36.8336,-2.2523
-fluxnet,train,12,-13.0769,131.1178
-fluxnet,train,13,37.0979,-2.9658
-fluxnet,train,14,47.8064,11.3275
-fluxnet,train,15,30.4978,91.0664
-fluxnet,train,16,50.8706,6.4497
-fluxnet,train,17,54.7252,90.0022
-fluxnet,train,18,44.3869,142.3186
-fluxnet,train,19,26.7414,115.0581
-fluxnet,train,20,52.1666,5.7436
-fluxnet,train,21,35.2617,137.0788
-fluxnet,train,22,60.8986,23.5134
-fluxnet,train,23,70.8291,147.4943
-fluxnet,train,24,70.4696,-157.4089
-fluxnet,train,25,53.9872,-105.1178
-fluxnet,train,26,45.2009,9.061
-fluxnet,train,27,41.7902,111.8971
-fluxnet,train,28,-36.6732,145.0294
-fluxnet,train,29,36.9695,-3.4758
-fluxnet,train,30,42.3804,12.0266
-fluxnet,train,31,2.973,102.3062
-fluxnet,train,32,56.4842,9.5872
-fluxnet,train,33,-22.283,133.249
-fluxnet,train,34,53.8759,12.889
-fluxnet,train,35,-14.5636,132.4776
-fluxnet,train,36,-34.9893,146.2907
-fluxnet,train,37,46.6869,-91.1528
-fluxnet,train,38,13.2829,30.4783
-fluxnet,train,39,45.8126,8.6336
-fluxnet,train,40,-33.4648,-66.4598
-fluxnet,train,41,49.0996,13.3047
-fluxnet,train,42,34.2547,-89.8735
-fluxnet,train,43,44.4992,-121.6224
-fluxnet,train,44,42.7102,-80.3574
-fluxnet,train,45,-3.018,-54.9714
-fluxnet,train,46,67.9972,24.2092
-fluxnet,train,47,23.1733,112.5361
-fluxnet,train,48,-23.8587,148.4746
-fluxnet,train,49,42.4025,128.0958
-fluxnet,train,50,68.4865,-155.7503
-fluxnet,train,51,47.2858,7.7319
-fluxnet,train,52,42.0467,116.2836
-fluxnet,train,53,-12.5452,131.3072
-fluxnet,train,54,68.613,161.3414
-fluxnet,train,55,55.8796,-98.4808
-fluxnet,train,56,15.4028,-15.4322
-fluxnet,train,57,67.3624,26.6386
-fluxnet,train,58,-15.4391,23.2525
-fluxnet,train,59,37.6086,101.3269
-fluxnet,train,60,51.8922,14.0337
-fluxnet,train,61,5.2685,-2.6942
-fluxnet,train,62,31.8214,-110.8661
-fluxnet,train,63,37.37,101.18
-fluxnet,train,64,44.5934,123.5092
-fluxnet,train,65,-17.1507,133.3502
-fluxnet,train,66,49.4944,18.5429
-fluxnet,train,67,45.8059,-90.0799
-icos-ww,train,0,49.035975,17.9699
-icos-ww,train,1,56.4476,32.9019
-icos-ww,train,2,37.914998,-3.227659
-icos-ww,train,3,36.940046,-2.033208
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+multiple,test,4,40.0201,-83.0183
+multiple,test,5,46.7393,-91.1663
+multiple,test,6,49.0247,14.7704
+multiple,test,7,50.9626,13.5651
+multiple,test,8,48.8442,1.9519
+multiple,test,9,45.8444,7.5781
+multiple,test,10,51.11218,3.85043
+multiple,test,11,50.3049,5.9981
+multiple,test,12,51.0792,10.4522
+multiple,test,13,56.6358,-99.9483
+multiple,test,14,51.3076,4.5198
+multiple,test,15,42.7068,-80.3483

metadata/target_names.csv

@@ -1,11 +1,14 @@
-index,name,unit,description
-0,Height,m,Vegetation canopy height
-1,AGB,kgC m-2 yr-1,Aboveground biomass
-2,SC,kgC m-2 yr-1, Soil carbon
-3, LAI,1,Leaf area index
-4, GPP,kgC m-2 yr-1,Gross primary production
-5, NPP,kgC m-2 yr-1,Net primary production
-6, Rh,kgC m-2 yr-1,Heterotrophic respiration
-7, NEE,kgC m-2 yr-1,Net ecosystem exchange
-8, Ra,kgC m-2 yr-1,Autotrophic respiration
-9, RECO,kgC m-2 yr-1,Ecosystem respiration
+target_group,index,name,unit,description
+simulation,0,Height,m,Vegetation canopy height
+simulation,1,AGB,kgC m-2 yr-1,Aboveground biomass
+simulation,2,SC,kgC m-2 yr-1, Soil carbon
+simulation,3, LAI,1,Leaf area index
+simulation,4, GPP,kgC m-2 yr-1,Gross primary production
+simulation,5, NPP,kgC m-2 yr-1,Net primary production
+simulation,6, Rh,kgC m-2 yr-1,Heterotrophic respiration
+simulation,7, NEE,kgC m-2 yr-1,Net ecosystem exchange
+simulation,8, Ra,kgC m-2 yr-1,Autotrophic respiration
+simulation,9, RECO,kgC m-2 yr-1,Ecosystem respiration
+observed,0,GPP,kgC m-2 yr-1,Gross primary production
+observed,1,RECO,kgC m-2 yr-1,Ecosystem respiration
+observed,2,NEE,kgC m-2 yr-1,Net ecosystem exchange