Merge branch 'master' into pre-commit-ci-update-config

notebooks/intercomparison-results.csv

@@ -2,6 +2,7 @@
 Togo,copernicus,Togo,0.61,0.18,0.78,0.03,0.57,0.05,0.87,0.02,0.66,0.07,0.82,0.03,58,132,115,17,25,33
 Togo,worldcover-v100,Togo,0.73,0.14,0.86,0.03,0.62,0.05,0.97,0.01,0.9,0.05,0.85,0.03,58,132,128,4,22,36
 Togo,worldcover-v200,Togo,0.75,0.15,0.87,0.02,0.66,0.05,0.96,0.02,0.88,0.05,0.86,0.03,58,132,127,5,20,38
+Togo,worldcereal-v100,Togo,0.76,0.14,0.87,0.02,0.67,0.05,0.96,0.02,0.89,0.05,0.87,0.03,58,132,127,5,19,39
 Togo,glad,Togo,0.71,0.14,0.85,0.03,0.59,0.05,0.97,0.01,0.89,0.05,0.84,0.03,58,132,128,4,24,34
 Togo,asap,Togo,0.47,0.17,0.63,0.03,0.53,0.05,0.67,0.02,0.42,0.06,0.77,0.04,58,132,89,43,27,31
 Togo,dynamicworld,Togo,0.26,0.1,0.74,0.03,0.16,0.02,0.99,0.01,0.9,0.1,0.73,0.03,58,132,131,1,49,9
@@ -18,6 +19,7 @@ Togo,ensemble-subset,Togo,0.57,0.15,0.81,0.03,0.43,0.04,0.97,0.01,0.86,0.07,0.8,
 Kenya,copernicus,Kenya,0.49,0.19,0.91,0.01,0.69,0.07,0.92,0.01,0.38,0.06,0.98,0.01,36,538,497,41,11,25
 Kenya,worldcover-v100,Kenya,0.41,0.24,0.94,0.01,0.31,0.06,0.99,0.0,0.61,0.12,0.96,0.01,36,538,531,7,25,11
 Kenya,worldcover-v200,Kenya,0.48,0.22,0.95,0.01,0.36,0.06,0.99,0.0,0.72,0.11,0.96,0.01,36,538,533,5,23,13
+Kenya,worldcereal-v100,Kenya,0.36,0.22,0.94,0.01,0.25,0.05,0.99,0.0,0.64,0.13,0.95,0.01,36,538,533,5,27,9
 Kenya,glad,Kenya,0.73,0.21,0.96,0.01,0.78,0.06,0.98,0.01,0.68,0.07,0.98,0.01,36,538,525,13,8,28
 Kenya,asap,Kenya,0.36,0.2,0.9,0.01,0.44,0.08,0.93,0.01,0.3,0.06,0.96,0.01,36,538,500,38,20,16
 Kenya,dynamicworld,Kenya,0.42,0.18,0.89,0.01,0.61,0.08,0.91,0.01,0.31,0.06,0.97,0.01,36,538,490,48,14,22
@@ -34,6 +36,7 @@ Kenya,ensemble-subset,Kenya,0.77,0.2,0.97,0.01,0.78,0.06,0.98,0.0,0.76,0.07,0.99
 Malawi,copernicus,Malawi,0.65,0.23,0.85,0.03,0.71,0.07,0.88,0.02,0.59,0.08,0.92,0.02,31,125,110,15,9,22
 Malawi,worldcover-v100,Malawi,0.73,0.22,0.88,0.02,0.77,0.07,0.91,0.02,0.69,0.08,0.94,0.02,31,125,114,11,7,24
 Malawi,worldcover-v200,Malawi,0.77,0.2,0.9,0.02,0.87,0.06,0.9,0.02,0.69,0.07,0.97,0.02,31,125,113,12,4,27
+Malawi,worldcereal-v100,Malawi,0.65,0.23,0.85,0.03,0.71,0.07,0.88,0.02,0.59,0.08,0.92,0.02,31,125,110,15,9,22
 Malawi,glad,Malawi,0.61,0.25,0.86,0.03,0.55,0.07,0.94,0.02,0.68,0.1,0.89,0.03,31,125,117,8,14,17
 Malawi,asap,Malawi,0.32,0.21,0.67,0.03,0.39,0.08,0.74,0.02,0.27,0.07,0.83,0.04,31,125,93,32,19,12
 Malawi,dynamicworld,Malawi,0.33,0.24,0.82,0.03,0.23,0.05,0.97,0.01,0.64,0.15,0.83,0.03,31,125,121,4,24,7
@@ -50,6 +53,7 @@ Malawi,ensemble-subset,Malawi,0.67,0.24,0.87,0.03,0.68,0.07,0.91,0.02,0.66,0.09,
 Tanzania,copernicus,Tanzania,0.8,0.05,0.85,0.01,0.72,0.02,0.94,0.01,0.88,0.02,0.83,0.01,519,777,728,49,144,375
 Tanzania,worldcover-v100,Tanzania,0.86,0.04,0.9,0.01,0.79,0.02,0.97,0.01,0.95,0.01,0.87,0.01,519,777,754,23,111,408
 Tanzania,worldcover-v200,Tanzania,0.84,0.04,0.88,0.01,0.76,0.02,0.97,0.01,0.94,0.01,0.86,0.01,519,777,750,27,124,395
+Tanzania,worldcereal-v100,Tanzania,0.8,0.04,0.86,0.01,0.71,0.02,0.96,0.01,0.93,0.01,0.83,0.01,519,777,748,29,150,369
 Tanzania,glad,Tanzania,0.8,0.04,0.86,0.01,0.69,0.02,0.98,0.01,0.95,0.01,0.83,0.01,519,777,760,17,159,360
 Tanzania,asap,Tanzania,0.74,0.05,0.77,0.01,0.79,0.02,0.76,0.01,0.69,0.02,0.84,0.01,519,777,594,183,109,410
 Tanzania,dynamicworld,Tanzania,0.58,0.04,0.76,0.01,0.42,0.01,0.99,0.0,0.95,0.01,0.72,0.01,519,777,766,11,303,216
@@ -66,6 +70,7 @@ Tanzania,ensemble-subset,Tanzania,0.82,0.04,0.87,0.01,0.7,0.02,0.99,0.0,0.98,0.0
 Mali,copernicus,Mali,0.42,0.27,0.93,0.01,0.55,0.1,0.95,0.01,0.33,0.08,0.98,0.01,20,407,385,22,9,11
 Mali,worldcover-v100,Mali,0.57,0.26,0.95,0.01,0.75,0.09,0.96,0.01,0.45,0.09,0.99,0.01,20,407,389,18,5,15
 Mali,worldcover-v200,Mali,0.62,0.26,0.95,0.01,0.8,0.08,0.96,0.01,0.5,0.09,0.99,0.01,20,407,391,16,4,16
+Mali,worldcereal-v100,Mali,0.62,0.3,0.96,0.01,0.65,0.09,0.98,0.01,0.59,0.11,0.98,0.01,20,407,398,9,7,13
 Mali,glad,Mali,0.51,0.31,0.95,0.01,0.55,0.1,0.97,0.01,0.48,0.11,0.98,0.01,20,407,395,12,9,11
 Mali,asap,Mali,0.33,0.17,0.86,0.01,0.75,0.09,0.86,0.01,0.21,0.05,0.99,0.01,20,407,351,56,5,15
 Mali,dynamicworld,Mali,0.2,0.26,0.93,0.01,0.2,0.08,0.96,0.0,0.2,0.09,0.96,0.01,20,407,391,16,16,4
@@ -81,6 +86,7 @@ Mali,ensemble-subset,Mali,0.47,0.32,0.95,0.01,0.45,0.1,0.98,0.01,0.5,0.12,0.97,0
 Namibia,copernicus,Namibia,0.3,0.39,0.97,0.0,0.6,0.21,0.97,0.0,0.2,0.11,1.0,0.0,5,447,435,12,2,3
 Namibia,worldcover-v100,Namibia,0.15,0.44,0.98,0.0,0.2,0.18,0.98,0.0,0.12,0.12,0.99,0.0,5,447,440,7,4,1
 Namibia,worldcover-v200,Namibia,0.19,0.34,0.96,0.0,0.4,0.21,0.97,0.0,0.12,0.09,0.99,0.0,5,447,433,14,3,2
+Namibia,worldcereal-v100,Namibia,0.3,0.39,0.97,0.0,0.6,0.21,0.97,0.0,0.2,0.11,1.0,0.0,5,447,435,12,2,3
 Namibia,glad,Namibia,0.23,0.3,0.96,0.0,0.6,0.21,0.96,0.0,0.14,0.08,1.0,0.0,5,447,429,18,2,3
 Namibia,asap,Namibia,0.08,0.08,0.81,0.0,0.8,0.18,0.81,0.0,0.04,0.02,1.0,0.0,5,447,360,87,1,4
 Namibia,dynamicworld,Namibia,0.16,0.29,0.95,0.0,0.4,0.22,0.96,0.0,0.1,0.07,0.99,0.0,5,447,429,18,3,2
@@ -97,6 +103,7 @@ Namibia,ensemble-subset,Namibia,0.44,0.71,0.99,0.0,0.4,0.2,1.0,0.0,0.5,0.29,0.99
 Rwanda,copernicus,Rwanda,0.7,0.16,0.73,0.04,0.69,0.05,0.76,0.04,0.71,0.06,0.74,0.05,61,72,55,17,19,42
 Rwanda,worldcover-v100,Rwanda,0.81,0.13,0.84,0.03,0.74,0.04,0.93,0.03,0.9,0.04,0.81,0.04,61,72,67,5,16,45
 Rwanda,worldcover-v200,Rwanda,0.78,0.13,0.82,0.03,0.69,0.05,0.93,0.03,0.89,0.05,0.78,0.04,61,72,67,5,19,42
+Rwanda,worldcereal-v100,Rwanda,0.48,0.05,0.68,0.04,0.31,0.03,1.0,0.0,1.0,0.0,0.63,0.05,61,72,72,0,42,19
 Rwanda,glad,Rwanda,0.67,0.14,0.75,0.04,0.54,0.04,0.93,0.03,0.87,0.06,0.71,0.05,61,72,67,5,28,33
 Rwanda,asap,Rwanda,0.27,0.16,0.56,0.04,0.18,0.03,0.88,0.03,0.55,0.11,0.56,0.05,61,72,63,9,50,11
 Rwanda,dynamicworld,Rwanda,0.42,0.04,0.66,0.04,0.26,0.02,1.0,0.0,1.0,0.0,0.62,0.05,61,72,72,0,45,16
@@ -112,6 +119,7 @@ Rwanda,ensemble-subset,Rwanda,0.77,0.14,0.81,0.03,0.69,0.05,0.92,0.03,0.88,0.05,
 Uganda,copernicus,Uganda,0.41,0.19,0.78,0.02,0.68,0.08,0.79,0.01,0.29,0.06,0.95,0.02,28,217,171,46,9,19
 Uganda,worldcover-v100,Uganda,0.49,0.24,0.91,0.02,0.36,0.06,0.99,0.01,0.77,0.12,0.92,0.02,28,217,214,3,18,10
 Uganda,worldcover-v200,Uganda,0.43,0.27,0.89,0.02,0.36,0.07,0.96,0.01,0.56,0.12,0.92,0.02,28,217,209,8,18,10
+Uganda,worldcereal-v100,Uganda,0.44,0.25,0.86,0.02,0.5,0.08,0.9,0.01,0.4,0.08,0.93,0.02,28,217,196,21,14,14
 Uganda,glad,Uganda,0.56,0.24,0.88,0.02,0.68,0.08,0.9,0.01,0.48,0.08,0.96,0.01,28,217,196,21,9,19
 Uganda,asap,Uganda,0.28,0.11,0.55,0.02,0.79,0.07,0.52,0.01,0.17,0.03,0.95,0.02,28,217,112,105,6,22
 Uganda,dynamicworld,Uganda,0.29,0.27,0.88,0.02,0.21,0.06,0.97,0.01,0.46,0.14,0.91,0.02,28,217,210,7,22,6
@@ -128,6 +136,7 @@ Uganda,ensemble-subset,Uganda,0.55,0.26,0.89,0.02,0.57,0.08,0.94,0.01,0.53,0.09,
 Zambia,copernicus,Zambia,0.62,0.34,0.95,0.01,0.82,0.11,0.96,0.01,0.5,0.12,0.99,0.01,11,206,197,9,2,9
 Zambia,worldcover-v100,Zambia,0.86,0.31,0.99,0.01,0.82,0.11,1.0,0.0,0.9,0.1,0.99,0.01,11,206,205,1,2,9
 Zambia,worldcover-v200,Zambia,0.67,0.36,0.96,0.01,0.82,0.11,0.97,0.01,0.56,0.13,0.99,0.01,11,206,199,7,2,9
+Zambia,worldcereal-v100,Zambia,0.72,0.36,0.97,0.01,0.82,0.11,0.98,0.01,0.64,0.13,0.99,0.01,11,206,201,5,2,9
 Zambia,glad,Zambia,0.83,0.31,0.98,0.01,0.91,0.08,0.99,0.01,0.77,0.12,1.0,0.0,11,206,203,3,1,10
 Zambia,asap,Zambia,0.2,0.14,0.7,0.01,0.73,0.13,0.7,0.01,0.11,0.04,0.98,0.01,11,206,144,62,3,8
 Zambia,dynamicworld,Zambia,0.56,0.43,0.96,0.01,0.45,0.12,0.99,0.01,0.71,0.18,0.97,0.01,11,206,204,2,6,5
@@ -144,6 +153,7 @@ Zambia,ensemble-subset,Zambia,0.91,0.26,0.99,0.01,0.91,0.08,1.0,0.0,0.91,0.09,1.
 Hawaii,copernicus,Hawaii,0.0,,0.98,0.01,0.0,0.0,0.99,0.0,0.0,0.0,0.98,0.01,6,354,352,2,6,0
 Hawaii,worldcover-v100,Hawaii,0.0,,0.98,0.01,0.0,0.0,1.0,0.0,0.0,-0.0,0.98,0.01,6,354,354,0,6,0
 Hawaii,worldcover-v200,Hawaii,0.0,,0.98,0.01,0.0,0.0,1.0,0.0,0.0,-0.0,0.98,0.01,6,354,354,0,6,0
+Hawaii,worldcereal-v100,Hawaii,0.0,,0.98,0.01,0.0,0.0,1.0,0.0,0.0,-0.0,0.98,0.01,6,354,354,0,6,0
 Hawaii,glad,Hawaii,0.0,,0.98,,0.0,,1.0,,0.0,,0.98,0.01,6,354,353,1,6,0
 Hawaii,dynamicworld,Hawaii,0.13,0.38,0.96,0.01,0.17,0.15,0.98,0.0,0.11,0.11,0.99,0.01,6,354,346,8,5,1
 Hawaii,gfsad-gcep,Hawaii,0.24,0.19,0.91,0.01,0.83,0.15,0.91,0.01,0.14,0.06,1.0,0.0,6,354,323,31,1,5
@@ -156,6 +166,7 @@ Hawaii,ensemble-subset,Hawaii,0.0,,0.98,0.01,0.0,0.0,1.0,0.0,0.0,-0.0,0.98,0.01,
 BlueNile2020,copernicus,BlueNile2020,0.73,0.07,0.81,0.02,0.65,0.02,0.91,0.01,0.82,0.03,0.81,0.02,252,414,377,37,87,165
 BlueNile2020,worldcover-v100,BlueNile2020,0.71,0.07,0.82,0.01,0.58,0.02,0.96,0.01,0.9,0.02,0.79,0.02,252,414,398,16,106,146
 BlueNile2020,worldcover-v200,BlueNile2020,0.66,0.07,0.79,0.02,0.54,0.02,0.94,0.01,0.85,0.03,0.77,0.02,252,414,390,24,115,137
+BlueNile2020,worldcereal-v100,BlueNile2020,0.69,0.07,0.8,0.02,0.58,0.02,0.93,0.01,0.84,0.03,0.79,0.02,252,414,385,29,105,147
 BlueNile2020,glad,BlueNile2020,0.73,0.07,0.82,0.01,0.63,0.02,0.93,0.01,0.85,0.03,0.81,0.02,252,414,387,27,93,159
 BlueNile2020,asap,BlueNile2020,0.29,0.08,0.59,0.02,0.23,0.02,0.8,0.01,0.41,0.04,0.63,0.02,252,414,333,81,195,57
 BlueNile2020,dynamicworld,BlueNile2020,0.78,0.07,0.84,0.01,0.76,0.02,0.88,0.01,0.8,0.03,0.86,0.02,252,414,365,49,60,192
@@ -172,6 +183,7 @@ BlueNile2020,ensemble-subset,BlueNile2020,0.62,0.07,0.78,0.02,0.48,0.02,0.96,0.0
 BlueNile2019,copernicus,BlueNile2019,0.77,0.05,0.82,0.01,0.67,0.02,0.95,0.01,0.92,0.02,0.77,0.02,337,404,385,19,112,225
 BlueNile2019,worldcover-v100,BlueNile2019,0.75,0.05,0.81,0.01,0.61,0.02,0.98,0.01,0.96,0.01,0.75,0.02,337,404,396,8,131,206
 BlueNile2019,worldcover-v200,BlueNile2019,0.71,0.05,0.79,0.01,0.57,0.02,0.97,0.01,0.94,0.02,0.73,0.02,337,404,391,13,144,193
+BlueNile2019,worldcereal-v100,BlueNile2019,0.74,0.05,0.81,0.01,0.61,0.02,0.98,0.01,0.97,0.01,0.75,0.02,337,404,397,7,133,204
 BlueNile2019,glad,BlueNile2019,0.79,0.05,0.83,0.01,0.69,0.02,0.95,0.01,0.92,0.02,0.79,0.02,337,404,383,21,103,234
 BlueNile2019,asap,BlueNile2019,0.33,0.07,0.54,0.02,0.25,0.02,0.79,0.01,0.5,0.04,0.56,0.02,337,404,318,86,252,85
 BlueNile2019,dynamicworld,BlueNile2019,0.79,0.06,0.83,0.01,0.72,0.02,0.92,0.01,0.88,0.02,0.8,0.02,337,404,370,34,95,242
@@ -188,6 +200,7 @@ BlueNile2019,ensemble-subset,BlueNile2019,0.66,0.05,0.76,0.02,0.5,0.02,0.98,0.01
 AlGadaref2019,copernicus,AlGadaref2019,0.75,0.06,0.68,0.02,0.86,0.01,0.47,0.02,0.66,0.02,0.74,0.03,370,314,148,166,51,319
 AlGadaref2019,worldcover-v100,AlGadaref2019,0.81,0.05,0.79,0.02,0.85,0.02,0.71,0.02,0.78,0.02,0.81,0.02,370,314,224,90,54,316
 AlGadaref2019,worldcover-v200,AlGadaref2019,0.79,0.05,0.75,0.02,0.89,0.01,0.58,0.02,0.71,0.02,0.82,0.03,370,314,182,132,40,330
+AlGadaref2019,worldcereal-v100,AlGadaref2019,0.82,0.05,0.77,0.02,0.95,0.01,0.57,0.02,0.72,0.02,0.9,0.02,370,314,179,135,20,350
 AlGadaref2019,glad,AlGadaref2019,0.77,0.06,0.73,0.02,0.82,0.02,0.62,0.02,0.72,0.02,0.75,0.03,370,314,196,118,65,305
 AlGadaref2019,asap,AlGadaref2019,0.28,0.07,0.42,0.02,0.21,0.02,0.67,0.02,0.43,0.04,0.42,0.02,370,314,210,104,293,77
 AlGadaref2019,dynamicworld,AlGadaref2019,0.72,0.05,0.64,0.02,0.86,0.01,0.39,0.02,0.62,0.02,0.7,0.03,370,314,122,192,52,318
@@ -204,6 +217,7 @@ AlGadaref2019,ensemble-subset,AlGadaref2019,0.79,0.06,0.76,0.02,0.84,0.02,0.68,0
 BureJimma2019,copernicus,BureJimma2019,0.72,0.08,0.79,0.02,0.75,0.03,0.81,0.02,0.69,0.03,0.85,0.02,212,377,304,73,52,160
 BureJimma2019,worldcover-v100,BureJimma2019,0.67,0.09,0.78,0.02,0.61,0.03,0.88,0.01,0.74,0.03,0.8,0.02,212,377,331,46,83,129
 BureJimma2019,worldcover-v200,BureJimma2019,0.75,0.08,0.82,0.02,0.72,0.03,0.88,0.01,0.78,0.03,0.85,0.02,212,377,333,44,60,152
+BureJimma2019,worldcereal-v100,BureJimma2019,0.68,0.08,0.8,0.02,0.59,0.03,0.93,0.01,0.82,0.03,0.8,0.02,212,377,349,28,87,125
 BureJimma2019,glad,BureJimma2019,0.68,0.08,0.81,0.02,0.56,0.02,0.94,0.01,0.85,0.03,0.79,0.02,212,377,356,21,93,119
 BureJimma2019,asap,BureJimma2019,0.54,0.09,0.62,0.02,0.61,0.03,0.63,0.02,0.48,0.03,0.74,0.02,212,377,239,138,83,129
 BureJimma2019,dynamicworld,BureJimma2019,0.63,0.08,0.78,0.02,0.5,0.02,0.94,0.01,0.83,0.03,0.77,0.02,212,377,356,21,106,106
@@ -220,6 +234,7 @@ BureJimma2019,ensemble-subset,BureJimma2019,0.76,0.08,0.84,0.02,0.71,0.03,0.92,0
 BureJimma2020,copernicus,BureJimma2020,0.72,0.09,0.81,0.02,0.77,0.03,0.82,0.02,0.67,0.03,0.89,0.02,168,357,293,64,38,130
 BureJimma2020,worldcover-v100,BureJimma2020,0.76,0.09,0.85,0.02,0.71,0.03,0.92,0.01,0.81,0.03,0.87,0.02,168,357,328,29,48,120
 BureJimma2020,worldcover-v200,BureJimma2020,0.8,0.09,0.88,0.01,0.77,0.03,0.93,0.01,0.84,0.03,0.89,0.02,168,357,332,25,39,129
+BureJimma2020,worldcereal-v100,BureJimma2020,0.75,0.08,0.86,0.02,0.64,0.03,0.97,0.01,0.9,0.03,0.85,0.02,168,357,345,12,60,108
 BureJimma2020,glad,BureJimma2020,0.68,0.09,0.83,0.02,0.57,0.03,0.95,0.01,0.84,0.03,0.82,0.02,168,357,339,18,73,95
 BureJimma2020,asap,BureJimma2020,0.52,0.1,0.66,0.02,0.58,0.03,0.69,0.02,0.47,0.03,0.78,0.02,168,357,247,110,71,97
 BureJimma2020,dynamicworld,BureJimma2020,0.67,0.09,0.82,0.02,0.56,0.03,0.95,0.01,0.84,0.03,0.82,0.02,168,357,339,18,74,94
@@ -236,6 +251,7 @@ BureJimma2020,ensemble-subset,BureJimma2020,0.76,0.09,0.86,0.02,0.7,0.03,0.93,0.
 Tigray2021,copernicus,Tigray2021,0.55,0.1,0.68,0.02,0.53,0.03,0.77,0.02,0.57,0.04,0.74,0.02,185,322,248,74,87,98
 Tigray2021,worldcover-v100,Tigray2021,0.63,0.09,0.71,0.02,0.68,0.03,0.73,0.02,0.59,0.03,0.8,0.02,185,322,235,87,60,125
 Tigray2021,worldcover-v200,Tigray2021,0.65,0.09,0.73,0.02,0.7,0.03,0.75,0.02,0.62,0.03,0.81,0.02,185,322,242,80,56,129
+Tigray2021,worldcereal-v100,Tigray2021,0.55,0.1,0.73,0.02,0.45,0.03,0.89,0.01,0.71,0.04,0.74,0.02,185,322,287,35,101,84
 Tigray2021,glad,Tigray2021,0.48,0.1,0.67,0.02,0.42,0.03,0.81,0.02,0.56,0.04,0.71,0.02,185,322,261,61,108,77
 Tigray2021,asap,Tigray2021,0.41,0.09,0.54,0.02,0.44,0.03,0.6,0.02,0.39,0.03,0.65,0.03,185,322,193,129,103,82
 Tigray2021,dynamicworld,Tigray2021,0.54,0.1,0.72,0.02,0.45,0.03,0.87,0.01,0.67,0.04,0.73,0.02,185,322,281,41,102,83
@@ -252,6 +268,7 @@ Tigray2021,ensemble-subset,Tigray2021,0.58,0.1,0.74,0.02,0.5,0.03,0.87,0.01,0.69
 Tigray2020,copernicus,Tigray2020,0.58,0.08,0.67,0.02,0.53,0.02,0.78,0.02,0.65,0.03,0.69,0.02,304,398,312,86,143,161
 Tigray2020,worldcover-v100,Tigray2020,0.71,0.06,0.79,0.02,0.61,0.02,0.92,0.01,0.86,0.02,0.76,0.02,304,398,367,31,119,185
 Tigray2020,worldcover-v200,Tigray2020,0.74,0.06,0.8,0.01,0.65,0.02,0.92,0.01,0.87,0.02,0.77,0.02,304,398,368,30,107,197
+Tigray2020,worldcereal-v100,Tigray2020,0.61,0.06,0.74,0.02,0.47,0.02,0.94,0.01,0.86,0.03,0.7,0.02,304,398,375,23,161,143
 Tigray2020,glad,Tigray2020,0.51,0.07,0.69,0.02,0.37,0.02,0.93,0.01,0.81,0.03,0.66,0.02,304,398,371,27,190,114
 Tigray2020,asap,Tigray2020,0.52,0.08,0.6,0.02,0.5,0.02,0.68,0.02,0.54,0.03,0.64,0.02,304,398,269,129,153,151
 Tigray2020,dynamicworld,Tigray2020,0.52,0.07,0.69,0.02,0.38,0.02,0.92,0.01,0.79,0.03,0.66,0.02,304,398,368,30,188,116
@@ -268,6 +285,7 @@ Tigray2020,ensemble-subset,Tigray2020,0.55,0.06,0.71,0.02,0.4,0.02,0.95,0.01,0.8
 Senegal,copernicus,Senegal,0.61,0.16,0.9,0.01,0.72,0.05,0.92,0.01,0.53,0.06,0.96,0.01,61,486,447,39,17,44
 Senegal,worldcover-v100,Senegal,0.64,0.17,0.91,0.01,0.69,0.05,0.94,0.01,0.6,0.06,0.96,0.01,61,486,458,28,19,42
 Senegal,worldcover-v200,Senegal,0.66,0.16,0.91,0.01,0.74,0.05,0.94,0.01,0.59,0.06,0.97,0.01,61,486,455,31,16,45
+Senegal,worldcereal-v100,Senegal,0.63,0.17,0.92,0.01,0.66,0.05,0.95,0.01,0.62,0.06,0.96,0.01,61,486,461,25,21,40
 Senegal,glad,Senegal,0.61,0.17,0.9,0.01,0.69,0.05,0.93,0.01,0.55,0.06,0.96,0.01,61,486,452,34,19,42
 Senegal,asap,Senegal,0.32,0.14,0.78,0.01,0.46,0.06,0.82,0.01,0.25,0.04,0.92,0.01,61,486,400,86,33,28
 Senegal,dynamicworld,Senegal,0.42,0.18,0.88,0.01,0.41,0.05,0.93,0.01,0.44,0.07,0.93,0.01,61,486,454,32,36,25

notebooks/intercomparison_script.ipynb

@@ -70,7 +70,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "# Load the test data points\n",
@@ -107,10 +109,11 @@
     "# comp_maps = ['copernicus', 'worldcover-v100', 'glad', 'asap', 'dynamicworld', 'gfsad-gcep',\n",
     "#              'digital-earth-africa', 'esa-cci-africa', 'globcover-v23', 'esri-lulc', 'nabil-etal-2021']\n",
     "# Ensemble maps that are available globally\n",
-    "comp_maps = ['copernicus', 'worldcover-v200', 'glad','dynamicworld', 'gfsad-gcep', 'globcover-v23', 'esri-lulc']\n",
+    "comp_maps = ['copernicus', 'worldcover-v200', 'glad', 'worldcereal-v100',\n",
+    "             'dynamicworld', 'gfsad-gcep', 'globcover-v23', 'esri-lulc']\n",
     "\n",
     "for country in countries:\n",
-    "    ensemble_subset = extracted[country][comp_maps].mode(axis='columns')\n",
+    "    ensemble_subset = extracted[country][comp_maps].mode(axis='columns')[0]\n",
     "    extracted[country]['ensemble-subset'] = ensemble_subset\n",
     "    \n",
     "covermap_test.sampled_maps = extracted\n",

src/area_utils.py

@@ -118,7 +118,7 @@ def load_raster(
     in_raster: str, boundary: Optional[gpd.GeoDataFrame] = None
 ) -> Tuple[Optional[np.ma.core.MaskedArray], dict]:
     """
-    Chcked if the raster is projected in the correct CRS.
+    Check if the raster is projected in the correct CRS.
     If not, reproject it.
     Clip the raster to the boundary. If no boundary is provided, clip to map bounds.
     in_raster: path to the input raster
@@ -131,7 +131,7 @@ def load_raster(
             print(
                 """WARNING: The map CRS is EPSG:4326. This means the map unit is degrees \
                 and the pixel-wise areas will not be in meters.
-                \n You need to project the  project the map to using the local UTM Zone \
+                \n You need to project the map to the local UTM Zone \
                 (EPSG:XXXXX)."""
             )
             t_srs = input("Input EPSG Code; EPSG:XXXX:")
@@ -144,10 +144,12 @@ def load_raster(
             return clip_raster(in_raster, boundary)
 
 
-def binarize(raster: np.ma.core.MaskedArray, meta: dict, threshold: float = 0.5) -> np.ndarray:
+def binarize(
+    raster: np.ma.core.MaskedArray, meta: dict, threshold: Optional[float] = 0.5
+) -> np.ma.core.MaskedArray:
     raster.data[raster.data < threshold] = 0
     raster.data[((raster.data >= threshold) & (raster.data != meta["nodata"]))] = 1
-    return raster.data.astype(np.uint8)
+    return raster.astype(np.uint8)
 
 
 def cal_map_area_class(

src/compare_covermaps.py

@@ -507,6 +507,17 @@ def generate_report(dataset_name: str, country: str, true, pred) -> pd.DataFrame
         resolution=10,
         crop_labels=[40],
     ),
+    "worldcereal-v100": Covermap(
+        "worldcereal-v100",
+        """ee.ImageCollection(
+            ee.ImageCollection("ESA/WorldCereal/2021/MODELS/v100")
+            .filter('product == "temporarycrops"')
+            .select("classification")
+            .mosaic()
+        )""",
+        resolution=10,
+        crop_labels=[100],
+    ),
     "glad": Covermap(
         "glad",
         'ee.ImageCollection("users/potapovpeter/Global_cropland_2019")',
@@ -625,6 +636,11 @@ def generate_report(dataset_name: str, country: str, true, pred) -> pd.DataFrame
                 ee.Image("users/adadebay/Tanzania_cropland_2019"),
                 ee.Image("users/eutzschn/Ethiopia_Bure_Jimma_2020_v1"),
                 ee.Image("users/izvonkov/Ethiopia_Bure_Jimma_2019_v1"),
+                ee.Image(
+                    "users/izvonkov/Rwanda_2019_skip_era5_min_lat--3"
+                    "-035_min_lon-28-43_max_lat--0-76_max_lon-31-013"
+                    "_dates-2019-02-01_202"
+                )
             ]
         )""",
         resolution=10,
@@ -642,6 +658,7 @@ def generate_report(dataset_name: str, country: str, true, pred) -> pd.DataFrame
             "BureJimma2020",
             "Tigray2021",
             "Tigray2020",
+            "Rwanda",
         ],
     ),
 }