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Abstract. Smallholder agriculture is the bedrock of the food production system in sub-Saharan Africa. Yields in Africa are significantly below potentially attainable yields for a number of reasons, and they are particularly vulnerable to climate change impacts. Monitoring of these highly heterogeneous landscapes is needed to respond to farmer needs, develop appropriate policy and ensure food security, and Earth Observation (EO) must be part of these efforts. There is a lack of ground data for developing and testing EO methods in West Africa, and in this paper, we present data on (i) crop locations, (ii) biophysical parameters and (iii) crop yield and biomass was collected in 2020 and 2021 in Ghana and is reported in this paper. In 2020, crop type was surveyed in more than 1800 fields in three different agro-ecological zones across Ghana (Guinea Savannah, Transition and Deciduous zones). In 2021, a smaller number of fields were surveyed in the Guinea Savannah zone, and additionally, repeated measurements of leaf area index (LAI) and leaf chlorophyll concentration were made on a set of 56 maize fields. Yield and biomass were also sampled at harvesting. LAI in the sampled fields ranged from 0.1 to 5.24 m2m−2, whereas leaf chlorophyll concentration varied between 6.1 and 60.3 μgcm−2. Yield varied between 190 and 4580 kg ha−1, with an important withinfield variability (average per field standard deviation 381 kg ha−1). The data are used in this paper to: (i) evaluate the Digital Earth Africa 2019 cropland masks where 61 % of sampled 2020/21 cropland is flagged as cropland by the data set; (ii) develop and test an LAI retrieval method from Earth Observation Planet surface reflectance data (validation correlation coefficient R = 0.49, RMSE 0.44 m2m−2; (iii) create a maize classification dataset for Ghana for 2021 (overall accuracy within the region tested: 0.84); and (iv) explore the relationship between maximum LAI and crop yield using a linear model (correlation coefficient R = 0.66 and R = 0.53 for in situ and Planet-derived LAI, respectively). The data set, made available here within the context of the GEOGLAM initiative, is an important contribution to understanding crop evolution and distribution in smallholder farming systems, and will be useful for researchers developing/validating methods to monitor these systems using Earth Observation data. The data described in this paper are available from https://doi.org/10.5281/zenodo.6632083 (Gomez-Dans et al., 2022).

Abstract. Smallholder agriculture is the bedrock of the food production system in sub-Saharan Africa. Yields in Africa are significantly below potentially attainable yields for a number of reasons, and they are particularly vulnerable to climate change impacts. Monitoring of these highly heterogeneous landscapes is needed to respond to farmer needs, develop an appropriate policy and ensure food security, and Earth observation (EO) must be part of these efforts, but there is a lack of ground data for developing and testing EO methods in western Africa, and in this paper, we present data on (i) crop locations, (ii) biophysical parameters and (iii) crop yield, and biomass was collected in 2020 and 2021 in Ghana and is reported in this paper. In 2020, crop type was surveyed in more than 1800 fields in three different agroecological zones across Ghana (the Guinea Savannah, Transition and Deciduous zones). In 2021, a smaller number of fields were surveyed in the Guinea Savannah zone, and additionally, repeated measurements of leaf area index (LAI) and leaf chlorophyll concentration were made on a set of 56 maize fields. Yield and biomass were also sampled at harvesting. LAI in the sampled fields ranged from 0.1 to 5.24 m2 m−2, whereas leaf chlorophyll concentration varied between 6.1 and 60.3 µg cm−2. Yield varied between 190 and 4580 kg ha−1, with an important within-field variability (average per-field standard deviation 381 kg ha−1). The data are used in this paper to (i) evaluate the Digital Earth Africa 2019 cropland masks, where 61 % of sampled 2020/21 cropland is flagged as cropland by the data set, (ii) develop and test an LAI retrieval method from Earth observation Planet surface reflectance data (validation correlation coefficient R=0.49, root mean square error (RMSE) 0.44 m2 m−2), (iii) create a maize classification data set for Ghana for 2021 (overall accuracy within the region tested: 0.84), and (iv) explore the relationship between maximum LAI and crop yield using a linear model (correlation coefficient R=0.66 and R=0.53 for in situ and Planet-derived LAI, respectively). The data set, made available here within the context of the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) initiative, is an important contribution to understanding crop evolution and distribution in smallholder farming systems and will be useful for researchers developing/validating methods to monitor these systems using Earth observation data. The data described in this paper are available from https://doi.org/10.5281/zenodo.6632083 (Gomez-Dans et al., 2022).