The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest data sources as input for crop-water balance models, thereby improving the crop water requirements (CWR) and yield estimates from the field to the regional scale. Satellite imagery and numerical weather prediction outputs offer high resolution (in time and space) gridded data that can compensate for the paucity of crop parameter field measurements and ground weather observations, as required for assessments of CWR and yield. In this study, the AquaCrop model was used to assess CWR and yield of tomato on a farm in Southern Italy by assimilating Sentinel-2 (S2) canopy cover imagery and using CM-SAF satellite-based radiation data and ERA5-Land reanalysis as forcing weather data. The prediction accuracy was evaluated with field data collected during the irrigation season (April-July) of 2021. Satellite estimates of canopy cover differed from ground observations, with a RMSE of about 11%. CWR and yield predictions were compared with actual data regarding irrigation volumes and harvested yield. The results showed that S2 estimates of crop parameters represent added value, since their assimilation into crop growth models improved CWR and yield estimates. Reliable CWR and yield estimates can be achieved by combining the ERA5-Land and CM-SAF weather databases with S2 imagery for assimilation into the AquaCrop model.

Assessing Crop Water Requirement and Yield by Combining ERA5-Land Reanalysis Data with CM-SAF Satellite-Based Radiation Data and Sentinel-2 Satellite Imagery

Pelosi A.
;
2022-01-01

Abstract

The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest data sources as input for crop-water balance models, thereby improving the crop water requirements (CWR) and yield estimates from the field to the regional scale. Satellite imagery and numerical weather prediction outputs offer high resolution (in time and space) gridded data that can compensate for the paucity of crop parameter field measurements and ground weather observations, as required for assessments of CWR and yield. In this study, the AquaCrop model was used to assess CWR and yield of tomato on a farm in Southern Italy by assimilating Sentinel-2 (S2) canopy cover imagery and using CM-SAF satellite-based radiation data and ERA5-Land reanalysis as forcing weather data. The prediction accuracy was evaluated with field data collected during the irrigation season (April-July) of 2021. Satellite estimates of canopy cover differed from ground observations, with a RMSE of about 11%. CWR and yield predictions were compared with actual data regarding irrigation volumes and harvested yield. The results showed that S2 estimates of crop parameters represent added value, since their assimilation into crop growth models improved CWR and yield estimates. Reliable CWR and yield estimates can be achieved by combining the ERA5-Land and CM-SAF weather databases with S2 imagery for assimilation into the AquaCrop model.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4814527
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