Abstract
In this study, we used Landsat Earth observations and gridded weather data along with global soil datasets available in Google Earth Engine (GEE) to estimate crop yield at 30 m resolution. We implemented a remote sensing and evapotranspiration-based light use efficiency model globally and integrated abiotic environmental stressors (temperature, soil moisture, and vapor deficit stressors). The operational model (Global Yield Mapper in Earth Engine (GYMEE)) was validated against actual yield data for three agricultural schemes with different climatic, soil, and management conditions located in Lebanon, Brazil, and Spain. Field-level crop yield data on wheat, potato, and corn for 2015–2020 were used for assessment. The performance of GYMEE was statistically evaluated through root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), relative error (RE), and index of agreement (d). The results showed that the absolute difference between the modeled and predicted field-level yield was within ±16% for the analyzed crops in both Brazil and Lebanon study sites and within ±15% in the Spain site (except for two fields). GYMEE performed best for wheat crop in Lebanon with a low RMSE (0.6 t/ha), MAE (0.5 t/ha), MBE (−0.06 t/ha), and RE (0.83%). A very good agreement was observed for all analyzed crop yields, with an index of agreement (d) averaging at 0.8 in all studied sites. GYMEE shows potential in providing yield estimates for potato, wheat, and corn yields at a relative error of ±6%. We also quantified and spatialized the soil moisture stress constraint and its impact on reducing biomass production. A showcasing of moisture stress impact on two emphasized fields from the Lebanon site revealed that a 12% difference in soil moisture stress can decrease yield by 17%. A comparison between the 2017 and 2018 seasons for the potato culture of Lebanon showed that the 2017 season with lower abiotic stresses had higher light use efficiency, above-ground biomass, and yield by 5%, 10%, and 9%, respectively. The results show that the model is of high value for assessing global food production.
Highlights
Crop yield modeling and prediction have broad implications on global food security and food production monitoring
The soil moisture stress trigger θVstress−trigger is the critical value under which plants get stressed [88] computed as a function of the soil moisture at field capacity (θ FC ), soil moisture at permanent wilting point (θ PWP ), and the fraction of the total available water that can be depleted from the root zone before moisture stress (p-factor) [65]: θVstress−trigger = θ FC − p factor × (θ FC − θ PWP )
This study presents the crop yield estimate at a field scale generated by modifying and applying light use efficiency (LUE) and APAR models via the integration of environmental stresses: vapor stress (VS), plant temperature stress (TS), and soil moisture stress (SMS)
Summary
Crop yield modeling and prediction have broad implications on global food security and food production monitoring. Many researchers suggest that the radiation or light use efficiency (LUE)-based biomass model proposed by Monteith [27] has significant potential for estimating the crop yield when combined with satellite data [28,29]. Campos et al [34] used the water productivity model to estimate crop yield and suggested considering the effect of the incoming radiation as a limitation for biomass production of wheat fields but without considering the environmental stresses. Where AGB (kg/ha) is the dry above-ground biomass production for the day of the satellite overpass; APAR is the absorbed photon flux by the canopy photosynthetic elements; LUEmax is the maximum light use efficiency (g/MJ); TS, VS, and SMS are the temperature, vapor, and soil moisture stressors, respectively; and 0.864 is a unit conversion factor [41]. Where atmospheric transmissivity can be calculated from water vapor and extraterrestrial solar radiation (R a ) can be calculated from the solar constant, solar declination, and day of the year [55]
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