ALBERT

Description: Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

Description: 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.