No abstract

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.

Evapotranspiration (ET) provides a robust connection between hydrological cycles and surface energy balance. Accurate and near‐daily ET estimation has utility in water resources, agricultural management applications, crop yields and drought monitoring. This study describes the implementation of an ET modeling system based on a Priestley‐Taylor version of the Two‐Source (soil and vegetation) Energy Balance Model (TSEB‐PT) within Google Earth Engine environment. TSEB‐PT performance was compared with the simpler single‐source HSEB (Hybrid Surface Energy Balance) ET model to assess relative advantages and disadvantages for operational application. Results were evaluated across multiple biomes and climatic zones across the US, Europe, and Australia in comparison with eddy covariance data from 30 flux tower sites. Both models produced similar results when considering all biomes at daily, weekly, and monthly timescales. Daily ET metrics for all sites combined yielded comparable results for both models, with a slightly lower root‐mean‐square error for TSEB‐PT (HSEB) of 1.2 (1.3) mm/d and a higher correlation (r) of 0.83 (0.80), but a larger mean percent bias error (MPBE = −9%) than HSEB (MPBE = 1%). TSEB‐PT performance was lowest for sites in warm summer humid continental and hot semi‐arid climates and in evergreen broadleaf forest cover, while HSEB showed lowest performance in tropical savanna hot semi‐arid climates and in savanna covers. Model performance was improved for both cropland and non‐cropland sites when TSEB‐PT and HSEB ET estimates were combined through simple averaging due to cancellation of opposing errors, showing a promise as potential tools for water resource management on a global scale.

No abstract

With the foreseen increase in population and the reliance on water as a key input for agricultural production, greater demand will be placed on freshwater supplies. The objective of this work was to present the newly developed Android smartphone application to calculate crop evapotranspiration in real-time to support field-scale irrigation management. As part of the answer to water shortage, we embraced technology by developing AgSAT, a Google Earth Engine-based application that optimizes water use for food production. AgSAT uses meteorological data to calculate daily water requirements using the ASCE-Penman–Monteith method (ETref) and vegetation indices from satellite imagery to derive the basal crop growth coefficient, Kcb. The performance of AgSAT to estimate ETref was assessed using climatic data from 18 meteorological stations distributed over several climatic zones worldwide. ETref estimation through the app showed acceptable results with values of 1.27, 0.9, 0.79, 0.95, and 0.5 for root mean square error (RMSE), correlation coefficient (r), modeling efficiency (NSE), concordance index (d), and percentage bias (Pbias), respectively. AgSAT guides gross irrigation requirements for crops and rationalizes water quantities used in agricultural production. AgSAT has been released, is currently in use by research scientists, agricultural producers, and irrigation managers, and is freely accessible from the Google Play and IOS Store and also at agsat.app. Our work is geared towards the development of remote sensing-based technologies that transfer significant benefits to farmers and water-saving efforts.