Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Abstract

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas.

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

“…The combination of Senitnel-2 data with models such as FAO-56 Penman-Monteith and Aquacrop in the case of tomato have shown to be successful [70,71]. Moreover, the combination of FAO-56 Penman-Monteith with Sentinel-2 derived LAI have also shown relatively high accuracy in ET retrieval [72]. Several studies also take advantage of the red and red-edge bands to predict the crop coefficient (Kc) and thereafter use it together with ET o [73,74].…”

On the Joint Exploitation of Optical and Sar Satellite Imagery for Grassland Monitoring

“…It prevents meeting the requirements of dense surveillance of grasslands (Kolecka et al, 2018). Most monitoring approaches integrating optical satellite data are based on the exploitation of derived vegetation indices (Ali et al, 2017, Pasqualotto et al, 2019, Tiscornia et al, 2019. Among these indices, NDVI is still widely used for its simplicity and ease of interpretation (Stendardi et al, 2019, Solano-Correa et al, 2020, Belgiu, Csillik, 2018.…”

Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery

“…According to previous studies, the uncertainty of Sentinel-2 LAI estimates was generally 0.54-1.16 m 2 /m 2 for crops [36,37,39,46,47,[50][51][52] and 1.55 m 2 /m 2 for forest [49]. In terms of Sentinel-2 FAPAR estimates, the uncertainty was 0.11 for crops [39] and 0.16-0.24 for forests [38].…”