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

Nieves Pasqualotto,Guido D’Urso,José Moreno,Jesús Delegido,Salvatore Falanga Bolognesi,Alejandro Pezzola,Cristina Winschel,Oscar Rosario Belfiore,Shari Van Wittenberghe

doi:10.3390/agronomy9100663

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.

Highlights

Monitoring the growth of agricultural crops during the whole growing season is important for increasing crop yields and reducing costs and input resources for the agricultural sector [1].Spatially-explicit knowledge of biophysical variables, such as the leaf area index (LAI) and the chlorophyll content (Chl), is fundamental for the understanding of agricultural ecosystems [2].the variables as LAI are used as inputs of important agricultural models, such as the adaptedFAO-56 Penman-Monteith (FAO-56 PM) model [3] which derives the reference (ETo ) and potential (ETc ) crop evapotranspiration
This section is composed of the different results obtained with each of the LAI retrieval methods, i.e., ANN S2 LAI product, the Clevers leaf area index by reflectance (CLAIR) model and vegetation indices (VIs), applied to the four in situ datasets
The calibration consists of a regression analysis technique applied to the observed and CLAIR estimated LAI values of the satellite image where there are more in situ values

Summary

Introduction

Monitoring the growth of agricultural crops during the whole growing season is important for increasing crop yields and reducing costs and input resources for the agricultural sector [1].Spatially-explicit knowledge of biophysical variables, such as the leaf area index (LAI) and the chlorophyll content (Chl), is fundamental for the understanding of agricultural ecosystems [2].the variables as LAI are used as inputs of important agricultural models, such as the adaptedFAO-56 Penman-Monteith (FAO-56 PM) model [3] which derives the reference (ETo ) and potential (ETc ) crop evapotranspiration. The variables as LAI are used as inputs of important agricultural models, such as the adapted. There has been a consistent effort to estimate vegetation parameters from remotely sensed data, allowing to adapt the Penman-Monteith equation for direct use with Earth observation (EO) based LAI and surface albedo retrieval [4], minimizing time and cost. Nowadays it is the most commonly used method for the estimation of ET [5,6,7]

Methods

Results

Discussion

Conclusion