A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

Abstract

Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetationand agricultural land monitoring and modelling studies. This paper is aimed at comparing, validating and discussing different LAI satellite products from operational services and customized solution based on innovative Earth Observation (EO) data such as Landsat-7/8 and Sentinel-2A. The comparison was performed to assess overall quality of LAI estimates for rice, as a fundamental input of different scale (regional to local) operational crop monitoring systems such as the ones developed during the "An Earth obseRvation Model based RicE information Service" (ERMES) project. We adopted a multiscale approach following international recognized protocols of the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) guidelines in different steps: (1) acquisition of representative field sample measurements, (2) validation of decametric satellite product (10-30 m spatial resolution), and (3) exploitation of such data to assess quality of medium-resolution operational products (~1000 m). The study areas were located in the main European rice areas in Spain, Italy and Greece. Field campaigns were conducted during three entire rice seasons (2014, 2015 and 2016-from sowing to full-flowering) to acquire multi-temporal ground LAI measurements and to assess Landsat-7/8 LAI estimates. Results highlighted good correspondence between Landsat-7/8 LAI estimates and ground measurements revealing high correlations (R 2 ≥ 0.89) and low root mean squared errors (RMSE ≤ 0.75) in all seasons. Landsat-7/8 as well as Sentinel-2A high-resolution LAI retrievals, were compared with satellite LAI products operationally derived from MODIS (MOD15A2), Copernicus PROBA-V (GEOV1), and the recent EUMETSAT Polar System (EPS) LAI product. Good agreement was observed between high-and medium-resolution LAI estimates. In particular, the EPS LAI product was the most correlated product with both Landsat/7-8 and Sentinel-2A estimates, revealing R 2 ≥ 0.93 and RMSE ≤ 0.53 m 2 /m 2 . In addition, a comparison exercise of EPS, GEOV1 and MODIS revealed high correlations (R 2 ≥ 0.90) and RMSE ≤ 0.80 m 2 /m 2 in all cases and years. The temporal assessment shows that the three satellite products capture well the seasonality during the crop phenological cycle. Discrepancies are observed mainly in absolute values retrieved for the peak of rice season. This is the first study that provides a quantitative assessment on the quality of available operational LAI product for rice monitoring to both the scientific community and users of agro-monitoring operational services.

Quality Assessment of PROBA-V LAI, fAPAR and fCOVER Collection 300 m Products of Copernicus Global Land Service

“…Indeed, the comparison at a spatial resolution of 3 km shows similar product accuracy (just slightly better) to that at a resolution of 300 m. The accuracy of Collection 300 m V1 is similar to that of the other reference satellite 1 km products for LAI and improved for fAPAR. All the satellite products show large overestimations for the Albufera rice crop site during the early and growing periods of development (June-July), and this result was previously observed for LAI products by Campos-Taberner et al [68] and by Fang et al [69] for rice crops in China. Satellite retrieval algorithms misinterpret the decreased reflectance due to strong water absorption as a denser canopy (i.e., increasing artificially LAI, fAPAR and fCOVER values).…”

Global Estimation of Biophysical Variables from Google Earth Engine Platform

“…Similar underestimation behaviour was found by other studies when comparing MODIS LAI products and LAI retrievals from RTM inversion [59,63]. Yan et al [59] found RMSE = 0.66 m 2 /m 2 and RMSE = 0.77 m 2 /m 2 when comparing MODIS C6 LAI estimates with ground LAI actual and LAI e f f measurements respectively, as well as larger uncertainties in high LAI values.…”

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].…”