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

Leaf area index (LAI) is a key biophysical variable fundamental in natural vegetation
and 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.