Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

Abstract

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R 2 > 0.93) and good accuracies (RMSE < 0.83, rRMSE m < 23.6% and rRMSE r < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

Global Estimation of Biophysical Variables from Google Earth Engine Platform

“…The solution can be achieved by means of numerical optimization or Monte Carlo approaches which are computationally expensive and do not guarantee the convergence to an optimal solution. Recently, new and more efficient algorithms relying on Machine Learning (ML) techniques have emerged and have become the preferred choice for most RTM inversion applications [16][17][18][19]21]. In this work, we have followed the latter hybrid approach, combining radiative transfer modeling and the parallelized machine learning RFs implementation available in GEE to retrieve the selected biophysical variables.…”

“…The use of RTMs implies modeling leaf and canopy structural and biochemical parameters. The ranges and distribution of parameters used for running the simulations are usually based on field measurements that are very useful for simulating specific land covers [16,17]. Nevertheless, when the objective is to simulate a wide range of vegetation situations and land covers, ground data is often a limitation.…”

“…In particular, the MODIS LAI/FAPAR product is based on a three-dimensional RTM which links surface spectral bi-directional reflectance factors (BRFs) to both canopy and soil spectral and structural parameters [15]. On the other hand, hybrid methods couple statistical with physically-based approaches inverting a database generated by an RTM [16][17][18]. For example, CYCLOPES global products [19] were derived inverting the PROSAIL radiative transfer model [20] using neural networks, while the global EUMETSAT LAI/FAPAR/FVC products are being produced inverting PROSAIL with multi-output Gaussian process regression from the EUMETSAT Polar System (EPS) [21].…”

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

“…This dataset includes both Sentinel-2A and Landsat-7/8 LAI retrievals. The Sentinel-2A products were recently obtained and validated during the 2016 rice season over the three rice areas [15]. The LAI retrieval approach is based on a hybrid method combining radiative transfer modelling and machine learning regression.…”

“…Destructive methods are time consuming, involve laborious work and are not well suited for continuous validation studies. In turn, non-destructive methods are based on simplified models of light transmission into the canopy and have been applied for validating remote sensing high-resolution LAI estimates in many works [13][14][15]23,24]. Non-destructive methods include the use of classical instrumentation (i.e., plant canopy analyzers, ceptometers, and digital hemispherical photography (DHP)) [33,34] as well as the employment of new technologies (e.g., PocketLAI smartphone application [35]).…”