Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data

Abstract

The synergy of a time series of optical satellite observations from a variety of sensors can be exploited to improve the retrieval of biophysical variables. Information from different sensors may assist in the variable retrieval by limiting potential ambiguities. This involves observations at different spatial, spectral, temporal and angular resolutions, etc. Furthermore, using timely data is of much importance for vegetation monitoring in environmental modeling. While the other necessary variables for such models can be collected daily (e.g. meteorological variables), the temporal resolution of optical sensors (high to intermediate spatial resolutions) does not allow having temporally frequent products of vegetation characteristics due to the revisit time of the sensors and cloud coverage. A multi-temporal, multi-sensor approach applied to a temporal sequence of radiometric data acquired by different sensors can improve mapping and monitoring of vegetation state variables over time. Even when no observations are available due to cloudiness or orbital configuration, the prior retrievals are taken.The study provides a prototype proof of concept for a multi-temporal, multi-sensor approach to retrieve vegetation state variables using data collected by different imaging spectral-radiometers over time. Focus is given to the retrieval of LAI, fCover and chlorophyll content over the agricultural test site in Barrax, Spain. The approach is evaluated over a limited number of remotely sensed data acquired during a short period in the Sentinel-3 Experiment (SEN3EXP 2009) field campaign. A variety of satellite observations including CHRIS-Proba, Landsat TM and ASTER are integrated and combined by means of the multi-temporal, multi-sensor approach and through inversion of a coupled surface-atmosphere radiative transfer model. We applied the iterative Bayesian model inversion in which retrievals of the current observation are incorporated as prior information to the successor observation.This paper presents an overview of the results and challenges in utilizing the multi-temporal, multi-sensor approach and the Bayesian inversion technique in the retrieval of terrestrial vegetation properties. Overall, the accuracy obtained with data acquired by multiple sensors over time was higher than when using a single sensor. LAI and fCover were retrieved with RMSE= 0.7 (m2/m2) and 0.1 respectively (multiple sensors), while RMSE= 1.09 (m2/m2) and 0.15 respectively, when using data acquired by a single sensor.