Derivation of biophysical parameters from fused remote sensing data

Resent launches of optical space-borne remote sensing systems with high resolution, high temporal repetition rate, and constant viewing angles (e.g.: Venμs, Sentinel-2) will fortify the potential of remote sensing applications in the context of agricultural monitoring (e.g.: the derivation of biophysical parameters). The practicality of optical and/or thermal remote sensing data is often limited by tile and cloud coverage. Spatial-Temporal-Adaptive Fusion Model (e.g.: STARFM) can be used to combine data from different remote sensing sensors to overcome these limitations. In order to investigate the reliability of synthesized remote sensing data in agricultural monitoring, we evaluated the quality and integrity of predicted FPAR and LAI data on maize. In this context, we used synthesized daily LANDSAT-like data products and a RandomForest to fill possible spatial and/or temporal data gaps and to predict FPAR and LAI for the entire growing period in 2015. The evaluation of the biophysical time series was concluded using a weekly to bi-weekly ground measurements in different phenological stages of the maize plant. The quality assessment of the entire growth period revealed the high potential of synthetic remote sensing data for agricultural monitoring. The quality of the results range between R
²
= 0.68; RMSE = 0.79 (LAI) and R
²
= 0.76; RMSE = 0.12 (FPAR).