BiophyNet: A Regression Network for Joint Estimation of Plant Area Index and Wet Biomass From SAR Data

Abstract

In this study, we propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean. The PAI and wet biomass have considerable importance for crop growth stage mapping and monitoring. RADARSAT-2 quad-pol data along with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements of canola and soybean obtained from the SMAPVEX16 campaign over Manitoba, Canada, are utilized for evaluating the efficiency and accuracy of the proposed estimation methodology. The analysis indicates promising results for the two crops with a correlation coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r)$ </tex-math></inline-formula> in the range of 0.69–0.87. The results also confirm intercorrelation between the PAI and wet biomass for canola and soybean.