Unverified paper record
BiophyNet: A Regression Network for Joint Estimation of Plant Area Index and Wet Biomass From SAR Data
IEEE Geoscience and Remote Sensing Letters · 1 Oct 2021 · 10.1109/lgrs.2020.3008757
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 within situmeasurements 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$(r)$in the range of 0.69–0.87. The results also confirm intercorrelation between the PAI and wet biomass for canola and soybean.
Plant phenotyping relevance
SARデータから植物面積指数と湿潤バイオマスを推定するニューラルネットワーク手法を提案・評価しており、植物形質の取得方法が研究の中心である。
abstractwe propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean
abstractRADARSAT-2 quad-pol data along within situmeasurements 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.
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