Combining multitemporal optical and SAR data for LAI imputation with BiLSTM network

TL;DR

This paper presents a BiLSTM-based method to impute Leaf Area Index (LAI) using multitemporal Sentinel-1 SAR and Sentinel-2 optical data, improving accuracy over traditional methods especially under cloud cover conditions.

Contribution

The study introduces a novel BiLSTM approach for LAI imputation combining SAR and optical data, demonstrating superior performance in diverse conditions and limited data scenarios.

Findings

BiLSTM outperforms traditional regression methods.

The method is robust across different growing conditions.

BiLSTM surpasses LSTM, especially during senescence.

Abstract

The Leaf Area Index (LAI) is vital for predicting winter wheat yield. Acquisition of crop conditions via Sentinel-2 remote sensing images can be hindered by persistent clouds, affecting yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery, and the ratio between its cross- and co-polarized channels (C-band) shows a high correlation with time series LAI over winter wheat regions. This study evaluates the use of time series Sentinel-1 VH/VV for LAI imputation, aiming to increase spatial-temporal density. We utilize a bidirectional LSTM (BiLSTM) network to impute time series LAI and use half mean squared error for each time step as the loss function. We trained models on data from southern Germany and the North China Plain using only LAI data generated by Sentinel-1 VH/VV and Sentinel-2. Experimental results show BiLSTM outperforms traditional regression methods, capturing nonlinear dynamics between multiple time series. It proves robust in various growing conditions and is effective even with limited Sentinel-2 images. BiLSTM's performance surpasses that of LSTM, particularly over the senescence period. Therefore, BiLSTM can be used to impute LAI with time-series Sentinel-1 VH/VV and Sentinel-2 data, and this method could be applied to other time-series imputation issues.