Work

Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features

Year: 2025

Type: article

Abstract: The Leaf Area Index (LAI) is an essential parameter for assessing vegetation growth. LAI derived from optical data can suffer from gaps caused by cloud cover. Synthetic Aperture Radar (SAR) presents a solution with its all-weather observation capability. To address these issues, this study proposes a new deep learning approach for reconstructing time series LAI using SAR and optical data in two steps. Firstly, the two-dimensional Convolutional Neural Network-Transformer (2D CNN-Transformer) is applied to bridge SAR and optical data. Secondly, the 2D CNN-Transformer predicted LAI and the Sentinel-2 LAI are input into the Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion (EDCSTFN) model to further improve the accuracy. The novelty lies in a two-step framework combining a 2D CNN-Transformer for spatiotemporal feature extraction and a deep learning fusion algorithm refining accurate LAI reconstruction. Results showed that the 2D CNN-Transformer achieved a higher accuracy (R2 = 0.64, RMSE = 0.38 m 2 /m 2 ) in establishing a relationship between SAR and optical data, compared to 1D CNN, 2D CNN-LSTM, and 1D CNN-Transformer. In the second step, the EDCSTFN reconstructed LAI achieved the highest accuracy of an R 2 of 0.81 and an RMSE of 0.22 m 2 /m 2 , with an average R 2 of 0.61 and RMSE of 0.37 m 2 /m 2 across croplands and forests in millions of pixels, further improving the accuracy based on the first step. The approach effectively fills gaps in spatial details and achieves a more continuous spatial distribution. The proposed approach demonstrates good generalizability in millions of pixels under frequent cloud cover and complex surface conditions and provides a new strategy for the fusion of optical and SAR data.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104745

Authors: Mingqi Li, Pengxin Wang, Kevin Tansey, Fengwei Guo, Ji Zhou

Institutions: Ministry of Agriculture and Rural Affairs, China Agricultural University, University of Leicester, University of Electronic Science and Technology of China

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,550