Leaf area index retrieval in Shanxi province of China using Sentinel-1 data
(2023) 2023 Dragon 5 Symposium — Location: Hohhot, Inner Mongolia, P.R. China (11.September.2023)
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- Abstract
- (en) The green area index (GAI), i.e., half of the green leaf and stem area per unit of horizontal ground surface area, is a key variable for assessing the development, health, and productivity of crops. Currently, most large-scale and cost-effective methods for its estimation exploit optical remote sensing data. Frequent cloud cover can, however, hinder their reliability by blocking the view of the sensors on which they rest. As a result in many parts of the world, its timely monitoring cannot be ensured using optical systems alone. Synthetic aperture radars (SARs), however, thanks to their cloud-penetrating ability, are capable of producing dense time series that can be used to improve the spatial and temporal coverage of their optical counterparts. In this study, SAR-to-optical GAI regression has been performed using a transformer encoder with past and current values of SAR backscatter and interferometric coherence, as well as past values of LAI when available. Sentinel-1 and -2 images acquired from 2018 to 2021 over the Hesbaye region of Belgium have been used for cross-validation. The model has been trained on three growing seasons and tested on the fourth for each fold. The results show that the model can successfully predict Sentinel-2-derived GAI with a cross-validation average R2=0.88 and RMSE=0.74, outperforming methods relying on radiative transfer model (e.g., the Water Cloud model) inversion. The model is also particularly effective compared to non-recurrent regression models, such as Random Forest and Multi-layer Perceptron, over long temporal gaps in the GAI time series, i.e., 30 to 60 days (15 to 30% of the growing season), a common occurrence in Belgium and many other parts of the world. These promising results pave the way for the generation of accurate, dense GAI time series throughout the growing season, allowing for timely crop monitoring in cloud-prone regions.
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Bouchat, J., Deffense, Q., Liao, Y., Song, Y., Saelens, S., Su, Q., Fan, J., & Defourny, P. (2023). Leaf area index retrieval in Shanxi province of China using Sentinel-1 data. 2023 Dragon 5 Symposium, Hohhot, Inner Mongolia, P.R. China. https://hdl.handle.net/2078.5/213526