Retrieval of Leaf Area Index From MODIS Surface Reflectance by Incorporating the Subpixel Information From Decametric-Resolution Data

High-frequency leaf area index (LAI) dataset is essential for vegetation dynamic monitoring and crop yield estimation; however, due to the negative impacts of land surface heterogeneity, current hectometric-resolution LAI products cannot satisfy the uncertainty requirement of LAI dataset in practice. Here, we proposed a method named “use of subpixel information” (USPI) that leverages fine-scale remote sensing data to improve the accuracy of hectometric-resolution LAI retrieval. Specifically, based on machine learning (ML) models trained by representative samples, we retrieved the USPI LAI from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance by incorporating the subpixel information from Sentinel-2 LAI estimates. The USPI LAI was comprehensively evaluated using 30-m LAI reference maps in three aspects: the performance of different ML models, the comparison with MODIS LAI products, and the potential correction of USPI LAI for clumping effect. Results showed that the Gaussian process regression (GPR) model outperformed other ML models for deriving LAI estimates; furthermore, USPI LAI exhibited better performance than MODIS LAI product, with bias, root mean square error (RMSE), and
$R^{2}$
of −0.308, 0.593, and 0.826, respectively, especially for pixels contaminated by atmospheric conditions. The underestimation of USPI LAI should be, nevertheless, noted because the effective LAI provided by Sentinel-2 was involved in the GPR training process; thus, it is necessary to introduce the accurate clumping index (CI) dataset for further improvement of USPI LAI retrievals. Our study indicates that incorporating the subpixel information from decametric-resolution data can effectively reduce the uncertainty of hectometric-resolution LAI retrieval, which is promising for generating the high-accuracy LAI time series dataset.