A MWCMLAI-Net method for LAI inversion in maize and rice using GF-3 and Lutan radar data

Abstract

ABSTRACTThis study aimed at alleviating the problems of unsatisfactory inversion accuracy and weak model stability in LAI remote sensing quantitative inversion. The properties and complex scattering mechanism of SAR data specify the polarization combinations and frequencies. This paper proposes an improved water cloud model combined with a deep neural network (MWCMLAI-Net) for high-precision inversion. The polarized GF-3 (C-band) and Lutan (L-band) were used to investigate the potential of SAR images to estimate LAI, a strong indicator of crop productivity. The study selected xiangfu district in the eastern part of Kaifeng City, Henan Province, as the test area and investigated the LAI of maize and rice. The [Formula: see text] Model, backward scattering coefficient extracted by the modified cloud and water model (MWCM), and LAI obtained by the inversion of the MWCM were used as the inputs, and the MWCMLAI-Net inversion of the LAI was constructed. The results showed that the model’s inverted LAI fitting accuracies of maize and rice for the three fertility periods were better than the other models, with R2 above 0.8516 and RMSE below 0.3999 m2/m2. The addition of noise did not affect the results.

Keywords

• Leaf area index (LAI)
• GF-3
• Lutan
• MWCMLAI-Net
• maize
• rice