Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (10): 2070-2083.doi: 10.12082/dqxxkx.2023.220777

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Retrieval of Corn Residue Biomass Based on SAR Data with Soil Scattering Interference Removed

XIE Xiaoman1,2(), HONG Zixiang1,2, LI Li1,2,*(), QIU Bingqi1,2, SU Yiran1,2   

  1. 1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China
    2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
  • Received:2022-10-11 Revised:2023-01-09 Online:2023-10-25 Published:2023-09-22
  • Contact: * LI Li, E-mail:;
  • Supported by:
    National Natural Science Foundation of China(42171324);Technical Service Project from Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences(202205511910797)


As a main way of conservation tillage, crop residue has important influence on the cycle of material and energy in farmland ecosystem. Acquisition of the biomass information of corn residues timely and accurately is of great significance for understanding the implementation of conservation tillage and evaluating the impact of residues quantitatively. However,compared with crops, residues have lower coverage and contain less water,which makes it more difficult to acquire the biomass. To address this issue, we developed a residue backscattering model based on AIEM-Oh model and the Water Cloud Model (WCM) to remove the soil scattering interference. Using Sentinel-1 SAR images as the main data source, a regression model of corn residue biomass was constructed based on radar features selected to retrieve and map the corn residue biomass in Lishu County. Results show that the residue backscattering model can eliminate the interference of soil backscattering contribution effectively, and the inversion model based on the residue backscattering coefficient can improve the biomass inversion accuracy. The autumn biomass inversion model based on the dual-polarized scattering product (Product) has an R2 greater than 0.75, and an RMSE less than 85 g/m2, showing an increase of at least 0.12 in R2 and a decrease of 17 g/m2 in RMSE compared to the biomass inversion model before soil scattering contribution removed. This study verifies the feasibility of residue biomass inversion model based on backscattering data with soil scattering interference removed, and provides an attempt for the dynamic monitoring of corn residue biomass using SAR remote sensing data in the future.

Key words: Sentinel-1 SAR, biomass retrieval, corn residue, Residue Backscattering Model