A point-surface fusion method for vegetation water content retrievalconsidering optimization of GNSS sites and modeling elements

• Published in: Measurement science & technology Vol. 34; no. 3; p. 35112
• Main Authors: Liang, Yueji, Hu, Xinmiao, Ren, Chao, Lu, Xianjian, Yan, Hongbo, Ding, Qin, Lai, Jianmin
• Format: Journal Article
• Language: English
• Published: 01.03.2023
• ISSN: 0957-0233; 1361-6501
• DOI: 10.1088/1361-6501/ac9f13

Abstract The normalized microwave reflectance index (NMRI) based on global navigation satellite system (GNSS) interferometric reflectometry has been proven to reflect the changes in vegetation water content (VWC) effectively, but it is limited to point data. A spatially continuous NMRI product can be obtained by fusing NMRI data (point) and moderate-resolution imaging spectroradiometer (MODIS) data (surface). However, the time resolution of the existing results is limited to 16 d, and the research on the selection of modeling elements is not deep enough. In this paper, a point-surface fusion method for VWC retrieval considering the optimization of GNSS sites and elements is proposed. This method is aimed at using MODIS multi-band to synthesize vegetation indices with 8 d spatial-temporal resolution and establishing the initial element set by combining meteorological elements, followed by optimizing the GNSS sites and modeling elements for best modeling scheme, finally constructing the point-surface fusion method for VWC retrieval, and comprehensively evaluating the performance of the method. The results indicate that optimizing GNSS sites and modeling elements are particularly critical to improving modeling accuracy. Compared with other vegetation elements, normalized difference vegetation index (NDVI), gross primary productivity, and leaf area index are essential elements that affect the modeling effect. Among them, NDVI is the critical element. The NMRI products with 8 d/500 m resolution obtained by GA-BPNN can better reflect the change of VWC. Furthermore, the spatial performance of NMRI products is consistent with the fire forecast products and is suitable for drought and fire forecasts.