A point-surface fusion method for vegetation water content retrieval considering optimization of GNSS sites and modeling elements
A point-surface fusion method for vegetation water content retrieval considering optimization of GNSS sites and modeling elements
复制标题
考虑GNSS站点和建模元素优化的植被水分反演点面融合方法
DOI：
10.1088/1361-6501/ac9f13
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发表时间：
2023
影响因子：
2.4
通讯作者：
Jianmin Lai
中科院分区：
文献类型：
--
作者：
Yueji Liang;Xinmiao Hu;Chao Ren;Xianjian Lu;Hongbo Yan;Qin Ding;Jianmin Lai
基于全球导航卫星系统（GNSS）的归一化微波反射指数（NMRI）已被证明能有效反映植被含水量（VWC）的变化，但仅限于点数据。通过融合NMRI数据（点）和中分辨率成像光谱仪（MODIS）数据（面），可以获得空间连续的NMRI产品。但现有成果的时间分辨率仅限于16 d，对建模元素的选取研究还不够深入。本文提出了一种考虑GNSS站点和要素优化的点面融合VWC检索方法。该方法利用MODIS多波段数据合成8维时空分辨率的植被指数，结合气象要素建立初始要素集，优化GNSS站点和建模要素，得到最佳建模方案，构建点面融合VWC反演方法，并对方法性能进行综合评价。结果表明，优化GNSS站点和建模元素对提高建模精度尤为关键。与其他植被要素相比，归一化植被指数（NDVI）、总第一性生产力和叶面积指数是影响建模效果的关键要素。其中，NDVI是关键要素。GA-BPNN得到的分辨率为8 d/500 m的NMRI产品能较好地反映VWC的变化。此外，NMRI产品的空间性能与火灾预报产品一致，适用于干旱和火灾预报。
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.