Abstract
Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point–surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71–0.83 and RMSEs of 0.025–0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.
Highlights
In recent years, with the development of imaging spectrometry, using remote sensing data to detect the chemical characteristics of vegetation has become an important topic in the study of global change
We propose the idea of fusing site-level normalized microwave reflection index (NMRI) products and optical remote sensing Vegetation water content (VWC)-related indices using machine learning methods to compensate for the spatial limitations of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) dataset
The NMRI was validated at four sites in Montana, and the results showed that the NMRI is correlated strongly with VWC and normalized difference vegetation index (NDVI) [33]
Summary
With the development of imaging spectrometry, using remote sensing data to detect the chemical characteristics of vegetation has become an important topic in the study of global change. There have been some studies combining these two kinds of data to retrieve VWC with a higher resolution [19,20]; the huge spatial resolution difference between optical and microwave remote sensing products makes the accuracy and spatial resolution of the fusion results poor in practical applications. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides us with a new mode to monitor the vegetation information in a long time series It acts as a relatively new L-band remote sensing technique with relevance for measuring vegetation state using reflected GNSS. The previous studies show that GNSS-IR NMRI data have better potential advantages in detecting the change of VWC than the traditional remote sensing techniques. We propose the idea of fusing site-level NMRI products and optical remote sensing VWC-related indices using machine learning methods to compensate for the spatial limitations of GNSS-IR dataset. The daily NMRI data can be obtained for each site
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