Spatial Downscaling of TRMM Precipitation Using an Optimal Regression Model with NDVI in Inner Mongolia, China

Xie, Shengjin; Liu, Yonghe; Yao, Fengxin

doi:10.1134/s0097807820060123

Cited by 4 publications

(2 citation statements)

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“…Therefore, satellite products with a high temporal resolution are often required to be spatially downscaled (disaggregation of a coarse cell into many finer cells) for various environmental applications. In the past 10 years, numerous spatial downscaling studies have been conducted on many satellite-derived products, such as precipitation [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], soil moisture [15], [16], [17], [18], [19], [20], [21], [22], [23], land surface temperature [2], [24], [25], [26], [27], [28], [29], [30], night-time light [31], solar radiation [32], evapotranspiration [33], [34], [35], chlorophyll [36], and wind speed [37]. The primary goal of spatial downscaling research is to improve the downscaling performance of satellite-derived products which is generally performed from two main aspects [38]: the introduction of new auxiliary variables [5], [8], [39], [40] and the development of new downscaling models [6], [13], [22],…”

Section: Introductionmentioning

confidence: 99%

“…In this study, Tropical Rainfall Measuring Mission (TRMM) monthly precipitation which has been one of the most frequent downscaled satellite products [8], [42], [48], [49], and geographically weighted regression (GWR) which has been one of the most commonly employed downscaled models [7], [31], [42], [47], [48], [50] were chosen as the target variable to be downscaled and the downscaling model, respectively. The elevation, slope, and normalized difference vegetation index (NDVI)), which have been widely adopted as predictors in the spatial downscaling of monthly TRMM products [4], [50], [51], [52], [53], [54], were selected as the basic predictors.…”

Section: Introductionmentioning

confidence: 99%

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Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

et al. 2023

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Are the variations of the fine predictors at the spatial scale of the target variable to be downscaled helpful for spatial downscaling? However, few studies have explored this topic. In this study, one of the most frequently downscaled satellite products (Tropical Rainfall Measuring Mission (TRMM) precipitation) and one of the most commonly employed downscaled models (geographically weighted regression (GWR)) were chosen as the target variable to be downscaled and the downscaling model, respectively. Three widely adopted auxiliary variables were selected as basic predictors. Variations of the three 1-km basic predictors at the 25-km (a TRMM cell) spatial scale (hereafter termed variation predictors (VP)) were captured by the employment of the ''standard deviation'' operators. A procedure was designed to determine the monthly optimal trend component model, and area-to-point kriging (ATPK) was applied to retrieve residual components. The monthly TRMM precipitation in the main body of the north-south transitional zone of China (MBNSTZC) from January 2010 to December 2019 (120 months in total) was spatially downscaled. When VP was introduced into the predictor family, performance improvements were observed for more than two-thirds of 120 months, and the average relative improvements in the coefficient of determination(R 2 ), root-mean-square error (RMSE), mean absolute error (MAE), and information entropy (IE) were 9.01%, 9.37%, 10.56%, and 28.21%, respectively. Our study suggests that: i) VP incorporation, which can improve downscaling performance to some extent, is important for GWR downscaling modeling; ii) Residual correction is unnecessary, especially for GWRs with VP incorporation; iii) GWRs with VP incorporation can not only downscale target variable but also have a certain interpolation ability.INDEX TERMS Variation predictor, spatial downscaling, disaggregation, unmixing, scaling, geographically weighted regression (GWR), tropical rainfall measuring mission (TRMM), precipitation, north-south transitional zone of China.The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang .

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

et al. 2023

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A review of downscaling methods of satellite-based precipitation estimates

2021

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Downscaling Correction and Hydrological Applicability of the Three Latest High-Resolution Satellite Precipitation Products (GPM, GSMAP, and MSWEP) in the Pingtang Catchment, China

Sun

Wei

Wang

et al. 2022

Advances in Meteorology

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The emergence of various high-resolution satellite precipitation products (SPPs) solves the problem of precipitation data sources for areas with a lack of precipitation data and is recognized as a reliable supplement to rain gauge observations in hydrometeorological applications. However, there still exists a shortcoming of coarse spatial resolution when applying these products to small and microscale river basins. In this study, a typical karst watershed in Southwest China—the Pingtang River Basin (PTRB)—was selected, and based on the relationship between precipitation and normalized difference vegetation index (NDVI), aspect, slope, and elevation, we used the geographically weighted regression (GWR) to downscale three SPPs, namely, global precipitation measurement (GPM), global satellite mapping of precipitation (GSMAP), and multisource weighted-ensemble precipitation (MSWEP), to 1 km × 1 km, respectively. Combined with rain gauge stations, the geographical differential analysis (GDA) was used to carry out error corrections to obtain three downscaling correction satellite precipitation products (DC-SPPs) with a 1 km spatial resolution, including DC-GPM, DC -GSMAP, and DC-MSWEP. Several statistical indices were used to perform error evaluation and precipitation capture ability analysis on SPPs and DC-SPPs, and the Grid-Xin’anjiang (the Grid-XAJ) model was used to compare their hydrological utility. The results show the following: (1) The downscaling correction method is effective. GWR can effectively improve the spatial resolution of SPPs, while GDA can reduce errors and further improve the accuracy of precipitation estimation. In addition, (2) the precipitation event characterization capabilities of GPM and GSMAP have been improved after downscaling correction, while the ability to capture precipitation events before and after the MSWEP correction is poor, showing a high hit rate and a high false alarm rate, which is unreliable to monitor precipitation events in the PTRB. Finally, (3) compared with SPPs, the hydrological performances of the three kinds of DC-SPPs have been significantly improved, and the NSE are all above 0.75 with low error. In general, the overall performance of DC-GSMAP is satisfactory. The accuracy of different SPPs after downscaling correction is different, but the applicability has been improved to different degrees.

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Spatial Downscaling of TRMM Precipitation Using an Optimal Regression Model with NDVI in Inner Mongolia, China

Cited by 4 publications

References 33 publications

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

Introducing Variations of Predictors as Optional Predictors Offers the Potential to Improve the Downscaling Performance of Geographically Weighted Regression Model

A review of downscaling methods of satellite-based precipitation estimates

Downscaling Correction and Hydrological Applicability of the Three Latest High-Resolution Satellite Precipitation Products (GPM, GSMAP, and MSWEP) in the Pingtang Catchment, China

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