A geographically weighted regression kriging approach for mapping soil organic carbon stock

Kumar, Sandeep; Lal, Rattan; Liu, Desheng

doi:10.1016/j.geoderma.2012.05.022

Cited by 243 publications

(141 citation statements)

References 36 publications

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“…Residuals are considered as errors, and it is possible that the errors have some spatial correlation structure that can be modeled. It could be considered that the errors are the component of the model which cannot be explained by the deterministic component, but is important to add as it helps explain the variation of the target variable across space [88]. GWRK has been proven to be an effective method for spatial prediction [88][89][90].…”

Section: The Downscaling Proceduresmentioning

confidence: 99%

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Zhang

et al. 2018

Remote Sensing

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Abstract:Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial resolution maps of precipitation over a mountainous watershed affected by a monsoon climate. Based on the relationships between precipitation and other environmental variables, such as the Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM), a regression model with a residual correction method was applied to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 product from coarse resolution (25 km) to fine resolution (1 km). Two methods, geographical difference analysis (GDA) and geographical ratio analysis (GRA), were used to calibrate the downscaled TRMM precipitation data. Monthly 1 km precipitation data were obtained by disaggregating 1 km annual downscaled and calibrated precipitation data using monthly fractions derived from original TRMM data. The downscaled precipitation datasets were validated against ground observations measured by rain gauges. According to the comparison of different regression models and residual interpolation methods, a geographically-weighted regression kriging (GWRK) method was accepted to conduct the downscaling of TRMM data. The downscaled TRMM precipitation data obtained using GWRK described the spatial patterns of precipitation reasonably well at a spatial resolution of 1 km with more detailed information when compared with the original TRMM precipitation. The results of validation indicated that the GRA method provided results with higher accuracy than that of the GDA method. The final annual and monthly downscaled precipitation not only had significant improvement in spatial resolution, but also agreed well with data from the validation rain gauge stations (i.e., R 2 = 0.72, RMSE = 161.0 mm, MAE = 127.5 mm, and Bias = 0.050 for annual downscaled precipitation during 2001 to 2015; and R 2 = 0.91, RMSE = 22.2 mm, MAE = 13.5 mm, and Bias = 0.048 for monthly downscaled precipitation during 2001 to 2015). In general, the downscaling-calibration procedure is useful for complex mountainous areas with insufficient ground gauges.

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Section: The Downscaling Proceduresmentioning

confidence: 99%

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Zhang

et al. 2018

Remote Sensing

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“…Because the MGWR is an extension of GWR and MLR, we conducted GWR and MLR to predict the SOM spatial distribution and compared the mapping results, accuracies and regression coefficients of GWR, MLR and MGWR. Besides, there are some other competitive methods in digital soil mapping, such as GWRK (GWR-kriging) (Kumar et al, 2012;Sun et al, 2015), LRK (local regression-kriging) (Sun et al, 2012;Sun et al, 2015), and KED (kriging with an external drift) (Song et al, 2016). We also compared the MGWR method with GWRK (GWRkriging), LRK (local regression-kriging), KED (kriging with an external drift), and OK (ordinary kriging).…”

Section: Evaluationsmentioning

confidence: 99%

“…Many studies show higher accuracy than feature space-only models, due to the inclusion of spatial autocorrelation of model residuals in the models (Brus and Heuvelink, 2007;Odeh and Mcbratney, 2000;Sumfleth and Duttmann, 2008;Sun et al, 2012). However, the model residuals can't always tally with the first order or second order stationarity and seldom of these methods can be adapted to fit data locally with varying coefficients over space for the regression (Kumar et al, 2012;Walter et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

et al. 2016

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The present regression models in digital soil mapping usually assume that relationships between soil properties and environmental variables are always fixed (as in MLR) or varying (as in GWR) in geographical space. In reality, some of the environmental variables may be fixed in affecting soil property variation and some are local varying. In this study, a mixed geographically weighted regression (MGWR) method which can deal with fixed and varying spatial relationships between a target variable and its environmental variables were proposed and used to predict topsoil soil organic matter (SOM) concentration in two study areas (Heshan, Heilongjiang province and Xuancheng, Anhui province, China) at two scales. Three groups of sample sets were created based on the total samples in the study areas to evaluate the robustness and stability of the model. Multiple linear regression (MLR), geographically weighted regression (GWR), GWR-kriging (GWRK), local regression-kriging (LRK), kriging with an external drift (KED), and ordinary kriging (OK) were used for comparison with MGWR. The validation results showed that the use of MGWR reduced the RMSE of GWR by 10.5% and 7.6% on average, reduced the RMSE of MLR by 12.8% and 9.9% on average for Heshan and Xuancheng study areas respectively. MGWR also showed a good competitiveness when compared with GWRK, LRK, KED and OK. In Heshan study area, the influence of flow length, relative position index, foot slope and distance to the nearest drainage were constant, whereas the elevation, topographic wetness index and valley index showed different influence in different regions. In Xuancheng study area, the fixed environmental variables were profile curvature, topographic wetness index and slope, whereas the varying environmental variables were precipitation, temperature, elevation, and limestone. The results indicate that the accuracy of predictions can be improved by adaptive coefficient according to the variation of environmental variables as implemented in MGWR compared with others considering only the local or global relationships. It was concluded that mixed geographically weighted regression model could be a potential method for digital soil mapping.

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“…Kriging is a geostatistical method that is commonly used to interpolate an SOCD data set from discrete points to a spatially continuous surface (Kumar et al, 2012;Khalil et al, 2013), and the semivariable function can be used to quantify the spatial autocorrelation and provides an input parameter for a spatial interpolation . All of the calculations for mapping SOC within individual soil depth ranges were performed using the ArcGIS software (Version 9.3).…”

Section: Estimation Of Soc Storagementioning

confidence: 99%

Soil organic carbon in the Sanjiang Plain of China: storage, distribution and controlling factors

Mao

Wang

et al. 2015

Biogeosciences

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Abstract. The accurate estimation of soil organic carbon (SOC) storage and determination of its pattern-controlling factors is critical to understanding the ecosystem carbon cycle and ensuring ecological security. The Sanjiang Plain, an important grain production base in China, is typical of ecosystems, yet its SOC storage and pattern has not been fully investigated because of insufficient soil investigation. In this study, 419 soil samples obtained in 2012 for each of the three soil depth ranges 0-30, 30-60, and 60-100 cm and a geostatistical method are used to estimate the total SOC storage and density (SOCD) of this region. The results give rise to 2.32 Pg C for the SOC storage and 21.20 kg m −2 for SOCD, which is higher than the mean value for the whole country. The SOCD shows notable changes in lateral and vertical distribution. In addition, vegetation, climate, and soil texture, as well as agricultural activities, are demonstrated to have remarkable impacts on the variation in SOCD of this region. Soil texture has stronger impacts on the distribution of SOCD than climate in the Sanjiang Plain. Specifically, clay content can explain the largest proportion of the SOC variations (21.2 % in the top 30 cm) and is the most dominant environmental controlling factor. Additionally, the effects of both climate and soil texture on SOCD show a weakening with increasing soil layer depth. This study indicates that reducing the loss of SOC requires effective conservation and restoration efforts of wetlands and forestlands, as well as sensible fertilization. The results from this study provide the most up-to-date knowledge on the storage and pattern of SOC in the Sanjiang Plain and have important implications for the determination of ecosystem carbon budgets and understanding ecosystem services.

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A geographically weighted regression kriging approach for mapping soil organic carbon stock

Cited by 243 publications

References 36 publications

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

Soil organic carbon in the Sanjiang Plain of China: storage, distribution and controlling factors

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