Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

Jin, Qiutong; Zhang, Jutao; Shi, Mingchang; Huang, Jixia

doi:10.3390/w8060266

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…It assumes that the model residuals are independent and that the regression coefficients are global (Song et al, 2016). The RK model is a spatial prediction technique, which is combined with a linear regression forecast of explanatory variables and ordinary kriging interpolation of the regression residuals (Bishop & Mcbratney, 2001; Hengl et al, 2007; Jin et al, 2016; Zhu & Lin, 2010). This model can be combined with different regression models to generate many combinations of methods (Li & Heap, 2014).…”

Section: Methodsmentioning

confidence: 99%

Spatial non‐stationary effects of explanatory variables on soil bulk density in the critical zone of the Chinese Loess Plateau

Liu

Wang

Zhou

et al. 2022

European J Soil Science

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Bulk density (BD) is one of the most important soil physical properties in the hydrological processes of Earth's Critical Zone (CZ). The heterogeneity and related controlling factors of surface BD have received extensive research attention. However, it is unclear whether the effects of controlling factors on BD are homogeneous or vary spatially. This study quantified topsoil BD with a high-density sampling strategy (10 m Â 10 m; n = 1134) across the ShuangChaGou watershed in the critical zone of the Chinese Loess Plateau (CLP-CZ). Stepwise multiple linear regression (SMLR), regression kriging (RK), random forest (RF) and geographically weighted regression (GWR) were established as modelling approaches to (i) compare the performance of these four models in explaining BD variation, (ii) assess the main factors driving BD, and (iii) evaluate the spatial non-stationary relationship between BD and its explanatory variables. GWR explained the BD variation to the same extent as the RK model (35%), outperforming both the SMLR (15%) and RF models (19%). The variation in topsoil BD depended mainly on: soil organic carbon (SOC) content, normalised difference vegetation index (NDVI), plan curvature, and slope length. The spatial distribution of the local R 2 , regression coefficient magnitude, significance and sign calculated by GWR revealed that the effects of variables accounting for variation in BD varied spatially. The explanatory power of these variables in terms of BD variation was strongest in northern and northeastern areas of the watershed and weakest in southern areas of the watershed. Both significant negative and positive correlations between BD and silt content, NDVI, and plan curvature were found. This study enriched traditional research linked to the complicated relationships between BD and controlling factors by incorporating spatial non-stationarity, which is useful for improving the accuracy of hydrological modelling in the CLP-CZ and in similar CZs worldwide. Highlights• Non-stationary relationships between BD (N = 1134) and its explanatory variables were captured at the watershed scale.

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Section: Methodsmentioning

confidence: 99%

Spatial non‐stationary effects of explanatory variables on soil bulk density in the critical zone of the Chinese Loess Plateau

Liu

Wang

Zhou

et al. 2022

European J Soil Science

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“…Multiple linear regression kriging (MLRK) is a combination of MLR and kriging (Jin et al, 2016) that uses linear regression obtained by the ordinary least squares (OLS) method and can be optimized for kriging methods. A kriging estimation analysis is performed on the residuals generated from MLR prediction.…”

Section: Multiple Linear Regression Krigingmentioning

confidence: 99%

A multiscale geographically weighted regression kriging method for spatial downscaling of satellite-based ozone datasets

Cheng,

Zhang,

Yang

et al. 2024

Front. Environ. Sci.

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Accurate monitoring of ozone (O3) concentrations by remote sensing is essential for achieving pollution control and ecological protection. However, the existing O3 remote sensing data with a low spatial resolution do not facilitate fine-grained studies of small-scale urban clusters. In this study, the multiscale geographically weighted regression kriging (MGWRK) method was used to spatially downscale O3 remote sensing products (10 km × 10 km). Downscaling factors were selected from meteorological factors and vegetation, aerosol optical thickness (AOD), and air pollutant emission inventory data. Spatial heterogeneity and scale differences among the factors were considered and compared via multiple regression kriging (MLRK) and geographically weighted regression kriging (GWRK) to generate 1-km annual and seasonal O3 remote sensing products. The results showed that I) the downscaling accuracy of each model can be expressed as MGWRK > GWRK > MLRK; the local downscaling model yields data that are more consistent with the actual spatial distribution of O3 after considering the spatial heterogeneity of the influencing factors; and the downscaled annual and seasonal data exhibit satisfactory spatial texture characteristics and consistency with the original spatial distribution of O3, while the distribution boundary problem of image elements is eliminated. II) Nitrogen oxide (NOx) and volatile organic compound emissions and temperature exhibit strong positive correlations with O3, while wind speed, humidity, the normalized difference vegetation index, and AOD indicate weak positive correlations with O3. Moreover, precipitation exhibits a weak negative correlation with O3. III) The coefficient of determination (R2) of the 1-km resolution annual O3 concentration data after downscaling based on the MGWRK model reaches 0.93, while the RRMSE and MAE values are only 3% and 1.86, respectively, with a coefficient of variation of 9.55%; the downscaling accuracy of the seasonal O3 concentration data is higher in summer and winter than during the other seasons, with R2 greater than 0.85, further confirming the spatial and temporal downscaling advantages of the MGWRK model for O3 in the Chang-Zhu-Tan city cluster. This further corroborates the feasibility of the MGWRK model for spatial and temporal O3 downscaling in the Chang-Zhu-Tan urban area.

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“…By establishing the multiple linear regression relationship between the precipitation and mean temperature and interpolation auxiliary factors, the residuals of the real value and predicted value were obtained. The kriging spatial interpolation method was used to carry out the spatial interpolation of the residual values, and the residual interpolation results of the regions to be predicted were added to the predicted values of the linear regression to obtain the final interpolation results [37]. The formula is as follows:…”

Section: Meteorological Data Interpolationmentioning

confidence: 99%

Spatio-Temporal Patterns and Driving Forces of Desertification in Otindag Sandy Land, Inner Mongolia, China, in Recent 30 Years

Yang

Shi

et al. 2023

Remote Sensing

Self Cite

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Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification.

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Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

Cited by 13 publications

References 32 publications

Spatial non‐stationary effects of explanatory variables on soil bulk density in the critical zone of the Chinese Loess Plateau

Spatial non‐stationary effects of explanatory variables on soil bulk density in the critical zone of the Chinese Loess Plateau

A multiscale geographically weighted regression kriging method for spatial downscaling of satellite-based ozone datasets

Spatio-Temporal Patterns and Driving Forces of Desertification in Otindag Sandy Land, Inner Mongolia, China, in Recent 30 Years

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