Comparison of sampling schemes for the spatial prediction of soil organic matter in a typical black soil region in China

2015 ◽  
Vol 75 (1) ◽  
Author(s):  
Yongcun Zhao ◽  
Xianghua Xu ◽  
Kang Tian ◽  
Biao Huang ◽  
Nan Hai
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spatial Prediction ◽  
Black Soil ◽  
Sampling Schemes ◽  
Black Soil Region

Walkley-Black Recovery Factor to Reassess Soil Organic Matter: Indo-Gangetic Plains and Black Soil Region of India Case Studies

2015 ◽  
Vol 46 (20) ◽  
pp. 2628-2648 ◽  
Author(s):  
T. Bhattacharyya ◽  
P. Chandran ◽  
S. K. Ray ◽  
C. Mandal ◽  
P. Tiwary ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Case Studies ◽  
Black Soil ◽  
Recovery Factor ◽  
Gangetic Plains ◽  
Black Soil Region

Spatial prediction of soil organic matter in northern Kazakhstan based on topographic and vegetation information

2007 ◽  
Vol 53 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Yusuke Takata ◽  
Shinya Funakawa ◽  
Kanat Akshalov ◽  
Norio Ishida ◽  
Takashi Kosaki
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spatial Prediction ◽  
Northern Kazakhstan

Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information

Geoderma ◽  
2012 ◽  
Vol 171-172 ◽  
pp. 35-43 ◽  
Author(s):  
Shiwen Zhang ◽  
Yuanfang Huang ◽  
Chongyang Shen ◽  
Huichun Ye ◽  
Yichun Du
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Auxiliary Information ◽  
Spatial Prediction ◽  
Categorical Variables ◽  
Terrain Indices

scholarly journals Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

2017 ◽  
Vol 9 (5) ◽  
pp. 754 ◽  
Author(s):  
Ying-Qiang Song ◽  
Lian-An Yang ◽  
Bo Li ◽  
Yue-Ming Hu ◽  
An-Le Wang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Extreme Learning Machine ◽  
Ordinary Kriging ◽  
Spatial Prediction ◽  
Geostatistical Model ◽  
Learning Machine

scholarly journals Comparison of Different Spatial Regression Methods for Predicting Organic Matter Content in Soil

2022 ◽  
Author(s):  
Xumeng Zhang ◽  
Wuping Zhang ◽  
Mingjing Huang ◽  
Li Gao ◽  
Lei Qiao ◽  
...  
Keyword(s):  
Organic Matter ◽  
Spatial Distribution ◽  
Soil Organic Matter ◽  
Land Use Planning ◽  
Prediction Accuracy ◽  
Meteorological Data ◽  
Organic Matter Content ◽  
Spatial Prediction ◽  
Matter Content ◽  
Soil Organic Matter Content

Abstract Dynamic changes in soil organic matter content affects the sustainable supply of soil water and fertilizer and impacts the stability of soil ecological function. Understanding the spatial distribution characteristics of soil organic matter will help deepen our understanding of the differences in soil organic matter content, soil formation law; such understanding would be useful for rational land use planning. Taking terrain data, meteorological data, and remote sensing data as auxiliary variables and the ordinary Kriging (OK) method as a control, this study compares the spatial prediction accuracies and mapping effects of various models (MLR, RK, GWR, GWRK, MGWR, and MGWRK) on soil organic matter. Our results show that the spatial distribution trend of soil organic matter predicted by each model is similar, but the prediction of composite models can reflect more mapping details than that of unitary models. The OK method can provide better support for spatial prediction when the sampling points are dense; however, the local models are superior in dealing with spatial non-stationarity. Notably, the MGWR model is superior to the GWR model, but the MGWRK model is inferior to the GWRK model. As a new method, the prediction accuracy of MGWRK reached 47.72% for the OK and RK methods and 40.08% for the GWRK method. The GWRK method achieved a better prediction accuracy. The influence mechanism of soil organic matter is complex, but the MGWR model more clearly reveals the complex nonlinear relationship between soil organic matter content and factors influencing it. This research can provide reference methods and mapping technical support to improve the spatial prediction accuracy of soil organic matter.

scholarly journals Exploring Appropriate Preprocessing Techniques for Hyperspectral Soil Organic Matter Content Estimation in Black Soil Area

2020 ◽  
Vol 12 (22) ◽  
pp. 3765
Author(s):  
Xitong Xu ◽  
Shengbo Chen ◽  
Zhengyuan Xu ◽  
Yan Yu ◽  
Sen Zhang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Dimensionality Reduction ◽  
Near Infrared ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Data Transformation ◽  
Black Soil ◽  
Locally Linear Embedding ◽  
Soil Area

Black soil in northeast China is gradually degraded and soil organic matter (SOM) content decreases at a rate of 0.5% per year because of the long-term cultivation. SOM content can be obtained rapidly by visible and near-infrared (Vis–NIR) spectroscopy. It is critical to select appropriate preprocessing techniques for SOM content estimation through Vis–NIR spectroscopy. This study explored three categories of preprocessing techniques to improve the accuracy of SOM content estimation in black soil area, and a total of 496 ground samples were collected from the typical black soil area at 0–15 cm in Hai Lun City, Heilongjiang Province, northeast of China. Three categories of preprocessing include denoising, data transformation and dimensionality reduction. For denoising, Svitzky-Golay filter (SGF), wavelet packet transform (WPT), multiplicative scatter correction (MSC), and none (N) were applied to spectrum of ground samples. For data transformation, fractional derivatives were allowed to vary from 0 to 2 with an increment of 0.2 at each step. For dimensionality reduction, multidimensional scaling (MDS) and locally linear embedding (LLE) were introduced and compared with principal component analysis (PCA), which was commonly used for dimensionality reduction of soil spectrum. After spectral pretreatments, a total of 132 partial least squares regression (PLSR) models were constructed for SOM content estimation. Results showed that SGF performed better than the other three denoising methods. Low-order derivatives can accentuate spectral features of soil for SOM content estimation; as the order increases from 0.8, the spectrum were more susceptible to spectral noise interferences. In most cases, 0.2–0.8 order derivatives exhibited the best estimation performance. Furthermore, PCA yielded the optimal predictability, the mean residual predictive deviation (RPD) and maximum RPD of the models using PCA were 1.79 and 2.60, respectively. The application of appropriate preprocessing techniques could improve the efficiency and accuracy of SOM content estimation, which is important for the protection of ecological and agricultural environment in black soil area.

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