Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China

Geoderma ◽  
2017 ◽  
Vol 305 ◽  
pp. 250-263 ◽  
Author(s):  
Shuai Wang ◽  
Qianlai Zhuang ◽  
Qiubing Wang ◽  
Xinxin Jin ◽  
Chunlan Han
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Liaoning Province ◽  
Soil Total Nitrogen

scholarly journals Estimating the Spatial Distribution of Soil Organic Carbon and Soil Total Nitrogen Stocks by Remote Sensing Methods: A Case Study in a Forest-dominated Alpine Watershed, Western China

2021 ◽  
Author(s):  
chao wang ◽  
Chuanyan Zhao ◽  
Kaiming Li ◽  
Shouzhang Peng ◽  
Ying Wang
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Vegetation Index ◽  
Multiple Linear Regression Model ◽  
Western China ◽  
Linear Regression Models ◽  
Soil Total Nitrogen

Abstract Soil organic carbon and soil total nitrogen stocks are important indicators for evaluating soil health and stability. Accurately predicting the spatial distribution of soil organic carbon and total nitrogen stocks is an important basis for mitigating global warming, ensuring regional food security, and maintaining the sustainable development of ecologically fragile areas. On the basis of field sampling data and remote sensing technology, this study divided the topsoil (0–30 cm) into three soil layers of 0–10 cm, 10–20 cm, and 20–30 cm to carry out soil organic carbon and soil total nitrogen stocks estimation experiments in the Qilian Mountains in western China. A multiple linear regression model and a stepwise multiple linear regression model were used to estimate soil organic carbon and soil total nitrogen stocks. A total of 119 topsoil samples and nine remotely sensed environmental variables were collected and used for model development and validation. The results indicated that these two linear regression models showed good performance. The modified soil-adjusted vegetation index (MSAVI), perpendicular vegetation index (PVI), aspect, elevation, and solar radiation were the key environmental variables affecting soil organic carbon and total nitrogen stocks. In topsoil, remote sensing technology could be used to predict the soil properties in layers; however, as the soil depth increased, the performance of the linear regression models gradually decreased.

scholarly journals An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9126
Author(s):  
Shuai Wang ◽  
Kabindra Adhikari ◽  
Qianlai Zhuang ◽  
Zijiao Yang ◽  
Xinxin Jin ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Total Nitrogen ◽  
Coastal Region ◽  
Northeastern China ◽  
Mean Annual Temperature ◽  
Soil Total Nitrogen ◽  
Vegetation Landscape ◽  
Sampling Points ◽  
The Mean

Soil organic carbon (SOC) and soil total nitrogen (STN) are major soil indicators for soil quality and fertility. Accurate mapping SOC and STN in soils would help both managed and natural soils and ecosystem management. This study developed an improved similarity-based approach (ISA) to predicting and mapping topsoil (0–20 cm soil depth) SOC and STN in a coastal region of northeastern China. Six environmental variables including elevation, slope gradient, topographic wetness index, the mean annual temperature, the mean annual temperature, and normalized difference vegetation index were used as predictors. Soil survey data in 2012 was designed based on the clustering of the study area into six climatic vegetation landscape units. In each landscape unit, 20–25 sampling points were determined at different landform positions considering local climate, soil type, elevation and other environmental factors, and finally 126 sampling points were obtained. Soil sampling from the depth of 0–20 cm were used for model prediction and validation. The ISA model performance was compared with the geographically weighted regression (GWR), regression kriging (RK), boosted regression trees (BRT) considering mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R2), and maximum relative difference (RD) indices. We found that the ISA method performed best with the highest R2 and lowest MAE, RMSE compared to GWR, RK, and BRT methods. The ISA method could explain 76% and 83% of the total SOC and STN variability, respectively, 12–40% higher than other models in the study area. Elevation had the largest influence on SOC and STN distribution. We conclude that the developed ISA model is robust and effective in mapping SOC and STN, particularly in the areas with complex vegetation-landscape when limited samples are available. The method needs to be tested for other regions in our future research.

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