ASSESSMENT OF LAND COVER CHANGES AND THEIR EFFECT ON SOIL ORGANIC CARBON AND SOIL TOTAL NITROGEN IN DAQING PREFECTURE, CHINA

2012 ◽  
Vol 25 (6) ◽  
pp. 520-531 ◽  
Author(s):  
B. Yu ◽  
P. Stott ◽  
X. Y. Di ◽  
H. X. Yu
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Cover ◽  
Total Nitrogen ◽  
Land Cover Changes ◽  
Soil Total Nitrogen

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.

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