Regional predictions of soil organic carbon content from spectral reflectance measurements

2009 ◽  
Vol 104 (3) ◽  
pp. 442-446 ◽  
Author(s):  
H. Aïchi ◽  
Y. Fouad ◽  
C. Walter ◽  
R.A. Viscarra Rossel ◽  
Zohra Lili Chabaane ◽  
...  
2021 ◽  
Author(s):  
Chenbo Yang ◽  
Meichen Feng ◽  
Lifang Song ◽  
Chao Wang ◽  
Wude Yang ◽  
...  

Abstract Hyperspectral remote sensing technology can realize the rapid, real-time, and non-destructive monitoring of soil nutrient changes, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were taken as the research object to study the influence of different water treatment on soil organic carbon content, and the relationship between soil organic carbon content and spectral reflectance. After spectral preprocessing, the hyperspectral monitoring models of SOC content were constructed by partial least squares regression(PLSR) with five different sample allocation ratios of calibration to validation sets. The results showed that the effects of drought stress on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not effective, but the RPD values of the models were constructed by simple mathematical transformation(T0-T5) and first-order differential transformation(T6-T11) can reach more than 1.4. The simple mathematical transformation(T0-T2, T4-T5) and the first-order differential transformation(T6-T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the construction of SOC hyperspectral monitoring model.


2021 ◽  
Vol 24 ◽  
pp. e00367
Author(s):  
Patrick Filippi ◽  
Stephen R. Cattle ◽  
Matthew J. Pringle ◽  
Thomas F.A. Bishop

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245040
Author(s):  
Feng Zhang ◽  
Shihang Wang ◽  
Mingsong Zhao ◽  
Falv Qin ◽  
Xiaoyu Liu

Soil organic carbon content has a significant impact on soil fertility and grain yield, making it an important factor affecting agricultural production and food security. Dry farmland, the main type of cropland in China, has a lower soil organic carbon content than that of paddy soil, and it may have a significant carbon sequestration potential. Therefore, in this study we applied the CENTURY model to explore the temporal and spatial changes of soil organic carbon (SOC) in Jilin Province from 1985 to 2015. Dry farmland soil polygons were extracted from soil and land use layers (at the 1:1,000,000 scale). Spatial overlay analysis was also used to extract 1282 soil polygons from dry farmland. Modelled results for SOC dynamics in the dry farmland, in conjunction with those from the Yushu field-validation site, indicated a good level of performance. From 1985 to 2015, soil organic carbon density (SOCD) of dry farmland decreased from 34.36 Mg C ha−1 to 33.50 Mg C ha−1 in general, having a rate of deterioration of 0.03 Mg C ha−1 per year. Also, SOC loss was 4.89 Tg from dry farmland soils in the province, with a deterioration rate of 0.16 Tg C per year. 35.96% of the dry farmland its SOCD increased but 64.04% of the area released carbon. Moreover, SOC dynamics recorded significant differences between different soil groups. The method of coupling the CENTURY model with a detailed soil database can simulate temporal and spatial variations of SOC at a regional scale, and it can be used as a precise simulation method for dry farmland SOC dynamics.


Sign in / Sign up

Export Citation Format

Share Document