Mapping soil organic carbon and clay using remote sensing to predict soil workability for enhanced climate change adaptation

Geoderma ◽  
2020 ◽  
Vol 363 ◽  
pp. 114177 ◽  
Author(s):  
S.S. Paul ◽  
N.C. Coops ◽  
M.S. Johnson ◽  
M. Krzic ◽  
A. Chandna ◽  
...  
2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Rodrigo Antón ◽  
Francisco Javier Arricibita ◽  
Alberto Ruiz-Sagaseta ◽  
Alberto Enrique ◽  
Isabel de Soto ◽  
...  

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Susanne Rolinski ◽  
Alexander V. Prishchepov ◽  
Georg Guggenberger ◽  
Norbert Bischoff ◽  
Irina Kurganova ◽  
...  

AbstractChanges in land use and climate are the main drivers of change in soil organic matter contents. We investigated the impact of the largest policy-induced land conversion to arable land, the Virgin Lands Campaign (VLC), from 1954 to 1963, of the massive cropland abandonment after 1990 and of climate change on soil organic carbon (SOC) stocks in steppes of Russia and Kazakhstan. We simulated carbon budgets from the pre-VLC period (1900) until 2100 using a dynamic vegetation model to assess the impacts of observed land-use change as well as future climate and land-use change scenarios. The simulations suggest for the entire VLC region (266 million hectares) that the historic cropland expansion resulted in emissions of 1.6⋅ 1015 g (= 1.6 Pg) carbon between 1950 and 1965 compared to 0.6 Pg in a scenario without the expansion. From 1990 to 2100, climate change alone is projected to cause emissions of about 1.8 (± 1.1) Pg carbon. Hypothetical recultivation of the cropland that has been abandoned after the fall of the Soviet Union until 2050 may cause emissions of 3.5 (± 0.9) Pg carbon until 2100, whereas the abandonment of all cropland until 2050 would lead to sequestration of 1.8 (± 1.2) Pg carbon. For the climate scenarios based on SRES (Special Report on Emission Scenarios) emission pathways, SOC declined only moderately for constant land use but substantially with further cropland expansion. The variation of SOC in response to the climate scenarios was smaller than that in response to the land-use scenarios. This suggests that the effects of land-use change on SOC dynamics may become as relevant as those of future climate change in the Eurasian steppes.


2021 ◽  
Vol 13 (4) ◽  
pp. 769
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Jie Liu ◽  
Xiangyu Ge ◽  
Junyong Zhang

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.


Author(s):  
Ziwei Xiao ◽  
Xuehui Bai ◽  
Mingzhu Zhao ◽  
Kai Luo ◽  
Hua Zhou ◽  
...  

Abstract Shaded coffee systems can mitigate climate change by fixation of atmospheric carbon dioxide (CO2) in soil. Understanding soil organic carbon (SOC) storage and the factors influencing SOC in coffee plantations are necessary for the development of sound land management practices to prevent land degradation and minimize SOC losses. This study was conducted in the main coffee-growing regions of Yunnan; SOC concentrations and storage of shaded and unshaded coffee systems were assessed in the top 40 cm of soil. Relationships between SOC concentration and factors affecting SOC were analysed using multiple linear regression based on the forward and backward stepwise regression method. Factors analysed were soil bulk density (ρb), soil pH, total nitrogen of soil (N), mean annual temperature (MAT), mean annual moisture (MAM), mean annual precipitation (MAP) and elevations (E). Akaike's information criterion (AIC), coefficient of determination (R2), root mean square error (RMSE) and residual sum of squares (RSS) were used to describe the accuracy of multiple linear regression models. Results showed that mean SOC concentration and storage decreased significantly with depth under unshaded coffee systems. Mean SOC concentration and storage were higher in shaded than unshaded coffee systems at 20–40 cm depth. The correlations between SOC concentration and ρb, pH and N were significant. Evidence from the multiple linear regression model showed that soil bulk density (ρb), soil pH, total nitrogen of soil (N) and climatic variables had the greatest impact on soil carbon storage in the coffee system.


2017 ◽  
Vol 72 (3) ◽  
pp. 191-204 ◽  
Author(s):  
E.D.v.L. Maas ◽  
R. Lal ◽  
K. Coleman ◽  
A. Montenegro ◽  
W.A. Dick

2010 ◽  
Vol 24 (4) ◽  
pp. 271-281 ◽  
Author(s):  
Moslem Ladoni ◽  
Seyed Kazem Alavipanah ◽  
Hosein Ali Bahrami ◽  
Ali Akbar Noroozi

Author(s):  
K.K. Vikrant ◽  
D.S. Chauhan ◽  
R.H. Rizvi

Climate change is one of the impending problems that have affected the productivity of agroecosystems which calls for urgent action. Carbon sequestration through agroforestry along altitude in mountainous regions is one of the options to contribute to global climate change mitigation. Three altitudes viz. lower (286-1200m), middle (1200-2000m), and upper (2000-2800m) have been selected in Tehri district. Ten Quadrates (10m × 10 m) were randomly selected from each altitude in agrisilviculture system. At every sampling point, one composite soil sample was taken at 30 cm soil depth for soil organic carbon analysis. For the purpose of woody biomass, Non destructive method and for crop biomass assessment destructive method was employed. Finally, aboveground biomass (AGB), belowground biomass carbon (BGB), Total tree Biomass (TTB), Crop biomass (CB), Total Biomass (TB), Total biomass carbon (TBC), soil organic carbon (SOC), and total carbon stock (TC) status were estimated and variables were compared using one-way analysis of variance (ANOVA).The result indicated that AGB, BGB, TTB, CB , TB, TBC, SOC, and TC varied significantly (p < 0.05) across the altitudes. Results showed that total carbon stock followed the order upper altitude ˃ middle altitudes ˃ lower altitude. The upper altitude (2000-2800 m) AGB, BGB,TTB, TBC,SOC, and TC stock was estimated as 2.11 Mg ha-1 , 0.52 Mg ha-1, 2.63 Mg ha-1, 2.633 Mg ha-1, 1.18 Mg ha-1 , 26.53 Mg ha-1, 38.48 Mg ha-1 respectively, and significantly higher than the other altitudes. It was concluded that agrisilviculture system hold a high potential for carbon storage at temperate zones. Quercus lucotrichophora, Grewia oppositifolia and Melia azadirach contributed maximum carbon storage which may greatly contribute to the climate resilient green economy strategy and their conservation should be promoted.


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