Soil Organic Carbon Estimation Using Remote Sensing Technique

2020 ◽  
pp. 405-412
Author(s):  
Akhil Francis ◽  
R. Kevin ◽  
Girigan Gopi ◽  
Rajesh Baby
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Remote Sensing Technique

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2

scholarly journals Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

2021 ◽  
Vol 13 (4) ◽  
pp. 769
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Jie Liu ◽  
Xiangyu Ge ◽  
Junyong Zhang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Lake Basin ◽  
Arid Areas ◽  
Annual Average ◽  
Machine Learning Methods ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

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.

Remote Sensing of Soil Organic Carbon in Semi-Arid Region of Iran

2010 ◽  
Vol 24 (4) ◽  
pp. 271-281 ◽  
Author(s):  
Moslem Ladoni ◽  
Seyed Kazem Alavipanah ◽  
Hosein Ali Bahrami ◽  
Ali Akbar Noroozi
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Arid Region ◽  
Semi Arid Region ◽  
Semi Arid

Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture

CATENA ◽  
2019 ◽  
Vol 182 ◽  
pp. 104141 ◽  
Author(s):  
Nikou Hamzehpour ◽  
Hossein Shafizadeh-Moghadam ◽  
Roozbeh Valavi
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Texture ◽  
Driving Forces ◽  
Digital Mapping

Prediction of Soil Organic Carbon and its Mapping Using Regression Analyses and Remote Sensing Data in GIS and R

2019 ◽  
pp. 429-450
Author(s):  
Gouri Sankar Bhunia ◽  
Pravat Kumar Shit ◽  
Hamid Reza Pourghasemi ◽  
Mohsen Edalat
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Remote Sensing Data ◽  
Regression Analyses ◽  
Sensing Data

scholarly journals Evaluating the feasibility of GF-1 remote sensing comparison with hyperspectral data for soil organic carbon prediction and mapping

2020 ◽  
Vol 545 ◽  
pp. 012016
Author(s):  
Yan Guo ◽  
Jia He ◽  
Shimin Li ◽  
Guoqing Zheng ◽  
Laigang Wang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Hyperspectral Data

scholarly journals Evaluation of Composite Burn Index and Land Surface Temperature for Assessing Soil Burn Severity in Mediterranean Fire-Prone Pine Ecosystems

Forests ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 494 ◽  
Author(s):  
Elena Marcos ◽  
Víctor Fernández-García ◽  
Alfonso Fernández-Manso ◽  
Carmen Quintano ◽  
Luz Valbuena ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Surface Temperature ◽  
Soil Properties ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Burn Severity ◽  
Soil Burn Severity ◽  
Changes In Soil Properties

We analysed the relationship between burn severity indicators, from remote sensing and field observations, and soil properties after a wildfire in a fire-prone Mediterranean ecosystem. Our study area was a large wildfire in a Pinus pinaster forest. Burn severity from remote sensing was identified by studying immediate post-fire Land Surface Temperature (LST). We also evaluated burn severity in the field applying the Composite Burn Index (CBI) in a total of 84 plots (30 m diameter). In each plot we evaluated litter consumption, ash colour and char depth as visual indicators. We collected soil samples and pH, soil organic carbon, dry aggregate size distribution (MWD), aggregate stability and water repellency were analysed. A controlled heating of soil was also carried out in the laboratory, with soil from the control plots, to compare with the changes produced in soils affected by different severity levels in the field. Our results shown that changes in soil properties affected by wildfire were only observed in soil aggregation in the high severity situation. The laboratory-controlled heating showed that temperatures of about 300 °C result in a significant reduction in soil organic carbon and MWD. Furthermore, soil organic carbon showed a significant decrease when LST values increased. Char depth was the best visual indicator to show changes in soil properties (mainly physical properties) in large fires that occur in Mediterranean pine forests. We conclude that CBI and post-fire LST can be considered good indicators of soil burn severity since both indicate the impact of fire on soil properties.

Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data

2020 ◽  
pp. 1-17 ◽  
Author(s):  
Santanu Mallik ◽  
Tridip Bhowmik ◽  
Umesh Mishra ◽  
Niladri Paul
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon
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