Soil organic carbon mapping using remote sensing techniques and multivariate regression model. Issue 2 (28th January 2019)

• Record Type: Journal Article
• Authors: Bhunia, Gouri Sankar; Kumar Shit, Pravat; Pourghasemi, Hamid Reza
• Is Part Of: Geocarto international. Volume 34:Issue 2(2019)
• Page Start: 215
• Page End: 226
• Publication Date: 2019-01-28
• Subjects: Soil organic carbon -- satellite data -- bare soil index -- normalized difference vegetation index, India; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2017.1381179
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship ( R 2 = 0.54; p < 0.0075). A significant statistical relationship ( r = −0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management.