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Environmental and Earth Sciences Proceedings
Volume 36
Issue 1
10.3390/eesp2025036003
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Proceeding Paper

Generation of Synthetic Hyperspectral Image Cube for Mapping Soil Organic Carbon Using Proximal Remote Sensing †

by
Rajan G. Rejith
1,*,
Rabi N. Sahoo
1,
Tarun Kondraju
1,
Amrita Bhandari
1,
Rajeev Ranjan
1 and
Ali Moursy
2
1
Division of Agricultural Physics, Indian Council of Agricultural Research (ICAR)–Indian Agricultural Research Institute (IARI), New Delhi 110012, India
2
Soil and Water Department, Faculty of Agriculture, Sohag University, Sohag 82524, Egypt
*
Author to whom correspondence should be addressed.
†
Presented at the 2nd International Electronic Conference on Land (IECL 2025), 4–5 September 2025; Available online: https://sciforum.net/event/IECL2025.
Environ. Earth Sci. Proc. 2025, 36(1), 3; https://doi.org/10.3390/eesp2025036003
Published: 18 November 2025
(This article belongs to the Proceedings of The 2nd International Electronic Conference on Land)
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Abstract

The advent of hyperspectral remote sensing represented a breakthrough in the accurate, fast, and non-invasive estimation of important soil fertility parameters. The present study utilizes non-imaging hyperspectral data in the spectral range of 350–2500 nm for estimating soil organic carbon (SOC) content. When partial least squares (PLS) scores were taken as independent variables, support vector machine (SVM) outperformed artificial neural network (ANN) and partial least squares regression (PLSR), achieving an R2 value of 0.83. After pre-processing, the proximal spectral values were spatially interpolated to construct a synthetic hyperspectral image of the experimental fields. By applying the regression model to this synthetic hyperspectral imagery, a high-resolution SOC map showing the variability of organic carbon content in the soil was generated.
Keywords: soil organic carbon (SOC); hyperspectral remote sensing; machine learning; ordinary kriging soil organic carbon (SOC); hyperspectral remote sensing; machine learning; ordinary kriging

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MDPI and ACS Style

Rejith, R.G.; Sahoo, R.N.; Kondraju, T.; Bhandari, A.; Ranjan, R.; Moursy, A. Generation of Synthetic Hyperspectral Image Cube for Mapping Soil Organic Carbon Using Proximal Remote Sensing. Environ. Earth Sci. Proc. 2025, 36, 3. https://doi.org/10.3390/eesp2025036003

AMA Style

Rejith RG, Sahoo RN, Kondraju T, Bhandari A, Ranjan R, Moursy A. Generation of Synthetic Hyperspectral Image Cube for Mapping Soil Organic Carbon Using Proximal Remote Sensing. Environmental and Earth Sciences Proceedings. 2025; 36(1):3. https://doi.org/10.3390/eesp2025036003

Chicago/Turabian Style

Rejith, Rajan G., Rabi N. Sahoo, Tarun Kondraju, Amrita Bhandari, Rajeev Ranjan, and Ali Moursy. 2025. "Generation of Synthetic Hyperspectral Image Cube for Mapping Soil Organic Carbon Using Proximal Remote Sensing" Environmental and Earth Sciences Proceedings 36, no. 1: 3. https://doi.org/10.3390/eesp2025036003

APA Style

Rejith, R. G., Sahoo, R. N., Kondraju, T., Bhandari, A., Ranjan, R., & Moursy, A. (2025). Generation of Synthetic Hyperspectral Image Cube for Mapping Soil Organic Carbon Using Proximal Remote Sensing. Environmental and Earth Sciences Proceedings, 36(1), 3. https://doi.org/10.3390/eesp2025036003

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