Author Details

Scroll

Refine your search

Collections

Basic & Applied Science Collection

Co-Authors

Journals

Current Science

Year

2023

Authors

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All

Swami, Sanjay

Mapping and Monitoring of Soil Organic Carbon Using Regression Analysis of Spectral Indices

Abstract Views :97 | PDF Views:61

Authors

Bullo Yami ¹, N. J. Singh ¹, B. K. Handique ², Sanjay Swami ¹

Affiliations
1 College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, Imphal–Umroi Road, Umiam 793 103, IN
2 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN

Source

Current Science, Vol 124, No 12 (2023), Pagination: 1431-1444

Abstract

The soil carbon sinking ability is dominantly controlled by local topographical settings, soil–crop management and traditional farming practices on which the food demand of the major population is dependent. The degradation of natural resources causing poor soil health is likely to strain the hilly and mountain ecosystem. This study aims to map soil organic carbon (SOC) of rice–fallow system under varying slopes and its changes during the past 20 years under traditional management practice using geospatial tools and techniques. Regression models of SOC were derived from remote sensing (RS)-based indices using multiple linear regression-stepwise (MLR-stepwise), partial least square regression (PLSR) and principal component analysis-regression (PCA-R). The MLR-stepwise model was found to be superior in performance with high R² (0.87) and least RMSE (0.026) compared to PLSR (R² = 0.71 and RMSE = 0.05) and PCA-R (R² = 0.27 and RMSE = 0.11) models for SOC prediction.

Keywords

Regression Models, Remote Sensing, Rice–Fallow System, Soil Organic Carbon, Spectral Indices.

Full Text

References

Rajasekaran, B. and Whiteford, M. B., Rice–crop production system: The role of indigenous knowledge in designing food security policies. Food Policy, 1992, 18(3), 237–247.

Gadgil, M., Berkes, F. and Folke, C., Indigenous knowledge for biodiversity conservation. Ambio, 1993, 22, 151–156.

Kala, C. P., Traditional ecological knowledge on characteristics, conservation and management of soil in tribal communities of Pachmarhi Biosphere Reserve, India. J. Soil Sci. Plant Nutr., 2013, 13(1), 201–214.

Sanders, D., Soil conservation. In Land Use, Land Cover and Soil Sciences (ed. Willy, H. V.), UNESCO, Eolss Publishers, Oxford, UK, 2004, 4, 1–21.

Arunrat, N., Kongsurakan, P., Sereenonchai, S. and Hatano, R., Soil organic carbon in sandy paddy fields of northeast Thailand: a review. Agronomy, 2020, 10(8), 1061.

Zhang, Y. et al., Prediction of soil organic carbon based on Landsat 8 monthly NDVI data for the Jianghan Plain in Hubei Province, China. Remote Sensing, 2019, 11, 1683; doi:10.3390/rs11141683.

Meetei, T. T., Kundu, M. C. and Devi, Y. B., Long-term effect of rice-based cropping systems on pools of soil organic carbon in farmer’s field in hilly agroecosystem of Manipur, India. Environ. Monit. Assess., 2020, 192(4), 1–17.

Wu, J. S. and Xiao, H. A., Measuring the gross turnover time of soil microbial biomass C under incubation. Acta Pedol. Sin., 2004, 41(3), 401–407.

Lui, D. et al., Spatial distribution of soil organic carbon and analysis of related factors in cropland of the black soil region, Northeast China. Agric. Ecosyst. Environ., 2006, 113, 73–81.

Li, Z. W., Nie, X. D., Chen, X. L., Lu, Y. M., Jiang, W. G. and Zeng, G. M., The effects of land use and landscape position on labile organic carbon and carbon management index in red soil hilly region, sourthern China. J. Mt. Sci., 2015, 12(3); doi:10.1007/s11629-013-2964-2.

Bayer, C. and Mielniczuk, J., Capítulo 2: Dinâmica da matéria orgânica. In Fundamentos da Matéria orgânica no solo: Ecossistemas Tropicais e Subtropicais (eds Santos, G. A. et al.), 07–18, Porto Alegre, Metropole, Brazil, 2008.

Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J. and Skjemstad, J. O., Visible, near infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 2006, 131, 59–75.

Shi, P. et al., Land-use types and slope topography affect the soil labile carbon fractions in the loess hilly–gully area of Shaanxi, China. Arch. Agron. Soil Sci., 2020, 66(5), 638–650.

Zhang, X. et al., Which slope aspect and gradient provides the best afforestation-driven soil carbon sequestration on the China’s Loess plateau? Ecol. Eng., 2020, 147, 105782.

Jendoubi, D., Liniger, H. and Ifejika Speranza, C., Impacts of land use and topography on soil organic carbon in a Mediterranean landscape (north-western Tunisia). Soil, 2019, 5(2), 239–251.

Rengel, Z., The role of crop residues in improving soil fertility. In Nutrient Cycling in Terrestrial Ecosystems, Springer, Berlin, Germany, 2007, pp. 183–214.

Jiang, D., Zhuang, D. and Huang, Y., Crop residues as an energy feedstock: availability and sustainability. In Sustainable Bioenergy Production, CRC Press, Boca Raton, Florida, USA, 2014, pp. 236–249.

Zhang, L. et al., Soil labile organic carbon fractions and soil enzyme activities after 10 years of continuous fertilization and wheat residue incorporation. Sci. Rep., 2020, 10, 11318; https://doi.org/10.1038/s41598-020-68163-3.

The Meghalayan, Dispatches from the farmlands of-Ri-bhoi; https://themeghalayan.com/dispatches-from-the-farmlands-of-RiBhoi (accessed on 6 April 2022).

Singh, P. and Benbi, D. K., Soil organic carbon pool changes in relation to slope position and land-use in Indian lower Himalayas. Catena, 2018, 166, 171–180.

Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G. and Bochtis, D., Remote sensing techniques for soil organic carbon estimation: a review. Remote Sensing, 2019, 11(6), 676; https://doi.org/10.3390/rs11060676.

Vohland, M., Ludwig, B., Seidel, M. and Hutengs, C., Quantification of soil organic carbon at regional scale: benefits of fusing Vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches. Geoderma, 2022, 405, 115426.

Bhunia, G. S., Shit, P. K. and Pourghasemi, H. R., Soil organic carbon mapping using remote sensing techniques and multivariate regression model. Geocarto Int., 2019, 34(2), 215–226.

Partyka, T. and Hamkalo, Z., Estimation of oxidizing ability of organic matter of forest and arable soil. Zemdirbyste-Agric., 2010, 97, 33–40.

Wu, H. Y., Zeng, F. P., Song, T. Q., Peng, W. X., Li, X. H. and OuYang, Z. W., Spatial variations of soil organic carbon and nitrogen in peak-cluster depression areas of Karst region. Plant Nutr. Fert. Sci., 2009, 15, 1029–1036.

Zhang, L., Gao, P., Wang, C., Liu, S. and Li, X., Spatial distribution of soil organic carbon in the forestland of the Yaoxiang small watershed in central and southern Shandong Province. Sci. Soil Water Conserv., 2015, 13, 83–89.

Kumar, S., Lal, R. and Liu, D., A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma, 2012, 189, 627–634.

Chabala, L. M., Mulolwa, A. and Lungu, O., Application of ordinary kriging in mapping soil organic carbon in Zambia. Pedosphere, 2017, 27, 338–343.

Lu, F., Zhao, Y., Huang, B. and Wang, J., Comparison of predicting methods for mapping the spatial distribution of topsoil organic matter content in cropland of hailun. J. Soil Sci., 2012, 43, 662–667.

Xu, E. and Zhang, H., Multi-scale analysis of kriging interpolation and conditional simulation for soil organic matters in newly reclaimed area in Yili. Soils, 2013, 45, 91–98.

Zhao, D., Zhao, H., Rao, J. and Gao, X., Analysis of the spatial distribution pattern of cultivated land quality and the influential factors based on trend-surface. Res. Soil Water Conserv., 2015, 22, 219–223.

Martín, J. A. R. et al., Soil organic carbon stock on the Majorca Island: temporal change in agricultural soil over the last 10 years. Catena, 2019, 181, 104087.

Zhao, M. S., Qiu, S. Q., Wang, S. H., Li, D. C. and Zhang, G. L., Spatial–temporal change of soil organic carbon in Anhui Province of East China. Geoderma Reg., 2021, 26, e00415.

Guo, L., Linderman, M., Shi, T. Z., Chen, Y. Y., Duan, L. J. and Zhang, H. T., Exploring the sensitivity of sampling density in digital mapping of soil organic carbon and its application in soil sampling. Remote Sensing, 2018, 10, 27.

Lin, Y., Zhu, A., Qin, C., Li, B. and Pei, T., A soil sampling method based on representativeness grade of sampling points. Acta Pedol. Sin., 2011, 48, 938–946.

Liu, Y., Guo, L., Jiang, Q., Zhang, H. and Chen, Y., Comparing geospatial techniques to predict soc stocks. Soil Till. Res., 2015, 148, 46–58.

Malone, B. P., Jha, S. K., Minasny, B. and McBratney, A. B., Comparing regression-based digital soil mapping and multiple-point geostatistics for the spatial extrapolation of soil data. Geoderma, 2016, 262, 243–253.

Žížala, D., Minařík, R. and Zádorová, T., Soil organic carbon mapping using multispectral remote sensing data: prediction ability of data with different spatial and spectral resolutions. Remote Sensing, 2019, 11(24), 2947.

Yu, H., Zha, T., Zhang, X., Nie, L., Ma, L. and Pan, Y., Spatial distribution of soil organic carbon may be predominantly regulated by topography in a small revegetated watershed. Catena, 2020, 188, 104459.

Zhong, Z. et al., Relationship between soil organic carbon stocks and clay content under different climatic conditions in Central China. Forests, 2018, 9(10), 598.

Churchman, G. J., Singh, M., Schapel, A., Sarkar, B. and Bolan, N., Clay minerals as the key to the sequestration of carbon in soils. Clays Clay Miner., 2020, 68(2), 135–143.

Zhang, H., Kang, J., Xu, X. and Zhang, L., Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: a case study of Yi’an County, Heilongjiang Province, China. Comput. Electron. Agric., 2020, 176, 105618.

Ferugoson, B., Lukens, W. E., El Masri, B. and Stinchcomb, G. E., Alluvial landform and the occurrence of paleosols in a humid–subtropical climate have an effect on long-term soil organic carbon storage. Geoderma, 2020, 371, 114388.

Department of Agriculture, Agriculture contingency plan for district RiBhoi. Directorate of Agriculture, Government of Meghalaya. 2014, pp. 1–3.

Sehgal, J. L., Sys, C., Stoops, G. and Tavernier, R., Morphology, genesis and classification of two dominant soils of the warm temperate and humid region of the central Himalayas. J. Indian Soc. Soil Sci., 1985, 33(4), 846–857.

Chen, J. and Zhu, W., Comparing Landsat-8 and Sentinel-2 top of atmosphere and surface reflectance in high latitude regions: case study in Alaska. Geocarto Int., 2021, 37(9), 1–20; doi:10.1080/10106049.2021.1924295.

Mondal, A., Khare, D., Kundu, S., Mondal, S., Mukherjee, S. and Mukhopadhyay, A., Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data. Egypt. J. Remote Sensing Space Sci., 2017, 20(1), 61–70.

Young, N. E., Anderson, R. S., Chignell, S. M., Vorster, A. G., Lawrence, R. and Evangelista, P. H., A survival guide to Landsat preprocessing. Ecology, 2017, 98(4), 920–932.

Thaler, E. A., Larsen, I. J. and Yu, Q., A new index for remote sensing of soil organic carbon based solely on visible wavelengths. Soil Sci. Soc. Am. J., 2019, 83(5), 1443–1450.

Singh, R. P. et al., Retrieval of wheat leaf area index using price approach based on inversion of canopy reflectance model. J. Indian Soc. Remote Sensing, 2005, 33(2), 307–313.

Ray, S. S., Sood, A., Panigrahy, S. and Parihar, J. S., Derivation of indices using remote sensing data to evaluate cropping systems. J. Indian Soc. Remote Sensing, 2005, 33(4), 475.

Kumari, M. and Sarma, K., Changing trends of land surface temperature in relation to land use/cover around thermal power plant in Singrauli district, Madhya Pradesh, India. Spat. Inf. Res., 2017, 25(6), 769–777.

Nageswara, P. P. R., Shobha, S. V., Ramesh, K. S. and Somashekhar, R. K., Satellite-based assessment of agricultural drought in Karnataka state. J. Indian Soc. Remote Sensing, 2005. 33(3), 429–434.

Gbolo, P., Gerla, P. J. and Vandeberg, G. S., Using high resolution, multispectral imagery to assess the effect of soil properties on vegetation reflectance at an abandoned feedlot. Geocarto Int., 2015, 30(7), 1–17.

Rouse, J. W., Haas, R. H., Schelle, J. A., Deering, D. W. and Harlan, J. C., Monitoring the vernal advancement or retrogradation of natural vegetation. NASA/GSFC, Type III, Final Report, NASA, Greenbelt, MD, USA, 1974, p. 371.

Tucker, C. J., Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing Environ., 1979, 8(2), 127–150.

Lillesaeter, E., Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling. Remote Sensing Environ., 1982, 12, 247–254.

Holben, B. N., Kaufman, Y. J. and Kendall, J. D., NOAA-11 AVHRR visible and near-IR in flight calibration. Int. J. Remote Sensing, 1990, 11(8), 1511–1519.

Holben, B. and Fraser, R. S., Red and near-infrared sensor response to off-nadiir viewing. Int. J. Remote Sensing, 1984, 5(1), 145–160.

Huete, A. R., A soil-adjusted vegetation index (SAVI). Remote Sensing Environ., 1988, 25, 295–309.

Pye, K. and Tsoar, H., Aeolian Sand and Sand Dunes, Springer, Berlin, Germany, 2009, p. 458.

Mayhew, S. and Penny, A., The Concise Oxford Dictionary of Geography, University Press, Oxford, UK, 1992, p. 250.

Geo University, Spectral indices with multispectral satellite data; https://www.geo.university›pages›blo (accessed on 18 December 2022).

Bartholomeus, H., Schaepman, M., Kooistra, L., Stevens, A., Hoogmoed, W. and Spaargaren, O., Spectral reflectance based indices for soil organic carbon quantification. Geoderma, 2008, 145, 28–36; doi:10.1016/j.geoderma.2008.01.010.

Zhou, W., Han, G., Liu, M. and Li, X., Effects of soil pH and texture on soil carbon and nitrogen in soil profiles under different land uses in Mun River Basin, Northeast Thailand. Peer J., 2019, 7, e7880.

Page, K. L., Dang, Y. P. and Dalal, R. C., The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield. Front. Sustain. Food Syst., 2020, 4, 31.

Nelson, D. W. and Sommers, L. E., Total carbon, organic carbon, and organic matter. In Methods of Soil Analysis: Part 3 – Chemical Methods, SSA Book Series, Madison, 1996, vol. 5, pp. 961–1010.

He, T., Wang, J., Lin, Z. and Cheng, Y., Spectral features of soil organic matter. Geo-spat. Inf. Sci., 2009, 12(1), 33–40.

Lin, L., Wang, Y., Teng, J. and Wang, X., Hyperspectral analysis of soil organic matter in coal mining regions using wavelets, correlations, and partial least squares regression. Environ. Monit. Assess., 2016, 188(2), 1–11.

Jaber, S. M., Lant, C. L. and Al-Qinna, M. I., Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra. Int. J. Remote Sensing, 2012, 32(18), 5077–5103.

Somvanshi, S. S., Kunwar, P., Tomar, S. and Singh, M., Comparative statistical analysis of the quality of image enhancement techniques. Int. J. Image Data Fusion, 2018, 9(2), 131–151.

Rasool, S. N., Gaikwad, S. W. and Talat, M. A., Relationship between soil properties and slope segments of Sallar Wullarhama watershed in the Liddar catchment of Jammu and Kashmir. Asian J. Eng. Res., 2014, 2(2), 1–10.

Jaksic, S., Ninkov, J., Milic, S., Vasin, J., Zivanov, M., Jaksic, D. and Komlen, V., Influence of slope gradient and aspect on soil organic carbon content in the region of Nis, Serbia. Sustainability, 2021, 13(15), 8332.

Wilding, L. P., Spatial variability: its documentation, accommodation and implication to soil surveys. In Soil Spatial Variability. Proceedings of a Workshop of the ISSS and the SSA (eds Nielsen, D. R. and Bouma, J.), Las Vegas NV, Wageningen, USA, 30 November–1 December 1985, pp. 166–187.

Hamzehpour, N., Shafizadeh-Moghadam, H. and Valavi, R., Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture. Catena, 2019, 182, 104141.

Wang, S., Zhuang, Q., Jin, X., Yang, Z. and Liu, H., Predicting soil organic carbon and soil nitrogen stocks in topsoil of forest ecosystems in Northeastern China using remote sensing data. Remote Sensing, 2020, 12(7), 1115.

Singh, N. J., Kudrat, M. and Jain, K., Effect of land use and topography on spatial distribution of soil organic carbon in semi-arid sub-tropical ecosystems in Uttar Pradesh, India. Int. J. Ecol. Environ. Sci., 2014, 40, 189–197.

Challam, L. L., Singh, N. J., Debbarma, K., Ray, L. I. and Swami, S., Effect of topographical settings on distribution of soil organic carbon fractions in rice ecosystem of North East India. Int. J. Ecol. Environ. Sci., 2016, 42(5), 129–135.

Viscarra Rossel, R. V. and Behrens, T., Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 2010, 158(1–2), 46–54.

Castaldi, F., Palombo, A., Santini, F., Pascucci, S., Pignatti, S. and Casa, R., Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sensing Environ., 2016, 179, 54–65.

Son, N. T., Chen, C. F., Chen, C. R., Minh, V. Q. and Trung, N. H., A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agric. For. Meteorol., 2014, 197, 52–64.

Nuarsa, I. W., Nishio, F. and Hongo, C., Relationship between rice spectral and rice yield using MODIS data. J. Agric. Sci., 2011, 3(2), 80.

Soil and Water Conservation Department, Integrated Watershed Management Project, Detailed project report, Govt of Meghalaya, Umsning C & RD block, 2009; https://megsoil.gov.in›iwmp›2009-10

IMP, Rainfall statistics of India, India Meteorological Department, Report no. MoES/IMD/HS/RAINFALL REPORT/30, 2019.

Pulak, G., Bhagwat, P. P., Satpute, U. S., Menon P., Prasad, A. K., Sable, S. T. and Advani, S. C., Observed rainfall variability and changes over Meghalaya state. India Meteorological Department, Pune.

Arunrat, N., Pumijumnong, N. and Hatano, R., Predicting local-scale impact of climate change on rice yield and soil organic carbon sequestration: a case study in RoiEt Province, Northeast Thailand. Agric. Syst., 2018, 164, 58–70.

Laxminarayana, K. and Bharali, S., Distribution of inorganic N fractions and N availability indices in the rice soils of Meghalaya. ORYZA – Int. J. Rice, 2010, 47(2), 128–135.

Gabarrón-Galeote, M. A., Trigalet, S. and van Wesemael, B., Effect of land abandonment on soil organic carbon fractions along a Mediterranean precipitation gradient. Geoderma, 2015, 249, 69–78.

Rouse Jr, J. W., Haas, R. H., Schell, J. A. and Deering, D. W., Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, Greenbelt, MD, USA, NASA Goddard Space Flight Center, 1974, vol. 351, p. 309.

Escadafal, R., Belghith, A. and Ben-Moussa, H., Indices spectraux pour la télédétction de la dégradation des milieux naturels en Tunisie aride. In Actes du 6eme Symp. Int. sur les mesures physiques et signatures en teledetection, Val d’Isère (France), 17–24 January 1994, pp. 253–259.

Roujean, J. L. and Breon, F. M., Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing Environ., 1995, 51(3), 375–384.

Mokarram, M., Roshan, G. and Negahban, S., Landform classification using topography position index (case study: salt dome of Korsia-Darab plain, Iran). Model. Earth Syst. Environ., 2015, 1(4), 1–7.

Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. and Sorooshian, S., A modified soil adjusted vegetation index. Remote Sensing Environ., 1994, 48(2), 119–126.

Jiang, Z., Huete, A. R., Kim, Y. and Didan, K., 2-band enhanced vegetation index without a blue band and its application to AVHRR data. In Remote Sensing and Modeling of Ecosystems for Sustainability IV, SPIE, 2007, vol. 6679, pp. 45–53.

Madeira, J., Bedidi, A., Cervelle, B., Pouget, M. and Flay, N., Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: the application of a Thematic Mapper (TM) image for soil-mapping in Brasilia, Brazil. Int. J. Remote Sensing, 1997, 18(13), 2835–2852.

Pouget, J. P., Jozefowicz, M. E., Epstein, A., Tang, X. and Mac-Diarmid, A. G., X-ray structure of polyaniline. Macromolecules, 1991, 24(3), 779–789.

Jamalabad, M. and Abkar, A., Forest canopy density monitoring using satellite images. In 20th ISPRS Congress on International Society for Photogrammetry and Remote Sensing, Istanbul, 2004, Turkey, pp. 12–23.

Deng, Y., Wu, C., Li, M. and Chen, R., RNDSI: a ratio normalized difference soil index for remote sensing of urban/suburban environments. Int. J. Appl. Earth Obs. Geoinf., 2015, 39, 40–48.

Rogers, A. S. and Kearney, M. S., Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices. Int. J. Remote Sensing, 2004, 25(12), 2317–2335.

McFeeters, S. K., The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sensing, 1996, 17(7), 1425–1432.

Username
Password
Remember me