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Automated delineation of salt-affected lands and their progress in coastal India using Google Earth Engine and machine learning techniques

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Abstract

Assessment of salt-affected land (SAL) is still a major challenging task worldwide, especially in developing nations. The advancement of remotely sensed digital satellite images of different spectral bands has enabled the assessment of soil salinity. Sentinel-2 and Landsat 8 and 5 images of 2020, 2015 and 2009 and Shuttle Radar Topographical Mission data of 2014 were obtained from the Google Earth Engine data catalogue. Twenty spectral indices have been used which include four vegetation indices, twelve soil salinity indices, four topographical characteristics and their spectral bands. The Random Forest model was used to detect SAL. A total of 593 soil samples were used in the model. Of the electrical conductivity values of samples collected in the field, 70% of the soil samples were used for the model training, and the remaining 30% were used for validation. Also, fivefold cross-validation was carried out to validate the model prediction. The predicted SAL extent identified during 2020 was 134.4 sq. km with an overall accuracy of 93% using fivefold cross-validation. In 2015 and 2009, the total SAL was 128.42 and 120.41 sq. km, respectively. The total SAL has increased by 11.6% during the study period. The present study demonstrated the strength of remote sensing techniques to assess the SAL, which will help quantify the unproductive lands at the state or national level for reclamation or other productive use.

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Data availability

Sources of all the data have been described properly. Derived data supporting the findings of this study are available from the corresponding author on request.

Code availability

The code used in the present study is available from the corresponding author on request.

References

  • Abrol, I. P., Yadav, J. S. P., & Massoud, F. I. (1988). Salt-affected soils and their management (No. 39). FAO Soils Bulletin 39, Food & Agriculture Org.

  • Akramkhanov, A., Martius, C., Park, S., & J., Hendrickx, J, M, H. (2011). Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma, 163(1–2), 52–55. https://doi.org/10.1016/j.geoderma.2011.04.001

    Article  Google Scholar 

  • Aksoy, S., Yildirim, A., Gorji, T., Hamzehpour, N., Tanik, A., & Sertel, E. (2022). Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data. Advances in Space Research, 69(2), 1072–1086. https://doi.org/10.1016/j.asr.2021.10.024

    Article  Google Scholar 

  • Allbed, A., Kumar, L., & Sinha, P. (2014). Mapping and modelling spatial variation in soil salinity in the Al Hassa Oasis based on remote sensing indicators and regression techniques. Remote Sensing, 6(2), 1137–1157. https://doi.org/10.3390/rs6021137

    Article  Google Scholar 

  • Arora, S., & Sharma, V. (2017). Reclamation and management of salt-affected soils for safeguarding agricultural productivity. Journal of Safe Agriculture, 1(1), 1–10.

    Google Scholar 

  • Carrasco, L., O’Neil, A., & W., Morton, R, D., Rowland, C, S. (2019). Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sensing, 11(3), 288. https://doi.org/10.3390/rs11030288

    Article  Google Scholar 

  • Das, R. S., Rahman, M., Sufian, N. P., Rahman, S. M. A., & Siddique, M. A. M. (2020). Assessment of soil salinity in the accreted and non-accreted land and its implication on the agricultural aspects of the Noakhali coastal region, Bangladesh. Heliyon, 6(9), e04926. https://doi.org/10.1016/j.heliyon.2020.e04926

  • Davis, E., Wang, C., & Dow, K. (2019). Comparing Sentinel-2 MSI and Landsat 8 OLI in soil salinity detection: A case study of agricultural lands in coastal North Carolina. International Journal of Remote Sensing, 40(16), 6134–6153. https://doi.org/10.1080/01431161.2019.1587205

    Article  Google Scholar 

  • Douaoui, A., & E, K., Nicolas, H., Walter, C. (2006). Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma, 134(1–2), 217–230. https://doi.org/10.1016/j.geoderma.2005.10.009

    Article  Google Scholar 

  • FAO. (2021). The world map of salt affected soil [WWW Document]. FOOD Agric. Organ, United Nations. Retrieved April 27, 2022, from https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/global-map-of-salt-affected-soils/en/

  • Fathizad, H., Ardakani, M. A. H., Sodaiezadeh, H., Kerry, R., & Taghizadeh-Mehrjardi, R. (2020). Investigation of the spatial and temporal variation of soil salinity using random forests in the central desert of Iran. Geoderma, 365, 114233. https://doi.org/10.1016/j.geoderma.2020.114233

  • Fathololoumi, S., Vaezi, A. R., Alavipanah, S. K., Ghorbani, A., Saurette, D., & Biswas, A. (2020). Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: a case study in Iran. Science of the Total Environment, 721, 137703. https://doi.org/10.1016/j.scitotenv.2020.137703

  • Gangai, I., & P, D., Ramachandran, S. (2010). The role of spatial planning in coastal management—a case study of Tuticorin coast (India). Land Use Policy, 27(2), 518–534. https://doi.org/10.1016/j.landusepol.2009.07.007

    Article  Google Scholar 

  • Gorji, T., Sertel, E., & Tanik, A. (2017). Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey. Ecological Indicators, 74, 384–391. https://doi.org/10.1016/j.ecolind.2016.11.043

    Article  CAS  Google Scholar 

  • Habibi, V., Ahmadi, H., Jafari, M., & Moeini, A. (2021). Mapping soil salinity using a combined spectral and topographical index with artificial neural network. PLoS One, 16(5), e0228494. https://doi.org/10.1371/journal.pone.0228494

  • Huete, A., & R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X

    Article  Google Scholar 

  • Huete, A., Didan, K., Miura, T., Rodriguez, E., & P., Gao, X., Ferreira, L, G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2

    Article  Google Scholar 

  • Ijaz, M., Ahmad, H., & R., Bibi, S., Ayub, M, A., Khalid, S. (2020). Soil salinity detection and monitoring using Landsat data: A case study from Kot Addu, Pakistan. Arabian Journal of Geosciences, 13(13), 1–9. https://doi.org/10.1007/s12517-020-05572-8

    Article  CAS  Google Scholar 

  • Ivushkin, K., Bartholomeus, H., Bregt, A. K., Pulatov, A., Kempen, B., & de Sousa, L. (2019). Global mapping of soil salinity change. Remote Sensing of Environment, 231, 111260. https://doi.org/10.1016/j.rse.2019.111260

  • Jiang, C., Chen, S., Pan, S., Fan, Y., & Ji, H. (2018). Geomorphic evolution of the Yellow River Delta: Quantification of basin-scale natural and anthropogenic impacts. Catena, 163, 361–377. https://doi.org/10.1016/j.catena.2017.12.041

    Article  Google Scholar 

  • Jiang, H., & Shu, H. (2018). Optical remote sensing data based research on detecting soil salinity at different depth in an arid area oasis, Xinjiang, China. Earth Science Informatics, 12(1), 43–56. https://doi.org/10.1007/s12145-018-0358-2

    Article  Google Scholar 

  • Khan, N., & M., Sato, Y. (2001). Monitoring hydro-salinity status and its impact in irrigated semi-arid areas using IRS-1B LISS-II data. Asian Journal of Geoinform, 1(3), 63–73.

    Google Scholar 

  • Khan, N. M., Rastoskuev, V., & V., Sato, Y., Shiozawa, S. (2005). Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77(1–3), 96–109. https://doi.org/10.1016/j.agwat.2004.09.038

    Article  Google Scholar 

  • Kılıc, O. M., Budak, M., Gunal, E., Acır, N., Halbac-Cotoara-Zamfir, R., Alfarraj, S., & Ansari, M. J. (2022). Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey. PLOS ONE, 17(4), e0266915. https://doi.org/10.1371/journal.pone.0266915

  • Kumar, P., Joshi, P. K., & Mittal, S. (2016). Demand vs supply of food in India-futuristic projection. Proceedings of the Indian National Science Academy, 82(5), 1579–1586. https://doi.org/10.16943/ptinsa/2016/48889

  • Kumar, P., & Sharma, P. K. (2020). Soil salinity and food security in India. Frontiers in Sustainable Food Systems, 4, 533781. https://doi.org/10.3389/fsufs.2020.533781

  • Kumar, S., Gautam, G., Saha, S., & K. (2015). Hyperspectral remote sensing data derived spectral indices in characterizing salt-affected soils: A case study of Indo-Gangetic plains of India. Environ. Earth Science, 73(7), 3299–3308. https://doi.org/10.1007/s12665-014-3613-y

    Article  Google Scholar 

  • Li, Y., Wang, C., Wright, A., Liu, H., Zhang, H., & Zong, Y. (2021). Combination of GF-2 high spatial resolution imagery and land surface factors for predicting soil salinity of muddy coasts. Catena, 202, 105304. https://doi.org/10.1016/j.catena.2021.105304

  • Lu, W., Lu, D., Wang, G., Wu, J., Huang, J., & Li, G. (2018). Examining soil organic carbon distribution and dynamic change in a hickory plantation region with Landsat and ancillary data. Catena, 165, 576–589. https://doi.org/10.1016/j.catena.2018.03.007

  • Machado, R., & Serralheiro, R. (2017). Soil salinity: effect on vegetable crop growth. Management practices to prevent and mitigate soil salinization. Horticulturae, 3(2), 30. https://doi.org/10.3390/horticulturae3020030

  • Mandal, S., Raju, R., Kumar, A., Kumar, P., Sharma, P., & C. (2018). Current status of research, technology response and policy needs of salt-affected soils in India—a review. Journal of the Indian Society of Coastal Agricultural Research, 36(2), 40–53.

    Google Scholar 

  • Mehrjardi, R., & T., Minasny, B., Sarmadian, F., Malone, B, P. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15–28. https://doi.org/10.1016/j.geoderma.2013.07.020

    Article  CAS  Google Scholar 

  • Metternicht, G., & I., Zinck, J, A. (2003). Remote sensing of soil salinity: Potentials and constraints. Remote Sensing of Environment, 85(1), 1–20. https://doi.org/10.1016/S0034-4257(02)00188-8

    Article  Google Scholar 

  • Mushtak, T., & J., Zhou, J. (2012). Assessment of soil salinity risk on the agricultural area in Basrah Province, Iraq: Using remote sensing and GIS techniques. Journal of Earth Science, 23(6), 881–891. https://doi.org/10.1007/s12583-012-0299-5

    Article  CAS  Google Scholar 

  • Nguyen, K. A., Liou, Y. A., Tran, H. P., Hoang, P. P., & Nguyen, T. H. (2020). Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam. Progress in Earth and Planetary Science, 7(1), 1–16. https://doi.org/10.1186/s40645-019-0311-0

  • Paliwal, A., Laborte, A., Nelson, A., Singh, R., & K. (2019). Salinity stress detection in rice crops using time series MODIS VI data. International Journal of Remote Sensing, 40(21), 8186–8202. https://doi.org/10.1080/01431161.2018.1513667

    Article  Google Scholar 

  • Peng, J., Biswas, A., Jiang, Q., Zhao, R., Hu, J., Hu, B., & Shi, Z. (2019). Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337, 1309–1319. https://doi.org/10.1016/j.geoderma.2018.08.006

    Article  Google Scholar 

  • Periasamy, S., & Ravi, K. P. (2020). A novel approach to quantify soil salinity by simulating the dielectric loss of SAR in three-dimensional density space. Remote Sensing of Environment, 251, 112059. https://doi.org/10.1016/j.rse.2020.112059

  • Qadir, M., Quillérou, E., Nangia, V., Murtaza, G., Singh, M., Thomas, R., Drechsel, P., & Noble, A. (2014). Economics of salt-induced land degradation and restoration. In Natural Resources Forum, 38(4), 282–95. https://doi.org/10.1111/1477-8947.12054

  • Sahana, M., Rehman, S., Patel, P., & P., Dou, J., Hong, H., Sajjad, H. (2020). Assessing the degree of soil salinity in the Indian Sundarban Biosphere Reserve using measured soil electrical conductivity and remote sensing data–derived salinity indices. Arabian Journal of Geosciences, 13, 1289. https://doi.org/10.1007/s12517-020-06310-w

    Article  Google Scholar 

  • Satheeskumar, V., Subramani, T., Lakshumanan, C., Roy, P. D., & Karunanidhi, D. (2021). Groundwater chemistry and demarcation of seawater intrusion zones in the Thamirabarani delta of south India based on geochemical signatures. Environmental Geochemistry and Health, 43, 757–770. https://doi.org/10.1007/s10653-020-00536-z

    Article  CAS  Google Scholar 

  • Scudiero, E., Skaggs, T., & H., Corwin, D, L. (2014). Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA. Geoderma Regional, 2–3, 82–90. https://doi.org/10.1016/j.geodrs.2014.10.004

    Article  Google Scholar 

  • Selvam, S., Manimaran, G., & Sivasubramanian, P. (2013). Hydrochemical characteristics and GIS-based assessment of groundwater quality in the coastal aquifers of Tuticorin corporation, Tamilnadu, India. Applied Water Science, 3(1), 145–159. https://doi.org/10.1007/s13201-012-0068-8

    Article  CAS  Google Scholar 

  • Sheik, M., & Chandrasekar, N. (2011). A shoreline change analysis along the coast between Kanyakumari and Tuticorin, India, using digital shoreline analysis system. Geo-Spatial Information Science, 14(4), 282–293. https://doi.org/10.1007/s11806-011-0551-7

    Article  Google Scholar 

  • Sheik, M. (2011). A shoreline change analysis along the coast between Kanyakumari and Tuticorin, India, using digital shoreline analysis system. Geo-Spatial Information Science, 14(4), 282–293. https://doi.org/10.1007/s11806-011-0551-7

    Article  Google Scholar 

  • Siebert, S., Henrich, V., Frenken, K., & Burke, J. (2013). Update of the digital global map of irrigation areas to version 5. In: Rheinische Friedrich-Wilhelms-Universit at, Bonn, Germany and Food and Agriculture Organization of the United Nations, Rome, Italy.

  • Singaraja, C. (2017). Relevance of water quality index for groundwater quality evaluation: Thoothukudi District, Tamil Nadu, India. Applied Water Science, 7, 2157–2173. https://doi.org/10.1007/s13201-017-0594-5

    Article  CAS  Google Scholar 

  • Sparks, D, L. (2003). Environmental soil chemistry. Elsevier Academic Press, 352. Retrieved November 14, 2019, from www.elsevier.com https://doi.org/10.1016/C2009-0-02455-6

  • Strobl, C., Boulesteix, A., & L., Kneib, T., Augustin, T., Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(1), 307. https://doi.org/10.1186/1471-2105-9-307

    Article  CAS  Google Scholar 

  • Wang, F., Chen, X., Luo, G., & Han, Q. (2014). Mapping of regional soil salinities in Xinjiang and strategies for amelioration and management. Chinese Geographical Science, 25(3), 321–336. https://doi.org/10.1007/s11769-014-0718-x

  • Wang, J., Ding, J., Yu, D., Teng, D., He, B., Chen, X., Ge, X., Zhang, Z., Wang, Y., Yang, X., Shi, T., & Su, F. (2020). Machine learning-based detection of soil salinity in an arid desert region, Northwest China: a comparison between Landsat-8 OLI and Sentinel-2 MSI. Science of the Total Environment, 707, 136092. https://doi.org/10.1016/j.scitotenv.2019.136092

  • Wang, J., Ding, J., Yu, D., Ma, X., Zhang, Z., Ge, X., & Guo, Y. (2019). Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China. Geoderma, 353, 172–187. https://doi.org/10.1016/j.geoderma.2019.06.040

  • Wu, W., Zucca, C., Muhaimeed, A. S., Al‐Shafie, W. M., Fadhil Al‐Quraishi, A. M., Nangia, V., & Liu, G. (2018). Soil salinity prediction and mapping by machine learning regression in C entral M esopotamia, Iraq. Land degradation & development, 29(11), 4005–4014. https://doi.org/10.1002/ldr.3148

  • Wu, W., Al-Shafie, W., & M., Mhaimeed, A, S., Ziadat, F., Nangia, V., Payne, W, B. (2014). Soil salinity mapping by multiscale remote sensing in Mesopotamia, Iraq. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 7(11), 4442–4452. https://doi.org/10.1109/JSTARS.2014.2360411

    Article  Google Scholar 

  • Yahiaoui, I., Bradaï, A., Douaoui, A., Abdennour, M., & A. (2021). Performance of random forest and buffer analysis of Sentinel-2 data for modelling soil salinity in the Lower-Cheliff plain (Algeria). International Journal of Remote Sensing, 42(1), 148–171. https://doi.org/10.1080/01431161.2020.1823515

    Article  Google Scholar 

  • Zhu, S., Zhu, X., & M., Wang, Z, L., Liu, Z, U. (2012). Zeolite diagenesis and its control on petroleum reservoir quality of Permian in northwestern margin of Junggar Basin. China. Science China Earth Sciences, 55(3), 386–396. https://doi.org/10.1007/s11430-011-4314-y

    Article  CAS  Google Scholar 

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Acknowledgements

Our sincere thanks to the Director, ICAR-Central Institute of Brackishwater Aquaculture, for providing support and facilities.

Funding

The present research work was funded by the Indian Council of Agricultural Research. Funding support provided for the project on resource mapping for aquaculture in Tamil Nadu by the Department of Fisheries, Government of Tamil Nadu, and ICAR-Extramural project.

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  1. ICAR-Central Institute of Brackishwater Aquaculture, Chennai, India

    Sabyasachi Kabiraj, Marappan Jayanthi, Muthusamy Samynathan & Selvasekar Thirumurthy

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  1. Sabyasachi Kabiraj
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  2. Marappan Jayanthi
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  4. Selvasekar Thirumurthy
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Contributions

S.Kabiraj — GIS analysis, sample collection, and manuscript writing. M. Jayanthi — conceptualization and manuscript writing. M. Samynathan — GIS analysis. S.Thirumurthy — sample collection.

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Correspondence to Marappan Jayanthi.

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Kabiraj, S., Jayanthi, M., Samynathan, M. et al. Automated delineation of salt-affected lands and their progress in coastal India using Google Earth Engine and machine learning techniques. Environ Monit Assess 195, 418 (2023). https://doi.org/10.1007/s10661-023-11007-0

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