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Review Article

Leveraging Remote Sensing and Geospatial Technologies in Soil and Water Resources Mapping and Characterization: A Review

Sagar Nandulal Ingle1, Bhabani Prasad Mondal1,*, Chandrabhan Patel1, Jagdish Prasad2
  • https://orcid.org/0000-0002-3132-8952
1Department of Soil Science and Agricultural Chemistry, Bihar Agricultural University, Sabour, Bhagalpur-813 210, Bihar, India.
2National Bureau of Soil Survey and Land Use Planning, Nagpur-440 033, Maharashtra, India.

ABSTRACT

Soil and groundwater, important natural resources, are essential for sustaining ecosystem health and maintaining agricultural production. However, both resources are under severe threat due to increasing demand for human consumption, agricultural operations and industrial activities. This situation warrants accurate monitoring and mapping to ensure their sustainable use. Since conventional methods are expensive and cumbersome, there is a need for innovative alternative approaches. The integration of remote sensing, Geographical Information Systems (GIS) and advanced geospatial technologies offers effective solutions for mapping and monitoring soil and groundwater. These tools provide accurate, cost-effective methods for estimating soil properties, detecting land degradation processes such as soil salinization and identifying potential groundwater zones. High-resolution satellite imagery, combined with machine learning, enables digital soil mapping, improving assessments of soil variability and land suitability. The adoption of these advanced technologies has revolutionized soil and groundwater assessment. Digital soil mapping enhances understanding of land resources, while remote sensing and GIS facilitate environmental conservation and sustainable agriculture. This article systematically underscores the role of these technologies in soil and groundwater management, highlighting their importance in resource sustainability.

INTRODUCTION

Soil and groundwater, both finite and invaluable natural resources, play fundamental roles in supporting ecosystems, regulating water cycles and sustaining agricultural productivity. However, the growing global demand for raw materials, food and energy, has intensified pressure on land, resulting in resource depletion, soil degradation and groundwater depletion (Cronin, 2009). Prasad (2004) emphasized that soil degradation in India threatens food security, urging urgent measures to protect soil health and here soil survey and soil classification help in better understanding of their characteristics, constraints and potential. Effective management of these resources is essential to maintain environmental sustainability, particularly in the face of climate change and land degradation. Soil, as the foundation of all production systems, exhibits significant spatial variability of variety of soil properties depending on spatial locations. Understanding its characteristics, spatial distribution and quality is crucial for ecosystem balance (Gessler, 1996). In this regard, soil survey is a potential tool which provides a systematic and scientific understanding of different soil types, their characteristics and distribution, enabling predictions about their potential uses (Mandal and Sharma, 2005). These surveys also classify soils and offer valuable insights that enhance natural resource management.
       
Groundwater a vital source of irrigation and human consumption, also require careful management. In regions, where irrigation is crucial, groundwater depletion poses a significant challenge. Additionally, issues such as soil salinization and pollution from solid waste further degrade soil health, necessitating monitoring and remediation efforts. Traditional methods of assessing soil and groundwater quality, although valuable, are time consuming, labour intensive and expensive (Laake, 2000). These conventional techniques are limited in their ability to capture spatial variability of soil properties and groundwater dynamics across large areas.
       
Recent advancements in geospatial technologies, particularly Geographical Information System (GIS), Remote Sensing (RS) and also Digital Soil Mapping (DSM), have revolutionized how soil and ground water are characterized, monitored and managed. These technologies allow for more efficient, precise and comprehensive approaches to natural resources management by integrating high resolution satellite imageries, with machine learning, geostatistics and advanced modelling techniques (Yeung and Lo, 2002; Shrestha, 2006). The integration of RS and GIS not only accelerates soil and groundwater mapping processes but also enhances the detail and accuracy of the data, reducing time and cost involved (Kalra et al., 2010). Such advancements offer significant potential to address growing challenges related to land degradation, soil salinization and ground water depletion, while promoting sustainable resource use.
       
In this review article, we explore the role of advanced geospatial techniques in soil survey, RS and GIS in soil resource mapping and characterization, groundwater mapping and discuss the application in addressing the pressing issues of land degradation, resource depletion and environmental sustainability.
 
RS and GIS in terrain characterization
 
Traditionally, landform mapping relied on aerial photograph interpretation (Dent  and Young, 1981). With remote sensing, more accurate resource mapping is possible (Karale et al., 1988). Satellite data and GIS enable spatial analysis, aiding in erosion risk assessment, watershed characterization and soil conservation. Drainage morphometry offers insights into land resource distribution (Strahler, 1964). Topographical variations significantly influence soil variability, necessitating geomorphological understanding and field surveys (McBratney et al., 2000; Velmurugan and Carlos, 2009). Satellite imagery and topographic maps help delineate physiographic variations like slope and land-cover (Speight, 1990). High-resolution satellite data improve geographic unit inventories. IRS-ID LISS-III data enhances geomorphological delineation by integrating lithology, drainage and contour information. Soil-landform units exhibit homogeneity in parent material, influencing soil-geomorphic processes (Wilson and Gallant, 2000; Hengl  and Reuter, 2008). Digital Elevation Models (DEMs) enhance slope classification for soil surveys (Hammer et al., 1995). DEM-derived terrain attributes effectively delineate soil boundaries (McBratney et al., 2003). Integrating high-resolution satellite data, GIS and field validation improves terrain characterization and soil inventory in a toposequence (Reddy et al., 2012).
 
RS and GIS in soil resource mapping and characterization
 
Soil is a vital resource requiring efficient management for sustainable agriculture. Soil mapping spatially defines soil types (Abuzar  and Ryan, 2001) and high-resolution satellite data enables detailed assessment of soil distribution and challenges. Advances in technology have improved soil mapping methods (Borse et al., 2018; Ingle et al., 2019; Kuchnwar et al., 2021). Spatial databases aid planning and development assessments using repeated satellite coverage. Satellite imagery reduces soil mapping time by 60-80% compared to manual methods (Liengsakul et al., 1993). Ingle et al., (2018) used geostatistics and GIS-based kriging to assess soil fertility, revealing high spatial variability in organic carbon and potassium levels for site-specific management. Mondal et al., (2020) assessed spatial variability of available phosphorus under three landuses using geostatistics, revealing highest variability in poplar-wheat system and strong spatial dependence using nugget-sill ratio of semi-variogram. They also assessed spatial variability of Soil Organic Carbon (SOC) under three landuses (berseem, rice-wheat and poplar-wheat) using geostatistics and found highest SOC in poplar-wheat and also identified Gaussian, spherical, exponential models as best fitting semi-variogram models in respective landuse systems (Mondal et al., 2021). Researchers highlight the effectiveness of satellite data Indian Remote Sensing (IRS-P6) (Velmurugan and Carlos, 2009) and IRS-P6, Linear Imaging Self-Scanning Sensor (LISS-IV) (Hiese et al., 2011, Reddy et al., 2012) for large-scale soil mapping. Panchromatic (PAN) merged satellite data provide detailed soil inventories (Walia et al., 2010). RS and GIS are powerful tools for soil characterization (Srivastava  and Saxena, 2004; Reddy et al., 2008; Pareta  and Pareta, 2012). GIS-based soil information supports developmental projects and agricultural research (Ramakrishnan  and Guruswamy, 2009). Improved soil mapping enhances land-use planning, aiding farmers in sustainable land management (Scull et al., 2003).
 
Role of RS and GIS in soil survey
 
RS and GIS have revolutionized soil surveys by providing high-precision mapping tools and detailed spatial analysis. Traditional methods are labour-intensive, whereas RS and GIS enable efficient soil characterization. High-resolution imagery, such as PAN and LISS III, is invaluable for updating cadastral maps, aiding local land management (Barnes  and Eckl, 1996). Satellite images facilitate soil series delineation, improving planning and resource allocation (Karale et al., 1988). RS allows large-area coverage with high spatial resolution, supporting soil-landscape relationship studies. Physiographic mapping combines topographic maps with satellite imagery, using spectral variations for classification (Velmurugan  and Carlos, 2009). Global Positioning System (GPS) integration enhances soil surveys by providing precise location data, minimizing errors and improving field verification (Stombaugh et al., 2002). High-resolution GPS data refines soil profile mapping and supports accurate classification (Longley et al., 1999; Panhalkar, 2011).
       
Post-survey, GIS organizes soil databases, enabling spatial analysis and overlay with thematic layers such as landuse and hydrology (Burrough  and McDonnell, 1998). GIS tools assist in land capability classification, soil resource mapping and land degradation assessment (Ekanayake  and Dayawansa, 2003). Integrating RS, GPS and GIS enhances soil mapping precision and supports sustainable land management.
       
Prasad (2021) emphasized the significance of geospatial technologies, noting that multi-spectral and multi-temporal RS data improve accuracy, cost-effectiveness and efficiency in soil surveys. These tools are essential for addressing soil variability, land degradation and resource management challenges.
 
Practical utility of RS data for micro-level planning
 
Geo-spatial technology is crucial for sustainable watershed management, aiding in resource mapping, monitoring and prioritization (Khan et al., 2001; Gosain  and Rao, 2004; Borse et al., 2018; Ingle et al., 2019; Kuchnwar et al., 2021). RS and GIS techniques help analyze watershed morphometry (Agarwal, 1998; Biswas et al., 1999) and support land-use planning. PAN merged with LISS III and IV data has been widely used for watershed characterization (Singh, 2009; Vittala et al., 2010; Das et al., 2012; Shanwad et al., 2012). Cartosat-1 stereo data aids in DEM construction for watershed demarcation and mapping (Kumar et al., 2008; Mohamed and Murthy, 2008; Sharma et al., 2010; Pareta  and Pareta, 2012).  Summary of different satellites used in micro-level planning is listed in Table 1.

Table 1: Summary of different satellites used at micro level planning.


 
Soil suitability mapping
 
Soil suitability is essential for maximizing the use of available land resources to support sustainable agricultural production (Hanna et al., 2000; Ekanayake  and Dayawansa, 2003). The advent of high-resolution satellite imagery, coupled with GIS-based terrain analysis, has significantly expanded and improved the efficiency of soil suitability mapping. This method focuses on soil characteristics, climate, landuse and topography, with the assumption that these factors vary continuously across space (Lagacherie  and McBratney, 2007). Recently, soil suitability for millets in Banka district has been done by Vimal et al., (2024). Karthika et al., (2024) used Sentinel-2 data in characterization of soil resources for sorghum suitability in Mahbubnagar, Telangana and reported that only 1.51% area was highly suitable and 33.99% area was marginally suitable due to soil limitations. The spatial data can be easily visualized and analysed within a GIS framework. Singha and Swain (2016) reviewed land suitability analysis methods, highlighting the effectiveness of integrating GIS, remote sensing, fuzzy logic and  Analytic Hierarchy Process (AHP) for optimizing agricultural landuse planning. Several researchers have created soil suitability maps using multi-criteria evaluation techniques, along with Quick Bird (60 cm) and LISS-IV imagery (Ceballos-Silva and Lopez-Blanco, 2003), while (Ingle et al., 2021, 2024) employed IRS-P6 LISS-III, LISS-IV satellite data for land suitability assessments of watershed for cotton depicted in Fig 1.

Fig 1: Land suitability map for cotton cultivation.


 
RS in digital soil mapping
 
Understanding the spatial variation of soil properties is crucial for effective soil management (Mondal et al., 2024). High-resolution soil mapping is essential, as conventional methods fail to capture soil variability and require extensive sampling, making them costly and time-consuming. Digital Soil Mapping (DSM) addresses these limitations by integrating remote sensing data with statistical, geostatistical, Machine Learning (ML) and Deep Learning (DL) models to predict soil properties at unsampled locations (Hengl et al., 2014; Ma et al., 2019).
       
DSM utilizes the SCORPAN (S=Soil, C=Climate, O=Organisms, R= Relief, P=Parent material, A=Age, N=Spatial location) framework, incorporating soil samples and environmental variables to improve prediction accuracy (Batjes et al., 2020). Techniques like regression kriging enhance interpolation (Wang et al., 2022), while ML models outperform geostatistics in capturing non-linear relationships (Taghizadeh-Mehrjardi et al., 2021). DL, with its multilayer neural networks, excels in large-scale mapping by extracting complex patterns from remote sensing data (Behrens et al., 2018; Padarian et al., 2019). Prediction uncertainty is evaluated using prediction intervals (Wadoux et al., 2020).
       
DSM is widely used to map soil properties such as moisture (Das et al., 2023; Mondal et al., 2024), pH (Pahlavan-Rad and Akbarimoghaddam, 2018), electrical conductivity (Ranjbar  and Jalali, 2016) and organic carbon (Mponela et al., 2020). Global-scale digital maps like the Global-Soil-Map aid in soil characterization (Arrouays et al., 2014; Poggio et al., 2021). In India, national and regional DSM initiatives have mapped soil texture, pH, SOC and micronutrients, supporting site-specific nutrient management and sustainable agriculture (Dharumarajan et al., 2017; Santra et al., 2017; Reddy et al., 2021; Dasgupta et al., 2023).
 
RS in soil salinity mapping
 
Soil salinity is a major land degradation issue, especially in arid, semi-arid and sub-humid regions (Dehni  and Lunis, 2012). It results from natural processes like mineral weathering and seawater intrusion, but human activities, particularly poor irrigation practices, exacerbate the problem (El-Rawy et al., 2024). Climate change is expected to intensify soil salinity, further threatening agriculture (Lekka et al., 2024). Traditional soil salinity assessment methods are costly and time-consuming (El-Rawy et al., 2024), necessitating advanced geospatial technologies. Earth observation (EO) and RS provide efficient, large-scale soil salinity mapping (Dehni  and Lunis, 2012). Satellite sensors like Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), Operational Land Imager (OLI) and IRS LISS-III integrate with GIS for accurate mapping. Spectral indices such as the Salinity Index (SI) and Normalized Difference Salinity Index (NDSI) are commonly used for soil salinity classification. Recent advancements in ML and DL enable digital soil salinity mapping by incorporating topographic and hyperspectral RS data. Google Earth Engine (GEE) is increasingly used for soil salinity analytics, with studies demonstrating the effectiveness of ML models like random forest and support vector machine (Aksoy et al., 2022). Artificial Neural Network (ANN) models help analyze soil-water-salt dynamics (Zickus et al., 2002; Rahouma  and Aly, 2020), while Convolution Neural Network (CNN)-based DL models enhance soil salinity mapping (El-Rawy et al., 2024). Integrating geospatial technology with ML and DL facilitates efficient, large-scale soil salinity monitoring, supporting sustainable agricultural management.
 
RS in detection, monitoring and mapping of dumping/waste disposal sites
 
Accurate identification and mapping of illegal dumping sites are essential to mitigate environmental hazards triggered by climate change (Fraternali et al., 2024). Poorly managed sites emit greenhouse gases, degrade air quality and cause leachate contamination, threatening ecosystems and human health (Alberti et al., 2022). Traditional on-site inspections are costly and time-consuming, but RS offers a cost-effective solution for monitoring landfill sites (Fraternali et al., 2024).
       
Drone-based RS enables detailed local-scale mapping, while satellite RS provides large-area coverage. EO satellites, such as Sentinel and Landsat, capture multispectral and hyperspectral imagery, offering high spatial and temporal resolution for landfill monitoring (Jutz  and MilagroPerez, 2018). Landsat-8 and Landsat-9 with OLI and Thermal Infrared Sensors (TIRS) and Sentinel-1’s microwave data, are widely used for detecting land-cover changes, stressed vegetation and surface temperatures (Gill et al., 2019). India’s IRS series and European Space Agency’s (ESA’s) Sentinel-2 provide free high-resolution optical data for large-scale monitoring. Meanwhile, WorldView and GeoEye-1 offer very high-resolution images suited for small-scale urban waste sites (Fraternali et al., 2024).
       
RS-derived indices like Normalized Difference Vegetation Index (NDVI)  and Soil Adjusted Vegetation Index (SAVI) detect poor vegetation health linked to landfill presence, while Land Surface Temperature (LST) is used for identifying heat anomalies in dumping areas (Cadau et al., 2013). Multi-factor analysis integrates geospatial data to assess environmental impacts (Abou El-Magd et al., 2022). Techniques like feature extraction, object classification and computer vision enhance landfill site detection and mapping (Seror and Portnov, 2018; Nazari et al., 2020).
 
RS and geospatial technology in groundwater mapping
 
Groundwater, constituting 26% of Earth’s freshwater, is vital for drinking, agriculture and industry (Arulbalaji et al., 2019). Its significance is higher in arid, semi-arid and mountainous regions with scarce surface water (Algaydi et al., 2019). Groundwater meets 40% of agricultural, 30% of industrial and half of the drinking water needs globally (Famiglietti, 2014). However, traditional assessment methods are costly, time-consuming and fail to capture key sub-surface dynamics (Kumar and Krishna, 2018; Acharya et al., 2019). Geospatial technologies, particularly RS and GIS, provide cost-effective solutions for mapping groundwater potential and scarcity zones (Rampheri et al., 2023; Bennett, 2025). RS offers critical hydrological data like soil moisture, vegetation conditions and land surface displacement (Hilbich et al., 2022). Satellite and drone-based RS enhance groundwater storage assessment (Adams et al., 2022), aiding in delineating groundwater zones and monitoring fluctuations (Ni et al., 2018; Akhter et al., 2021; Sreekanth et al., 2023). In India, integrating geospatial and geophysical techniques with RS data identified groundwater potential in Dehradun using the Weighted Index Overlay method and 2D Electrical Resistivity Tomography (Gyeltshen et al., 2020). RS-derived elevation models further aid in groundwater storage analysis (Massoud et al., 2021). Multispectral and hyperspectral RS helps assess land surface and soil spectral properties influencing groundwater occurrence (Kumar et al., 2022). While RS supports large-scale monitoring, limitations like sensor resolution and atmospheric variability necessitate refined methodologies to improve accuracy in groundwater mapping.

CONCLUSION

The integration of GIS and remote sensing along with other advanced geospatial technology has made revolution in sustainable management of soil and groundwater by providing precise, scalable and cost-effective solutions. These advanced geospatial techniques provide critical insights into soil variability, salinity levels and groundwater availability, enabling sustainable resource management practices. The use of high-resolution satellite data coupled with machine learning techniques, significantly improves the efficiency and accuracy of soil and groundwater mapping and their sustainable management. Thus, such innovative technological approaches make it easier to address environmental challenges such as soil degradation and water scarcity. Since global demands for these two important natural resources continue to grow, these advanced technologies will definitely play a vital role in ensuring their sustainable uses as well as their preservation for future generations.

ACKNOWLEDGEMENT

The authors are thankful to the Directorate of Research as well as constituted publication team of the Bihar Agricultural University for supporting and providing communication number for this review article (BAU communication no. 1980/250127).

CONFLICT OF INTEREST

The authors declare no conflict of interest.

REFERENCES


  1. Abou El-Magd, I., Attwa, M., El Bastawesy, M., Gad, A., Henaish, A., Zamzam, S. (2022). Qualitative and quantitative characterization of municipal waste in uncontrolled dumpsites and landfills using integrated remote sensing, geological and geophysical data: A case study. Sustainability. 14(8): 4539. 

  2. Abuzar, M., Ryan, S. (2001). Integrating multi-source remote sensing data for soil mapping in Victoria. Geoscience and Remote Sensing. 6: 2495-2497. 

  3. Acharya, T., Kumbhakar, S., Prasad, R., Mondal, S., Biswas, A. (2019). Delineation of potential groundwater recharge zones in the coastal area of north-eastern India using geoinformatics. Sustainable Water Resources Management. 5: 533-540. 

  4. Adams, K.H., Reager, J.T., Rosen, P., Wiese, D.N., Farr, T.G., Rao, S., Haines, B.J., Argus, D.F., Liu, Z., Smith, R., Famiglietti, J.S. (2022). Remote sensing of groundwater: Current capabilities and future directions. Water Resources Research. 58(10): e2022WR032219. 

  5. Agarwal, C.S. (1998). Study of drainage pattern through aerial data in Naugarh area of Varanasi district, U.P. Journal of Indian Society of Remote Sensing. 26: 169-175. 

  6. Akhter, G., Ge, Y., Iqbal, N., Shang, Y., Hasan, M. (2021). Appraisal of remote sensing technology for groundwater resource management perspective in Indus Basin. Sustainability. 13(17): 9686. 

  7. 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. 

  8. Alberti, P. (2022). The ‘land of fires’: epidemiological research and public health policy during the waste crisis in Campania, Italy. Heliyon. 8(12): 1-9. e12331. 

  9. Algaydi, B.A., Subyani, A.M., Hamza, M.H. (2019). Investigation of groundwater potential zones in hard rock terrain, Wadi Na’man, Saudi Arabia. Groundwater. 57(6): 940-950. 

  10. Arrouays, D., Grundy, M.G., Hartemink, A.E., Hempel, J.W., Heuvelink, G.B., Hong, S.Y., Lagacherie, P., Lelyk, G., McBratney, A.B., McKenzie, N.J., dL Mendonca-Santos, M. (2014). Global soil map: Toward a fine-resolution global grid of soil properties. Advances in Agronomy. 125: 93-134. 

  11. Arulbalaji, P., Padmalal, D., Sreelash, K. (2019). GIS and AHP techniques-based delineation of groundwater potential zones: A case study from southern Western Ghats, India. Scientific Reports. 9(1): 2082. 

  12. Barala, T.N. (2010). A sustainable land use model for Nepal-a case study of Nawalparasi. Applied Geoinformatics for Society and Environment. 55-65. 

  13. Barnes, G.B., Eckl, M. (1996). Pioneering a GPS methodology for cadastral surveying: experience in Albania and Belize. Geomatics Programme, University of Florida, Gainesville, USA. 

  14. Batjes, N.H., Ribeiro, E., Van Oostrum, A. (2020). Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth System Science Data. 12(1): 299-320. 

  15. Behrens, T., Schmidt, K., MacMillan, R.A., Viscarra Rossel, R.A. (2018). Multi-scale digital soil mapping with deep learning. Scientific Reports. 8(1): 15244. 

  16. Bennett, G. (2025). A review of the data used to validate ground- water recharge maps created with GIS techniques over the past two decades. Hydro Research. 8: 276-285.

  17. Biswas, S., Sudhakar, S., Desai, V.R. (1999). Prioritisation of sub- watersheds based on morphometric analysis of drainage basin-a remote sensing and GIS approach. Journal of the Indian Society of Remote Sensing. 27: 155-166. 

  18. Borse, A.A., Nagaraju, M.S.S., Dash, B., Sahu, N., Ingle, S.N., Srivastava, R. (2018). Characterization and evaluation of land resources for management of Barela village in Seoni district, Madhya Pradesh using high resolution satellite data and GIS. Agropedology. 28(01): 48-59. 

  19. Burrough, P.A., McDonnel, R.A. (1998). Principles of Geographical Information Systems. Oxford University Press. 

  20. Cadau, E.G., Putignano, C., Aurigemma, R., Melchiorre, A., Bosco, P., Tesseri, A., Battazza, F. (2013). SIMDEO: An integrated system for landfill detection and monitoring using EO data. IEEE International Geoscience and Remote Sensing Symposium-IGARSS. 3305-3308. 

  21. Ceballos-Silva, A., Lopez-Blanco, J. (2003). Delineation of suitable areas for crops using a multi-criteria evaluation approach and land use/cover mapping: A case study in Central Mexico. Agricultural Systems. 77: 117-136. 

  22. Cronin, R. (2009). Exploiting natural resources: Growth, instability and conflict in the Middle East and Asia. The Henry L. Stimson Centre. Washington. 

  23. Das, A., Mondal, M., Dar, B., Ghosh, A.R. (2012). Analysis of drainage morphometry and watershed prioritization of Bandu watershed, Purulia, WB through RS and GIS technology- A case study. International Journal of Geomatics and Geosciences. 2(4): 995-1013. 

  24. Das, B., Rathore, P., Roy, D., Chakraborty, D., Bhattacharya, B.K., Mandal, D., Jatav, R., Sethi, D., Mukherjee, J., Sehgal, V. K., Singh, A.K., Kumar, P. (2023). Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations.  Agricultural and Forest Meteorology. 339: 109567. 

  25. Dasgupta, S., Debnath, S., Das, A., Biswas, A., Weindorf, D.C., Li, B., Shukla, A.K., Das, S., Saha, S., Chakraborty, S. (2023). Developing regional soil micronutrient management strategies through ensemble learning-based digital soil mapping. Geoderma. 433: 116457. 

  26. Dehni, A., Lounis, M. (2012). Remote sensing techniques for salt affected soil mapping: Application to the Oran region of Algeria. Procedia Engineering. 33: 188-198. 

  27. Dent, D., Young, A. (1981). Soil survey and land evaluation. Goerge Allen and Unwin. London.

  28. Dharumarajan, S., Hegde, R., Singh, S.K. (2017). Spatial prediction of major soil properties using random forest techniques- A case study in semi-arid tropics of South India. Geoderma Regional. 10: 154-162. 

  29. Ekanayake, G.K., Dayawansa, N.D.K. (2003). Land suitability identification for a production forest through GIS technique. Forestry and Biodiversity. Map India Conference. 

  30. El-Rawy, M., Sayed, S.Y., AbdelRahman, M.A., Makhloof, A., Al- Arifi, N., Abd-Ellah, M.K. (2024). Assessing and segmenting salt-affected soils using in-situ EC measurements, remote sensing and a modified deep learning MU-NET convolutional neural network. Ecological Informatics. 102652. 

  31. Famiglietti, J.S. (2014). The global groundwater crisis. Nature Climate Change. 4(11): 945-948. 

  32. Fraternali, P., Morandini, L. and González, S.L.H. (2024). Solid waste detection, monitoring and mapping in remote sensing images: A survey. Waste Management. 189: 88-102. 

  33. Gessler, P.E. (1996). Statistical soil-landscape modelling for environmental management.

  34. Gill, J., Faisal, K., Shaker, A. and Yan, W.Y. (2019). Detection of waste dumping locations in landfill using multi-temporal Landsat thermal images. Waste Management and Research. 37(4): 386-393. 

  35. Gosain, A.K., Rao, S. (2004). GIS-based technologies for watershed management. Current Science. 87: 948-953.

  36. Gyeltshen, S., Tran, T.V., Teja Gunda, G.K., Kannaujiya, S., Chatterjee, R.S. and Champatiray, P.K. (2020). Groundwater potential zones using a combination of geospatial technology and geophysical approach: Case study in Dehradun, India. Hydrological Sciences Journal. 65(2): 169-182. 

  37. Hammer, R.D., Young, N.C. and Wolenhaupt, T.L. (1995). Slope class maps form soil survey and digital elevation models. Soil Science Society of America Journal. 59: 509-519.

  38. Hanna, F., Wahab, M.A., Ali, R.R. (2000). Land suitability classi- fication of some soils south and west Nile Delta Egypt. 3rd Conference of Egyptian Soil Science Society (ESSS), Cairo, Egypt.

  39. Hengl, T., De Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J.G., Walsh, M.G., Gonzalez, M.R. (2014). SoilGrids1km- global soil information based on automated mapping. Plos One. 9(8): e105992. 

  40. Hengl, T., Reuter, H.I. (2008). Geomorphometry: Concepts, Software, Applications (33rd ed.). Elsevier, Amsterdam. pp: 772.

  41. Hiese, N., Mich, N., Yanthan, M., Yeptho, H., Mor, S. (2011). Application of geoinformatics in land resource management at micro- watershed level for sustainable development of Sanis, Wokha district, Nagaland. Geospatial World Forum. 18- 21: 1-10.

  42. Hilbich, C., Hauck, C., Mollaret, C., Wainstein, P., Arenson, L.U. (2022). Towards accurate quantification of ice content in permafrost of the Central Andes-Part 1: Geophysics- based estimates from three different regions. The Cryosphere. 16(5): 1845-1872. 

  43. Ingle, S.N., Nagaraju, M.S.S., Sahu, N., Srivastava, R., Tiwary, P., Sen, T.K., Nasre, R.A. (2018). Mapping of spatial variability in soil properties and soil fertility for site-specific nutrient management in Bareli watershed, Seoni district of Madhya Pradesh using geostatistics and GIS. International Journal of Current Microbiology and Applied Sciences. 7(10): 2299-2306.

  44. Ingle, S.N., Nagaraju, M.S.S., Sahu, N., Kumar, N., Tiwary, P., Srivastava, R., Sen, T.K., Nasre, R.A. (2019). Characterization, classification and evaluation of land resources for management of Bareli watershed in Seoni district, Madhya Pradesh using remote sensing and GIS. Journal of Soil and Water Conservation. 18(1): 1-10.

  45. Ingle, S.N., Nagaraju, M.S.S., Gadge, P.S., Deshmukh, D.P., Dange, N.R. (2021). GIS-based land use suitability of diversified cropping systems in Bareli watershed. International Journal of Economic Plants. 8(4): 231-236.

  46. Ingle, S.N., Nagaraju, M.S.S., Kumar, N., Prasad, J., Tiwary, P., Srivastava, R., Sahu, N., Lal, B., Das, S.P., Pradhan, A.K., Beura, K., Karad, G.U. (2024). Soil quality assessment and mapping in basaltic terrain of Central India for sustainable soil and crop management using integrated PCA and GIS. Plant Science Today (Early Access). https:/ /doi.org/10.14719/pst.4607. 

  47. Jutz, S., Milagro-Pérez, M. (2018). 1.06-Copernicus program. In: Comprehensive Remote Sensing. Liang, S. (Ed.), Elsevier, Oxford: 150-191.

  48. Kalra, N.K., Singh, L., Kachhwah, R., Joshi, D.C. (2010). Remote sensing and GIS in identification of soil constraints for sustainable development in Bhilwara district, Rajasthan. Journal of the Indian Society of Remote Sensing. 38(2): 279-290. 

  49. Karale, R.L., Saini, K.M., Narula, K.K. (1988). Mapping and monitoring ravines using remotely sensed data. Journal of Soil and Water Conservation. 32(1): 75.

  50. Karthika K.S., Kumar Anil K.S., Srinivasan R., Chandrakala M., Parvathy S., Prasad J. (2024). Understanding the sorghum- growing soils in a semi-arid ecosystem of Telangana, India and assessing their land suitability for sorghum cultivation. Indian Journal of Agricultural Research. 58(2024): 1100-1108. doi: 10.18805/IJARe.A-6322. 

  51. Khan, M.A., Gupta, V.P., Moharana, P.C. (2001). Watershed prioritization using remote sensing and geographical information system: A case study from Guhiya, India. Journal of Arid Environments. 49: 465-475.

  52. Kuchnwar, O.D., Gabhane, V.V., Ingle, S.N. (2021). Remote sensing and GIS application for land resources appraisal of Ridhora watershed in Nagpur district, Maharashtra. Journal of Soil and Water Conservation. 20(2): 139-153.

  53. Kuchnwar, O.D., Gabhane, V.V., Ingle, S.N. (2022). Spatial variability assessment and mapping of soil properties for sustainable agricultural production using remote sensing technology and geographic information systems (GIS). Emerging Life Sciences Research. 8(1): 50-59.

  54. Kumar, S., Kumar, A., Saha, S.K., Kumar, A. (2008). Stero cartosat- 1 satellite remote sensing data in assessing topographic potential of soil erosion. Journal of the Indian Society of Remote Sensing. 36: 159-165.

  55. Kumar, A., Krishna, A.P. (2018). Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach. Geocarto International. 33(2): 105-129.

  56. Kumar, M., Singh, S.K., Kundu, A., Tyagi, K., Menon, J., Frederick, A., Raj, A., Lal, D. (2022). GIS-based multi-criteria approach to delineate groundwater prospect zone and its sensitivity analysis. Applied Water Science. 12(4): 71. 

  57. Laake, P.E. (2000). Spatial tools for laymen, natural resource management by experts. International Archives of Photo- grammetry and Remote Sensing. 33: 1593-1600.

  58. Lagacherie, P., McBratney, A.B. (2007). Spatial soil information systems and spatial soil inference systems: Perspectives for digital soil mapping. Developments in Soil Science. 31: 3-22.

  59. Liengsakul, M., Mekpaiboonwatana, S., Pramojanee, P., Bronsveld, K., Huizing, H. (1993). Use of GIS and remote sensing for soil mapping and for locating new sites for permanent cropland: A case study in the highlands of northern Thailand. Geoderma. 60(1-4): 293-307.

  60. Lekka, C., Detsikas, S.E., Petropoulos, G.P., Chalkias, C. (2024). Mapping and monitoring of salt-affected soils: The contribution of geoinformation. In Remote Sensing in Precision Agriculture. Academic Press. pp: 71-91. 

  61. Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W. (Eds.) (1999). Geographical information systems (2nd ed.). John Wiley and Sons, Inc., New York.

  62. Ma, Y., Minasny, B., Malone, B.P., McBratney, A.B. (2019). Pedology and digital soil mapping (DSM). European Journal of Soil Science. 70(2): 216-235. 

  63. Massoud, E.C., Liu, Z., Shaban, A. and El Hage, M. (2021). Groundwater depletion signals in the Beqaa Plain, Lebanon: Evidence from GRACE and Sentinel-1 data. Remote Sensing. 13(5): 915. 

  64. McBratney, A.B., Odeh, I.O.A., Bishop, T.F.A., Dunbar, M.S. and Shatar, T.M. (2000). An overview of pedometric techniques for use in soil survey. Geoderma. 97(3-4): 293-327. 

  65. McBratney, A.B., Mendonça, S.M.L., Minasny, B. (2003). On digital soil mapping. Geoderma. 117(1-2): 3-52. 

  66. Minasny, B., McBratney, A.B. (2016). Digital soil mapping: A brief history and some lessons. Geoderma. 264: 301-311. 

  67. Mandal, A.K., Sharma, R.C. (2005). Computerization database on salt-affected soils of Haryana state. Journal of the Indian Society of Remote Sensing. 33(3): 447-455. 

  68. Mohamed, G.S., Murthy, R.L.N. (2008). Cartosat Stereo Data for Watershed and Water Resources GIS. 14th ARSPC, Darwin, Australia.

  69. Mondal, B.P., Sekhon, B.S., Sadhukhan, R., Singh, R.K., Hasanain, M., Mridha, N., Das, B., Dhyay, A.C. and Banerjee, K. (2020). Spatial variability assessment of soil available phosphorus using geostatistical approach. Indian Journal of Agricultural Sciences. 90(6): 1170-1175.

  70. Mondal, B.P., Sekhon, B.S., Setia, R.K. and Sadhukhan, R. (2021). Geostatistical assessment of spatial variability of soil organic carbon under different land uses of northwestern India. Agricultural Research. 10(3): 407-416. DOI:10. 1007/s40003-020-00509-9.

  71. Mondal, B.P., Sahoo, R.N., Das, B., Chakraborty, D., Kumar, N., Raj, G.B. (2024). Digital soil mapping: concepts, methods and applications-remote sensing and GIS perspectives. In Remote Sensing of Soils Elsevier. pp: 195-212. 

  72. Mponela, P., Snapp, S., Villamor, G.B., Tamene, L., Le, Q.B., Borgemeister, C. (2020). Digital soil mapping of nitrogen, phosphorus, potassium, organic carbon and their crop response thresholds in smallholder managed escarpments of Malawi. Applied Geography. 124: 102299. 

  73. Nazari, R., Alfergani, H., Haas, F., Karimi, M.E., Fahad, M.G.R., Sabrin, S., Everett, J., Bouaynaya, N., Peters, R.W. (2020).  Application of satellite remote sensing in monitoring elevated internal temperatures of landfills. Applied Sciences. 10(19): 6801. 

  74. Ni, B., Wang, D., Deng, Z., Xu, H., Wang, D., Jiang, X. (2018). Review on the groundwater potential evaluation based on remote sensing technology. In IOP Conference Series: Materials Science and Engineering IOP Publishing. 394(5): 052038. 

  75. Padarian, J., Minasny, B., McBratney, A.B. (2019). Using deep learning for digital soil mapping. Soil. 5(1): 79-89. 

  76. Pahlavan-Rad, M.R., Akbarimoghaddam, A. (2018). Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran). Catena. 160: 275-281. 

  77. Panhalkar, S. (2011). Land capability classification for integrated watershed development by applying RS and GIS techniques. Journal of Agricultural and Biological Sciences. 6(4): 46-55.

  78. Pareta, K., Pareta, U. (2012). Integrated watershed modeling and characterization using GIS and RS techniques. Indian Journal of Engineering. 1(1): 81-91.

  79. Poggio, L., De Sousa, L.M., Batjes, N.H., Heuvelink, G.B., Kempen, B., Ribeiro, E., Rossiter, D. (2021). Soil grids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil. 7(1): 217-240. 

  80. Prasad, J. (2004). Environmental implications of soil degradation in India-A review. Agricultural Review. 25(1): 57-63.

  81. Prasad, J. (2021). Application of remote sensing in soil characte- rization and mapping. Journal of the Indian Society of Soil Science. 69: S103-S112.

  82. Praveen, K.R., Singh, S., Mishra, A. (2010). Village mapping using high-resolution remote sensing data and field survey. 11th ESRI India User Conference. pp. 1-6.

  83. Rahouma, K.H., Aly, R.H. M. (2020). Applying polynomial learning for soil detection based on Gabor wavelet and Teager Kaiser energy operator. In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). Springer International Publishing. 4: 771-783.

  84. Raj, S., Koshal, A.K. (2012). GIS and remote sensing for site specific farming area mapping. International Journal of Engineering and Innovative Technology. 1(4): 198-200.

  85. Ramakrishnan, S.S., Guruswamy, V. (2009). GIS Applications in soil data analysis, Centre for GIS Applications, Anna University, India.

  86. Rampheri, M.B., Dube, T., Dondofema, F., Dalu, T. (2023). Progress in the remote sensing of groundwater-dependent eco- systems in semi-arid environments. Physics and Chemistry of the Earth, Parts A/B/C. 130: 103359. 

  87. Ranjbar, F., Jalali, M. (2016). The combination of geostatistics and geochemical simulation for the site-specific management of soil salinity and sodicity. Computers and Electronics in Agriculture. 121: 301-312. 

  88. Reddy, G.P.O., Maji, A.K., Nagaraju, M.S.S., Thayalan, S., Ramamurthy, V. (2008). Ecological evaluation of land resources and land use systems for sustainable development at water- shed level in different agro-ecological zones of Vidarbha region, Maharashtra using remote sensing and GIS techniques, Project Report, NBSS  and LUP, Nagpur. pp: 270.

  89. Reddy, G.P.O., Nagaraju, M.S.S., Ramteke, I.K., Sarkar, D. (2012). Terrain characterization for soil resource mapping using IRS-P6 data and GIS-A case study from basaltic terrain of central India. Journal of the Indian Society of Remote Sensing. 41(2): 331-343. 

  90. Reddy, N.N., Chakraborty, P., Roy, S., Singh, K., Minasny, B., McBratney, A.B., Biswas, A., Das, B.S. (2021). Legacy data-based national-scale digital mapping of key soil properties in India. Geoderma. 381: 114684. 

  91. Santra, P., Kumar, M., Panwar, N. (2017). Digital soil mapping of sand content in arid western India through geostatistical approaches. Geoderma Regional. 9: 56-72. 

  92. Scull, P., Franklin, J., Chadwick, O. A., McArthur, D. (2003). Predictive soil mapping: A review. Progress in Physical Geography. 27(2): 171-197. 

  93. Seror, N., Portnov, B. A. (2018). Identifying areas under potential risk of illegal construction and demolition waste dumping using GIS tools. Waste management. 75: 22-29. 

  94. Shanwad, U.K., Patil, V.C., Gowda, H.H., Shashidhar, K.C. (2012). Remote sensing and GIS for integrated resource management policy-a case study in Medak Nala watershed, Karnataka, India. American-Eurasian Journal of Agriculture and Environmental Sciences. 12(6): 790-806.

  95. Sharma, S.K., Rajput, G.S., Tiganth, S., Pandey, R.P. (2010). Morphometric analysis and prioritization of a watershed using GIS. Journal of Indian Water Resources Society. 30(2): 33-39.

  96. Shrestha, H.L. (2006). Using GPS and GIS in participatory mapping of community forest in Nepal. Electronic Journal of Information Systems in Developing Countries. 25(5): 1-11.

  97. Singh, T.P. (2009). Satellite-image-based water and land development plan e-governance: Case studies, Chapter-10. pp: 180- 190.

  98. Singha, C. and Swain, K.C. (2016). Land suitability evaluation criteria for agricultural crop selection: A review. Agricultural Reviews. 37(2): 125-132.  

  99. Speight, J.G. (1990). Australian Soil and Land Survey: Field Handbook (2nd ed.). Inkata Press. Melbourne.

  100. Sreekanth, J., Nair, A.S., Indu, J., Pagendam, D., Kaushika, G.S. (2023). Estimation of groundwater storage loss for the Indian Ganga basin using multiple lines of evidence. Scientific Reports. 13(1): 1797. 

  101. Srivastava, R., Saxena, R. K. (2004). Techniques of large-scale soil mapping in basaltic terrain using satellite remote sensing data. International Journal of Remote Sensing. 25(4): 679-688. 

  102. Strahler, A.N. (1964). Quantitative geomorphology of basins and channel networks. In Handbook of Applied Hydrology McGraw Hill Book Company, New York.

  103. Stombaugh, T., Shearer, S., Fulton, J. (2002). GPS simplified University of Kentucky Ext. Rept. PA-5.

  104. Taghizadeh-Mehrjardi, R., Hamzehpour, N., Hassanzadeh, M., Heung, B., Goydaragh, M.G., Schmidt, K., Scholten, T. (2021). Enhancing the accuracy of machine learning models using the super learner technique in digital soil mapping. Geoderma. 399: 115108. 

  105. Tomar, A.S., Singh, U.C. (2012). Geomorphological mapping using remote sensing and GIS a tool for land use planning around Shivpuri City, M.P. Indian Journal of Computer Engineering. 5(1): 28-30.

  106. Velmurugan, A., Carlos, G.G. (2009). Soil resource assessment and mapping using remote sensing and GIS. Journal of the Indian Society of Remote Sensing. 37(3): 511-525.

  107. Vimal, B.K., Ingle, S.N., Das, S.P., Srinivasaraghvan, A., Kumari, R., Padbhushan, R., Alam, F., Kumari, S. (2024). Multi- criteria decision making and GIS approached land suitability analysis for pearl millet cultivation in Banka block of Banka district, Bihar. AATCC Review. 12(02): 278-283. 

  108. Vittala, S.S., Govindaiah, S., Honne Gowda, H. (2010). Prioritization of sub-watersheds for sustainable development and management of natural resources: An integrated approach using remote sensing, GIS and socio-economic data. Current Science. 95(3): 345-354.

  109. Wadodkar, M.R., Ravisankar, T. (2011). Soil resource database at village level for developmental planning. Journal of the Indian Society of Remote Sensing. 39(4): 529-536. 

  110. Wadoux, A.M.C., Minasny, B., McBratney, A.B. (2020). Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth-Science Reviews. 210: 103359. 

  111. Walia, C.S., Singh, S.P., Dhankar, R.P., Ram, J., Kamble, K.H., Katiyar, D.K. (2010). Watershed characterization and soil resource mapping for land use planning of Moolbari watershed, Shimla, H.P. in lesser Himalayas. Current Science. 98(2): 176-182.

  112. Wang, J., Zhao, D., Zare, E., Sefton, M., Triantafilis, J. (2022). Unravelling drivers of field-scale digital mapping of topsoil organic carbon and its implications for nitrogen practices. Computers and Electronics in Agriculture. 193: 106640. 

  113. Wilson, J.P., Gallant, J.C. (2000). Terrain analysis: Principles and applications. John Wiley and Sons, New York.

  114. Yong, Q., Wu, H., Wang, J., Chen, R., Yu, B., Zuo, J., Du, L. (2023). Automatic identification of illegal construction and demolition waste landfills: A computer vision approach. Waste Management. 172: 267-277. 

  115. Zickus, M., Greig, A.J., Niranjan, M. (2002). Comparison of four machine learning methods for predicting PM10 concentrations in Helsinki, Finland. Water, Air and Soil Pollution: Focus. 2: 717-729. 
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