Geospatial analysis of soil moisture variability and its implications for irrigation management using GIS

Introduction

Soil moisture (SM) refers to the water contained within the pore spaces of soil. It controls water availability for plants, influences evapotranspiration and runoff, affects nutrient movement, and modulates hydrological and ecological processes. In agricultural regions, variability in soil moisture-across both space and time-poses significant challenges for efficient water allocation, irrigation scheduling, and yield optimisation. Traditional irrigation planning often assumes uniform soil-water conditions across a field or region. But soils vary in texture, structure, depth; topography influences water retention and drainage; rainfall and climate are heterogeneous; land use and vegetation cover change dynamically. Ignoring this variability leads to over-irrigation in some areas (wasting water and causing waterlogging) and under-irrigation in others (stressing crops) [1,2] . Geospatial tools-primarily Geographic Information Systems (GIS) and remote sensing (RS)-enable mapping, monitoring, and analysis of soil moisture variation over large areas and over time. Advances in sensor technology (microwave, multispectral, thermal), data assimilation, and spatial-statistical modelling enable high-resolution soil-moisture estimates beyond the limited coverage of ground sensors [2,3] . This review examines the current state of geospatial analysis of soil moisture variability, the methods employed, the observed spatio-temporal patterns, and their applications in irrigation management. Special attention is given to studies in India and similar agroclimatic regions, highlighting potential for precision irrigation, drought preparedness, and sustainable water use.

Importance of Soil Moisture in Agriculture and Irrigation

 Water availability for crops: Soil moisture determines the water accessible to plant roots.

Adequate soil moisture ensures sustained plant growth; deficits stress crops, reducing yield.

In irrigated and rain-fed agriculture alike, timely knowledge of soil moisture enables welltimed irrigation to match crop water demand [3,4] .

 Soil health and nutrient dynamics: Soil water is essential for dissolution, movement and uptake of nutrients. It influences soil microbial activity, nutrient cycling, soil temperature, and the chemical and biological processes necessary for soil fertility [4,5] .  Environmental and hydrological balance: Soil moisture regulates infiltration, surface runoff, groundwater recharge, evapotranspiration, and runoff generation. These processes influence flood risk, drought resilience, and water resource sustainability. For water-scarce or monsoon-driven climates (like many parts of India), managing soil moisture is vital for drought and flood mitigation [5,6] .  Efficient irrigation and water conservation: With increasing water scarcity and pressure on freshwater resources, efficient irrigation becomes critical. Mapping soil moisture variability helps tailor irrigation -applying water where and when needed -minimising waste and ensuring equitable distribution across heterogeneous lands.  Climate resilience and sustainable farming: In regions with variable or uncertain rainfall (e.g., monsoon variability), soil moisture analysis aids forecasting and planning -helps anticipate drought, schedule irrigation or rainfall-recharge, plan cropping patterns -ultimately supporting resilient agriculture under changing climate patterns [4,6] .

Given these roles, having accurate spatial and temporal soil moisture information is foundational for modern irrigation management, water resource planning, and sustainable agriculture.

Methods for Soil Moisture Estimation and Geospatial Analysis

There are three broad approaches to estimating and mapping soil moisture: ground-based measurements, remote sensing, and GIS-based spatial interpolation and modelling.

Ground-based methods

Ground-based methods include gravimetric sampling, in-situ sensors (e.g., dielectric sensors, moisture probes), and automated observation stations. The gravimetric method involves collecting soil samples and measuring mass before and after drying to calculate volumetric moisture content. These methods provide accurate point-scale measurements, often considered the " ground truth [6,7] ." Recent developments include low-cost sensor networks using microprocessors and IoT technologies. For example, a system developed at a research institution in India used a Raspberry Pibased sensor network to measure soil moisture and temperature, demonstrating a high correlation (r ≈ 0.9) with standard gravimetric observations. arXiv However, ground methods have limited spatial coverage; scaling to fields, districts, or watersheds requires interpolation or combination with spatial methods [7,8] .

Remote sensing approaches

Remote sensing enables large-area monitoring. Sensors on satellites or drones measure reflectance (optical, multispectral, thermal) or microwave backscatter-data that can be correlated with soil moisture. A recent comprehensive review (2016-2024) of remotesensing-based soil moisture estimation identified satellite data as the dominant method (used in ~ 73% of studies) [8,9] . Microwave sensors (passive or active SAR) are particularly valuable because they can sense soil moisture independent of cloud cover and are sensitive to soil dielectric properties. Active radar (e.g., SAR) is increasingly used to estimate soil moisture, potentially at high spatial resolution. For instance, a 2025 study using satellite-based SAR (Sentinel-1) tested soil moisture prediction for wheat fields, using backscatter coefficients and vegetation-correcting indices, and was validated against ground measurements collected via a portable probe at 5 cm depth [9,10] . among methods [10,11] . Hybrid models may include soil and topographic variablese.g., the Soil Moisture Proxy Model (SMPM), which combines soil properties, slope, aspect, drainage patterns, and land use to estimate potential soil moisture across a watershed. Semi-distributed hydrological models incorporating remote sensing and GIS (e.g., DPHM-RS) integrate processes such as interception, evapotranspiration, soil moisture, runoff, and routing to simulate water fluxes across basins. These methods allow estimation of soil moisture at locations without direct measurements, generation of continuous soil moisture maps, and analysis of spatial patterns in relation to terrain, soil type, rainfall, and land use [12,13] . Understanding these patterns-and their drivers-is essential for designing effective irrigation strategies and drought/flood management.

GIS

4.1 Spatial variability: soil, topography, land use  Soil texture and structure: Soil moisture retention depends heavily on soil texture, organic matter content, porosity, and bulk density. For example, a 2025 study in East Kazakhstan found that heavy soils (loam, silt-loam, clay-loam) consistently maintained higher soil moisture than light-textured soils (sands) across different years [13,14] .  Topography and drainage characteristics: Terrain influences water accumulation and drainage. Indices like the Topographic Wetness Index (TWI) combine slope and upslope contributing area to identify zones likely to accumulate moisture [14,15] .

For example, in watershed-scale analyses, areas in depressions, gentle slopes, or flow-path convergence often show higher soil moisture potential than ridges and convex slopes, where runoff and drainage reduce retention. The SMPM applied in a semi-arid watershed in Mesa Verde (USA) demonstrated how topographic and soil variables together highlight zones with higher waterholding potential, offering guidance for water management and dryland agriculture [15,16] .  Land use and vegetation cover: Vegetation influences evapotranspiration, shading, and interception of rainfall; land cover changes alter infiltration and evaporation. Areas under dense vegetation may exhibit different soil moisture dynamics than bare land or cropland. Seasonal crops, crop rotations, and cropping intensity also affect spatial soil moisture distribution [16,17] .  Heterogeneous field-scale variability: Even within a single field, moisture can vary depending on microtopography, soil heterogeneity, irrigation method, and previous water application. A 2023 study in a corn-soybean production field, using a 55 m × 55 m grid (44 sampling points) and moisture sensors at multiple depths (31, 61, 76 cm), documented substantial spatial and temporal variation.

The authors used interpolation (IDW) to map soil water tension (SWT) across the field to support site-specific irrigation management [17,18] . [18,19] .

The study used statistical measures (e.g., coefficient of variation and correlation with rainfall) and identified areas that consistently experienced moisture deficits -a valuable approach for drought-risk mapping.

 Drought and moisture persistence: Periods of delayed or poor rainfall produce soil moisture deficits. Geospatial timeseries mapping helps identify persistent dry zones, enabling analysis of drought frequency. In the 20-year Indian soil moisture dataset, some regions experienced 4-6 years of drought in June, highlighting the vulnerability [20,21] .  Irrigation events and water application: In irrigated agriculture, artificial water application alters soil moisture independently of rainfall. Integration of soil moisture maps with irrigation data enables assessment of irrigation effectiveness, detection of over-or under-irrigation, and guidance for future scheduling. Sensor-based field studies show that spatially distributed moisture measurements and interpolation can support precision irrigation by matching water supply to localised field moisture conditions.  Sub-seasonal and interannual dynamics: Soil moisture can fluctuate within a cropping season (before/after rainfall, irrigation, evapotranspiration), and across years depending on rainfall variability, land use change, or management practices.

Applications for Irrigation Management and Water Resource Planning

Geospatial analysis of soil moisture has direct and practical applications in irrigation planning, efficiency improvement, drought and flood risk management, and policy-level water resource planning [22,23] .

Precision irrigation and site-specific water management

By mapping spatial variability at the field or watershed scale, GIS-based moisture maps can guide where to apply water, how much, and when. This reduces over-irrigation (water waste and salinity risk) and under-irrigation (crop stress), thereby improving water-use efficiency. Field-level sensor networks combined with interpolation methods (e.g., IDW, Kriging) support micro-zone-based irrigation scheduling. For instance, in fields under pivot or drip irrigation, moisture sensors plus GIS interpolation help detect zones with lower moisture retention, allowing targeted water application rather than blanket irrigation [24,25] .

Drought risk assessment and water scarcity management

Long-term geospatial soil moisture datasets enable drought mapping, vulnerability assessment, drought frequency analysis, and early warning. In monsoon-dependent regions, identifying zones with recurring soil moisture deficits helps allocate irrigation resources, plan drought-resilient cropping patterns, and design water-saving strategies.

Watershed and basin-level water management, planning of water harvesting structures

At larger scales, GIS-based soil moisture and topographic analysis help identify zones likely to collect and retain water, suitable for water-harvesting infrastructure (e.g., small check dams, recharge ponds), rainwater harvesting, and groundwater recharge. Hybrid hydrological models that integrate soil moisture, rainfall, evapotranspiration, runoff, and land cover (e.g., DPHM-RS) support basin-scale water balance estimation and decision-making [26,27] .

Agricultural planning and crop water demand mapping

By overlaying soil moisture maps with soil type, land use, crop distribution, and climatic data, planners and farmers can estimate the spatial distribution of irrigation demand. GIS-based demand maps facilitate optimised water allocation, reduce water delivery inequality, and support judicious water management at the district or regional level [28,29] .

5.5

Monitoring and evaluating irrigation system performance, waterlogging, and salinity risk GIS-based soil moisture observations allow detection of zones of over-irrigation, poor drainage, waterlogging, or salinity buildup. Over time, such monitoring helps evaluate the performance of irrigation infrastructure, drainage design, and remedial measures. Also helpful in diagnosing inefficiencies or inequalities in water delivery systems. [30,31] . This approach provides a practical method for district-level soil moisture mapping, thereby enabling irrigation planning based on actual spatial variation rather than coarse assumptions.

Satellite-based soil moisture for crop irrigation: 2024 Study on Wheat Fields in India

A 2025 study used Sentinel-1 SAR imagery to predict soil moisture in wheat fields during critical phenological stages (tillering, flowering, grain filling)dates: 19 Feb, 9 Mar, 14 Aprwhen soil water availability is crucial for yield. Groundtruth data were collected using a portable moisture probe at a depth of 5 cm. Backscatter coefficients (VV, VH) and radar-vegetation index (RVI) were used as predictors [32,33] . This demonstrates the feasibility of satellite-based soil moisture estimation for guiding irrigation during key crop stages, offering a scalable, remote method for crop water management.

Long-term soil moisture spatio-temporal mapping across India (1991-2010)

A study using ESA-satellite-derived surface soil moisture over India (daily data) for 20 years (1991-2010) analysed interannual variability, correlation with rainfall, and drought frequency. Monthly mean images (e.g., June and July) showed significant spatial variability: many northern and central states had low moisture in early monsoon (June) with high year-toyear variability; by July, moisture increased with less variability. Regions like Uttar Pradesh, East Rajasthan, Punjab, Haryana, Bihar, Gujarat, and parts of Chhattisgarh and Jharkhand showed low mean moisture and high coefficient of variation (CV) [34,35] . Drought frequency mapping (years when soil moisture fell less than 90% of normal) identified significant drought-prone areas.

In June, about 32% of pixels experienced 4-6 years of drought; in July, 25% of pixels did [36,37] . These maps can inform regional irrigation planning, drought mitigation, reservoir and groundwater recharge strategies.

Watershed-scale soil moisture potential mapping in semiarid region (USA) -implications for water harvesting and dryland agriculture

Although not in India, the watershed-scale application of the Soil Moisture Proxy Model (SMPM) in a semi-arid watershed in Mesa Verde (Colorado, USA) illustrates how combining soil, slope, aspect, drainage, and land cover data in GIS can identify zones with higher water retention potential [38,39] . This has direct relevance for arid and semi-arid regions worldwide, including many parts of India where dryland farming or water harvesting is critical. Such identification enables targeted placement of rainwaterharvesting structures, selection of crops suited to local moisture conditions, and optimised land management for sustainable agriculture [40,41] . [42,43] .  Spatial resolution constraints: Coarse-resolution satellite products may miss small-scale variability; interpolation and modelling require sufficient ground-truth points, which are often lacking.  Vegetation and surface conditions interference:

Vegetation cover, crop residues, surface roughness, and land cover changes influence remote-sensed reflectance or backscatter, complicating soil moisture retrieval. The presence of crops during the growing season makes estimation harder. Recent reviews note that vegetation interference, limited penetration depth, and calibration requirements remain significant challenges [44,45] .  Sparse in-situ data: Many regions, especially in developing countries, lack dense soil moisture monitoring networks. Without in-situ data, validation of remote-sensing or model outputs remains uncertain. Studies often rely on limited or sporadic ground measurements [46,47] .

Temporal and dynamic complexity

Soil moisture dynamics are influenced by rainfall, evapotranspiration, irrigation, land use change, groundwater uptake, and soil processes. Capturing this complexity requires frequent observations and integration of multiple datasets (climate, land cover, soil properties). Many studies focus on snapshots (single dates) or short periods; long-term continuous monitoring at high spatial resolution remains difficult [48,49] .

Model and interpolation challenges

Spatial interpolation assumes spatial autocorrelation -that nearby points have similar moisture. Where soil, topography, or land use change abruptly, interpolation may yield misleading estimates. Hybrid models combining soil/topographic variables mitigate some issues, but require detailed ancillary data (soil maps, DEMs, land cover), which may be missing or outdated in many regions.

Integrating soil moisture data into operational irrigation decision-support systems (DSS)

Despite numerous studies, operational systems that integrate geospatial soil moisture maps into everyday irrigation planning remain limited. Challenges include data availability, temporal frequency, user capacity (farmers, water managers), infrastructure, and institutional uptake.

Future directions and research opportunities

 Multi-sensor fusion and machine learning: Recent trends show growing use of multi-sensor data (microwave, optical, thermal) combined with machine/deep learning for improved soil moisture estimation. These methods may better account for vegetation, soil heterogeneity, and surface conditions.  Root-zone moisture estimation: Research should focus on combining surface observations with soil hydraulic models, in-situ deep sensors, and data assimilation to estimate moisture in the root zone (not just surface). Approaches such as simultaneous estimation of hydraulic parameters and soil moisture distribution using remote sensing and modelling show promise [49,50] .  High-resolution, frequent monitoring: Use of UAVs/drones, dense sensor networks, and smartphoneenabled data collection (IoT) can fill spatial and temporal gaps, especially for smallholder farms.  Locally calibrated models for diverse soil and agro-climatic zones (especially in India): India' s agroecological diversity demands region-specific calibration and validation. The systematic establishment of ground observation networks, soil mapping, integration of climatic data, and local field studies will improve reliability.  Operational decision-support systems for farmers and water managers: Development of GIS-based DSS integrating soil moisture, crop calendar, climate forecasts, irrigation infrastructure, water availability and demand, to translate geospatial insights into actionable irrigation schedules [51,52] .

Conclusion

Soil moisture variability-across space and time-is a critical factor for efficient water use, sustainable agriculture, drought and flood risk management, and long-term water resource planning. Geospatial tools, primarily GIS and remote sensing, enable mapping and monitoring of soil moisture at scales and resolutions not feasible with ground-based measurements alone. A range of methods -from simple interpolation of in-situ measurements to satellite-based microwave sensing to hybrid models incorporating soil, topography, climate, and land cover data -have been successfully applied globally and in India [53,54] . Empirical evidence demonstrates that soil moisture distribution is highly heterogeneous even within small areas or single fields, driven by soil texture, topography, land use, and temporal factors such as rainfall and irrigation. GIS-based soil moisture mapping supports precision irrigation, targeted water application, drought and water-stress assessment, watershedlevel planning, and water-harvesting design. In India, interpolation-based mapping (e.g., in Varanasi), satellite-based monitoring (wheat-wheat cropping regions), and long-term moisture trend analysis highlight significant potential. However, challenges persist: remote sensing often provides only surface moisture; temporal frequency and spatial resolution remain limited; vegetation, surface conditions, and the lack of dense in-situ networks complicate reliable estimation; and the integration of data into operational irrigation scheduling frameworks remains limited [55] .

References

1. Abdulmanov R, Miftakhov I, Ishbulatov M, Galeev E, Shafeeva E. Comparison of the effectiveness of GIS-based interpolation methods for estimating the spatial distribution of agrochemical soil properties. Environ Technol Innov. 2021;24:101970.
2. Nikam BR, Garg V, Thakur PK, Aggarwal SP. Application of remote sensing and GIS in performance evaluation of an irrigation project at the disaggregated level. J Indian Soc Remote Sens. 2020;48(7):979-97.
3. Yousaf W, Awan WK, Kamran M, Ahmad SR, Bodla HU, Riaz M, et al. A paradigm of GIS and remote sensing for crop water deficit assessment in near real time to improve the irrigation distribution plan. Agric Water Manag. 2021;243:106443.
4. Zheng L, Li M, Ji R, Ye H, Wu C, Zhang J. Development of soil moisture management models based on GIS for farmland and its application. Trans Chin Soc Agric Eng. 2009;25(1):13-7.
5. Zhang L, Zhang K, Zhu X, Chen H, Wang W. Integrating remote sensing, irrigation suitability and statistical data for irrigated cropland mapping over mainland China. J Hydrol. 2022;613:128413.
6. Taghvaeian S, Neale CM, Osterberg JC, Sritharan SI, Watts DR. Remote sensing and GIS techniques for assessing irrigation performance: A case study in Southern California. J Irrig Drain Eng. 2018;144(6):05018002.
7. Masud MJ, Bastiaanssen WG. Remote sensing and GIS applications in water resources management. Water Resour Manag. 2017;31:351-73.
8. Mahmoud SH, Gan TY. Irrigation water management in arid regions of the Middle East: Assessing spatio-temporal variation of actual evapotranspiration through remote sensing techniques and meteorological data. Agric Water Manag. 2019;212:35-47.
9. Ray SS, Dadhwal VK. Estimation of crop evapotranspiration of the irrigation command area using remote sensing and GIS. Agric Water Manag. 2001;49(3):239-49.
10. Raza A, Khaliq A, Hu Y, Zubair N, Acharki S, Zubair M, et al. Water resources and irrigation management using GIS and remote sensing techniques: case of Multan district (Pakistan). In: Surface and groundwater resources development and management in semi-arid region. Cham: Springer; 2023. p. 137-56.
11. Tunçay T. Comparison of the quality of interpolation methods to estimate the spatial distribution of soil moisture content. Commun Soil Sci Plant Anal. 2021;52(4):353-74.
12. Malamos N, Tsirogiannis IL, Tegos A, Efstratiadis A, Koutsoyiannis D. Spatial interpolation of potential evapotranspiration for precision irrigation purposes. Eur Water. 2017;59:303-9.
13. Yao X, Fu B, Lü Y, Sun F, Wang S, Liu M. Comparison of four spatial interpolation methods for estimating soil moisture in a complex terrain catchment. PLoS One. 2013;8(1):e54660.
14. Fontanet M, Fernàndez-Garcia D, Ferrer F. The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields. Hydrol Earth Syst Sci. 2018;22(11):5889-900.
15. Bwambale E, Naangmenyele Z, Iradukunda P, Agboka KM, Houessou-Dossou EA, Akansake DA, et al. Towards precision irrigation management: A review of GIS, remote sensing and emerging technologies. Cogent Eng. 2022;9(1):2100573.
16. Lafond JA, Gumiere SJ, Hallema DW, Périard Y, Jutras S, Caron J. Spatial distribution patterns of soil water availability as a tool for precision irrigation management in Histosols: Characterization and spatial interpolation. Vadose Zone J. 2015;14(6):vzj2014-10.
17. Chen H, Fan L, Wu W, Liu HB. Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought. Environ Monit Assess. 2017;189(10):525.
18. Blatchford M, Mannaerts CM, Zeng Y, Nouri H, Karimi P. Influence of spatial resolution on remote sensing-based irrigation performance assessment using WaPOR data. Remote Sens. 2020;12(18):2949.
19. Belagalla N, Pranitha A, Arpitha B, Appoju S, Akanksha T. The role of pollinators in global food security: Protecting the unsung heroes of agriculture. Agrifrontline. 2025;1(9):73-6.
20. Zhu X, Zhu W, Zhang J, Pan Y. Mapping irrigated areas in China from remote sensing and statistical data. IEEE J Sel Top Appl Earth Obs Remote Sens. 2014;7(11):4490-504.
21. Arafa Y, El-Gindy AGM, El-Shirbeny M, Bourouah M, Abd-ElGawad AM, Rashad YM, et al. Improving the spatial deployment of the soil moisture sensors in smart irrigation systems using GIS. Cogent Food Agric. 2024;10(1):2361124.
22. Kharrou MH, Simonneaux V, Er-Raki S, Le Page M, Khabba S, Chehbouni A. Assessing irrigation water use with remote sensing-based soil water balance at an irrigation scheme level in a semi-arid region of Morocco. Remote Sens. 2021;13(6):1133.
23. Calera A, Campos I, Osann A, D' Urso G, Menenti M. Remote sensing for crop water management: From ET modelling to services for the end users. Sensors. 2017;17(5):1104.
24. Roychowdhury R, Najmusaqib S, Ahmad PT. Biofactories in bloom: Turning plants into sustainable sources. Agrifrontline. 2025;1(9):62-6.
25. El Nahry AH, Ali RR, El Baroudy AA. An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques. Agric Water Manag. 2011;98(4):517-31.
26. Hervai A, Pirkhoffer E, Fábián SÁ, Halmai Á, Nagy G, Lóczy D, et al. Interpolation and 3D visualization of soil moisture. Acta Geogr Debr Landsc Environ. 2017;11(1):2334.
27. Dari J, Brocca L, Quintana-Seguí P, Escorihuela MJ, Stefan V, Morbidelli R. Exploiting high-resolution remote sensing soil moisture to estimate irrigation water amounts over a Mediterranean region. Remote Sens. 2020;12(16):2593.
28. Belagalla N, Matam P, Rajitha R, Dhanya M, Keerthisri T. Insect pests and crop loss: A growing threat to food production in the 21st century. Agrifrontline. 2025;1(9):8791.
29. Mehra P. Remote sensing-based assessment of soil nutrient status for precision and site-specific fertilization. Agrifrontline. 2025;1(9):77-86.
30. Tamuly G, Majid B. Disaster forecasting in agriculture: Early warning through geospatial tools. Agrifrontline. 2025;1(9):102-9.
31. Anderson MC, Hain CR, Lannom KA. Remote sensing and GIS applications in agricultural water management. Agric Water Manag. 2015;162:1-13.
32. Bian J, Liu X. Mapping soil organic carbon stocks in cropland soils using remote sensing and GIS techniques. Remote Sens Environ. 2018;215:29-38.
33. Bishnoi S, Shukla A. Application of remote sensing and GIS in soil health assessment. Indian J Soil Conserv. 2020;48(2):145-52.
34. Bhattacharyya R, Yadav R. Monitoring soil quality using remote sensing and GIS: A case study of the upper Brahmaputra basin, India. Soil Use Manag. 2019;35(4):55867.
35. Brown JR, Fox J. Soil health assessment using remote sensing technologies. Agronomy. 2020;10(3):345.
36. Chen J, Zhang J, Li X. Soil organic carbon estimation using remote sensing data in the semi-arid region of China. Geoderma. 2016;265:57-66.
37. Das R, Chattopadhyay R. GIS-based mapping of soil fertility and its implications for sustainable agriculture in India. J Soil Sci Environ Manag. 2018;9(6):85-92.
38. Feng Q, Zhao Z. GIS and remote sensing integration for precision agriculture: Applications to soil moisture and organic carbon monitoring. Precis Agric. 2021;22(5):110012.
39. Ghosh S, Saha A. Integrating remote sensing and GIS for monitoring soil degradation in the Gangetic plains of India. Remote Sens Appl Soc Environ. 2019;12:10-22.
40. Guo X, Li W. Using remote sensing and GIS to assess soil salinity in arid regions. Land Degrad Dev. 2020;31(3):41223.
41. Jackson ML, Burnell J. Advances in remote sensing techniques for soil health monitoring. Soil Sci Soc Am J. 2017;81(6):1221-34.
42. Jha RK, Kumar A. Soil health monitoring using remote sensing data: A review of techniques and case studies in India. J Environ Manag. 2019;235:159-73.
43. Joshi P, Pandey V. Remote sensing techniques in soil health monitoring and their applications for sustainable agriculture. Int J Environ Sci Technol. 2020;17(4):2531-42.
44. Kharche SR, Singh M. Integration of remote sensing data with GIS for soil erosion assessment in the northern Himalayas. Hydrol Res. 2021;52(2):345-58.
45. Kundu A, Jain R. Precision agriculture in India: Soil health monitoring using remote sensing and GIS. Agric Syst. 2018;166:60-71.
46. Li H, Liu Y. Remote sensing and GIS for sustainable soil management. Environ Monit Assess. 2017;189(10):389402.
47. Liu D, Zhu Y. Soil organic carbon mapping using remote sensing and GIS techniques: A comprehensive review. Agron Sustain Dev. 2020;40(1):16-27.
48. Liu L, Chen Y. Applications of remote sensing and GIS in soil health management: A case study of Indian agriculture. J Environ Eng Sci. 2019;4(2):55-63.
49. Lu S, Zhang H. Soil moisture estimation using remote sensing and GIS: A case study in arid regions. Remote Sens. 2019;11(7):1234-48.
50. Mehdizadeh S, Sadeghi S. The integration of remote sensing and GIS for monitoring soil salinity and sodicity in arid regions. Agric Water Manag. 2018;204:128-38.
51. Mishra SK, Tripathi NK. GIS-based soil health monitoring in the Narmada River Basin, India. Environ Monit Assess. 2018;190(6):328.
52. Nadaf SA, Amara DMK, Patil PL. Distribution of micronutrients in selected pedons of sugarcane-growing Vertisols in Northern Karnataka State of India. Open J Soil Sci. 2022;12:1-12. doi:10.4236/ojss.2022.121001.
53. Panda S, Senapati P. Integration of remote sensing and GIS for monitoring soil erosion and degradation in the western Himalayas. Land Degrad Dev. 2021;32(5):1500-1511.
54. Amara DMK, Nadaf SA, Saidu DH, Vonu OS, Musa RM, Kamanda PJ, et al. Studies on land resource inventory for agricultural land use planning in Northern Transition Zone of India through remote sensing and GIS techniques. J Geogr Inf Syst. 2021;13:710-728. doi:10.4236/jgis.2021.136039.
55. Nadaf SA, Patil PL, Dasog GS. Identification of micronutrient constraints in sugarcane-growing Vertisols of Northern Transition Zone of Karnataka by GIS technique. Karnataka J Agric Sci. 2015;28(1).