1 Introduction

Groundwater is one of the most essential sources of fresh water that humanity can investigate and responsibly utilize for our everyday needs. It is stored in the upper region of the earth’s crust’s changing subsurface geological formations [1]. Groundwater reserves around the world can be used for a variety of domestic, industrial, agricultural, and other development purposes. The ever-increasing global demand for freshwater has prompted us to delve deeper into these underground freshwater deposits. Groundwater flow is primarily determined by two factors: porosity and permeability. Rainfall and water flow recharge the groundwater aquifers, and outflows include water runoff into streams and lakes, springs, evaporation, and pumping. Groundwater is a type of water that fills all of the spaces in a sub-surface geological layer. The most essential portion of the water cycle is groundwater, which is formed when part of the rainfall penetrates down into the ground and then becomes groundwater. Many natural factors influence the occurrence and distribution of this type of freshwater resource, including geology, soil, geomorphology, lineament patterns, drainage patterns and drainage density, rainfall, land use, and so on [2]. There are several methods for determining groundwater availability for human consumption based on these factors. Sustainable water resource management will boost agricultural productivity. As a result, conducting a reliable evaluation of water use for the limited available water supply, as well as effective planning, is crucial. Agriculture is the backbone of the Indian economy, accounting for around 15–20% of the national GDP, and India’s agriculture sector has already been essential to the country’s economic progress and poverty reduction. The installation of groundwater projects in areas where rainfall scarcity and variability are crucial environmental elements for boosting agricultural output. Groundwater mapping employing geospatial methods has become a major tool in the realm of agriculture and water management due to the increasing need for groundwater for irrigation [3, 4] for relative importance of thematic parameters analytical hierarchy process (AHP) has been used [5, 6]. Akhtar et al. [7] utilized geospatial approaches to find the best multi-purpose river projects and optimize irrigation potential for a better manner of site selection and better design of water supply schemes. Hu et al. [8] studied the irrigated agricultural potential by assessing crop evapotranspiration and groundwater availability. Worqlul et al. [9] assessed the irrigation potential using two factors i.e., ranking techniques and pair-wise comparison techniques. Groundwater and agriculture are interrelated in India. India depends heavily on groundwater in various sectors [10,11,12] of which the most important is agriculture. In rural areas, groundwater provides 80–90 percent of the domestic water supply. Groundwater is also responsible for 50 percent of total urban and industrial water demand. It also supplies water to more than half of the irrigated areas [13]. Around 60–65% of irrigation for agriculture is done using groundwater and about 90% of extracted groundwater is used in irrigation. In India, there is a notable and growing necessity regarding safe drinking water in light of the inevitable water crisis. Water quality and quantity are being impacted in both rural and urban areas of India as a result of rapid urbanization, modernization, and population growth [14]. The problem of water pollution and contamination worsens the problem [15]. As we all know, India is on the verge of a freshwater crisis, owing to poor water management and the destruction of the environment, which has resulted in millions of people lacking access to potable water. Water levels across several regions of the country have dropped drastically over the last two decades as exploitation has increased. The Mewat district of Haryana has groundwater issues in terms of quality as well as quantity [16,17,18,19,20], where people are buying water to fulfilling their needs. The main objective of the study is to delineate, identify and map the groundwater and agriculture potential of Mewat district, Haryana as an example of sustainable water resource and agriculture development and planning in the area.

2 Study area

The Mewat district of Haryana faces a major problem in its fresh groundwater supply and erratic supply of fresh water from the government’s side, it is necessary to find proper sources of groundwater, especially for its agricultural needs. Geographically, the district covers an area of 1507 sq. km and ranks 16th in the state and 544th in the Country in terms of size (Fig. 1). It is located at 28 °12′ N latitude, 77 °3′ E longitude, and 199 m above sea level. In the year 2019, forest land accounted for 7.38 percent of the total geographical area. It is bordered on the north by the Gurgaon district, on the west by the Rewari district, and on the east by the Faridabad and Palwal districts. The district’s soil is light and sandy. It is located in the subtropical region. The district is bisected by National Highway 1. The district’s climate is similar to that of other northern Indian locations. The district’s climate is extremely hot in the summers (40 °C) and moderately cold in the winters (10 °C). The monsoon season is when it rains the most. In the 2018–19 calendar year, the district received 525.8 mm of rain. The average annual rainfall in Mewat is about 595 mm [21]. The district is divided into four sub-districts, eight towns, and 439 villages. The Mewat region is primarily underlined by Quaternary alluvium, which serves as a primary groundwater reservoir. A small amount of groundwater can also be found in the district’s strike ridges’ cracks, rock joints, and crevices of hard rocks. The top zone’s groundwater is reported to exist down to a depth of 70 m and may retain water in phreatic circumstances. Deeper-level aquifers are either completely or partially enclosed. In the Mewat area, the Central Ground Water Board has conducted exploratory drilling at depths between 39 and 291 m. The results of the exploratory boreholes show that the alluvial deposit in the deeper zones contains different amounts of sand, clay, and kankar. These sediments lay on the Delhi System’s basement of rocks. The area’s alluvium thickness ranges from nearly nonexistent near hill ranges to over 291 m [21].

Fig. 1
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Study area map

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3 Materials and methods

3.1 Thematic layer preparation

In this work, a mix of RS and GIS methods was used to delineate the groundwater and agriculture potential zones. These are one of the most cutting-edge techniques and are also economical since it generates data that is suitable and of excellent quality for this kind of assessment [22,23,24,25,26,27]. Seven thematic layers namely Geomorphology, Soil, Slope, Drainage Density, Land Use/Land Cover, Lineament Density, and Geology were created for groundwater potential zonation based on the availability of groundwater, and six thematic layers namely Digital Elevation Model (DEM), Slope, Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Soil Moisture Index (SMI), and Rainfall were created for agriculture potential zonation as these factors affect the potentiality for agriculture. By overlaying all of the layers using a weighted overlay tool in the spatial analyst toolbox in ArcGIS 10.8.2, the Groundwater Potential Zones and the Agriculture Potential Zones were extracted.

LANDSAT 8 sensor pictures were used to create the land use/land cover map using the visual interpretation technique. Normalized difference vegetation index, land surface temperature map, and soil moisture index map were also prepared using Landsat 8 data with the help of the raster calculator tool. Slope and drainage density was generated using Global Multi-Resolution Terrain Elevation Data (GMTED) 2010 DEM data. Geospatial data on geomorphology, geology, and lineament density were obtained using the data supplied by the Bhukosh GSI. The United Nations Food and Agriculture Organization (UNFAO) soil statistics were used to extract the soil map. Rainfall data (2011–2020) was downloaded from the Climatic Research Unit (CRU data) of the UK’s National Centre for Atmospheric Sciences. The data downloaded was converted into point format in ArcGIS 10.8.2 and finally interpolated with the Inverse Distance Weighting (IDW) interpolation technique to produce the rainfall map.

3.2 AHP and assignment of weights and ranks

The analytical hierarchy process (AHP) approach was used to select the decision criteria for analyzing the data for identifying groundwater and agriculture potential zones using GIS. Based on professional judgment, weights were given to several thematic maps and their specific attributes. An AHP approach is a process that incorporates both objective expert opinion and empirical facts to accomplish effective decision-making. Making complicated judgments requires the connection of both important and unimportant elements to produce a ratio and an abstract scale of priorities. It helps with selection criteria identification and weighting, analysis, and decision-making process rapidly. By comparing each pair of components for each element in the various thematic layers, it was possible to determine the order in which the items in each portion of the hierarchy should be prioritized. The hierarchy is divided into sections using a pair-wise comparison matrix. A nine-point scale, from 1 (insignificance or equal significance) to 9 (strong preference or absolute), is used for analytic hierarchical decision-making. Tables 1 and 2 display the weights and rankings given to the research area’s thematic layers and attributes. The contribution of each element is evaluated independently in this pair-wise comparison. The relative importance of each pair of components within a particular element group is evaluated independently in AHP.

Table 1 Assigned weights and ranks for groundwater potential
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Table 2 Assigned weights and ranks for agriculture potential
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The groundwater and agriculture potential zones were delineated by integrating all the spatial layers using a weighted overlay approach [28,29,30]. Before the overlaying operation, all spatial layers were classified into a uniform ranking pattern ranging from 1 to 5, where 1 represents poor potential and 5 represents excellent potential. Geomorphology was assigned the highest weight as it is the most important parameter in governing the occurrence of groundwater in a region. whereas Geology, Lineament Density, Slope, and Soil were given moderate weight, and LULC and Drainage Density were assigned a low weight in the case of groundwater potential. A similar process was applied to produce the agriculture potential map in which LST and SMI were given a high rank as land surface temperature and soil moisture index heavily affects the potentiality for plant growth in a region, Slope and Rainfall moderate rank, and NDVI and DEM a low rank. The weighted overlay tool in ArcGIS was used to create the final integrated maps. By applying a uniform measuring scale of values to each raster, weighting each according to their relevance, and adding all of them up to create an integrated map, this methodology offers a way to combine different themed maps. In the current study, the weighted overlay process was conducted by ranking the various criteria and allocating weights to the various thematic layers by the degree of potential.

4 Results and discussions

4.1 Drainage density

The availability of groundwater is highly dependent on drainage density. Permeability has an opposite relationship with drainage density. As a result, it plays a crucial role in defining the groundwater potential zone. A drainage basin’s drainage density is calculated by dividing the sum of the lengths of all its rivers by its overall area. Low infiltration due to high drainage density makes the area’s potential for groundwater less favourable. Low drainage density translates to high infiltration, which raises the groundwater potential more. In the prepared drainage density map (Fig. 2) high weight is assigned for low density and low weight is allocated for high density in the zonation of groundwater potential.

Fig. 2
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Drainage density map of study area

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4.2 Soil

The quantity of water that may permeate subterranean formations and thus affect groundwater recharge depends significantly on the kind of soil. The major variables taken into account for estimating the rate of infiltration are the hydraulic properties of the soil and its texture. The soils of the Mewat area are largely impacted by salt. The soils are loamy sand with a medium texture and a low to medium organic content of 0.25 to 0.75 percent. The soil map (Fig. 3) of the study region was created using the soil data that was retrieved from the UNFAO.

Fig. 3
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Soil map of study area

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4.3 Lineament density

Lineaments are linear or curved characteristics that are structurally regulated. They are relatively straight alignments on the satellite photos which makes them easy to spot. Lineaments are the areas where faulting and fracture have occurred, leading to more permeability and secondary porosity. Based on the closeness of the lineaments, the ranks for lineament density are given. In the prepared lineament density map (Fig. 4) high weight and low weight are given to classes with high and low densities, respectively.

Fig. 4
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Lineament density map of study area

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4.4 Geomorphology

One of the key elements utilized frequently for the definition of groundwater potential zones is geomorphology, which describes the landform and topography of a region. It provides details on how different landform characteristics are distributed and located throughout the region The younger and older coastal/fluvial plains, water bodies, and valley fill are given higher weights, whilst the structural and denudational hills are given lesser weights. The geomorphology map (Fig. 5) was prepared with the spatial data downloaded from BHUKOSH GSI website.

Fig. 5
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Geomorphology map of study area

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4.5 Geology

The research area’s geology governs the occurrence, flow, and storage of groundwater. The geology map of the Mewat district shows three distinct divisions. The majority of the region is covered by undifferentiated aeolian/fluvial sediments which have high potentiality for groundwater. The elevated regions of Mewat are occupied by the rocks belonging to the Ajabgarh subgroup of the Delhi supergroup of rocks. As they are mostly composed of carbonaceous shales and arenites, they show a moderate potentiality for groundwater occurrence. There are small outcrops of Alwar group rocks located in the northwest region of the district and as they are composed of granites these rocks show very low potentiality for groundwater occurrence. Based on these observations the respective weights were assigned to the features shown on the geology map of the study region (Fig. 6).

Fig. 6
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Geology map of study area

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4.6 Slope

The slope is one of the most important topographical features that convey how steep the ground surface is. The slope provides crucial details about the type of geologic processes taking place at a local level. The slope of the surface has the most impact on surface run-off and infiltration rate. Because water from precipitation runs quickly down a steep slope after rainfall, larger slopes yield smaller recharge. It does not have enough time to stay there to penetrate and replenish the saturated zone. In the slope map (Fig. 7) for flat and easy slopes, a high weight is provided. For steep and very steeping slopes, low weight is allocated.

Fig. 7
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Slope map of study area

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4.7 Land use/land cover

We can learn more about the existing terrain by looking at the Land Use Land Cover (LULC) map (Fig. 8) of the area. To investigate how land is used and to prepare for and manage the future of the land resource, mapping of land use and land cover is done. In the research area, four types of land use have been identified: built-up, water bodies, vegetation (including agricultural land), and barren land. The LULC class vegetation (forest and agricultural land) stores significantly more water than built-up and barren land. Vegetation and water bodies are given a high weight. Built-up land and barren terrain are given a low weight.

Fig. 8
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Land use/land cover map of study area

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4.8 Groundwater potential

After combining all thematic maps, the final map was categorized into four groundwater potential zones: very poor, poor to moderate, moderate to very good, and excellent [28, 31]. Table 3 shows the assessment of the groundwater potential of Mewat according to the area. The groundwater potential map (Fig. 9) of Mewat district, Haryana, was created using the weighted overlay approach by integrating numerous thematic layers such as geomorphology, slope, lineament density, drainage density, soil, geology, and LULC. Different themes are awarded appropriate weightage based on their impact on groundwater circulation and storage. Rank and score are assigned to each feature in a thematic layer based on groundwater flow and potentiality criteria. The feature with the largest groundwater potentiality received the highest rank, while the lowest received the lowest. The themed layers are transformed into a raster format after assigning weightage to all themes and ranking to features. Finally, an integrated groundwater potential zone map was created in Arc GIS using the weighted overlay tool. The groundwater potential map clearly shows that the alluvial plain, which is made of sand, silt, and clay, has very good to exceptional potentiality due to its almost flat slope. Due to its lower water holding capacity, the pediment plain with a gentle slope has moderate potential, whereas the aeolian plain with coarse to fine aeolian sand has moderate to very good potential. The potential of structural hills and linear ridges ranges from poor to moderate. As a result, the created groundwater potential map serves as a starting point for future investigation.

Table 3 Assessment of groundwater potential of Mewat, Haryana
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Fig. 9
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Groundwater potential map of study area

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4.9 Land surface temperature

Land Surface Temperature (LST) is simply a measure of how hot the Earth’s surface would feel to the touch in a given area. Land surface temperature is an essential controlling factor for efficient farming. Higher land surface temperatures usually indicate major human activities, densely populated regions, asphalt, mining sectors, etc.… which are not suitable sites for agriculture. The LST map was reclassified and categorized as very low (5–10 °C), low (10–20 °C), medium (20–30 °C), high (30–40 °C), and very high (40–49 °C). Therefore, in the prepared LST map (Fig. 10) high weights were given to the areas with lower land surface temperatures, moderate weightage was given to areas with moderate land surface temperatures, and lowest weightage was given to regions having the highest land surface temperature readings.

Fig. 10
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Land surface temperature map of study area

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4.10 Normalized difference vegetation index

The Normalized Difference Vegetation Index (NDVI) measures the difference between near-infrared light, which vegetation strongly reflects, and red light, which vegetation absorbs. That is, it is helpful in providing us with information regarding the type of vegetation and vegetation density. When compared to other wavelengths, healthy vegetation (chlorophyll) reflects more near-infrared (NIR) and green light. It does, however, absorb more red and blue light. This is why our eyes perceive vegetation as green. High-vegetation-dense regions will be more favorable for crop planting. Therefore, in the prepared NDVI map (Fig. 11) high weights were assigned to higher NDVI values (4 and 5 on the map), and low weights were assigned to lower NDVI values (1 and 2 on the map) for agriculture potential mapping.

Fig. 11
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Normalized difference vegetation index (NDVI) map of study area

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4.11 Digital elevation model

A Digital Elevation Model (DEM) is a depiction of the Earth’s bare ground topographic surface, without any trees and other surface items. The digital elevation model was reclassified into two. Weights were assigned based on the reclassified digital elevation model. In the study area’s DEM (Fig. 12) high weights were given to land in the low-lying regions (1 on the map) and low weight was given to regions having higher elevations (2 on the map).

Fig. 12
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DEM map of study area

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4.12 Soil moisture index

The soil moisture index (SMI) measures the amount of accessible soil water for vegetation, as well as the relative soil water content [32]. Soil moisture content is an important soil property in agriculture for crop production. The amount of irrigation required depends on soil moisture. As a result, in the prepared SMI map (Fig. 13), high weights were given to regions having high soil moisture index (4 and 5 on the map), and moderate weights were assigned to regions having moderate soil moisture index (3 on the map), and low weights were given to regions with the lowest soil moisture index (1 on the map).

Fig. 13
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Soil moisture index (SMI) map of study area

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4.13 Rainfall

Rainfall is one of the important parameters that govern the agricultural potential of a region. Rainfall is a major factor that affects the growth and production of food crops both at the germination stage and during the fruiting period. The rainfall data (2011–2020) for the study region was obtained from the climate research unit (CRU) website which is mostly maintained by UK’s national centre for atmospheric sciences. The data obtained were processed and interpolated with IDW to produce the rainfall map (Fig. 14) of the study region. The rainfall map was reclassified into three regions. High weights were given to regions receiving more rain (390 mm) and low weights were given to regions receiving less rain (120 mm). And moderate weight for moderate rain (270 mm).

Fig. 14
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Rainfall map (2011–2020) of study area

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4.14 Slope

The rise or fall of the ground surface is referred to as a slope or slope is one of the most important topographical features that conveys how steep the ground surface is. It is critical for a farmer or irrigator to recognize the land’s slopes. In steep terrain, a slope is simple to spot. Also planting crops on level land is much easier and more efficient than having to do crop plantations on steeper lands. The slope values were reclassified and categorized into five classes nearly level (< 1%), very gentle (1–6%), gentle (6–20%), steep (20–40%), and very steep (> 40%). Giving regard to this the weights were assigned to the slope map (Fig. 15). High weights were given to regions having nearly level land, moderate weights were given to regions having gentle slopes, and lowest weights were given to regions having a steep slope.

Fig. 15
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Slope map of study area

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4.15 Agriculture potential

The final map was divided into four agriculture potential zones: very poor, poor to moderate, moderate to very good, and excellent after the six maps were combined. Table 4 shows the assessment of the groundwater potential of Mewat according to the area. By combining six thematic layers—LST, SMI, NDVI, DEM, Slope, and Rainfall—weighted overlay methodology was used to construct the agriculture potential map (Fig. 16) for the Mewat district of Haryana. Various topics are given the proper weighting according to how they could affect agriculture. Each parameter in a thematic layer receives a rank and grade depending on how well suited it is for agricultural production. The feature with the largest agriculture potentiality received the highest rank, while the lowest received the lowest. The themed layers are transformed into a raster format after assigning weightage to all themes and ranking to features. Finally, an integrated agriculture potential zone map was created in ArcGIS using the weighted overlay tool. The agriculture potential map clearly shows that most of the regions in the southeast part of Mewat have an excellent potential for agriculture due to the region having a nearly level land, low land surface temperature, high soil moisture index, and receiving higher rainfalls. As we can see from the generated agriculture potential map most of the Mewat region falls under the moderate to very good agriculture potential zone and other regions fall under the poor to moderate agriculture potential zone. Regions having higher slopes, less NDVI, less SMI, and high LST fall under the very poor agriculture potential zone.

Table 4 Assessment of agriculture potential of Mewat District, Haryana
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Fig. 16
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Agriculture potential map of study area

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A number of studies have been conducted by Krishan et al. [16,17,18,19,20] on the hydro-chemical characterization of groundwater of Mewat district. This study will be very much useful for policymakers, agriculturists, and water resources planners by identifying and demonstrating the potential areas for development and methodology used can be applied in the areas with similar topographical, and geomorphological features in the country as well as internationally the amount and its relation to precipitation amount.

For confirmation, the locations of the different wells were plotted (Fig. 17), from which the water level data were obtained to validate the groundwater potential zone map. Table 5 is shown below depicting the coordinates of test wells (the water level data of the year 2019 was used) and the class of groundwater potential zone identified using GIS techniques. As we can see from Fig. 17 and Table 5, the wells are located in places where there is a good groundwater potential. Hence we can use this map to identify suitable locations for finding a good yield of groundwater from Mewat district, Haryana.

Fig. 17
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Well location map with groundwater potential zones

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Table 5 Showing coordinates of wells and class of groundwater potential
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5 Conclusion

Remote sensing and geographic information systems (GIS) have proven to be critical tools in identifying groundwater and agriculture potential zones based on the integration of several thematic maps. The presence of groundwater is directly related to the geomorphology and slope of the land. The existence of lineaments influences the likelihood of groundwater occurrence in hard rock areas. Unconsolidated sediment layers and a relatively flat slope dominate the study region. The existence of lineaments influences the groundwater potential of residual hills, structural hills, and linear ridges with steep slopes, which are either very poor or poor to moderate. The alluvial plain, which is composed of sand, silt, and clay, has excellent potentiality because of its almost flat slope, as seen by the groundwater potential map. The pediment plain with a mild slope has modest potential because of its lower water holding capacity, but the aeolian plain with coarse to fine aeolian sand has moderate to very good potential. And in the case of agriculture potential, the majority of the regions in the southeast of Mewat have excellent potential (319 km2) for agriculture, as evidenced by the region’s virtually flat terrain, low land surface temperature, high soil moisture index, higher rainfall totals, as shown by the agricultural potential map. The majority of the Mewat area, as shown by the produced agriculture potential map, is located in the moderate to very good (865 km2) agriculture potential zone, while other sections are located in the poor to moderate (264 km2) agriculture potential zone. Areas with greater slopes, lower NDVI, lower SMI, and higher LST are classified as having very poor (2 km2) agriculture potential. The groundwater potential map and the agriculture potential map produced as a consequence provide a place to start more research. This study has demonstrated that groundwater and agriculture potential zones may be discovered using RS and GIS techniques as the sustainable use of groundwater is required to support long-term agricultural sustainability as well as socio-economic development in any region. Groundwater potential mapping has risen in recent years in response to the rising demand for water supplies. RS and geographic information systems (GIS) have shown to be a strong tool and a cost-effective way of estimating the groundwater potential and agriculture potential of a region. For exploration, effective planning, sustainable use, and management of groundwater resources, as well as to provide suggestions for future work and provide guidance for groundwater prospection, accurate groundwater delineation is required. Groundwater zones offer the thorough hydrogeology and geophysical investigations required for the setup and appropriate operation of wells, particularly for agricultural applications. According to the study, the integration of seven thematic maps for groundwater potential (geology, drainage density, geomorphology, soil, lineament density, slope, and land use/land cover) and the six thematic maps for agriculture potential (LST, SMI, NDVI, Slope, DEM, and Rainfall) provides local authorities, planners and farmers with first-hand knowledge of places ideal for groundwater exploration and efficient crop plantations.