Spatio-temporal assessment of land properties on soil moisture and vegetation using resourcesat-2 awifs sensor in hudgi sub watershed, Karnataka, India

Introduction

Availability of optimum soil moisture at different and important growth stages of a crop or any vegetation is crucial among all other requirement to complete its life cycle (Rajesh Kumar Soothar et. al. in 2021 & Aakansha Chadha et. al. in 2019) [31,32] . Role of other essentials like soil nutrients will effective under sufficient soil moisture status which ultimately decides qualitative and qualitative parameter of crop health and productivity (Pauline Welikhe et. al. in 2017) [20] . Soil and land characters like soil texture, structure, infiltration rate, maximum water holding capacity, drainage, soil depth, slope etc, have direct positive correlation with optimum soil moisture content and health of vegetation (V.K. Dadhwal et. al. in 2015, D. S. Kamat et. al. in 2015) [7] . Assessment and monitoring of soil moisture at different temporal stage will be an important indicator of potential of soil and land of given area (Mohamed A.E. Abdel [2,3,6] .

No doubt, application of geo statistical tools like NDMI and NDVI will help in drawing inference on status of moisture and vegetation for a given area but these important indicators are highly dynamic and influenced by several factors bellow and above the ground (Josef Lastovicka et. al. in 2020) [1] . A comparative study of these influential factors on NDMI and NDVI will enlighten the concept towards better understanding and application of the outcome effectively in management of natural calamities like drought, flood and degradation of natural resources. Surface and subsurface features of soil and land were characterized by conducting land resource inventory (LRI) through detailed soil survey (Jaya N. Surya et. al. in 2018) [4] Properties such as soil texture, depth, severity of erosion status in Hudgi sub watershed in Bidar district of Karnataka state, were assessed for their influence on NDMI and NDVI under normal and deficit rain fall situations to find out most influential factor among properties of soil on these geo statistical indicators (Alberto C. de C. Bernardi et. al

. in 2017)

Study area

Hudgi sub watershed encompassing seven micro watersheds in Humnabad Taluka of Bidar District, Karnataka, India and is located in between 17 ° 44' 0.388" North latitude and 77 ° 09 ' 33.575" East longitude covering the total geographical area of 5830.26 ha. The average annual rainfall is 780mm and temperature with daily mean temperature of 27.3 ℃ make the study area favorable for many crops during normal distribution. The topology of the study area is slightly undulating to plain terrain having maximum elevation of 684 MSL and minimum elevation of 640 MSL. Ten major soil series are identified in the study area indicate wide range in some soil and land characters like soil texture, depth, erosion, susceptibility, etc developed on different physiographic position on laterite constituting 79% of TGA. Character wise soil of the area texturally vary from sandy loan to clay, depth wise belong to very shallow to very deep, slope range from gently to very gently sloping, and with gravel cover percentage ranging from non-gravelly to very gravelly. Remaining 21% of lands are covered under waterbody, habitation, forestry and rockout crop. Major crops grown were Soybean, Red gram, Green gram, Black gram, Bengal gram, Jowar, sesamum, sugarcane, wheat and safflower. [25] . Major application of this sensor is agricultural crop discrimination and monitoring, yield estimate or crop acreage, precision farming, forest mapping, water resource rural development and so on (Meisam Amani, BahramSalehi et. al. 2017) [30] . For present study data used was of two years that is 2015 and 2017 for three different crop seasons which are Kharif (June to September), Rabi (October to March) and summer season (April and May) as to have a comparative studies in rainfall deficit year (2015) and normal rainfall year (2017). To obtain physical parameters of soil, Land Resource Inventory (LRI) field visit were carried out on along with grid points provided on cadastral map and base map. Cadastral map and base map were provided by KSRSAC along with its Shapefile so that field observations are inserted on them for preparation of maps such as Soil Depth map, Surface Soil Texture Map, Soil Gravelliness, Soil Slope, and Soil Erosion. Using all these physical properties of soil, Soil Phase map was generated based on its Taxonomy. Soil Phases are unit of soil that are designed according to the purpose of survey which are carried to recognize name of soil and landscape properties that are used in criteria of soil taxonomy as well as soil phase are used to recognize and name at relatively higher categorical level with soil properties that are used as differentiate with lower categorical level. The soil Phases recognized along with Soil series and all other physical properties that are included in this study are elaborated in below listed tables. [4,5] . Polygon of soil mapping units drawn on base map during detailed soil survey were confirmed with the help of characterization of soil through interpretation surface and profile soil sample lab data and spatial data from Carto Sat-I and LISS-IV at the same Scale.

Various layers such as soil texture, depth, slope, erosion, gravel percentage of study area were generated on GIS Platform. [21]

Zonal statistics or correlation

Results and discussion

The study area Hudgi Sub watershed was covered under Land resource inventory for the ground data with respect to surface and sub surface characters like soil depth, texture, surface gravelliness, slope and erosion status. Results of interpretation of all the above said parameters are explained as below. [17] Soil gravelliness It is confirmed from the results that the surface gravelliness of the study area belongs to non-gravelly to very gravelly classes.

An area of 1478 ha (31.34% of TGA) belong to non-gravelly class. Remaining 1393 ha (29.54 % TGA) is covered with gravelly class, and1416 ha (30.02 % of TGA) belongs to very gravelly class.

Slope

The slope classes of the study area ranged from nearly level in the lowland to very gently sloping uplands in the sub watershed. An area of 754 ha (15.99%) is covered with gently sloping classes whereas an area of 3533 ha (74.92%) belong to very gently sloping classes.

Soil erosion

It is found that the present study area is suffering from moderate to severe soil erosion hazard. Out of total area surveyed 3057 ha (64.83%) is affected with moderate erosion hazard and an area of 1230 ha (28.08%) was found suffering from severe erosion hazard. [16,18] . It is found that among all the factors positive influence of texture was observed as all the higher values of NDMI were recorded under the influence of soil texture as compare to other factors. (J. W. [11,13] ) [1,12] . Whereas during 2017 that is normal rainfall year the correlated NDVI values ranged between 0.490 to 0.500 in Kharif season, 0.467 to 0.488 in rabi season and during summer the correlated values ranged from 0.254 to 0.260. [11,14] . Within soil series several mapping units have been mapped as imported indicator of status of phases of soil series. Basic soil forming processes like addition and losses will decide status of mapping unit of soil series. As different phases of soil series will decide the capacities of soil, major soil series and their respective mapping units were used for NDMI and NDVI under different seasons and rainfall conditions.

Discussion

Similar results were observed by Josef Lastovicka et.al. In June 2020 [1] where under NDVI values reflected the availability of sufficient soil moisture for optimum growth of crop plants.

During NDVI & NDMI when water is the limiting factor, more available water the plants grow better resulting in higher values within season NDVI level & higher of season crop and root biomass at the north west side compare to south east side of field.

Lower value of NDVI where recorded in sandy soil texture in their study that is "Botswana" area by (S. E. Nicholson et.al. in 1994) [24] where they had carried linear relationship between NDVI and rainfall. Here the author reported that strong correlation between NDVI values and soil texture, where they recorded higher NDVI values in fine texture soils and lower NDVI values in sand rich coarse texture soils belonging to arenosols.

Conclusion

In our study area, we evaluated the changes in NDMI and NDVI The analysis revealed that rainfall variability had a significant influence on both moisture availability and vegetation growth across all crop seasons. Comparatively higher NDMI and NDVI values were observed during the normal rainfall year, indicating improved soil moisture conditions and healthier vegetation, whereas reduced values during the drought year reflected moisture stress and poor crop vigor. Among the evaluated land characteristics, soil texture emerged as the most influential factor, with clay-rich and finer-textured soils consistently exhibiting greater moisture retention and higher vegetation indices. In contrast, areas affected by severe soil erosion, coarse surface conditions, and higher gravelliness showed relatively lower NDMI and NDVI values because of their limited waterholding capacity.

The study also confirmed that soil series and mapping units derived through detailed LRI surveys effectively explained the spatial variability in moisture distribution and vegetation performance. Variations among soil mapping units highlighted the importance of local soil characteristics in controlling crop response under different rainfall conditions. The combined interpretation of remotely sensed indices and field-based soil information therefore provides a more comprehensive understanding of watershed behaviour than either approach alone. Overall, the study establishes that integrating remote sensing, GIS, and detailed soil resource information is an efficient approach for monitoring watershed health, identifying moisturestressed areas, and supporting site-specific land and water management. The findings can assist planners and decisionmakers in developing sustainable agricultural practices, prioritizing soil and water conservation measures, improving drought preparedness, and enhancing watershed management in semi-arid regions. Future studies may further improve the accuracy of soil moisture assessment by incorporating higherresolution satellite data, longer temporal datasets, and fieldbased soil moisture observations for validation. The present study assessed the spatial and temporal variations in soil moisture and vegetation dynamics in the Hudgi Subwatershed, Humnabad Taluk, Bidar District, Karnataka, using NDMI and NDVI derived from ResourceSat-2 AWiFS imagery for two contrasting rainfall years, namely 2015 (rainfall-deficit year) and 2017 (normal rainfall year). The analysis was carried out for the three major agricultural seasons-Kharif, Rabi, and summer. The satellite-derived indices were integrated with Land Resource Inventory (LRI) data, and their relationships with soil physical properties were evaluated using GIS-based spatial analysis in ArcGIS. The results demonstrated that soil texture exerted the strongest influence on moisture availability and vegetation health. The highest NDMI values were observed during the Kharif season of the normal rainfall year (2017), particularly in areas with favourable soil texture, whereas the lowest values occurred during the summer season of the drought year (2015) in locations affected by severe soil erosion. These findings indicate that both rainfall distribution and inherent soil characteristics play a crucial role in controlling seasonal soil moisture conditions. Analysis at the soil series and mapping unit level revealed considerable spatial variability in NDMI and NDVI. The Overall, the study confirms that the integration of remote sensing indices with detailed soil resource information provides an effective approach for assessing seasonal soil moisture dynamics and vegetation conditions. The findings demonstrate the importance of soil texture, erosion status, and rainfall variability in influencing moisture availability and crop performance. This integrated methodology can support watershed planning, precision agriculture, drought monitoring, and sustainable land and water resource management in semiarid regions.

References

1. Lastovicka J, et al. Sentinel-2 data in an evaluation of the impact of the disturbances on forest vegetation. Remote Sens. 2020;12:1914. DOI:10.3390/rs12121914.
2. Abdel Rahman MAE, Natarajan A, Hegde R. Assessment of land suitability and capability by integrating remote sensing and GIS for agriculture in Chamarajanagar district, Karnataka, India. Egypt J Remote Sens Space Sci. 2016;19:125-141.
3. Niu CY, et al. Analysis of soil moisture condition under different land uses in the arid region of Horqin sandy land, northern China. Solid Earth. 2015;6:1157-1167.
4. Surya JN, et al. Land resource inventory and characterization for planning soil conservation measures in Aravalli hill slopes. Int J Curr Microbiol Appl Sci. 2018;7(4). DOI:10.20546/ijcmas.2018.704.080.
5. Singh SK, et al. Land resource inventory of India for development of sustainable agricultural land use plans using geospatial techniques: avenues for investment. 2016. Available from: https://www.researchgate.net/publication/346441651
6. Liu Q, et al. Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images. Int J Remote Sens. 2015. DOI:10.1080/01431161.2014.995274.
7. Kamat DS, et al. Assessment of water-stress effects on crops. Int J Remote Sens. 2015. DOI:10.1080/01431168508948480.
8. Lin ML, et al. Spatial filtering analysis for quick drought assessment using MODIS images to detect drought affected areas. In: IEEE International Geoscience and Remote Sensing Symposium; 2011. DOI:10.1109/IGARSS.2011.6049243.
9. Metzler JW, Sader SA. Model development and comparison to predict softwood and hardwood percent cover using high and medium spatial resolution imagery. Int J Remote Sens. 2005;26(17):3749-3761. DOI:10.1080/01431160500104152.
10. Aliero MM, et al. Assessing soil physical properties variability and their impact on vegetation using geospatial tools in Kebbi State, Nigeria. IOP Conf Ser Earth Environ Sci. 2018;169:012111. DOI:10.1088/17551315/169/1/012111.
11. Firoozy Nejad M, et al. Assessment of LST and NDMI indices using MODIS and Landsat images in Karun riparian forest. J For Sci. 2019. DOI:10.17221/121/2018-JFS.
12. Ashraf M, et al. A comparison of change detection analyses using different band algebras for Baraila Wetland with NASA's multi-temporal Landsat dataset. J Geogr Inf Syst. 2015;7:1-19. DOI:10.4236/jgis.2015.71001.
13. Duran C, et al. Drought and vegetation analysis in Tarsus River Basin (Southern Turkey) using GIS and remote sensing data. Int J Hum Sci. 2015;12(2):1853-1866. DOI:10.14687/ijhs.v12i2.3370.
14. Deckers J, et al. Using NDVI and soil quality analysis to assess influence of agronomic management on within-plot spatial variability and factors limiting production. Plant Soil. 2009;317:41-59. DOI:10.1007/s11104-008-9787-x.
15. Öztürk HS, et al. Analysis of the spatial variability of soil properties in different physiographic units. Soil Sci Soc Am J. 2018. DOI:10.2136/sssaj2004.0154.
16. Gharechelou S, et al. Soil moisture mapping in an arid area using a land unit area (LUA) sampling approach and geostatistical interpolation techniques. ISPRS Int J Geo-Inf. 2016;5(3):35. DOI:10.3390/ijgi5030035.
17. Simelane DO. Influence of soil texture, moisture, and surface cracks on the performance of a root-feeding flea beetle, Longitarsus bethae (Coleoptera: Chrysomelidae), a biological control agent for Lantana camara (Verbenaceae). Environ Entomol. 2007;36. DOI:10.1603/0046225X(2007)36[512:IOSTMA]2.0.CO;2.
18. Lin TS, et al. Impact of soil moisture initialization and soil texture on simulated land-atmosphere interaction in Taiwan. J Hydrometeorol. 2016. DOI:10.1175/JHM-D-15-0024.1.
19. Welikhe P, et al. Estimation of soil moisture percentage using LANDSAT-based moisture stress index. J Remote Sens GIS. 2017. DOI:10.4172/2469-4134.1000200.
20. Shen L, et al. Spatiotemporally characterizing urban temperatures based on remote sensing and GIS analysis: a case study in Saskatoon, Canada. Open Geosci. 2015. DOI:10.1515/geo-2015-0005.
21. Kuzevic S, et al. Land cover and vegetation coverage changes in the mining area: a case study from Slovakia. Sustainability. 2022;14:1180. DOI:10.3390/su14031180.
22. Nicholson SE, Farrar TJ. The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall. Remote Sens Environ. 1994;50:107-120.
23. Lobell DB, Asner GP. Moisture effects on soil reflectance. Soil Sci Soc Am J. 2002;66(3):722-727. DOI:10.2136/sssaj2002.0722.
24. Dadhwal VK, et al. Crop growth and productivity monitoring and simulation using remote sensing and GIS. In: Satellite Remote Sensing and GIS Applications in Agricultural Meteorology. 2005. p.263-289.
25. Mobasheri MR, Amani M. Soil moisture content assessment based on Landsat 8 red, near-infrared, and thermal channels. J Appl Remote Sens. 2016;10(2):026011. DOI:10.1117/1.JRS.10.026011.
26. Jamali S, Seaquist JW, Ardö J, Eklundh L. Investigating temporal relationships between rainfall, soil moisture and MODIS-derived NDVI and EVI for six sites in Africa. Savanna. 2011;21:547-553.
27. Bernardi ACC, et al. Spatial variability of vegetation index and soil properties in an integrated crop-livestock system. 2017;21(8):513-518.
28. Amani M, Salehi B, et al. Temperature-vegetation-soil moisture dryness index (TVMDI). Remote Sens Environ. 2017. DOI:10.1016/j.rse.2017.05.026.
29. Soothar RK, et al. Effect of different soil moisture regimes on plant growth and water use efficiency of sunflower: experimental study and modeling. Bull Natl Res Cent. 2021;45:121.
30. Chadha A, et al. Influence of soil moisture regimes on growth, photosynthetic capacity, leaf biochemistry and reproductive capabilities of the invasive agronomic weed Lactuca serriola. PLoS One. 2019;14(6):e0218191. DOI:10.1371/journal.pone.0218191.