Spatio-Temporal Analysis of Vegetation Health and Soil Characteristics across Rajasthan using NDVI Anomalies

Abstract

Thisstudypresentsacomprehensivespatio-temporalanalysisofvegetationhealthandsoilpatternsacrossRajasthan,India, using NDVI (Normalized Difference Vegetation Index) anomalies derived from MODIS satellite data for the period 2020–2025. The researchleveragesGoogleEarthEngine(GEE)toprocessandvisualizeNDVItrends,generateanomalymaps,andcorrelatevegetation dynamics with soil properties and rainfall variability. District-level time series charts highlight spatial differences in vegetation re- sponse,whileSentinel-2imageryvalidatesregionalpatternswithfinerdetail.AGEE-baseddashboardfacilitatesinteractiveexploration of NDVI anomalies,vegetation-soil relationships,andseasonalvariationacrossall33 districts. The results reveal persistentvegetation stress in arid western districts and stronger recovery in eastern regions with clay-rich soils and higher rainfall. The integration of soil classification, land use, and climatic datasets supports improved understanding of ecological vulnerability in dryland systems. The study proposes scalable methods for environmental monitoring, with potential applications indrought assessment, land-useplanning, and agricultural decision-making in arid and semi-arid regions.

Introduction

This study analyzes vegetation health and dynamics across Rajasthan, India, using NDVI (Normalized Difference Vegetation Index) derived from MODIS satellite data over 2020–2025. Rajasthan’s ecological diversity—from arid deserts in the west to fertile agricultural zones in the east—makes it an ideal region for such analysis. The study integrates NDVI with soil, vegetation, rainfall, and land use data to understand spatial and temporal vegetation patterns and stress responses, especially under climatic extremes like drought.

Methodology:
NDVI was calculated from satellite reflectance data and anomalies (deviations from mean NDVI) were mapped district-wise using Google Earth Engine (GEE). The study also incorporated soil type and rainfall data to examine their correlation with vegetation stress. Sentinel-2 imagery was used for validation.

Key Findings:

• Eastern Rajasthan shows dense vegetation; western regions (e.g., Jaisalmer, Barmer) exhibit sparse, stressed vegetation with significant NDVI drops during drought and low monsoon years.

• District time series reveal vegetation recovery and stress cycles linked closely to rainfall patterns.

• Vegetation types vary by district—croplands dominate the east, sparse vegetation and sandy soils the west, and forests in hilly areas like Sirohi.

• Seasonal NDVI peaks align with monsoon rains, and irrigation reduces vegetation stress.

Dashboard:
An interactive GEE-based dashboard was developed for real-time geospatial exploration of NDVI, vegetation, and soil data, enabling policymakers and researchers to monitor drought vulnerability and vegetation health effectively.

Implications:
NDVI anomalies serve as early indicators of ecological stress and can support drought management, land use planning, and crop insurance decisions. The study proposes further enhancements with predictive modeling and integration of higher-resolution data for improved vegetation and drought forecasting.

Conclusion

ThisstudycomprehensivelydemonstratestheeffectivenessoftheNormalizedDifferenceVegetationIndex(NDVI)asa robust and scalable tool for assessing vegetation health, detecting drought-induced stress, and analyzing land-use dy- namics across ecologically diverse regions such as Rajasthan. By integrating NDVI anomalies with auxiliary datasets such as soil texture (from ISRIC SoilGrids) and rainfall variability (from CHIRPS and IMD records), this project suc- cessfully mapped patterns of ecological vulnerability, resilience, and land degradation over a six-year period (2020– 2025). Theanalysishighlightednotablespatialgradients —fromthesparselyvegetatedanddrought-pronedistrictsofwestern Rajasthan, like Barmer and Jaisalmer, to the greener, agriculturally dominant eastern regions like Jaipur and Udaipur. SeasonalNDVItimeseriesandmulti-yearanomalymappingofferedinsightsintotheeffectsofmonsoonvariability,soil moisture retention capacity, and land management practices. These findings are particularly critical for drought early warning systems, agro-ecological planning, and sustainable resource allocation in semi-arid and arid zones. A significant achievement of the project was the development of an interactive, district-wise dashboard built using Google Earth Engine (GEE). This dashboard empowers users—including policymakers, researchers, and land manag- ers—to dynamically explore NDVI anomalies, track vegetation trends, assess soil-vegetation relationships, and down- load custom CSV reports. The integration of real-time visualization capabilities with backend geospatial computation makes the dashboard a valuable decision-support tool for operational monitoring.Despiteitssuccess,thestudyacknowledgesseverallimitations.ThemoderatespatialresolutionofMODISimagery(250 meters)occasionallymasksfiner-scalelandcovervariability,especiallyinheterogeneousagriculturallandscapes.Cloud contamination, particularly during the monsoon months, poses challenges for generating seamless NDVI composites. Moreover, global soil datasets, while valuable, may not always capture localized anthropogenic impacts such as soil salinity increase, land degradation, or changing irrigation practices. Future improvements could involve the fusion of MODISdatawithhigher-resolutionSentinel-2NDVIproductsandtheincorporationofnear-real-timeground-truthdata for validation. Nevertheless,NDVIremainsanindispensabletoolinthefieldofremotesensingforenvironmentalmonitoring,precision agriculture,biodiversityconservation,andland-usemanagement.Itssimplicity,proveneffectiveness,andcompatibility with cloud computing platforms like GEE ensure its continued relevance in addressing 21st-century ecological and cli- matic challenges. TheresultsofthisresearchunderlinetheimmensepotentialofintegratingEarthobservationdatawithcloud-basedana- lyticsto enhanceresilience,ensuresustainableresource management,andfosterproactiveenvironmentalstewardshipat regional and local scales.

References

[1] Tucker,C.J.(1979).Redandphotographicinfraredlinearcombinationsformonitoring vegetation.RemoteSensingofEnvironment,8(2),127–150. [2] Huete, A., et al. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. [3] ESA WorldCover 10m 2020 v100. https://esa-worldcover.org [4] Hengl, T., et al. (2017). SoilGrids250m: Global gridded soil information. Geoderma, 324, 126–137. [5] CHIRPS Rainfall Dataset. Climate Hazards Centerhttps://www.chc.ucsb.edu/data/chirps [6] Roy, D.P., et al. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. [7] Jain, M., et al. (2016). The importance of satellite data in global agricultural monitoring. Nature Food, 2(4), 200–206. [8] Didan, K. (2015). MOD13Q1 MODIS/Terra Vegetation Indices. NASA. [9] ESA WorldCover 2020. European Space Agency. [10] Gorelick, N. et al. (2017). Google Earth Engine: Planetary-scale geospatial analysis. Remote Sensing of Environment. [11] FAO GAUL. Global Administrative Unit Layers. [12] ISRIC SoilGrids (2018). International Soil Reference and Information Centre. [13] Google Earth Engine Documentation. (2024). https://developers.google.com/earth-engine/ [14] ISRO Bhuvan Data Repository. (2024). https://bhuvan.nrsc.gov.in [15] Funk, C., Peterson,P.,Landsfeld,M.etal.(2015).CHIRPS:Anewprecipitationdatasetforclimatemonitoring.ScientificData. [16] Sentinel-2MissionGuide.CopernicusESA.https://sentinel.esa.int/web/sentinel/missions/sentinel-2

Copyright

Copyright © 2025 Shrijit Chatterjee , Subha Dey, Prabir Kumar Mahatha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.