Soil Moisture Index Estimation Using Land Surface Temperature Maps

Deepti Soni, Samina Yasmin, Aman Chandrakar, Anju Jangade, Dr. Ajay Kumar Garg,

doi:10.5281/zenodo.15735323

Research Paper | Open Access
Volume 02 | Issue 06 | Article Id IJSRT/250306084

Soil Moisture Index Estimation Using Land Surface Temperature Maps
Deepti Soni* Samina Yasmin Aman Chandrakar Anju Jangade Dr. Ajay Kumar Garg
Department of Civil Engineering, Government Engineering College, Raipur (C.G.)-492015

Abstract

Soil moisture plays a critical role in agricultural productivity, hydrological cycles, and environmental sustainability. Accurate and timely estimation of soil moisture is essential, particularly in agrarian regions vulnerable to climatic variability. This study aims to estimate the Soil Moisture Index (SMI) using Land Surface Temperature (LST) data derived from Landsat-8 imagery, with a focus on Dhamtari district, Chhattisgarh—a region heavily dependent on agriculture and monsoonal rainfall. The methodology leverages open-source platforms such as QGIS and Google Earth Engine (GEE) for data processing, visualization, and index calculation. Despite limitations such as the absence of ground-truth soil moisture data and the impact of cloud cover on some images, the study presents a cost-effective and scalable approach for regional soil moisture monitoring. Findings indicate a consistent pattern of moisture depletion during May, particularly severe in 2022 and 2023, indicating intensifying summer dryness. While October months initially showed substantial post-monsoon recovery, especially in 2022 and 2023, a declining trend was observed by 2024, suggesting growing inter-annual variability. These insights are vital for informing climate-resilient agricultural planning, early drought detection, and sustainable land-use policy in Dhamtari and comparable agro-ecological regions.

Keywords

LST, SMI, TVDI, NDVI, QGIS, OLI, TIRS, ISRO, USGS

Introduction

Understanding the spatial and temporal distribution of soil moisture is crucial for sustainable agricultural practices, hydrological modelling, and climate-resilient land management. With increasing pressure on water resources and changing land use dynamics, especially in agrarian districts like Dhamtari, there is a growing need for reliable, spatially continuous methods to monitor soil moisture variability. Traditional approaches often fall short in scale and frequency, prompting the adoption of remote sensing-based methods that leverage satellite data to estimate key environmental indicators. Among these indicators, the Soil Moisture Index (SMI) and Land Surface Temperature (LST), derived from remote sensing, offer significant insights into the dynamic relationship between soil moisture, land surface characteristics, and temperature patterns. The integration of these two indices enables comprehensive assessment of soil moisture variations across time and space. In recent years, the Temperature-Vegetation Dryness Index (TVDI) has also emerged as a powerful tool for evaluating the vegetation condition and its relationship with moisture availability. TVDI combines both temperature and vegetation data, making it highly effective in understanding vegetation stress and moisture depletion across different landscapes. By examining the TVDI, SMI, and LST together, we can obtain a multifaceted perspective of land surface dynamics, providing more accurate assessments of drought, soil health, and vegetation conditions. This report aims to analyze the temporal and seasonal variations of SMI, LST, and TVDI across Dhamtari district from 2021 to 2024, focusing on how these indices interact and influence land surface conditions. By integrating these indices, this study will explore key environmental patterns, correlations, and trends to enhance our understanding of soil moisture distribution, vegetation stress, and temperature variations, all of which play a critical role in shaping the region’s agricultural and environmental strategies.

2. Software and Tools Used

1. QGIS (v3.28): Used for NDVI, LST, and SMI computations, and map rendering
2. Google Earth Engine: Time-series visualization and data pre-processing
3. USGS Earth Explorer: Satellite imagery download
4. Microsoft Excel: Data integration and tabulation

3. METHODOLOGY

In order to meet the objectives, the following research methodology was adopted:

3.1 Study Design & Planning

Data Collection

Field surveys for spot soil moisture (2025)
Landsat-8 OLI/TIRS satellite imagery (2021–2024) for January, May, and October
Administrative boundary data (Bhuvan)
Land Use/Land Cover data (NRSC)
Population and census datasets
1. Software and tools used

3.2.1 QGIS (v3.28): Used for NDVI, LST, and SMI computations, and map rendering

3.2.2 Google Earth Engine: Time-series visualization and data pre-processing

3.2.3 USGS Earth Explorer: Satellite imagery download

3.2.4 Microsoft Excel: Data integration and tabulation

3.3 Soil Moisture Index (SMI) Estimation

3.4 Spatio-temporal Analysis

Seasonal comparisons: January, May, and October
Year-wise analysis: 2021–2024
Moisture classification into five distinct categories

3.5 Correlation & Statistical Analysis

SMI vs. LST
SMI vs. TVDI
SMI vs. NDVI
Pearson correlation coefficient analysis

3.6 Interpretation & Visualization

Map generation for LST, SMI, TVDI and NDVI
Identification of drought-prone and moisture-deficient regions

3.7 Findings and Discussion

Results and Discussion

This section presents an integrated interpretation of the observed patterns in Land Surface Temperature (LST), Soil Moisture Index (SMI), Normalized Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI) across spatial and temporal dimensions in Dhamtari district from 2021 to 2024. By analyzing these indices seasonally (January, May, and October), temporally (year-on-year), and through correlation, a comprehensive understanding of land surface dynamics, vegetation stress, and soil moisture fluctuations is derived. Findings are structured to reveal critical insights into climate stress responses, drought This chapter consolidates and interprets the analytical outcomes derived from the temporal, seasonal, and correlation analyses of Land Surface Temperature (LST), Soil Moisture Index (SMI), Temperature Vegetation Dryness Index (TVDI), and Normalized Difference Vegetation Index (NDVI) across Dhamtari district from 2021 to 2024. Drawing from spatial datasets and remote sensing indices analyzed for January, May, and October of each year, this section elaborates on the inter-seasonal variability, inter-annual fluctuations, and inter-variable relationships, offering insight into patterns of thermal stress, moisture availability, vegetation condition, and drought susceptibility. Tables and maps, referenced throughout, provide a visual and quantitative basis for understanding the spatio-temporal dynamics and how vegetation health (as indicated by NDVI) responds to changes in moisture and temperature regimes. Behavior, and their implications for sustainable land and water management.

5. Year-wise Correlation Results -

Table 5. 1 Pearson Correlation Coefficient between Variables

Year	Month	SMI–LST (r)	SMI–TVDI (r)	SMI-NDVI
2021	Jan	–0.62	–0.85	0.88
	May	–0.72	–0.89	0.79
	Oct	–0.60	–0.78	0.94
2022	Jan	–0.59	–0.81	0.86
	May	–0.77	–0.91	0.76
	Oct	–0.51	–0.76	0.95
2023	Jan	–0.64	–0.88	0.87
	May	–0.80	–0.92	0.78
	Oct	–0.53	–0.73	0.96
2024	Jan	–0.55	–0.83	0.89
	May	–0.74	–0.87	0.81
	Oct	–0.49	–0.69	0.97

CONCLUSION

mixed results, varying by year depending on monsoon performance. The comprehensive spatial-temporal assessment of Soil Moisture Index (SMI), Land Surface Temperature (LST), Temperature Vegetation Dryness Index (TVDI), and Normalized Difference Vegetation Index (NDVI) across Dhamtari district from 2021 to 2024 provides critical insights into the hydro-climatic and ecological dynamics of the region. The analysis, conducted across three key months (January, May, and October), reveals a recurring pattern of seasonal stress and partial recovery, with May emerging as the most hydro-thermally stressed period in all years.
LST reached its maximum values in May, with temperatures exceeding 42°C in 2023 and 2024, especially in the central-western zone (Map 1.6, Map 1.9). This thermal intensification was directly linked to the steep decline in SMI, particularly in May 2022 and 2023 when over 60% of the district fell under Class 1 and 2 (SMI < 0.4) indicating acute surface dryness (Table 2.5). January consistently exhibited higher SMI, reflecting cooler conditions and greater soil moisture retention, while October showed
TVDI values mirrored the trends in LST and SMI, with May showing peak dryness, where more than 2,000 sq.km of land in 2022 and 2023 registered TVDI > 0.8 (Map 2.12, Map 2.15). January values remained mostly below 0.4, indicating minimal drought stress during winter.

NDVI trends added a crucial ecological layer to the analysis. The lowest NDVI values (<0.2) were observed during May in all years, reflecting poor vegetation cover and high stress on crop and forest systems (Map 5.38, Map 5.41). January and October, by contrast, showed higher NDVI values in Class 3 and 4 (0.2–0.6), signifying partial to moderate greenness recovery (Table 5.10). NDVI not only helped spatially map vegetation condition but also responded effectively to changes in both LST and soil moisture availability.

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