Remote Sensing Based Soil Moisture Estimation Using In-situ Probes in Varanasi District, India

<p>Severely dry climate plays an important role in the occurrence of wildfires in Thailand. Soil water deficits increase dry conditions, resulting in more intense and longer burning wildfires. The temperature vegetation dryness index (TVDI) and the normalized difference drought index (NDDI) were used to estimate soil moisture during the dry season to explore its use for wildfire risk assessment. The results reveal that the normalized difference wet index (NDWI) and land surface temperature (LST) can be used for TVDI calculation. Scatter plots of both NDWI/LST and the normalized difference vegetation index (NDVI)/LST exhibit the triangular shape typical for the theoretical TVDI. However, the NDWI is more significantly correlated to LST than the NDVI. Linear regression analysis, carried out to extract the maximum and minimum LSTs (LST<sub>max</sub>, LST<sub>min</sub>), indicate that LST<sub>max </sub>andLST<sub>min</sub> delineated by the NDWI better fulfill the collinearity requirement than those defined by the NDVI. Accordingly, the NDWI-LST relationship is better suited to calculate the TVDI. This modified index, called TVDI<sub>NDWI-LST</sub>, was applied together with the NDDI to establish a regression model for soil moisture estimates. The soil moisture model fulfills statistical requirements by achieving 76.65% consistency with the actual soil moisture and estimated soil moisture generated by our model. The relationship between soil moisture estimated from our model and leaf fuel moisture indicates that soil moisture can be used as a complementary dataset to assess wildfire risk, because soil moisture and fuel moisture content (FMC) show the same or similar behavior under dry conditions. <strong></strong></p>

Temperature vegetation dryness index (TVDI) in a triangular or trapezoidal feature space can be calculated from the land surface temperature (LST) and normalized difference vegetation index (NDVI), and has been widely applied to regional drought monitoring. However, thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels. In cloudy areas, LST data include a large number of cloudy pixels, seriously degrading the spatial and temporal continuity of drought monitoring. In this paper, the Remotely Sensed Daily Land Surface Temperature Reconstruction model (RSDAST) is combined with the LST reconstructed (RLST) by the RSDAST and applied to drought monitoring in a cloudy area. The drought monitoring capability of the reconstructed temperature vegetation drought index (RTVDI) under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction. Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI (OTVDI) in 2011. In addition, RLST/NDVI scatter plots cover a wide range of values, with the fitted dry–wet boundaries more representative of real soil moisture conditions. Under continuously cloudy conditions, the OTVDI inverted from the original LST (OLST) loses its drought monitoring capability, whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area. The correlation between TVDI and soil moisture is stronger for RTVDI (R = −0.45) than that for OTVDI (R = −0.33). In terms of the spatial and temporal distributions, the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI. Hence, in continuously cloudy areas, RTVDI not only expands drought monitoring capability in time and space, but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.

Abstract. Soil moisture is one of key environmental variables that affect vegetation cover and energy exchange between the land surface and the atmosphere. Satellite remote sensing technology can provide information for monitoring large-scale soil moisture dynamics quickly. The temperature vegetation dryness index (TVDI) acts as an effective indicator of inferring soil moisture status which is calculated according to the empirical parameterization of composed of the land surface temperature (LST) and the normalized difference vegetation index (NDVI) characteristic space. In this paper, the MODIS TVDI was calculated based on MODIS LST product (MOD11A2, 1 km) and NDVI data (derived from MOD09A1, 500m). Meanwhile, LST and NDVI from Landsat8 OLI images were estimated to obtain Landsat-based TVDI. Then, a Kalman filter algorithm was used to simulate TVDI time series data with 30m resolution and a revisit period of 8 days combining TVDI derived from Landsat and MODIS data. We selected the west of the Songnen Plain, China as the test area and high quality cloudy-free images during growing season (April to October) of 2018 as the input data. The predicted TVDI time series data of medium resolution not only improved the temporal resolution to capture the changes at fine scale within a short period, but also made up for the deficiency of low spatial resolution MODIS data. The results show that it is feasible to generate medium or high resolution TVDI time series data by applying different remote sensing data by Kalman filtering algorithm.

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement.

This study proposes a modified vegetation-dependent temperature-vegetation dryness index (TVDI) model for analyzing regional drought disasters in the Beijing-Tianjin-Hebei Metropolitan Region (BTHMR) of China. First, MODIS monthly normalized difference vegetation index (NDVI), land surface temperature (LST) data and land use/cover data (Land cover type2) were pre-processed as a consistent big dataset. The land use/cover data were modified and integrated into six primary types. Then, these land types were used as the base data layer to calculate the TVDI by parameterizing the relationship between the MODIS NDVI and LST data. By emphasizing different types of land uses, this study was able to compare and analyze the differences of the TVDI indices between the entire study area (no consideration of the land types) and the six classified land uses. The soil moisture data were used to validate the modified TVDI values based on different land uses, which confirmed that the modified model more effectively reflected drought conditions. Finally, the aforementioned model was used to analyze the temporal and spatial variation of drought experienced by vegetation cover from 2000 to 2014. The results of the modified model were validated with the synchronized soil moisture and precipitation data. The case study clearly demonstrated that the modified TVDI model, which is based on different vegetation indexes, could better reflect the drought conditions of the study area.

This paper presents an approach to assess soil moisture status in Northern Basins using MODIS land surface temperature (Ts) and the normalized difference vegetation index (NDVI). The methodology is based on the interpretation of the land surface temperature- vegetation (Ts-NDVI) space. For the study site over the Mackenzie River Basin, a positive correlation was noted between surface temperature and NDVI. The dry edge and the wet edge of the Ts-NDVI space are function of the NDVI values. A satisfactory agreement was obtained between the temperature-vegetation dryness index (TVDI) and the precipitation and elevation patterns. The TVDI index was therefore able to describe the spatiotemporal variation of the soil moisture in a cold climate and humid area.

Soil moisture is important to understand the interaction between the land and the atmosphere, and has an influence on hydrological and agricultural processes such as drought and crop yield. In-situ measurements at stations have been used to monitor soil moisture. However, data measured in the field are point-based and difficult to represent spatial distribution of soil moisture. Remote sensing techniques using microwave sensors provide spatially continuous soil moisture. The spatial resolution of remotely sensed soil moisture based on typical passive microwave sensors is coarse (e.g., tens of kilometers), which is inadequate for local or regional scale studies. In this study, AMSR2 soil moisture was downscaled to 1km using MODIS products that are closely related to soil moisture through statistical ordinary least squares (OLS) and random forest (RF) machine learning approaches. RF (r2=0.96, rmse=0.06) outperformed OLS (r2=0.47, rmse=0.16) in modeling soil moisture possibly because RF is much flexible through randomization and adopts an ensemble approach. Both approaches identified T·V (i.e., multiplication between land surface temperature and normalized difference vegetation index) and evapotranspiration. AMSR2 soil moisture produced from the VUA-NASA algorithm appeared overestimated at high elevation areas because the characteristics of ground data for validation and correction used in the algorithm were different from those in our study area. In future study, AMSR2 soil moisture based on the JAXA algorithm will be evaluated with additional input variables including land cover, elevation and precipitation.

<p indent="0mm">Drought disasters happen frequently along the China–Pakistan Economic Corridor (CPEC), which harm the development and safety of the countries along it. It is therefore important to conduct drought monitoring along CPEC. While soil water content (SWC) is a fundamental indicator of drought prediction, temperature vegetation dryness index (TVDI) can predict drought by inverting surface soil water content. Based on a combination of MODIS normalized differential vegetation index (NDVI) and land surface temperature (LST) products and SRTM DEM data, the study used NDVI-LST spatial characteristics to extract the spatial edge of dryness and wetness along CPEC from 2000 – 2017, through which to obtain the monthly TVDI. The study area is from 41°25′24.49″N to 23°45′24.49″N and from 60°53′57.97″E to 79°52′27.97″E. The data is in GeoTiff format, with a spatial resolution of 1 km. We then compared the data with the precipitation and soil water content observed by meteorological stations. Results show that TVDI has a significantly negative correlation with Standardized Precipitation Index (SPI) and SWC . The dataset can be used to support drought monitoring and prediction along CPEC.

Droughts are common across various climates, typically caused by prolonged decreases in rainfall. Several factors contribute to drought, including the temperature, wind speed, and relative humidity and the timing, amount, and intensity of rainfall during the growing season. This study introduces the Soil Moisture and Evapotranspiration Revealed Drought Index (SERDI), a new index that combines soil moisture and evapotranspiration (calculated using the Penman–Monteith method) to enhance drought early warning systems. To validate the SERDI, we compared it with other established indices such as the Land Surface Temperature (LST), Vegetation Health Index (VHI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), using metrics like the R-squared (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), and p-value to assess the accuracy, data variability, and forecast conditions. The results showed a low RMSE and high R2 between the SERDI and the LST and VHI, indicating a strong correlation. However, weaker correlations were observed between the SERDI and NDVI/NDWI, as shown by the lower R2 and higher RMSE values in semi-arid areas. Regions across Iran, Iraq, Syria, Jordan, and Israel experienced mostly moderate to severe drought conditions, with a few areas in Iran and Syria showing normal conditions. The SERDI’s strong correlation with the LST and moderate correlation with the VHI can be attributed to the direct influence of the soil moisture and evapotranspiration on the surface temperature and vegetation health. On the other hand, the weaker correlation with the NDVI and NDWI is due to variability in the vegetation response, irrigation practices, and regional differences. This study concludes that the SERDI is an effective tool for the detection of drought based on soil moisture and evapotranspiration.

In arid and semi-arid regions, soil moisture and salinity are important elements to control regional ecology and climate, vegetation growth and land function. Soil moisture and salt content are more important in arid wetlands. The Ebinur Lake wetland is an important part of the ecological barrier of Junggar Basin in Xinjiang, China. The Ebinur Lake Basin is a representative area of the arid climate and ecological degradation in central Asia. It is of great significance to study the spatial distribution of soil moisture and salinity and its causes for land and wetland ecological restoration in the Ebinur Lake Basin. Based on the field measurement and Landsat 8 satellite data, a variety of remote sensing indexes related to soil moisture and salinity were tested and compared, and the prediction models of soil moisture and salinity were established, and the accuracy of the models was assessed. Among them, the salinity indexes D1 and D2 were the latest ones that we proposed according to the research area and data. The distribution maps of soil moisture and salinity in the Ebinur Lake Basin were retrieved from remote sensing data, and the correlation analysis between soil moisture and salinity was performed. Among several soil moisture and salinity prediction indexes, the normalized moisture index NDWI had the highest correlation with soil moisture, and the salinity index D2 had the highest correlation with soil salinity, reaching 0.600 and 0.637, respectively. The accuracy of the BP neural network model for estimating soil salinity was higher than the one of other models; R2 = 0.624, RMSE = 0.083 S/m. The effect of the cubic function prediction model for estimating soil moisture was also higher than that of the BP neural network, support vector machine and other models; R2 = 0.538, RMSE = 0.230. The regularity of soil moisture and salinity changes seemed to be consistent, the correlation degree was 0.817, and the synchronous change degree was higher. The soil salinity in the Ebinur Lake Basin was generally low in the surrounding area, high in the middle area, high in the lake area and low in the vegetation coverage area. The soil moisture in the Ebinur Lake Basin slightly decreased outward with the Ebinur Lake as the center and was higher in the west and lower in the east. However, the spatial distribution of soil moisture had a higher mutation rate and stronger heterogeneity than that of soil salinity.

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.

Combined with ground weather observation data from March 2005 to October, the MODIS vegetation index products was used to analysis the sensitivity of Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) under different coverage of surface. Together with the MODIS land surface temperature products to form the feature space of Ts/RVI, Ts/NDVI and Ts/EVI, from which obtaining the Temperature-Vegetation Dryness Index (TVDI). Based on TVDI, the 0-20 cm relative soil moisture is acquired. Results show that the average soil moisture retrieved from Ts/EVI is closer to in situ data. Average retrieved soil moisture relative to average verification soil moisture has a deviation of 2%-12%. However, the Ts/RVI has a better accuracy of retrieving soil moisture under vegetation coverage of higher than 0.6, while Ts/EVI performs better for inversing soil moisture under low vegetation coverage of lower than 0.4.

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

In Ethiopia's rainfed highlands, water use efficiency (WUE), defined as the ratio of net primary productivity (NPP) to evapotranspiration (ET), is essential for balancing carbon assimilation and water loss. However, no previous studies have quantified long-term WUE trends or examined their biophysical drivers at the sub-basin scale in the Gilgel Abay watershed. Moreover, earlier research in Ethiopia has largely focused on vegetation indices, without explicitly linking WUE to key biophysical variables at the watershed scale, despite 20–30% rainfall variability posing serious threats to hydropower generation and food security for nearly three million people in the Upper Blue Nile Basin. Although global WUE studies are extensive, watershed-specific WUE assessments in Ethiopia, particularly in the Gilgel Abay watershed, which contributes approximately 60% of Lake Tana’s inflow, remain scarce. This study explored the spatiotemporal variability of WUE from 2001 to 2024 and its biophysical drivers using MODIS and Sentinel-2 MSI imagery. Mann–Kendall trend tests and Sen’s slope estimators were used to detect trends, while pixel-wise Pearson correlations were applied to assess relationships between WUE and the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture index (SMI), and vegetation condition index (VCI). Results showed significant increases in ET (5.45 kg/m 2 /year) and NPP (10.11 gC/m 2 /year), but a non-significant upward trend in WUE (0.0005 gC/mm/year). Spatially, higher WUE, ET, and NPP were concentrated in the southern and central regions. WUE exhibited positive correlations with NPP, NDVI, SMI, and VCI, but negative correlations with ET and LST across most (≈75%) of the watershed. Land use/land cover (LULC) classification, with an overall accuracy of 94.4% and a Kappa coefficient of 0.93, indicated that cultivated land was dominant (72.39%), followed by shrubland (10.39%) and forest (3.48%). Forest areas exhibited the highest annual ET (596.80 kg/m 2 ), NPP (1055.90 gC/m 2 ), and WUE (1.78 gC/mm), whereas water bodies had the lowest values (ET: 456.40 kg/m 2 ; NPP: 625.80 gC/m 2 ; WUE: 1.29 gC/mm). These findings show that healthy vegetation and sufficient soil moisture together improve WUE, help ecosystems cope with high temperatures, and support restoration efforts and sustainable management of water and vegetation, ultimately enhancing the resilience of the Nile Basin.

Soil moisture is a key component of land surface parameterization. The triangle/trapezoid feature space can be used to monitor soil moisture effectively. This research aims to use enhanced vegetation index (EVI) as an alternative for the normalized difference vegetation index (NDVI) in estimation of temperature vegetation dryness index (TVDI) to improve its ability of retrieving soil moisture and to discuss the flexibility of EVI in retrieval of soil moisture. The result shows that LST-EVI has higher R2 in linear regression and more significant in validation at depth of 0-10cm than LST-NDVI. Influence caused by altitude for LST-EVI in monitoring soil moisture will be discussed in this study. In comparison with precipitation, LST-EVI and soil moisture shows a better mirror symmetry in dry season than in rainy season.