Extraction of the soil moisture index and land surface temperature for Newport County in Wales, UK through analysing Landsat 8-OLI using GIS

The soil moisture is an important component in the plant’s growth that decreases with time. Soil moisture content relies on climate change, urban heat island (UHI), and population. The purpose of the investigation is based on the soil moisture changes that are with different prospective in which soil moisture index (SMI) with normalized difference vegetation index (NDVI), SMI with land surface temperature (LST), and SMI with LST and NDVI involved. Similarly, the satellite images of Landsat 8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) and Landsat 4-5 TM (Thematic Mapper) were based on examination and co-operate the RS/GIS techniques. All satellite data images were downloaded from the website of earthexplorer. The ERDAS IMAGINE 9.2, Microsoft Excel, and ArcGIS 10.3.1 software from diverse techniques were adopted like LST, SMI with LST, SMI with LST and NDVI, NDVI, SMI with NDVI, and LST with NDVI and all of the graphs were developed except LST and LST with NDVI. The study concludes that the SMI with LST decreased reasons of LST increased plus SMI with NDVI and SMI with LST and NDVI both increased in the review area, and all changes were examined from the images (1992 and 2019) comparisons. Furthermore, all of the changes graphically observed the high values of the no-change classes.

Preventive control of desert locusts is based on monitoring recession areas to detect outbreaks. Remote sensing has been increasingly used in the preventive control strategy. Soil moisture is a major ecological driver of desert locust populations but is still missing in the current imagery toolkit for preventive management. By means of statistical analyses, combining field observations of locust presence/absence and soil moisture estimates at 1 km resolution from a disaggregation algorithm, we assess the potential of soil moisture to help preventive management of desert locust. We observe that a soil moisture dynamics increase of above 0.09 cm3/cm3 for 20 days followed by a decrease of soil moisture may increase the chance to observe locusts 70 days later. We estimate the gains in early warning timing compared to using imagery from vegetation to be 3 weeks. We demonstrate that forecasting errors may be reduced by the combination of several types of indicators such as soil moisture and vegetation index in a common statistical model forecasting locust presence. Policy implications. Soil moisture estimates at 1 km resolution should be used to plan desert locust surveys in preventive management. When soil moisture increases in a dry area of potential habitat for the desert locust, field surveys should be conducted two months later to evaluate the need of further preventive actions. Remote sensing estimates of soil moisture could also be used for other applications of integrated pest management.

Soil moisture takes an important part involving climate, vegetation and drought. This paper explains how to calculate the soil moisture index and the role of soil moisture. The objective of this study is to assess the moisture content in soil and soil moisture mapping by using remote sensing data in the selected study area. We applied the remote sensing technique which relies on the use of the soil moisture index (SMI) which uses the data obtained from satellite sensors in its algorithm. The relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) are based on experimental parameterization for the soil moisture index. Multispectral satellite data (visible, red and near-infrared (NIR) and thermal infrared sensor (TIRS) bands) were utilized for assessment of LST and to make vegetation indices map. Geographic Information System (GIS) and image processing software were utilized to determine the LST and NDVI. NDVI and LST are considered as essential data to obtain SMI calculation. The statistical regression analysis of NDVI and LST were shown in standardized regression coefficient. NDVI values are within range −1 to 1 where negative values present loss of vegetation or contaminated vegetation, whereas positive values explain healthy and dense vegetation. LST values are the surface temperature in °C. SMI is categorized into classes from no drought to extreme drought to quantitatively assess drought. The final result is obtainable with the values range from 0 to 1, where values near 1 are the regions with a low amount of vegetation and surface temperature and present a higher level of soil moisture. The values near 0 are the areas with a high amount of vegetation and surface temperature and present the low level of soil moisture. The results indicate that this method can be efficiently applied to estimate soil moisture from multi-temporal Landsat images, which is valuable for monitoring agricultural drought and flood disaster assessment.

Land surface temperature (LST) and soil moisture are important factors in environmental hazard modeling. The main objective of this research is to derive the LST and a soil moisture index (SMI) from thermal satellite images. A split-window algorithm is applied to derive the spectral radiance and emissivity from two thermal infrared (TIR) bands of the Landsat 8 satellite in four consecutive years (2015–2018) to serve as input for the LST analysis. First, the normalized difference vegetation index (NDVI) is computed from which an emissivity index is calculated using an object-based threshold technique. This is followed by the calculation of the LST via a split-window algorithm. Subsequently, the SMI is modeled to reflect the relationship between the surface temperature and the vegetation cover. A spatial analysis investigates the relationship between the LST and SMI with known geological faults. The results indicate that the areas with low-temperature and high-moisture overlap with fault zones. The authors discuss to what degree fault zones can be detected or predicted based on LST and SMI.

Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index (SMI), which aims to capture the temporal variability of SM, irrespective of cloud cover and solar illumination, was developed by using the L-band SM active passive (SMAP) radiometer observations. The SMI was proposed on the basis of two key foundations: 1) vegetation and roughness have similar effects on "depolarization" of microwave emission, while SM enhances polarization differences and 2) vegetation and roughness generally impose positive effects on surface emissivity, while SM and emissivity are negatively correlated. Based on the two physical principles, it is possible to decouple the effects of SM and those of vegetation and surface roughness in a 2-D space independent of vegetation type and roughness condition. The proposed SMI was then validated by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements from five dense SM networks covering different vegetation and climatic conditions and also compared with SMAP passive and European space agency climate change initiative (ESA CCI) SM products at a coarse resolution of 36 km, and SMAP-enhanced passive and Japan Aerospace Exploration Agency (JAXA) advanced microwave scanning radiometer (AMSR2) SM products at a medium resolution of 9 km. The results show that the new SMI is able to well reproduce the temporal dynamic of SM with a favorable averaged correlation coefficient value of 0.87 and 0.84 at 36 and 9 km, respectively, higher than that of SMAP passive (0.80), SMAP-enhanced passive (0.77), ESA CCI (0.69), and JAXA AMSR2 (0.53). After removing the systematic differences between satellite and site-specific SM data by using the cumulative distribution function (CDF) matching technique, the SMI can achieve an average root mean squared error (RMSE) of 0.031 and 0.036 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> at 36 and 9 km during the validation period, respectively, lower than that of the satellite SM products. In addition to surface temperature, the SMI does not need any further information from other sensors [e.g., the optical normalized difference vegetation index (NDVI) or leaf area index (LAI) data] to guarantee an all-weather monitoring. Therefore, it has great potential to estimate SM variability on a global scale.

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.

Soil moisture helps to determine the crop growth, disaster management, climatology and ecology. Soil moisture is highly influenced by vegetation cover. The current study was performed in Varanasi district of Uttar Pradesh on March 29, 2023, to estimate soil moisture using Landsat 8 data and biophysical parameters. This study also assessed the relationship between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) and their response to soil moisture. Additionally, it coupled NDVI-LST feature space-derived dry and wet edge best fit coefficient parameters with the Temperature Vegetation Dryness Index (TVDI) and Soil Moisture Index (SMI) equations to produce a spatial distribution of soil moisture availability. The LST and NDVI results help to extract Soil Surface Moisture (SSM) more effectively using an optical remote sensing approach. The soil moisture inversion model (TVDI) and SMI algorithm provide a better representation of the spatial distribution of soil moisture in Varanasi district. A linear and strong correlation exists between in-situ soil moisture data and TVDI (R2 = 0.6812), as well as a positive relationship between in-situ soil moisture data and SMI (R2 = 0.6848). It was found that LST and NDVI helped to generate the TVDI and SMI equations using the triangle model, and both equations demonstrated a strong correlation with in-situ data (R2 of TVDI = 0.6812, R2 of SMI = 0.6848).

O estudo da umidade do solo é fundamental não só para a determinação da resiliência de ecossistemas e sua recuperação, mas também na modelagem da relação água-vegetação-atmosfera. Na aquisição dessas informações o sensoriamento remoto perfaz uma ferramenta importante e de potencial adequado para monitoramento e mapeamento. Visando à espacialização de índices relacionados à umidade, vários métodos têm sido propostos, embora sua aplicação ainda seja limitada. Neste trabalho se aplicou o modelo de índice de umidade do solo (IUS) cujos objetivos foram: espacializar o IUS, estabelecer graus de desertificação, delimitar a área em processo de desertificação e verificar possíveis relações do IUS com parâmetros de água no solo. Na aplicação deste modelo se utilizaram, como dados de entrada, o NDVI (índice de vegetação da diferença normalizada) e a LST (temperatura da superfície) e se observou que o IUS representado pela média dos valores desses índices pode ser empregado na determinação do grau de degradação da superfície e para gerar classificação legendada, discriminando vários níveis de degradação ambiental. Constatou-se também que não houve relação direta do IUS com parâmetros físicos de retenção de umidade do solo. Desta forma, o sensoriamento remoto mostrou ser uma ferramenta significativa na avaliação de índices de umidade do solo em áreas degradadas tal como para delinear a dinâmica de borda em núcleo de desertificação.

Purpose This study aims to investigate the impact of wildfire on soil moisture, vegetation health and surface thermal conditions in a forested area east of Parnitha, Greece. It evaluates the ecological changes caused by the fire by analyzing key remote sensing indices. These include the normalized difference vegetation index (NDVI), soil moisture index (SMI) and the derived land surface temperature (LST), all of which support forest recovery efforts. Design/methodology/approach A quantitative approach was employed using open-source Landsat 8 collection 2 level 2 science product (L2SP) data from 2020 (pre-fire) and 2022 (post-fire), provided by the United States Geological Survey. The analysis focuses on a wildfire that started in 2021, leveraging the inverse relationship between land surface temperature and normalized difference vegetation index to calculate soil moisture index. Data preprocessing, including scaling and normalization, was conducted in ArcGIS Pro. Subsequently, LST equations for SMI estimation were derived using linear regression in MATLAB. To evaluate the reliability of the results, statistical analyses were performed. These included the coefficient of determination (R2), distribution analysis via histograms and calculation of the mean and standard deviation (Std). A correlation matrix was also used to assess variable interrelationships and temporal changes. Findings The results reveal substantial ecological changes post-fire: a marked increase in surface temperature, reduced soil moisture levels and significant degradation of vegetation health. Specifically, SMI values show a shift toward drier conditions, NDVI indicates a decline in vegetation cover and density and LST exhibits increased mean and variability. Originality/value This study employs a novel integration using remote sensing indices and the L2SP dataset, which provides preprocessed surface reflectance and LST products. This approach reduces computational effort while maintaining analytical effectiveness. It delivers more accurate and efficient insights to support sustainable forest management and reforestation strategies in fire-prone environments.

The Czech Republic has experienced collectivization, transforming individual land ownership into state ownership through agricultural land consolidation after World War II. Land consolidation has led to the expansion of agricultural soil blocks as well as the disappearance of vital landscape features such as shrubs, hedges, lone trees, and wetlands, all of which serve a variety of ecological functions. The disappearance of the landscape elements causes some important problems, such as soil degradation and erosion. The Czech Ministry of Agriculture has enforced a regulation that was developed by the European Union to improve agriculture by enforcing cross-compliance requirements, which encompass the observance of Good Agricultural and Environmental Condition (GAEC) standards. The regulation disincentivizes single-crop cultivation on areas larger than 30 hectares since 2020 to reduce the negative effects of land consolidations on agriculture and soil quality. In this context, the study examines the relationship between land surface temperature (LST) and moisture of agricultural plants and soil blocks in the South Bohemia, Czech Republic, using Landsat-8 remote sensing data from 2018 to 2021. The mono-window algorithm (MWA) was used to obtain LST, and the precision of Landsat LST data was evaluated using MODIS daily LST data. The NRMSE ≤ 0.15 and RMSE ≤ 3.5 C were found with a strong positive relationship r ≥ 0.65 between datasets. Additionally, the density of vegetation and the moisture index of soil and plants were calculated using spectral remote sensing indices such as the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), and soil moisture index (SMI). The statistical relationships among those spectral indices with LST were examined. The analyzes showed that the implementation of GAEC 7d improved the relationship between NDVI and NDMI, with the correlation increasing from r = 0.80 in 2019 to 0.92 in 2021. Meanwhile, the inverse relationship between NDMI and LST changed from − 0.71 to -0.67. These changes in correlation suggest that plant water retention in the region increased following the regulation, and the soil structure may have also improved.

Soil moisture (SM) is a key factor in climate, hydrology, and agronomy. Its assessment is therefore important and constitutes an alert parameter for desertification. It impacts the heat and mass transfers between soil and atmosphere. However, information access in the regional or global scale is very complicated which usually leads to a rough estimate, or completely disregard of this parameter. Today, remote sensing offers a solution to this problem by providing access to this information through the satellite images. The use of geospatial data to generate adequate information on droughts, we applied the remote sensing method based on the use of the Soil Moisture Index (SMI), which in its algorithm uses the data obtained from satellite sensors. As reported by Hunt et al., The index is based on actual water content (), water capacity and wilting point. The multispectral satellite images from the visible (red band) and infrared (near-infrared and thermal band) bands are essential for the calculation of the index, which is why we used Landsat 8 OLI/TIRS in this context, then SMOS disaggregated data is chosen to be compared with SMI and MI (Moisture Index) used in this work. On the other side, drought monitoring in Morocco faces complexity in the evidence that it depends on a lot of parameters including the SM. Thus, the aim of this paper is firstly to analyze the spatiotemporal variation of SM from SMOS in Casablanca-Settat region of Morocco site during five successive years 2013 to 2017. For that, the first temporal SM values mapping of the study area is established for each year of 2013 to 2017. Next, for each year we will compare SM from SMOS with SMI by drawing the graph line of SM variability in this period and generating differences maps. Secondly, we will evaluate the effect and the impact of Soil Moisture Index (SMI) in Drought monitoring by calculation of Land Surface Temperature (LST) from Landsat 8 OLI/TIRS, and define which indexes are more indicated for assessing soil conditions.

Malaria is a life-threatening disease influenced by environmental factors such as temperature, vegetation, water availability, altitude, and rainfall, which shape mosquito breeding and transmission. This study aimed to assess environmental factors influencing mosquito breeding and malaria transmission using geospatial techniques in the Wabi Shebele sub-basin of Ethiopia. Seven key environmental factors: Land use land cover (LULC) types, land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), elevation, and rainfall were employed to identify factors that influence malaria transmission. The results show that areas with high Land surface tempature (LST), particularly bare land (33.66°C), grassland (31.76°C), and agricultural land (31.42°C) provide favorable conditions for malaria vector breeding. Agricultural land, comprising 24.78%, enhances malaria risk by offering ideal breeding sites. Conversely, forested areas with lower LST (26.98°C) and areas with high NDVI and low NDWI (highland zones) were associated with low malaria risk due to cooler microclimates and limited water availability. The soil moisture index clearly shows that lower elevations are associated with higher soil moisture due to proximity to water bodies, which facilitates malaria vector breeding. Agricultural land, high LST, low elevation, high soil moisture, and moderate to high rainfall are the primary environmental factors facilitating malaria distribution. A malaria control approach in the study area should focus on managing environmental factors, particularly in high-risk areas like agricultural lands and low-elevation zones, while promoting sustainable land use practices to reduce mosquito breeding sites. This finding underscore the utility of geospatial analysis in guiding malaria control, particularly in identifying potential mosquito breeding sites and environmental risk zones for spatially targeted interventions.

Soil moisture is essential in monitoring agricultural lands, particularly in Jember, East Java which serves as one of Indonesian rice-producing regions. Scarcity of information related to the environment crucial to rice planting is evident. This includes detailed growth phase and soil moisture, where their estimation could be done through exploiting remote sensing data. This study aims to estimate soil moisture condition on paddy fields in selected study areas by applying Landsat 8 OLI/TIRS data, acquired in 2021. Thermal band of Landsat 8 was utilized to derive the Soil Moisture Index (SMI). This study also investigated the variation of vegetation index (studied using NDVI) and land surface temperature (LST) as parameters related to soil moisture conditions. Paddy fields in the study area were dominated by moderate soil moisture levels, with average SMI of 0.47, NDVI=0.45, and about 22°C temperature. The SMI ranged from 0.21 to 0.65. The low SMI values indicate low vegetation density and high surface temperature in paddy lands. This research suggested that SMI from Landsat 8 could serve as an efficient approach in monitoring soil moisture condition and understanding its correlation to surface temperature and vegetation condition in agricultural areas, particularly paddy fields.

This paper presents the potential for soil moisture (SM) retrieval using Sentinel-1 C-band Synthetic Aperture Radar (SAR) data acquired in Interferometric Wide Swath (IW) mode along with Land Surface Temperature (LST) estimated from analysis of LANDSAT-8 digital thermal data. In this study Sentinel-1 data acquired on 27 February 2020 was downloaded from Copernicus website and LANDSAT-8 OLI data acquired on 24 February 2020 from the website https://earthexplorer.usgs.gov/.The soil samples were collected from 70 test fields in different villages of three talukas for estimating soil moisture content using the gravimetric method. The Sentinel-1 SAR microwave data was analysed using open source tools of Sentinel Application Platform (SNAP) software for estimation of backscattering coefficient. Land surface temperature estimated using Landsat-8 thermal data. The Landsat-8, Thermal infrared sensor Band-10 data and operational land imager Band-4 and Band-5 data were used in estimating LST. The Soil Moisture Index (SMI) for all field test sites was computed using the LST values. The regression analysis using σ0VV and σ0VH polarization with soil moisture indicated that σ0VV polarization was more sensitive to soil moisture content as compared to σ0VH polarization. The multiple regression analysis using field measured soil moisture (MS %) as dependent variable, and σ0VV and SMI as independent variable was carried which resulted in the coefficient of determination (R2) of 0.788, 0.777 and 0.778 for Godhra, Goghamba and Kalol talukas, respectively. These linear regression equations were used to compute the predicted soil moisture in three talukas. The maps of spatial distribution of soil moisture in three talukas were generated using the respective regression equations of three talukas.

Abstract. Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.