Land Surface Temperature Normalized Difference Vegetation Index Research Articles

A regressive analysis of the main environmental risk factors of human echinococcosis in 370 counties in China.

Echinococcosis is a natural focal, highly prevalent disease in China. Factors influencing the spread of echinococcosis are not only related to personal exposure but also closely related to the environment itself. The purpose of this study was to explore the influence of environmental factors on the prevalence of human echinococcosis and to provide a reference for prevention and control of echinococcosis in the future. Data were collected from 370 endemic counties in China in 2018. By downloading Modis, DEM and other remote-sensing images in 2018. Data on environmental factors, i.e., elevation, land surface temperature (LST) and normalized difference vegetation index (NDVI) were collected. Rank correlation analysis was conducted between each environmental factor and the prevalence of echinococcosis at the county level. Negative binomial regression was used to analyze the impact of environmental factors on the prevalence of human echinococcosis at the county level. According to rank correlation analysis, the prevalence of human echinococcosis in each county was positively correlated with elevation, negatively correlated with LST, and negatively correlated with NDVI in May, June and July. Negative binomial regression showed that the prevalence of human echinococcosis was negatively correlated with annual LST and summer NDVI, and positively correlated with average elevation and dog infection rate. The prevalence of human cystic echinococcosis was inversely correlated with the annual average LST, and positively correlated with both the average elevation and the prevalence rate of domestic animals. The prevalence of human alveolar echinococcosis was positively correlated with both NDVI in autumn and average elevation, and negatively correlated with NDVI in winter. The prevalence of echinococcosis in the population is affected by environmental factors. Environmental risk assessment and prediction can be conducted in order to rationally allocate health resources and improve both prevention and control efficiency of echinococcosis.

Assessing Moisture Content and Its Mitigating Effect in an Urban Area Using the Land Surface Temperature–Vegetation Index Triangle Method

Nowadays, climate change and heat extremes are becoming highly challenging problems in many cities across the globe. One of the solutions to overcome this problem is the use of vegetation, and, in particular, extending the range of overgrown areas, which are sometimes referred to as “urban green areas.” In this paper, the moisture condition and its mitigating effect on Land Surface Temperature in urban areas were examined in Warsaw, Poland, using satellite data. To do so, the so-called “Triangle Method” was employed. The triangle method is based on a Land Surface Temperature–Normalized Difference Vegetation Index (LST-NDVI) scatterplot to calculate the Temperature Vegetation Dryness Index (TVDI) and its modification–quadratic Temperature Vegetation Dryness Index (qTVDI). This article discusses, in detail, the usefulness of the triangle method for the analyses of built-up areas. The drought satellite indices TVDI and qTVDI compared with those of LST, NDVI, and NDBI (Normalized Difference Building Index). The study shows that the triangle method based on LST-NDVI scatterplot analysis is a promising tool for establishing moisture conditions over urban areas and for studying the effect of vegetation impact on urban heat islands. Detailed analysis shows that over an urban area, qTVDI shows better agreement with LST than classic TVDI.

A Long-Term Analysis of the Dependency of Land Surface Temperature on Land Surface Indexes

This study attempted to analyze the long-term dependency of land surface temperature (LST) on four land surface indexes—namely modified normalized difference water index (MNDWI), normalized difference bareness index (NDBaI), normalized difference built-up index (NDBI), and normalized difference vegetation index (NDVI)—in Imphal City, India, using the summer and winter Landsat satellite data for 1991, 1996, 2001, 2006, 2011, 2016, and 2021. These land surface indexes have a different response to LST variation in the city. The results show that the LST–NDVI correlation (r = –0.40 in summer, −0.36 in winter) and LST–MNDWI correlation (r = –0.31 in summer, −0.51 in winter) are moderately negative. In contrast, the LST–NDBaI correlation (r = 0.38 in summer, 0.49 in winter) and LST–NDBI correlation (r = 0.56 in summer, 0.69 in winter) are moderately positive. The correlation is less variable in winter. A gradually increasing trend is observed in the winter correlation, whereas the summer image does not have the same type of relationship trend. The study reflects an application of remote sensing on land surface features.

Time series analysis and impact assessment of the temperature changes on the vegetation and the water availability: A case study of Bakun-Murum Catchment Region in Malaysia

The Bakun-Murum (BM) catchment region of the Rajang River Basin (RRB), Sarawak, Malaysia, has been under severe threat for the last few years due to urbanization, global warming, and climate change. The present study aimed to evaluate the time series analysis and impact assessment of the temperature changes on the vegetation/agricultural lands and the water availability within the BM region. For this purpose, the Landsat data for the past thirty years (1990–2020) were used. Remote sensing techniques for estimating the surface temperatures and variation within the vegetation and water bodies were utilized, and validation was done using on-ground weather stations. Google Earth Engine (GEE) and other RS & GIS tools were used for analyzing the time series trends of land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). The results exposed an overall rise of 1.06 °C in the annual mean temperatures over the last thirty years. A maximum annual mean NDVI of 0.48 was recorded for 2018 and 2019. The lowest annual mean NDVI (0.27) was observed in 2005. The annual mean NDWI increased to 0.48 in 2018 and 2019, respectively. The statistical correlation results revealed the coefficient of determination (R2) of 0.09 and 0.13 for the annual mean LST and annual mean NDVI and the annual mean LST and annual mean NDWI, respectively. Moreover, the Mann-Kendall trend test for the annual mean temperature series indicates a slightly increasing trend with Sen's slope of 0.03 °C/year. It is found that there is a positive trend in the annual mean rainfall patterns, as Sen's slope indicates a yearly increase of 50.58 mm/year. This study found significant changes in the LST, NDVI, and NDWI of the BM catchment region during the last thirty years, demanding the concerned authorities' instant attention to alleviate the adverse effects of such changes to protect the ecosystem.

Association of Different Satellite Driven Products with Evapotranspiration in Tamil Nadu, India (2010-2020)

Evapotranspiration is an important phenomenon of hydrological cycle mainly influenced by meteorological factors and other vegetation characteristics. In this study, the relationship between Actual Evapotranspiration (AET) with Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were analysed using Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA satellite data products with the help of Geographic Information System (GIS) software. The duration of study was during South West Monsoon (SWM) season from June to September, over a period of ten years (2011 to 2020) for all seven Agro Climatic Zones (ACZ) of Tamil Nadu, India. The results showed that moderate to strong positive correlation were observed between AET and NDVI (r= 0.3 to 0.9) in all seven ACZ. The AET and LST showed a weak to strong negative correlation (r= -0.4 to -0.7) in six ACZs except High Rainfall Zone (HRZ) which might be due to lower LST and high NDVI values recorded in the HRZ which exhibited a weak positive relationship (r=0.2). Relationship between AET, LST and NDVI are highly useful for parametrizing evapotranspiration in different climatic conditions, analyzing different crop growth and production and helps in planning water management strategies at regional scale.

Identification of desertified and preserved areas in a conservation unit in the state of Paraíba – Brazil

The objective of this study is to identify and analyse the main characteristics of areas potentially degraded by desertification and of preserved areas using the Soil Surface Moisture Index (SSMI), alongside the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). The study is based on a set of points obtained in the field and from the RGB false colour image for the Environmental Protection Areas (EPA) of the Cariri, in the semi-arid region of Paraíba, using a space-time cross-section covering both rainy and dry periods. The results showed that at all points in Desertified Areas, the main characteristics were a low SSMI, high LST and low NDVI in both periods. The Preserved Areas, on the other hand, presented a high SSMI, moderate LST and high NDVI in the rainy period, with the same characteristics repeated in the dry period for SSMI and NDVI, but with a low LST. Timely identification of these characteristics, both in areas degraded by desertification and in better preserved areas, can provide useful information for future decisions relating to the physical and territorial management of the Conservation Unit.

Modeling Vegetation Water Stress over the Forest from Space: Temperature Vegetation Water Stress Index (TVWSI)

The conventional Land Surface Temperature (LST)–Normalized Difference Vegetation Index (NDVI) trapezoid model has been widely used to retrieve vegetation water stress. However, it has two inherent limitations: (1) its complex and computationally intensive parameterization for multi-temporal observations and (2) deficiency in canopy water content information. We tested the hypothesis that an improved water stress index could be constructed by the representation of canopy water content information to the LST–NDVI trapezoid model. Therefore, this study proposes a new index that combines three indicators associated with vegetation water stress: canopy temperature through LST, canopy water content through Surface Water Content Index (SWCI), and canopy fractional cover through NDVI in one temporally transferrable index. Firstly, a new optical space of SWCI–NDVI was conceptualized based on the linear physical relationship between shortwave infrared (SWIR) and soil moisture. Secondly, the SWCI–NDVI feature space was parameterized, and an index d(SWCI, NDVI) was computed based on the distribution of the observations in the SWCI–NDVI spectral space. Finally, standardized LST (LST/long term mean of LST) was combined to d(SWCI, NDVI) to give a new water stress index, Temperature Vegetation Water Stress Index (TVWSI). The modeled soil moisture from the Australian Water Resource Assessment—Landscape (AWRA-L) and Soil Water Fraction (SWF) from four FLUXNET sites across Victoria and New South Wales were used to evaluate TVWSI. The index TVWSI exhibited a high correlation with AWRA-L soil moisture (R2 of 0.71 with p < 0.001) and the ground-based SWF (R2 of 0.25–0.51 with p < 0.001). TVWSI predicted soil moisture more accurately with RMSE of 21.82 mm (AWRA-L) and 0.02–0.04 (SWF) compared to the RMSE ranging 28.98–36.68 mm (AWRA-L) and 0.03–0.05 (SWF) were obtained for some widely used water stress indices. The TVWSI could also be a useful input parameter for other environmental models.

Exploring the Relationship Between Land Surface Temperature and Land Use Change in Lahore Using Landsat Data


 The present study focuses on determining the correlation of land surface temperature (LST) with normalized difference builtup index (NDBI) and normalized difference vegetation index (NDVI) of Lahore, a metropolitan city of Pakistan using landsat 5 and 8 dataset. This study also categorizes different types of land use through supervised image classification scheme and maximum likelihood algorithm (MLA), and assess the correlation between LST and land use type of different classes. The results of the study indicate that modifications in type of land use altered spatial variations of land surface temperature in 1990 and 2015. The findings also show that the ever increasing temperature caused by impervious surfaces such as builtup area, roads, construction sites and vacant land considerably contributes to heat island effect. However, vegetation cover, green and blue spaces decrease LST and effectively relieve the effect of heat island. LST builds a strong positive correlation with NDBI and strong negative correlation with NDVI. Based on the regression analysis between LST and NDBI and NDVI, these indices can be utilized as a sign to assess the impact of LU changes on temperature. The results further indicates that LST changes follow the pattern of LU changes in Lahore and the warmness intensity has been observed highest in the high density builtup area and vacant land, while low at the green and blue spaces. The analysis reveals that an increase in LST by 1.98 °C during the period of 25 years at the rate of 0.079 °C/year in high density builtup area was due to the excessive increase in settlement growth. The study concludes that change of land use has an effect on the LST in Lahore.
 

Estimation of Surface Soil Moisture Based on Improved Multi-index Models and Surface Energy Balance System

Soil moisture is a vital resource that plays a critical role in arid and semi-arid areas. In the present study, a new approach was adopted to estimate surface soil moisture based on multi-index models using reflective and thermal indices as well as surface energy balance system–Iran (SEBS–Iran) in pastures and farmlands in Qom province, Iran in 2016–2017. To select the best model based on remote sensing (RS) indices, 12 models were designed and after analysis, the best ones were selected. Afterward, the results of the SEBS–Iran algorithm and the improved multi-index model [normalized multi-band drought index (NMDI), normalized difference vegetation index (NDVI), land surface temperature (LST) and the temperature vegetation dryness index (TVDI)] were calibrated with field data in the two studied fields (pastures and farmlands). The findings indicated that the multi-index model NMDI–TDVI–LST–NDVI (R = 0.95) and SEBS–Iran (R = 0.93) both had significant correlations with measured soil moisture. Regarding both models in farmlands and pastures, the SEBS–Iran regression model was closer to the line of fit, and R2 in the two fields was 0.95 and 0.96, respectively. Compared to SEBS–Iran, the multi-index model showed lower coefficient of determination in pastures (0.71) due to the higher accuracy of SEBS–Iran in areas with lower vegetation density. Generally, both methods were found to be suitable for soil moisture estimation. The multi-index model can be used to estimate soil moisture in densely vegetated areas on a large scale due to its simplicity and good accuracy. Moreover, the highly accurate SEBS–Iran model can be used even in sparsely vegetated areas.

Impacts of Vegetation and Topography on Land Surface Temperature Variability over the Semi-Arid Mountain Cities of Saudi Arabia

Land surface temperature (LST) can fully reflect the water–heat exchange cycle of the earth surface that is important for the study of environmental change. There is little research on LST in the semi-arid region of Abha-Khamis-Mushyet, which has a complex topography. The study used LST data, retrieved from ASTER data in semi-arid mountain areas and discussed its relationship with land use/land cover (LULC), topography and the normalized difference vegetation index (NDVI). The results showed that the LST was significantly influenced by altitude and corresponding LULC type. In the study area, during the summer season, extreme high-temperature zones were observed, possibly due to dense concrete surfaces. LST among different types of land use differed significantly, being the highest in exposed rocky areas and built-up land, and the lowest in dense vegetation. NDVI and LST spatial distributions showed opposite trends. The LST–NDVI feature space showed a unique ABC obtuse-angled triangle shape and showed an overall negative linear correlation. In brief, the LST could be retrieved well by the emissivity derived NDVI TES method, which relied on upwelling, downwelling, and transmittance. In addition, the LST of the semi-arid mountain areas was influenced by elevation, slope zenith angle, aspect and LULC, among which vegetation and elevation played a key role in the overall LST. This research provides a roadmap for land-use planning and environmental conservation in mountainous urban areas.

Assessment of trends of land surface vegetation distribution, snow cover and temperature over entire Himachal Pradesh using MODIS datasets

AbstractWe examine spatial and temporal variability in normalized difference vegetation index (NDVI), snow cover and land surface temperature (LST) in Himachal Pradesh between 2001 and 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. Mann–Kendall trend tests and Sen's slope estimates indicate increasing NDVI trends during the postmonsoon period. Increasing snow cover trend is observed during winter and premonsoon whereas decreasing annual LST trends are observed for Himachal Pradesh. Pearson's correlation coefficient (PCC) indicate a strong positive correlation between NDVI and LST (PCC = .808) and strong negative correlation between LST and snow cover (PCC = −.809) and NDVI and snow cover (PCC = −.838). Coefficient of determination greater than .90, between MODIS LST and snow cover observations and weather station records, indicate fair representation of ground conditions using the MODIS dataset. Low (2.4°C/1,000 m) and steep (7.1°C/1,000 m) temperature lapse rate is observed during monsoon and winter, respectively.

An assessment on the relationship between land surface temperature and normalized difference vegetation index

The present study aims to assess the trend of spatiotemporal relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) under different ranges of LST and NDVI values for Raipur City of India using fifteen cloud-free Landsat data sets of the pre-monsoon season from 2002 to 2018. LST maintains a strong negative relationship with NDVI for the whole of the study area. The relationship is quite insignificant for both the high LST zones and low LST zones. The results also indicate that under the positive NDVI values, the LST–NDVI relationships are strong to moderately negative, whereas it is positive and non-consistent under the negative values of NDVI. The results also show that the relationship is stronger in the earlier times, whereas it is weaker in recent times. An increase in heterogeneous landscape inside the city boundary strongly supports the changing pattern of LST–NDVI relationship.

GENERATING TIME SERIES OF MEDIUM-RESOLUTION TEMPERATURE VEGETATION DROUGHT INDEX IMAGES USING A KALMAN FILTER METHOD FOR SOIL MOISTURE CHANGE ANALYSIS

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.

Effects of Surface Heterogeneity Due to Drip Irrigation on Scintillometer Estimates of Sensible, Latent Heat Fluxes and Evapotranspiration over Vineyards

Accurate estimates of sensible (H) and latent (LE) heat fluxes and actual evapotranspiration (ET) are required for monitoring vegetation growth and improved agricultural water management. A large aperture scintillometer (LAS) was used to provide these estimates with the objective of quantifying the effects of surface heterogeneity due to soil moisture and vegetation growth variability. The study was conducted over drip-irrigated vineyards located in a semi-arid region in Albacete, Spain during summer 2007. Surface heterogeneity was characterized by integrating eddy covariance (EC) observations of H, LE and ET; land surface temperature (LST) and normalized difference vegetation index (NDVI) data from Landsat and MODIS sensors; LST from an infrared thermometer (IRT); a data fusion model; and a two-source surface energy balance model. The EC observations showed 16% lack of closure during unstable atmospheric conditions and was corrected using the residual method. The comparison between the LAS and EC measurements of H, LE, and ET showed root mean square difference (RMSD) of 25 W m−2, 19 W m−2, and 0.41 mm day−1, respectively. LAS overestimated H and underestimated both LE and ET by 24 W m−2, 34 W m−2, and 0.36 mm day−1, respectively. The effects of soil moisture on LAS measurement of H was evaluated using the Bowen ratio, β. Discrepancies between HLAS and HEC were higher at β ≤ 0.5 but improved at 1 ≥ β > 0.5 and β > 1.0 with R2 of 0.76, 0.78, and 0.82, respectively. Variable vineyard growth affected LAS performance as its footprints saw lower NDVILAS compared to that of the EC (NDVIEC) by ~0.022. Surface heterogeneity increased during wetter periods, as characterized by the LST–NDVI space and temperature vegetation dryness index (TVDI). As TVDI increased (decreased) during drier (wetter) conditions, the discrepancies between HLAS and HEC, as well as LELAS and LEEC Re decreased (increased). Thresholds of TVDI of 0.3, 0.25, and 0.5 were identified, above which better agreements between LAS and EC estimates of H, LE, and ET, respectively, were obtained. These findings highlight the effectiveness and ability of LAS in monitoring vegetation growth over heterogonous areas with variable soil moisture, its potential use in supporting irrigation scheduling and agricultural water management over large regions.

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements.

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST–NDVI relations over Europe, using long-term (2000–2017) MODIS images. Overlaying the LST–NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March–August). During the spring season (March–May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June–August), most of the biomes—except the northern ones—turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST–NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes.

Identification of Concealed Faults in a Grassland Area in Inner Mongolia, China, Using the Temperature Vegetation Dryness Index

Fault identification in vegetated area (e.g., grassland) is a major challenge compared to that in outcrop areas. To identify concealed faults in a grassland covered area, a hybrid method combining the temperature vegetation dryness index (TVDI) and the singularity index was proposed in this paper to extract TVDI anomalies associated with concealed fractures in the Sonid Left Banner Grassland, Inner Mongolia, North China. In the triangle space of LST/NDVI (land surface temperature/normalized difference vegetation index), scattered points were concentrated in the areas of partial and full cover, while few data points were in the areas of bare soil with low values of NDVI and high values of LST; this result is consistent with the semiarid grassland landscape of the study area. Although TVDI imaging shows an obvious linear pattern with an NE-NNE trend, which indicates the existence of concealed faults, the surrounding background weakened and reduced the significance of the anomalies. To better delineate the concealed faults, the singularity index was employed to remove the influence of the background and enhance the TVDI anomalies associated with the concealed faults. The TVDI imaging and singularity index mapping showed NNE and NE orientations; this finding is consistent with the regional tectonic framework. Geological mapping footprints showed that the hybrid method is useful to identify concealed faults in covered areas of grassland.

Land surface temperature and its impact factors in Western Sichuan Plateau, China

The understanding influence of multiple factors variations on land surface temperature (LST) remains elusive. LST was retrieved by the atmospheric correction algorithms. Based on the correlation coefficients, stepwise regression analysis was developed to examine how multiple factors variability led to LST variations. The differences in LST between impact factors vary depending on time in a day. The elevation and land use types significantly affect the LST in sunny slope or shadow areas has a significantly quadratic curve correlation or a negative linear correlation with it, the influence of slope and aspect is not very significant. LST for forestland, grassland and bare land in the sunny slope and shadow area was the cubic polynomial related to its elevation. Normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) effectively express LST in mountainous. LST and NDMI or NDVI have a significantly negative correlation, NDMI is more effective and more applicable for the expression of LST.

Evaluation of LST downscaling algorithms on seasonal thermal data in humid subtropical regions of India

Thermal image downscaling algorithms use a unique relationship between land surface temperature (LST) and vegetation indices (e.g. normalized difference vegetation index (NDVI)). The LST–NDVI correlation and regression parameters vary in different seasons depending on land-use practices. Such relationships are dynamic in humid subtropical regions due to inter-seasonal changes in biophysical parameters. The present study evaluates three downscaling algorithms, namely disaggregation of radiometric surface temperature (DisTrad), sharpening thermal imagery (TsHARP), and local model using seasonal (25 February 2010, 14 April 2010, and 26 October 2011) thermal images. The aggregated Landsat LST of 960 m resolution is downscaled to 480, 360, 240, and 120 m using DisTrad, TsHARP, and the local model and validated with aggregated Landsat LSTs of a similar resolution. The results illustrate that the seasonal variability of the LST–NDVI relationship affects the accuracy of the downscaling model. For example, the accuracy of all algorithms is higher for the growing seasons (February and October) unlike the harvesting season (April). The root mean square error of the downscaled LST increases from 480 to 120 m spatial resolution in all seasons. The models are least suitable in water body and dry-river bed sand areas. However, the downscaling accuracy is higher for NDVI > 0.3. The present study is useful to understand the applicability of the downscaling models in seasonally varied landscapes and different NDVI ranges.

Development of an ATI-NDVI method for estimation of soil moisture from MODIS data

Estimation of soil moisture is essential for research of climatology, hydrology, and ecology. The commonly used remotely sensed approach is LST-NDVI (land-surface temperature-normalized difference vegetation index). In this study, the apparent thermal inertia (ATI) is used instead of surface temperature to develop an ATI-NDVI space for estimation of soil moisture. Comparison with ground-based measurements shows a root mean square error (RMSE) of 0.0378 m3 m−3 between retrieved and measured soil moistures. Validation with time series in situ data indicates the RMSE as 0.0162, 0.0285, 0.0368, and 0.0093 m3 m−3 for forest, shrub, cropland, and grassland, respectively, which is comparable to or even better than the results of previous studies. The proposed method in this study is a remote-sensing approach without elaborate ancillary data except for the percentage of sand in the soil, and it is practical and convenient to be applied to regions with surfaces from bare soil to full vegetation and the entire range of surface moisture contents from wet to dry.