ABSTRACT
The Land Surface Temperature (LST) is the fundamental concept of measuring the temperature of the ground or soil using remote sensing technologies. LST is an essential environmental parameter that has several uses in meteorology, climatology, agriculture, urban planning, and environmental monitoring. Remote sensing instruments, such as thermal infrared sensors aboard satellites, provide the means to capture LST data on a global scale. LST and ground air temperature ( ) are two related but distinct measures that provide valuable insights into different aspects: LST refers to the temperature of the Earth's surface, and it provides information about the temperature of the soil or crop canopy, which can impact plant growth and development, on the other hand measures the temperature of the surrounding air, which influences the rate of evaporation, transpiration, and photosynthesis in plants. In this study, a linear regression equation was developed to correlate the LST and using air temperature data of 19th March 2017, and 14th March 2021 from the two meteorological stations namely Veganpur and Main Maize Research Station (MMRS) of the Godhra region. The Landsat 8 thermal and optical bands were used to estimate LST, whereas Sentinel 2 optical data was used for the Land Use and Land Cover (LULC) purpose. To correlate LST with , eight ground truth (GT) points were collected from both stations, namely the minimum and maximum air temperatures in 2017 and 2021. The satellite derived LST data and the maximum ground air temperature ( ) were found to strongly concur, with an R2 value of 0.97. It was noted that a temperature difference of 2.9°C was found between the maximum ground air temperature and the Land Surface Temperature (LST). Study showed that from 2017 to 2021, the area used for agriculture, forestry, water bodies, and barren land decreased from 75.62 to 71.51%, 15.17 to 14.30%, 1.60 to 1.38%, and 0.03 to 0.02%, respectively, however the area used for build-up increased from 7.59 to 12.79%. During a five-year period, the study identified an enhancement in Land Surface Temperature (LST) due to an increase in built-up area. A temperature change was also seen throughout the course of the five-year period as a result of variations in LULC. Overall, it came to light that, under specific conditions and speculation, LST estimated using thermal data from Landsat 8 can be closely associated with ground air maximum temperature.
ABSTRACT
Accurate measurement and monitoring of surface and subsurface soil moisture is essential for understanding hydrological processes, crop growth modeling, crop water requirement, and climate studies. Accurate measurement of the soil moisture content (SMC) in the root zone is essential for precise irrigation authority and plant water stress evaluation. However, the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), that operate at L-band, can only estimate the top 5 cm of soil moisture. Microwave remote sensing has proven to be a valuable tool for non-invasive soil moisture estimation. This research aims to investigate and develop a methodology for estimating surface and subsurface soil moisture using microwave data from Sentinel-1. The study was conducted to establish the relationship between surface & the backscatter coefficient derived using the Sentinel-1 SAR microwave remote sensing satellite imagery, and relationship between surface and subsurface soil moisture at different depths, in the Godhra region. Two seasons namely summer (Zaid) and monsoon (Kharif) were taken into consideration to build up the relationship between surface soil moisture and co-polarization backscatter coefficient ( For the summer (Zaid) and monsoon (Kharif) seasons, the co-polarization backscatter coefficient ( and surface soil moisture (0-5, cm) were found to have a correlation in terms of R2 as 0.91 and 0.90, respectively. The study explores the relationship between microwave signals and surface soil moisture content (0-5, cm) and then the relationship between surface soil moisture and soil moisture at various depths were also modeled thereby contributing to improved soil moisture estimation techniques and applications. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, and 41-60 cm depths were found to be 0.60, 0.51, and 0.46, respectively, in the summer (Zaid) season. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm depths were found to be 0.83, 0.61, 0.51, 0.26, and 0.13, respectively. According to the study, it is observed that the relationship between co-polarization backscatter coefficient ( and soil moisture weakens as the depth of soil moisture increases. Overall, the regression models developed between the co-polarization backscatter coefficient ( and surface soil moisture showed very good results, whereas the regression models developed between the surface soil moisture and soil moisture at various depths showed reasonably acceptable results up to the depth of 60 cm. The findings in the present study suggest that Sentinel-1A C-band SAR data can be used to estimate surface soil moisture. It is also shown in this study that the surface soil moisture can be correlated with the subsurface soil moisture up to the depth of 60 cm, satisfactorily using regression equations.