Expanding urban areas exert a considerable anthropogenic strain on the natural environment, imposing a substantial burden on essential resources like space, water, and air quality. The climatic conditions in metropolitan regions diverge significantly from their rural counterparts, with alterations in radiation balance, moisture content, and thermal stability occurring as urbanization advances 2. Uncontrolled construction and economic activities contribute to rapid urban sprawl and land surface expansion, potentially escalating environmental crises 4. An indispensable variable for scrutinizing surface energy budgets, land surface processes, urban heat islands, and retrieving atmospheric variables is the LST, also referred to as land skin temperature 4. LST plays a crucial role in assimilating data into land surface models. Influenced by surface air temperature and various surface and subsurface factors such as soil moisture, texture, vegetation type, elevation, and radiations, LST reflects the complex interplay of these elements 4. Despite the local connection between surface air temperature and LST, notable disparities exist in the magnitudes of their diurnal oscillations 2.

Remote sensing, exemplified by the utilization of the NDVI, stands as a pivotal tool for monitoring plant growth and assessing regional-scale plant health and drought conditions 5. In tandem with NDVI, LST serves as a crucial parameter for understanding surface energy distribution. Challenges, however, arise from the delayed response of NDVI and LST to rainfall events. LULC changes, captured by satellites like Landsat, Terra, and Aqua, prove indispensable for managing disasters such as landslides, urban flooding, and the repercussions of climate change on a regional scale 6. These changes in land use and cover, driven by factors like population growth, urbanization, agricultural practices, and land use policies, transform landscapes into diverse mosaics with multifaceted implications for nutrient cycling, bio-geochemical cycles, hydrologic processes, and carbon sequestration 1, 7, 8.

In a specific case study focusing on the taluks of Bantval, Baltangvadi, Puttur, and Sulya in the Dakshina Kannada district, Karnataka, the analysis of LST using data from the LANDSAT 8 satellite provides valuable insights into landscape dynamics 1. The sensitivity of LST to various factors, including vegetation, the canopy layer, and soil moisture, makes it a potent tool for detecting and understanding LULC changes and their linkages to climatic variations 9, 10. This understanding not only aids in disaster management but also informs sustainable planning and management of natural resources at a temporal scale, showcasing the intricate interplay between LST variations and landscape dynamics 1.

In the present investigation, the analysis of LST in the taluks of Bantval, Baltangvadi, Puttur, and Sulya within the Dakshina Kannada district of Karnataka was conducted utilizing data acquired from the LANDSAT 8- OLI & TRS satellite. To gauge LST across the major taluks in the district, a comparative study involving the examination of NDVI data from the US Geological Survey (USGS) and LULC information from Environmental Systems Research Institute (ESRI) was carried out. Table 1 furnishes details about the source of the data and its spatial resolution, providing essential insights into the methodology employed for the study.

Figure 2 depicts the methodology for the proposed effort to estimate LST. Data from LANDSAT 8 can only be processed using this method. Band 10 is used in this study to determine NDVI while bands 4 and 5 are used to evaluate brightness temperature Figure 3 .

The following literature provides a full breakdown of the stages required for the planned work.

Step1: Geometrically corrected data were produced from the satellite data 2 .

Using the following equation, the DN (Digital Number) values of band 10 are first converted to at-sensor spectral radiance in the proposed study (1) 3 :

1TOALκ=ML*Qcal+A1-Qi

where,

TOA (L_ʎ) = TOA spectral radiance (watts/m²*srad*µm))

M_{L =} Band-specific multiplicative rescaling factor

Q_{cal =} Quantized and calibrated standard product pixel values (DN)

A_{l =} Band-specific additive rescaling factor

Q_{i =} is the correction value for Band 10 of Landsat 8

Step2: After converting DN values to at-sensor spectral radiance, the TIRS band data should be converted to brightness temperature (BT) using the thermal constants given in the metadata file and the following equation(2) 3 :

2BT=K2/Ln(K1/TOA)+1))-273.15

where,

BT = Top of atmosphere brightness temperature

Ln = Total spectral Radiance

K1 = COSNTANT_BAND 10

K2 = CONSTANT_BAND 10

Step 3: To identify the various land cover types in the study area, the NDVI (2) is crucial. The NDVI has a range of -1.0 to +1.0. The normalised difference between the near-infrared band (0.85-0.88m) and the red band (0.64 - 0.67 m) of the pictures is used to determine the NDVI on a per-pixel basis (3)using the equation (3).

3NDVI=NIR( Band 5) -Red( Band 4)/NIR(Band 5)+Red (Band 4) 

Where NIR(2) is the near-infrared band value of a pixel and RED is the red band value of the same pixel. Calculation of NDVI is necessary to further calculate proportional vegetation (Pv) and emissivity (ԑ) 3 .

Step 4: The next step (equation 4) is to calculate proportional vegetation (Pv)(2) from NDVI values obtained in step 3. This proportional vegetation gives the estimation of the area under each land cover type. The vegetation and bare soil proportions are acquired from the NDVI of pure pixels(3).

4Pv=NDVI-NDVImin/NDVImax-NDVImin2

where,

Pv = Proportion of Vegetation

NDVI = Normalised Difference Vegetation Index

NDVImin = refers to the previous result of NDVI minimum value

NDVImax = refers to the previous result of NDVI maximum value.

Step 5: Calculation of land surface emissivity (LSE)(2) is required to estimate LST since LSE (equation 5) is a proportionality factor that scales the black body radiance (Plank’s law) to measure emitted radiance and it is the ability to transmit thermal energy across the surface into the atmosphere. At the pixel scale, natural surfaces are heterogeneous in terms of variation in LSE. In addition, the LSE is largely dependent on the surface roughness, nature of vegetation cover,etc 3 .

5ϵ=0.004*Pv+0.986

where,

ϵ= Land surface emissivity

Pv = Proportion of Vegetation

0.986 = correction value for the equation.

Step 6: Using the brightness temperature (BT)2 of band 10 and the LSE obtained from Pv and NDVI, the last step is to compute LST. LST is retrievable 3 using the equation 6.

6Ts=BT/((1+(K*BT/p)*Ln(σ)))

where ρ is (h xc/σ) which is equal to 1.438 x 10-2 mK in which σ is the Boltzmann constant (1.38 x 10-23 J/K), h is Plank's constant (6.626 x 10-34), and c is the light velocity (3 x 108 m/s). Ts is the LST in Celsius (o C), BT is at-sensor BT (o C), and λ is the average wavelength of band 10 3.

The LULC obtained from ESRI has been masked with the study for a comparative study for analyzing the LST.

The study reveals intricate interactions between land cover change and basin-scale hydrology, radiation, heat fluxes, and surface temperature, highlighting their importance in calculating LST and understanding the environmental impacts of urban expansion 16. The research showcases its dynamic ability to estimate LST by utilizing brightness temperature data from a TIR sensor and LSE derived from proportional vegetation cover observed in optical bands from a LANDSAT 8 sensor 3. Within the study area, a moderate LST range of 53.7 to 78.67 has been identified. To decipher the reasons behind this moderate LST, a comprehensive comparison involving NDVI, LULC, and LST has been conducted, focusing on the significant taluks of Bantwal, Belthangady, Puttur, and Sullia, in addition to the district capital Mangalore. These taluks are emerging as important tourism destinations, warranting detailed analysis. The study suggests potential extensions, such as considering the entire district, exploring surface air temperature for a comprehensive examination, and delving deeper into the specifics of the research region 3.