Land Surface Temperature Estimation Using Remote Sensing Data

Solanky, Vijay; Singh, Sangeeta; Katiyar, Sunil Kumar

doi:10.1007/978-981-10-5801-1_24

Cited by 22 publications

(13 citation statements)

References 20 publications

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“…Land surface temperature (LST) is an important climate related parameter which affects the vegetation and biodiversity in an ecosystem. It has, therefore, been recognized as an essential environmental factor for monitoring the resilience of our planet [1]. It is systematically measured by satellite sensors and estimated from TOA brightness values of the infrared spectral bands [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…It has, therefore, been recognized as an essential environmental factor for monitoring the resilience of our planet [1]. It is systematically measured by satellite sensors and estimated from TOA brightness values of the infrared spectral bands [1,2]. LST affects the partition of energy between the ground and photosynthesizing chlorophyll and determines the surface air temperature [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

et al. 2022

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This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat-5 and Landsat-8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook’s distance, the effect of missing values in the analysis of the time series were examined. Results from the cross-correlation and cross-variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

et al. 2022

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“…An important approach is to obtain the Earth's surface temperature data. Great progress in this direction was made by V. Solanky et al [10]. In their research, they tried to obtain the Earth's surface temperature data using MODIS and Landsat-8 satellite imagery for the Bhopal region.…”

Section: Remote Sensing Of Different Environmental Variablesmentioning

confidence: 99%

Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach

Borovskaya

Krivoguz

Chernyi

et al. 2022

JMSE

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Knowledge of the spatio-temporal distribution of salinity provides valuable information for understanding different processes between biota and environment, especially in hypersaline lakes. Remote sensing techniques have been used for monitoring different components of the environment. Currently, one of the biggest challenges is the spatio-temporal monitoring of the salinity level in water bodies. Due to some limitations, such as the inability to be located there permanently, it is difficult to obtain these data directly. In this study, machine learning techniques were used to evaluate the salinity level in hypersaline East Sivash Bay. In total, 93 in situ data samples and 6 Sentinel-2 datasets were used, according to field measurements. Using linear regression, random forest and AdaBoost models, eight water salinity evaluation models were built (six with simple, one with random forest and one with AdaBoost). The accuracy of the best-fitted simple linear regression model was 0.8797; for random forest, it was equal, at 0.808, and for AdaBoost, it was −0.72. Furthermore, it was found that with an increase in salinity, the absorbing light shifts from the ultraviolet part of the spectrum to the infrared and short-wave infrared parts, which makes it possible to produce continuous monitoring of hypersaline water bodies using remote sensing data.

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“…Emissivity refers to the radioactive properties of an object and summarises its ability to emit radiation [20,21]. To obtain the LST it is necessary to measure land surface emissivity.…”

Section: Estimation Of Emissivitymentioning

confidence: 99%

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

Yuhendra¹,

Sumantyo²

2020

TELKOMNIKA

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West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C 0 to 32.59 C 0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C 0 to 32.59 C 0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from -8.5 C 0 to 5 C 0 .

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Land Surface Temperature Estimation Using Remote Sensing Data

Cited by 22 publications

References 20 publications

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

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