Evaluation of Three Land Surface Temperature Products From Landsat Series Using in Situ Measurements

Wang, Mengmeng; He, Chang; Zhang, Zhengjia; Hu, Tian; Duan, Si‐Bo; Mallick, Kaniska; Li, Hua; Liu, Xiuguo

doi:10.1109/tgrs.2022.3232624

Cited by 4 publications

(3 citation statements)

References 85 publications

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“…To further validate the land cover change (LCC) classifications, we have applied ground truth data in ArcGIS 10.4 which was acquired from Google Earth to calculate the overall accuracy, and this revealed an overall accuracy of more than 85% for all LCC classes. Further, the LCC maps were generated by employing the land change modeler (LCM) in IDRISI 2020, a specialized tool that facilitates the analysis and modeling of temporal land cover change [49][50][51]. LCM was utilized to evaluate the rate and spatial location of gains and losses, transitions, and exchanges in LCC over KPK in the last two decades (2000-2020).…”

Section: Land Cover Change and Transitionsmentioning

confidence: 99%

Investigating Land Cover Changes and Their Impact on Land Surface Temperature in Khyber Pakhtunkhwa, Pakistan

Ul Hussan,

Li,

Liu

et al. 2024

Sustainability

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Restoration of degraded land is a significant concern in the 21st century in order to combat the impacts of climate change. For this reason, the provisional government of Khyber Pakhtunkhwa (KPK), Pakistan, initialized a Billion Tree Tsunami Project (BTTP) in 2013 and finished it in 2017. Although a few researchers have investigated the land use transitions under BTTP in the short term by merging all the vegetation types into one, analysis of the long-term benefits of the project and future persistence were missing. Furthermore, the previous studies have not discussed whether the prime objective of the BTTP was achieved. Considering the existing gaps, this research mainly involves analyzing (i) fluctuations in the green fraction by employing a land change modeler (LCM), along with the spatial location of gain-loss and exchange analysis using a high-resolution dataset (GLC30); (ii) forest cover changes under the influence of the BTTP; (iii) impacts of green fraction changes towards land surface temperature (LST) by utilizing the less-explored technique of curve fit linear regression modeling (CFLR); and finally, (iv) assessing the persistence of the NDVI and LST trends by employing the Hurst exponent. Research findings indicate that as an output of BTTP, despite the government’s claim of increasing the forest cover by 2%, a significant gain of grassland (3904.87 km2) was observed at the cost of bare land. In comparison, the overall increase in forest cover was only 0.39%, which does not satisfy the main objective of this project. On the other hand, the CFLRM-based actual contributions of land cover change (LCC) transition to LST indicate a significant decline in LST in the areas with gains in green fraction for both grassland and forest. At the same time, an increase was observed with reverse transitions. Although the results appear positive for climatic impacts in the short term, the HURST model-based persistence analysis revealed that the spatial locations of increasing vegetation and decreasing LST trends fall under the weakly persistent category, therefore these trends may not continue in the near future. Despite some positive impact on LST attributed to the green fraction increase, this project cannot be regarded as a complete success due to its failure to achieve its prime objective.

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Section: Land Cover Change and Transitionsmentioning

confidence: 99%

Investigating Land Cover Changes and Their Impact on Land Surface Temperature in Khyber Pakhtunkhwa, Pakistan

Ul Hussan,

Li,

Liu

et al. 2024

Sustainability

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“…Many studies have evaluated meteorological variables for modeling suitability in remote sensing, such as soil moisture [21], precipitation [22], evapotranspiration (ET) [23], and temperature [24,25]. However, most studies have only assessed individual meteorological variables provided by multiple satellite products and reanalysis datasets.…”

Section: Introductionmentioning

confidence: 99%

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

Lin,

Fang,

Xia

et al. 2023

Remote Sensing

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Air temperature (Ta) is essential for studying surface processes and human activities, particularly agricultural cultivation, which is strongly influenced by temperature. Remote sensing techniques that integrate multi-source data can estimate Ta with a high degree of accuracy, overcoming the shortcomings of traditional measurements due to spatial heterogeneity. Based on in situ measurements in Guangdong Province from 2012 to 2018, this study applied three machine learning (ML) models and fused multi-source datasets to evaluate the performance of four data combinations in Ta estimation. Correlations of covariates were compared, focusing on rice planting areas (RA). The results showed that (1) The fusion of multi-source data improved the accuracy of model estimations, where the best performance was achieved by the random forest (RF) model combined with the ERA5 combination, with the highest R2 reaching 0.956, the MAE value of 0.996 °C, and the RMSE of 1.365 °C; (2) total precipitation (TP), wind speed (WD), normalized difference vegetation index (NDVI), and land surface temperature (LST) were significant covariates for long-term Ta estimations; (3) Rice planting improved the model performance in estimating Ta, and model accuracy decreased during the crop rotation in summer. This study provides a reference for the selection of temperature estimation models and covariate datasets. It offers a case for subsequent ML studies on remote sensing of temperatures over agricultural areas and the impact of agricultural cultivation on global warming.

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“…Evapotranspiration (ET) is the total flux of water vapor transferred from surface soil and vegetation to the atmosphere, mainly includes evaporation of soil moisture and transpiration of water in vegetation, and is an essential component of the land-atmosphere hydrological cycle and the surface water and heat balance [1,2]. In the context of global warming and surface environmental changes, investigating the spatiotemporal distribution of ET and its response to climatic factors is pivotal for climate change research [3][4][5], drought monitoring [6][7][8], water resource management [9,10], crop yield estimation [11,12], and environmental protection [13,14]. The Yangtze River Basin (YRB) is the largest basin in China, its water and hydropower resources are the richest in China [15], and its ecological security in the basin is receiving increasing attention as the region with the most rapid economic and social development in China in recent years.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Seasonal Effect of Climatic Factors on Evapotranspiration in the Monsoon Climate Zone: A Case Study of the Yangtze River Basin

Wang

et al. 2023

Atmosphere

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Evapotranspiration (ET) plays an essential role in water balance and ecological environment changes. The Yangtze River Basin (YRB) is a typical monsoon climate zone. Most existing studies on the impact of climatic factors on annual ET have overlooked the seasonal effect. This study quantitatively analyzed the spatiotemporal variation characteristics of ET and its relationship with climatic factors at the annual and monthly scales in the YRB using high−spatial−resolution PML_V2 ET data from 2001 to 2020. Results showed that: (1) the spatiotemporal distribution of the regions with significant correlation between ET and individual climatic factors (i.e., air temperature, solar radiation and precipitation) in the YRB showed obvious cyclical changes in month, and the spatial change pattern is strongly related to the elevation; (2) the area proportion of the dominant climatic factors affecting annual ET in the study area was characterized by solar radiation > specific humidity > precipitation > air temperature > wind speed. However, monthly ET in most areas of the YRB was driven by solar radiation and air temperature, especially in summer and autumn, while ET in spring and winter was mainly driven by solar radiation, air temperature, and specific humidity.

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Evaluation of Three Land Surface Temperature Products From Landsat Series Using in Situ Measurements

Cited by 4 publications

References 85 publications

Investigating Land Cover Changes and Their Impact on Land Surface Temperature in Khyber Pakhtunkhwa, Pakistan

Investigating Land Cover Changes and Their Impact on Land Surface Temperature in Khyber Pakhtunkhwa, Pakistan

Air Temperature Monitoring over Low Latitude Rice Planting Areas: Combining Remote Sensing, Model Assimilation, and Machine Learning Techniques

Investigating the Seasonal Effect of Climatic Factors on Evapotranspiration in the Monsoon Climate Zone: A Case Study of the Yangtze River Basin

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