Comparison and evaluation of different dryness indices based on vegetation indices-land surface temperature/albedo feature space

Liu, Ying; Qian, Jiaxin; Yue, Hui

doi:10.1016/j.asr.2021.05.007

Cited by 17 publications

(3 citation statements)

References 48 publications

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“…Typically, NDVI and LST values have inverse relationship but when NDVI and LST datapoints were extracted from a spatial region, they tend to form a triangle or sometimes trapezoidal in shape in a scatter space plots. These triangular or trapezoidal scatter space plots can be modelled with Triangular Vegetation Index (TVI) or Temperature Vegetation Dryness Index (TVDI) indices to assess the unknown LST values or to identify relative stress level, soil moisture deficit or drought conditions in a given region (Liu et al, 2021). However, in this study, we have developed PMLR functions for predicting missing LST values using the HANTS model predicted NDVI and emissivity values at each pixel locations.…”

Section: Discussionmentioning

confidence: 99%

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Mohanasundaram

Baghel

Thakur

et al. 2022

Preprint

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Critical applications of satellite data products include monitoring vegetation dynamics using vegetation indices such as Normalized Difference Vegetation Index (NDVI) and thermal characteristics such as Land Surface Temperature (LST) variations across different spatial and temporal scales. One of the major problems with passive remote sensing satellite data products is cloud and shadow cover that void the information from a specific image. The cloud cover problems further amplified during monsoon seasons as the formation of clouds are more frequent during these seasons. The present study proposes temporal aggregation of images and developing Harmonic Analysis of Time Series (HANTS) and Pixel-wise Multiple Linear Regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series of L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI data sets. The calculated root mean square (RMSE) values were ranging from 0.004 to 0.05 and 1.05°C to 3.11°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) calculated at province level show that most of the western provinces showed a healthy vegetation condition than other provinces in the northeast of Thailand.

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Section: Discussionmentioning

confidence: 99%

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Mohanasundaram

Baghel

Thakur

et al. 2022

Preprint

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“…This phenomenon ultimately affects urban ecosystems, local climates, and energy flow [ 13 ]. Thus, understanding the relationship between biophysical indices and LST is crucial for mitigating local climate impacts in cities [ 14 , 15 ]. One of the most serious problems in urban areas area is an increase in temperature due to conversion of the natural landscapes (such as vegetation cover and water cover) into impervious surface cover [ 16 ] as well as the conversion of vegetation cover into agricultural and open land.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Relationship between Urban Biophysical Composition and Land Surface Temperature in a Hot Desert Megacity (Saudi Arabia)

Addas

2023

IJERPH

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The deteriorations of the thermal environment due to extreme land surface temperature (LST) has become one of the most serious environmental problems in urban areas. The spatial distribution of urban biophysical composition (UBC) has a significant impact on the LST. Therefore, it is essential to understand the relationship between LST and biophysical physical composition (BPC) to mitigate the effects of UHIs. In this study, an attempt was made to understand the relationship between LST and BPC in a hot desert coastal megacity (Jeddah megacity) in Saudi Arabia. Principal component analysis (PCA) was used to understand the factors affecting LST based on remote sensing indices. Correlation and regression analyses were carried out to understand the relationship between LST and BPC and the impact of BPC on LST. The results showed that, in Jeddah city from 2000 to 2021, there was a substantial increase in the built-up area, which increased from 3085 to 5557.98 hectares. Impervious surfaces had a significant impact on the LST, and green infrastructure (GI) was negatively correlated with LST. Based on the PCA results, we found that the GI was a significant factor affecting the LST in Jeddah megacity. The findings of this study, though not contributing to further understanding of the impact of BPC on LST, will provide planners and policy makers with a foundation for developing very effective strategies to improve the eco-environmental quality of Jeddah megacity.

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“…Based on the above-mentioned reasons, scholars focused on the TVDI and found that the relationship between the relative changes in VI, LST, and SM was relatively stable under most climatic conditions and surface cover conditions (Carlson et al, 1990). The triangular or trapezoidal two-dimensional feature space formed by LST and NDVI (Liu et al, 2021a) was used to estimate the TVDI. The upper and lower thresholds of land surface temperature are represented on both sides of the characteristic space, and the calculated TVDI values are subsequently used to infer the degree of drought, which can more accurately determine the SM.…”

Section: Introductionmentioning

confidence: 99%

Inversion of soil moisture and its feedback on ecological restoration in arid and semi-arid areas of northwest China

Liu¹,

Yuanyuan²,

Zhou³

et al. 2022

Revista Brasileira De Ciência Do Solo

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Soil moisture (SM) plays an important role in regulating the global water cycle, especially in arid areas, and is one of the main indicators of ecological environmental health. Although traditional methods can accurately measure SM at a single sample site, they are limited in large-scale and dynamic SM monitoring. Therefore, we used the Landsat images as the data source and the soil adjusted vegetation index (SAVI) to build the adjusted SAVI (aSAVI) index by modifying the soil adjustment parameter L and introducing the short-wave infrared band. According to the theory of temperature vegetation dryness index (TVDI) and feature space, we introduced a model, combined the measured SM data (Minqin Basin, China) through a comparative analysis of four vegetation indices (NDVI, SAVI, MSAVI, aSAVI) determine the optimal model. Taking the Minqin Basin as the study area, the spatiotemporal variation characteristics of SM in three sub-regions (the entire study area, irrigated region, and periphery of the irrigated regions) were quantitatively analyzed and compared in four different periods: pre-Comprehensive Treatment Program of the Shiyang River Basin (pre-CTSRB) (2000)(2001)(2002)(2003)(2004)(2005), CTSRB I (2006-2010), CTSRB II (2011-2016), and CTSRB-end (2017-2021) to evaluate the ecological restoration effects of treatment programs from the SM perspective. The results showed that: 1) SM values derived from TVDI inversion and the aSAVI were more accurate, and the model sensitivity decreased with soil depth; 2) the mean value of SM fluctuated across the four periods but decreased slightly over the entire time series. The spatial variations of the SM were characterized by a "descending then ascending" trend. Soil moisture increased in 21.35 % of areas at 0.00-0.10 m in the past 22 years, and 59.66 % at 0.10-0.20 m. There was a negative correlation between the mean variation trend of SM and the percentage of area where SM fell in different periods; 3) the treatment program positively affected the ecological restoration of the Minqin Basin from the SM perspective. The area where SM increased was larger than that of decreasing SM, especially in 0.10-0.20 m soil layer. The increase can promote growth and confer resistance to desertification.

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Comparison and evaluation of different dryness indices based on vegetation indices-land surface temperature/albedo feature space

Cited by 17 publications

References 48 publications

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Understanding the Relationship between Urban Biophysical Composition and Land Surface Temperature in a Hot Desert Megacity (Saudi Arabia)

Inversion of soil moisture and its feedback on ecological restoration in arid and semi-arid areas of northwest China

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