Multi-temporal characterization of land surface temperature and its relationships with normalized difference vegetation index and soil moisture content in the Yellow River Delta, China

Chi, Yuan; Sun, Jingkuan; Sun, Yonggen; Liu, Shengjing; Fu, Zhanyong

doi:10.1016/j.gecco.2020.e01092

Cited by 27 publications

(22 citation statements)

References 51 publications

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“…The NDVI produced no significant correlations with soil moisture on UAV images, whereas Planet Scope NDVI variants with their higher spectral and spatial resolution positively correlated with bare soils, concurring with [39]. The ease of calculating the NDVI and the high temporal resolution of the data may mean that Sentinel-2 Planet Scope may play a future role in early warning systems of drought, as it enables high-resolution vegetation condition monitoring, which may be useful in detecting the onset of agricultural drought.…”

Section: Discussionmentioning

confidence: 57%

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Casamitjana

Torres-Madronero

et al. 2020

Applied Sciences

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Surface soil moisture is an important hydrological parameter in agricultural areas. Periodic measurements in tropical mountain environments are poorly representative of larger areas, while satellite resolution is too coarse to be effective in these topographically varied landscapes, making spatial resolution an important parameter to consider. The Las Palmas catchment area near Medellin in Colombia is a vital water reservoir that stores considerable amounts of water in its andosol. In this tropical Andean setting, we use an unmanned aerial vehicle (UAV) with multispectral (visible, near infrared) sensors to determine the correlation of three agricultural land uses (potatoes, bare soil, and pasture) with surface soil moisture. Four vegetation indices (the perpendicular drought index, PDI; the normalized difference vegetation index, NDVI; the normalized difference water index, NDWI, and the soil-adjusted vegetation index, SAVI) were applied to UAV imagery and a 3 m resolution to estimate surface soil moisture through calibration with in situ field measurements. The results showed that on bare soil, the indices that best fit the soil moisture results are NDVI, NDWI and PDI on a detailed scale, whereas on potatoes crops, the NDWI is the index that correlates significantly with soil moisture, irrespective of the scale. Multispectral images and vegetation indices provide good soil moisture understanding in tropical mountain environments, with 3 m remote sensing images which are shown to be a good alternative to soil moisture analysis on pastures using the NDVI and UAV images for bare soil and potatoes.

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Section: Discussionmentioning

confidence: 57%

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Casamitjana

Torres-Madronero

et al. 2020

Applied Sciences

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“…The LST-NDVI correlation is quite fascinating in remote sensing studies (Gutman and Ignatov 1998;Goward et al 2002). This LST-NDVI correlation is mostly negative observed in the many recent studies performed in the mixed urban areas (Alexander 2020;Chi et al 2020;Neinavaz et al 2020;Nse et al 2020).…”

Section: Introductionmentioning

confidence: 93%

Dynamic seasonal analysis on LST-NDVI relationship and ecological health of Raipur City, India

Guha

2021

Ecosyst Health Sustain

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Land surface temperature (LST) is a significant component of the ecological health of any city and the LST is closely related to the normalized difference vegetation index (NDVI). The present study evaluates the seasonal variability of the relationship of LST with NDVI by using a large dataset of Landsat sensors for different seasons from 1991-92 to 2018-19. Pearson's correlation coefficient technique was used to obtain the LST-NDVI relationship. The study also compares the ecological and thermal status of the city by applying the urban thermal field variance index (UTFVI). The results found that the mean LST increased considerably. The post-monsoon season produces the best correlation (-0.59), followed by the monsoon season (-0.53), premonsoon season (-0.45), and winter season (-0.22). Apart from this relationship, the ecological status of the city has also been estimated. Almost an equal portion of lands are under the excellent and worst categories of ecological condition. This study is beneficial for future ecological planning in any tropical city.

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“…The positive correlation between LST and NDVI, the positive correlation between LST and NDBI, and the negative correlation between LST and NDWI conform to earlier studies. Yuan Chi et al [8] analyzed relationships among LST and NDVI in the Yellow River Delta, China, the positive correlations were observed in all seasons of 2016-2017. Subhanil Guha et al [11] focuses on the study of land surface temperature with NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, and LST performs a strong correlation with NDBI (positive).…”

Section: Accuracy Assessment Of Lulc Classificationmentioning

confidence: 99%

“…The most common satellite-derived indicators for estimating the temporal and spatial changes of LST are the normalized difference vegetation index (NDVI), the normalized difference built-up index (NDBI), and the normalized difference water index (NDWI) [7]. Previous studies have analyzed the different relationships between LULC, LST, NDVI, NDBI, and NDWI [7][8][9][10][11][12][13]. These results are mainly attributable to the growth and expansion of cities brought about by urbanization and socioeconomic development, which affect land use and regional climate change.…”

Section: Introductionmentioning

confidence: 99%

Influence of Land Use and Land Cover Change on Land surface temperature

Sun

2021

E3S Web Conf.

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The rapid urbanization process has recently led to significant land use and land cover (LULC) changes, thereby affecting the climate and the environment. The purpose of this study is to analyze the LULC changes in Hefei City, Anhui Province, and their relationship with land surface temperature (LST). To achieve this goal, multitemporal Landsat data were used to monitor the LULC and LST between 2005 and 2015. The study also used correlation analysis to analyze the relationship between LST, LULC, and other spectral indices (NDVI, NDBI, and NDWI). The results show that the built-up land has expanded significantly, transforming from 488.26 km2 in 2005 to 575.64 km2 in 2015. It further shows that the mean LST in Hefei city has increased from 284.0 K in 2005 to 285.86 K in 2015. The results also indicate that there is a positive correlation between LST and NDVI and NDBI, while there is a negative correlation between LST and NDWI. This means that urban expansion and reduced water bodies will lead to an increase in LST.

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Multi-temporal characterization of land surface temperature and its relationships with normalized difference vegetation index and soil moisture content in the Yellow River Delta, China

Cited by 27 publications

References 51 publications

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

Dynamic seasonal analysis on LST-NDVI relationship and ecological health of Raipur City, India

Influence of Land Use and Land Cover Change on Land surface temperature

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