Investigating the Relationship Between Shallow Groundwater, Soil Moisture and Land Surface Temperature Using Remotely Sensed Data

2018 ◽  
Author(s):  
Saeid Hamzeh ◽  
Mohammad Mehrabi ◽  
Seyed Kazem Alavipanah ◽  
Majid Kiavar Moghadam
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Shallow Groundwater ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
The Relationship

scholarly journals Estimation of soil moisture using trapezoidal relationship between remotely sensed land surface temperature and vegetation index

2011 ◽  
Vol 15 (5) ◽  
pp. 1699-1712 ◽  
Author(s):  
W. Wang ◽  
D. Huang ◽  
X.-G. Wang ◽  
Y.-R. Liu ◽  
F. Zhou
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Antecedent Precipitation ◽  
Walnut Gulch ◽  
Semi Arid Region

Abstract. The trapezoidal relationship between land surface temperature (Ts) and Vegetation Index (VI) was used to estimate soil moisture in the present study. An iterative algorithm is proposed to estimate the vertices of the Ts ~ VI trapezoid theoretically for each pixel, and then Water Deficit Index (WDI) is calculated based on the Ts ~ VI trapezoid using MODIS remotely sensed measurements of surface temperature and enhanced vegetation index (EVI). The capability of using WDI based on Ts ~ VI trapezoid to estimate soil moisture is evaluated using soil moisture observations and antecedent precipitation in the Walnut Gulch Experimental Watershed (WGEW) in Arizona, USA. The result shows that, the Ts ~ VI trapezoid based WDI can capture temporal variation in surface soil moisture well, but the capability of detecting spatial variation is poor for such a semi-arid region as WGEW.

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

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