scholarly journals Surface Soil Water Content Estimation from Thermal Remote Sensing based on the Temporal Variation of Land Surface Temperature

2014 ◽  
Vol 6 (4) ◽  
pp. 3170-3187 ◽  
Author(s):  
Dianjun Zhang ◽  
Ronglin Tang ◽  
Wei Zhao ◽  
Bohui Tang ◽  
Hua Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temporal Variation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Thermal Remote Sensing

scholarly journals Earth Observations-Based Evapotranspiration in Northeastern Thailand

2019 ◽  
Vol 11 (2) ◽  
pp. 138 ◽  
Author(s):  
Chaolei Zheng ◽  
Li Jia ◽  
Guangcheng Hu ◽  
Jing Lu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Spatiotemporal Pattern ◽  
Monsoon Climate ◽  
Thermal Remote Sensing ◽  
Clear Sky

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d−1, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate.

Spatio-temporal variability behavior of land surface soil water content in shrub- and grass-land

Geoderma ◽  
2011 ◽  
Vol 162 (3-4) ◽  
pp. 260-272 ◽  
Author(s):  
Wei Hu ◽  
Mingan Shao ◽  
Fengpeng Han ◽  
Klaus Reichardt
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Temporal Variability ◽  
Land Surface ◽  
Surface Soil ◽  
Grass Land ◽  
Spatio Temporal

scholarly journals Thermal Remote Sensing: A tool to Determine Temporal Land Surface Temperature in Hawassa City, Ethiopia

2020 ◽  
Author(s):  
Mikias Biazen Molla
Keyword(s):  
Remote Sensing ◽  
Standard Deviation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Thermal Remote Sensing ◽  
The City

Abstract This investigation was conducted for the estimation of the temporal land surface temperature value using thermal remote sensing of Landsat-8 (OLI) Data in Hawassa City Administration, Ethiopia. Satellite datasets of Landsat-7 (ETM+) for 22nd March 2002 and Landsat-8 (OLI) of 22nd March 2019 were taken for this study. Different algorisms were used to estimate the Normalized Difference Vegetation Index threshold from the Red and Near-Infrared band and the ground earth's surface emissivity esteem is legitimately recovered from the thermal infrared by coordinating with the outcome got from MODIS information. The land use land cover map of the city was prepared with better accuracy using the on-screen classification technique. The spatial distribution of surface temperature of the city range from 6.62°C to 22.54°C with a mean of 14.58°C and a standard deviation of 11.25 in the year of march 22nd 2002. The LST result derived from Landsat 8 for March 22nd, 2019, ranges from 11.97°C to 35.5°C with a mean of 23.735 °C and a standard deviation of 16.64. In both years the higher LST values correspond to built-up/settlement and bare/open lands of the city; whereas, lower LST values were observed in vegetation (trees/woodlot, shrubs, and grass forested) area. Urban expansion (built-up area roads, and another impervious surface), decline in vegetation levels due to deforestation and increasing population density. Increasing an evergreen tree and green space coverage, design and develop city parks and rehabilitate the existing degraded natural environments are among the recommended strategy to reduce the rate of LST.

Thermal remote sensing of land surface temperature from satellites: Current status and future prospects

1995 ◽  
Vol 12 (3-4) ◽  
pp. 175-224 ◽  
Author(s):  
A. J. Prata ◽  
V. Caselles ◽  
C. Coll ◽  
J. A. Sobrino ◽  
C. Ottlé
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Current Status ◽  
Future Prospects ◽  
Thermal Remote Sensing

scholarly journals Decoupling of surface and near-surface soil water content: A remote sensing perspective

1997 ◽  
Vol 33 (6) ◽  
pp. 1383-1395 ◽  
Author(s):  
William J. Capehart ◽  
Toby N. Carlson
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Surface Soil ◽  
Near Surface

Estimating soil water content from surface digital image gray level measurements under visible spectrum

2011 ◽  
Vol 91 (1) ◽  
pp. 69-76 ◽  
Author(s):  
Yuanjun Zhu ◽  
Yunqiang Wang ◽  
Mingan Shao ◽  
Robert Horton
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Digital Image ◽  
Surface Soil ◽  
Visible Spectrum ◽  
Soil Surface ◽  
Soil Samples ◽  
Gray Level

Zhu, Y., Wang, Y., Shao, M. and Horton, R. 2011. Estimating soil water content from surface digital image gray level measurements under visible spectrum. Can. J. Soil Sci. 91: 69–76. Determining soil water content (SWC) is fundamental for soil science, ecology and hydrology. Many methods are put forward to measure SWC, such as drying soil samples, neutron probes, time domain reflectrometry (TDR) and remote sensing. Sampling and drying soil is time-consuming. A neutron probe cannot determine SWC of surface soil accurately because neutrons escape when they are emitted near soil surface and TDR is, to some extent, influenced by soil salinity and temperature. Remote sensing can obtain SWC over a large area across a range of temporal and spatial scales. Complicated terrain and atmospheric conditions often make remote sensing data unreliable. Determining SWC from surface gray level (GL) measurements in the visible spectrum may have advantages over other remote sensing techniques, because surface soil images can be easily acquired by digital cameras, even with complicated landforms and meteorological conditions. However, few studies use this method, and further work is required to develop the ability of visible spectrum digital images to accurately estimate SWC. In this study, 42 soil samples were collected to investigate the relationship between surface GL and SWC using computer processing of soil surface images acquired by a digital camera. After establishing an equation to describe this relationship, a simple calibrated model was developed. The calibrated model was validated by an independent set of 48 soil samples. The results indicate that surface GL was sensitive to SWC. There was a negative linear relationship between surface GL and the square of SWC for the 42 calibration soil samples (correlation coefficients >0.91). Based on this negative relationship, a model was established to estimate SWC from surface GL. The results of model validation showed the estimated SWCs by surface GL were very close to the measured SWCs (correlation coefficient=0.99 at a significant level of 0.01). Generally, SWC could be estimated from surface GL for a given soil, and the model could be used to quickly and accurately determineg SWC from surface GL measurements.

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