Soil moisture estimation with a remotely sensed dry edge determination based on the land surface temperature-vegetation index method

2019 ◽  
Vol 13 (02) ◽  
pp. 1 ◽  
Author(s):  
Jinfeng Yang ◽  
Dianjun Zhang
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Index Method ◽  
Moisture Estimation

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 Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

2018 ◽  
Vol 7 (7) ◽  
pp. 275 ◽  
Author(s):  
Bipin Acharya ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Laxman Khanal ◽  
Shahid Naeem ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Dengue Fever ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Remotely Sensed ◽  
Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

scholarly journals NEURAL NETWORK METHOD FOR DROUGHT MODELING USING SATELLITE DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 749-753
Author(s):  
R. Mokhtari ◽  
M. Akhoondzadeh
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Snow Cover ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Machine Learning Algorithms ◽  
Factors Affecting ◽  
Average Accuracy

Abstract. Drought is one of the natural crises in each region. Drought has a direct relationship with vegetation. Various factors affect vegetation. The relationship between these factors and vegetation can be expressed using methods of machine learning algorithms. Nowadays, using remote sensing images can be used to measure the factors affecting vegetation and investigate this phenomenon with high precision. In this research, vegetation and various factors affecting this factor, which can be measured using satellite imagery, are selected. The factors include land surface temperature (LST), evapotranspiration (ET), snow cover, rainfall, soil moisture that which are derived from the active and passive sensors of satellite sensors as the products of land surface temperature (LST), snow cover and vegetation, using images of products of the MODIS sensor and rainfall using the images of the TRMM satellite and soil moisture using the images of the SMOS satellite during a period from June 2010 to the end of 2018 for the central region of Iran has received and after that, primary processing was performed on these images. The vegetation index (NDVI) is modeled using artificial neural network algorithm for monthly periods. method have been able to achieve model with desirable accuracy. The average accuracy was RMSE = 0.048 and R2 = 0.867.

scholarly journals Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring

2021 ◽  
Vol 13 (9) ◽  
pp. 1778
Author(s):  
Soo-Jin Lee ◽  
Nari Kim ◽  
Yangwon Lee
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Crop Yield ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
The State ◽  
Agricultural Drought ◽  
Drought Monitoring

Various drought indices have been used for agricultural drought monitoring, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Soil Water Deficit Index (SWDI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Drought Response Index (VegDRI), and Scaled Drought Condition Index (SDCI). They incorporate such factors as rainfall, land surface temperature (LST), potential evapotranspiration (PET), soil moisture content (SM), and vegetation index to express the meteorological and agricultural aspects of drought. However, these five factors should be combined more comprehensively and reasonably to explain better the dryness/wetness of land surface and the association with crop yield. This study aims to develop the Integrated Crop Drought Index (ICDI) by combining the weather factors (rainfall and LST), hydrological factors (PET and SM), and a vegetation factor (enhanced vegetation index (EVI)) to better express the wet/dry state of land surface and healthy/unhealthy state of vegetation together. The study area was the State of Illinois, a key region of the U.S. Corn Belt, and the quantification and analysis of the droughts were conducted on a county scale for 2004–2019. The performance of the ICDI was evaluated through the comparisons with SDCI and VegDRI, which are the representative drought index in terms of the composite of the dryness and vegetation elements. The ICDI properly expressed both the dry and wet trend of the land surface and described the state of the agricultural drought accompanied by yield damage. The ICDI had higher positive correlations with the corn yields than SDCI and VegDRI during the crucial growth period from June to August for 2004–2019, which means that the ICDI could reflect the agricultural drought well in terms of the dryness/wetness of land surface and the association with crop yield. Future work should examine the other factors for ICDI, such as locality, crop type, and the anthropogenic impacts, on drought. It is expected that the ICDI can be a viable option for agricultural drought monitoring and yield management.

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

2010 ◽  
Vol 7 (6) ◽  
pp. 8703-8740 ◽  
Author(s):  
W. Wang ◽  
D. Huang ◽  
X.-G. Wang ◽  
Y.-R. Liu ◽  
F. Zhou
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Surface Soil Moisture ◽  
Antecedent Precipitation ◽  
Walnut Gulch ◽  
Semi Arid Region

Abstract. The trapezoidal relationship between 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 grid, and then WDI is calculated for each grid 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, Ts~VI trapezoid based WDI can well capture temporal variation in surface soil moisture, but the capability of detecting spatial variation is poor for such a semi-arid region as WGEW.

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