scholarly journals Modification of the Land Surface Temperature – Vegetation Index Triangle Method for soil moisture condition estimation by using SYNOP reports

2020 ◽  
Vol 119 ◽  
pp. 106823
Author(s):  
Karol Przeździecki ◽  
Jarosław Zawadzki
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Condition Estimation ◽  
Triangle Method

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 A multitemporal remote sensing approach to parsimonious streamflow modeling in a southcentral Texas watershed, USA

2007 ◽  
Vol 4 (1) ◽  
pp. 1-33 ◽  
Author(s):  
B. P. Weissling ◽  
H. Xie ◽  
K. E. Murray
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Streamflow Prediction ◽  
Remotely Sensed Data ◽  
Moisture Condition ◽  
Soil Moisture Condition

Abstract. Soil moisture condition plays a vital role in a watershed's hydrologic response to a precipitation event and is thus parameterized in most, if not all, rainfall-runoff models. Yet the soil moisture condition antecedent to an event has proven difficult to quantify both spatially and temporally. This study assesses the potential to parameterize a parsimonious streamflow prediction model solely utilizing precipitation records and multi-temporal remotely sensed biophysical variables (i.e.~from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite). This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A multiple regression model, accounting for 78% of the variance of observed streamflow for calendar year 2004, was developed based on gauged precipitation, land surface temperature, and enhanced vegetation Index (EVI), on an 8-day interval. These results compared favorably with streamflow estimations utilizing the Natural Resources Conservation Service (NRCS) curve number method and the 5-day antecedent moisture model. This approach has great potential for developing near real-time predictive models for flood forecasting and can be used as a tool for flood management in any region for which similar remotely sensed data are available.

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.

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