Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach

2012 ◽  
Vol 50 ◽  
pp. 152-161 ◽  
Author(s):  
Christopher M.U. Neale ◽  
Hatim M.E. Geli ◽  
William P. Kustas ◽  
Joseph G. Alfieri ◽  
Prasanna H. Gowda ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Modeling Approach ◽  
Evapotranspiration Modeling

Analysis of agronomic drought over North Africa using remote sensing satellite data

2021 ◽  
Author(s):  
Mehrez Zribi ◽  
Simon Nativel ◽  
Michel Le Page
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
North Africa ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Study Region ◽  
Semi Arid Region ◽  
Anomaly Index

<p>This paper aims to analyze the agronomic drought in a highly anthropogenic  semi-arid region, North Africa. In the context of the Mediterranean climate, characterized by frequent droughts, North Africa is particularly affected. Indeed, in addition to this climatic aspect, it is one of the areas most affected by water scarcity in the world. Thus, understanding and describing agronomic drought is essential. The proposed study is based on remote sensing data from TERRA-MODIS and ASCAT satellite, describing the dynamics of vegetation cover and soil water content through NDVI and SWI indices. Two indices are analyzed, the Vegetation Anomaly Index (VAI) and the Moisture Anomaly Index (MAI). The dynamics of the VAI is analyzed for different types of regions (agircultural, forest areas). The contribution of vegetation cover is combined with the effect of soil water content through a new drought index combining the VAI and MAI. A discussion of this combination is proposed on different study areas in the study region. It illustrates the complementarity of these two informations in analysis of agronomic drought.</p>

Inverting Study on Soil Water Content Based on Harmonic Analysis and Hyperspectral Remote Sensing

2017 ◽  
Vol 37 (10) ◽  
pp. 1028001 ◽  
Author(s):  
姜雪芹 Jiang Xueqin ◽  
叶 勤 Ye Qin ◽  
林 怡 Lin Yi ◽  
李西灿 Li Xican
Keyword(s):  
Remote Sensing ◽  
Harmonic Analysis ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Hyperspectral Remote Sensing

scholarly journals Separation of soil and canopy reflectance signatures of Mid German agricultural soils

2011 ◽  
Vol 51 (No, 7) ◽  
pp. 296-303 ◽  
Author(s):  
T. Behrens ◽  
K. Gregor ◽  
W. Diepenbrock
Keyword(s):  
Remote Sensing ◽  
Soil Properties ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Vegetation Index ◽  
Near Infrared ◽  
Field Experiments ◽  
Agricultural Soils ◽  
Soil Reflectance

Remote sensing can provide visual indications of crop growth during production season. In past, spectral optical estimations were well performed in the ability to be correlated with crop and soil properties but were not consistent within the whole production season. To better quantify vegetation properties gathered via remote sensing, models of soil reflectance under changing moisture conditions are needed. Signatures of reflected radiation were acquired for several Mid German agricultural soils in laboratory and field experiments. Results were evaluated at near-infrared spectral region at the wavelength of 850 nm. The selected soils represented different soil colors and brightness values reflecting a broad range of soil properties. At the wavelength of 850 nm soil reflectance ranged between 10% (black peat) and 74% (white quartz sand). The reflectance of topsoils varied from 21% to 32%. An interrelation was found between soil brightness rating values and spectral optical reflectance values in form of a linear regression. Increases of soil water content from 0% to 25% decreased signatures of soil reflectance at 850 nm of two different soil types about 40%. The interrelation of soil reflectance and soil moisture revealed a non-linear exponential function. Using knowledge of the individual signature of soil reflectance as well as the soil water content at the measurement, soil reflectance could be predicted. As a result, a clear separation is established between soil reflectance and reflectance of the vegetation cover if the vegetation index is known.

Predicting Soil Water Content through Remote Sensing of Vegetative Characteristics in a Turfgrass System

Crop Science ◽  
2008 ◽  
Vol 48 (2) ◽  
pp. 763-770 ◽  
Author(s):  
Jason K. Dettman-Kruse ◽  
Nick E. Christians ◽  
Michael H. Chaplin
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content

Retrieving soil-water retention curve at the wet part by remote sensing

2020 ◽  
Author(s):  
Zampela Pittaki-Chrysodonta ◽  
Per Moldrup ◽  
Bo V. Iversen ◽  
Maria Knadel ◽  
Lis W. de Jonge
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Spectral Data ◽  
Water Retention ◽  
Water Retention Curve ◽  
Soil Water Retention Curve ◽  
Soil Water Retention ◽  
Retention Curve

<p>The soil water retention curve (SWRC) at the wet part is important for understanding and modeling the water flow and solute transport in the vadose zone. However, direct measurements of SWRC is often laborious and time consuming processes. The Campbell function is a simple method to fit the measured data. The parameters of the Campbell function have been recently proven that can be predicted using visible-near-infrared spectroscopy. However, predicting the SWRC using image spectral data could be an inexpensive and fast method. In this study, 100-cm<sup>3</sup> soil samples from Denmark were included and the soil water content was measured at a soil-water matric potential from pF 1 [log(10)= pF 1] up to pF 3. The anchored Campbell soil-water retention function was selected instead of the original. Specifically, in this function the equation is anchored at the soil-water content at pF 3 (θ<sub>pF3</sub>) instead at the saturated water content. The image spectral data were correlated with the Campbell parameters [θ<sub>pF3</sub>, and the pore size distribution index (Campbell b). The results showed the potential of remote sensing to be used as a fast and alternative method for predicting the SWRC in a large-scale.</p>

Remote sensing in microwave and gamma ranges for the monitoring of soil water content of the root zone

2013 ◽  
Vol 34 (17) ◽  
pp. 6182-6201 ◽  
Author(s):  
Leonid Vulfson ◽  
Arthur Genis ◽  
Dan G. Blumberg ◽  
Alexey Kotlyar ◽  
Valentin Freilikher ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Root Zone
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