Retrievals of Turbulent Heat Fluxes and Surface Soil Water Content by Remote Sensing

2011 ◽  
pp. 469-502
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Surface Soil ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Turbulent Heat Fluxes

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.

scholarly journals SIMSPHERE: A DOWNLOADABLE SOIL/VEGETATION/ATMOSPHERE/TRANSFER (SVAT) MODEL FOR TEACHING AND RESEARCH

2021 ◽  
pp. 1-24
Author(s):  
Toby N. Carlson ◽  
Arthur A. Person ◽  
Thomas J. Canich
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Surface Soil ◽  
Teaching And Research ◽  
Penn State ◽  
Svat Model ◽  
The Web

AbstractSimsphere, a soil/vegetation/atmosphere/transfer (SVAT) model developed at Penn State, can be downloaded from the web for use by students and researchers. In existence for several decades, Simsphere has figured in both the classroom and in research at several universities. As such, Simsphere has been supported by a knowledgeable group of academic users and has been applied in a variety of applications, such as in remote sensing of surface soil water content, and in the assessment of water and ozone stresses on plants. This paper describes the model and how it can be downloaded and run.

Using soil surface gray level to determine surface soil water content

2010 ◽  
Vol 53 (10) ◽  
pp. 1527-1532 ◽  
Author(s):  
YuanJun Zhu ◽  
YunQiang Wang ◽  
MingAn Shao
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Surface Soil ◽  
Soil Surface ◽  
Gray Level

Evaluating Surface-Soil Water Content by Measuring Reflectance

1975 ◽  
Vol 39 (2) ◽  
pp. 238-242 ◽  
Author(s):  
E. L. Skidmore ◽  
J. D. Dickerson ◽  
H. Schimmelpfennig
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Surface Soil

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>

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