Applications of microwave remote sensing of soil moisture for water resources and agriculture

Engman, Edwin T.

doi:10.1016/0034-4257(91)90013-v

Cited by 193 publications

(89 citation statements)

References 25 publications

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“…Direct observations of ground measurements provide surface soil moisture with high accuracy and scalable frequency at the points measured. However, the most obvious limitations of the ground soil moisture measurements are their spatial discontinuity at specific locations [5], and, therefore, point-based or ground station measurements do not represent the spatial distribution since soil moisture varies spatiotemporally [6]. For the requirement of soil moisture estimation at a large scale (i.e., national scale), satellite remote sensing (RS) measurements are the preferred operational option.…”

Section: Introductionmentioning

confidence: 99%

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Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Zhang

Huang

et al. 2017

IJGI

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Abstract:In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR) were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R 2 = 0.9875 and a root mean square error (RMSE) of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive (SMAP) active radar soil moisture products and the Global Land data assimilation system (GLDAS) 0-10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale.

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Section: Introductionmentioning

confidence: 99%

“…J. Geo-Inf. 2017, 6, 130 2 of 20 situ soil moisture measurements using RS measurements via statistical regression models. However, conventional statistical regression models have difficulties in extracting complex correlations between large-scale RS data and in situ soil moisture.…”

Section: Introductionmentioning

confidence: 99%

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Zhang

Huang

et al. 2017

IJGI

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“…For radiometers working in shorter wavelength ranges, atmospheric attenuation and emission of the signal can be expressed as [41,42] Where, is the microwave brightness temperature, is the atmospheric transmission, is the surface reflectivity and , and are the temperatures of the sky, soil and atmosphere, respectively. For typical soil moisture applications using longer microwave wavelengths, atmosphere is transparent in most atmospheric conditions (~1K) and is much less (~3.5K); hence these terms can be neglected.…”

Section: Scientific Rationale Behind Soil Moisture Retrieval Using Pamentioning

confidence: 99%

“…For typical soil moisture applications using longer microwave wavelengths, atmosphere is transparent in most atmospheric conditions (~1K) and is much less (~3.5K); hence these terms can be neglected. Therefore, Where is the emissivity, which depends upon the dielectric constant of the medium [41].…”

Section: Scientific Rationale Behind Soil Moisture Retrieval Using Pamentioning

confidence: 99%

Passive Microwave Remote Sensing of Soil Moisture: A Step-ByStep Detailed Methodology using AMSR-E Data over Indian SubContinent

Singh

Srivastava

Shashi

et al. 2015

IJARSG

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This paper presents a detailed methodology to process AMSR-E soil moisture data to generate average soil moisture maps of desired durations be it weekly, monthly or yearly over a large geographical area like a continent or a sub-continent. The paper also explores utility of AMSR-E soil moisture product (AE_Land 3 product) to understand the soil moisture variations over Indian subcontinent by analysing daily soil moisture data for entire calendar year of 2009. In order to demonstrate the developed methodology the year 2009 was selected wherein a total of 730 AMSR-E daily scenes (365 each for ascending as well as descending passes) were processed and analysed. Although the absolute values of soil moisture derived from AMSR-E are not showing good agreement with soil moisture status on ground which is due to large variability in soil moisture within the coarseresolution cell offered by passive sensors [1] but in general AMSR-E derived soil moisture values are well explained on the basis of rainfall data and agricultural practices adopted in different states of Indian sub-continent. The soundness of the detailed methodology proposed in this paper has been well supported by studying the variations in AMSR-E derived soil moisture with seasonal variations and rainfall data. It has been observed that the soil moisture variations are in line with the seasonal changes as well as the rainfall variations.

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“…Directly active [Ulaby et al, 1996] and passive microwave [Njoku and Entekkabi, 1996] remote sensing approaches were developed to estimate surface soil moisture dynamics. However, the use of RS pixel-based data is limited due to the scale discrepancy between observed RS resolution and required modeling resolution [Engman, 1991;Entekhabi et al, 1999]. In this regard, downscaling schemes are necessary to improve the availability of subpixel soil moisture products from RS footprints/pixels for agriculture and water resources management at the field scale.…”

Section: Introductionmentioning

confidence: 99%

Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications

Shin

Mohanty

2013

Water Resour. Res.

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[1] Soil moisture (SM) at the local scale is required to account for small-scale spatial heterogeneity of land surface because many hydrological processes manifest at scales ranging from cm to km. Although remote sensing (RS) platforms provide large-scale soil moisture dynamics, scale discrepancy between observation scale (e.g., approximately several kilometers) and modeling scale (e.g., few hundred meters) leads to uncertainties in the performance of land surface hydrologic models. To overcome this drawback, we developed a new deterministic downscaling algorithm (DDA) for estimating fine-scale soil moisture with pixel-based RS soil moisture and evapotranspiration (ET) products using a genetic algorithm. This approach was evaluated under various synthetic and field experiments (Little Washita-LW 13 and 21, Oklahoma) conditions including homogeneous and heterogeneous land surface conditions composed of different soil textures and vegetations. Our algorithm is based on determining effective soil hydraulic properties for different subpixels within a RS pixel and estimating the long-term soil moisture dynamics of individual subpixels using the hydrological model with the extracted soil hydraulic parameters. The soil moisture dynamics of subpixels from synthetic experiments matched well with the observations under heterogeneous land surface condition, although uncertainties (Mean Bias Error, MBE: À0.073 to À0.049) exist. Field experiments have typically more variations due to weather conditions, measurement errors, unknown bottom boundary conditions, and scale discrepancy between remote sensing pixel and model grid resolution. However, the soil moisture estimates of individual subpixels (from the airborne Electronically Scanned Thinned Array Radiometer (ESTAR) footprints of 800 m Â 800 m) downscaled by this approach matched well (R: 0.724 to À0.914, MBE: À0.203 to À0.169 for the LW 13; R: 0.343-0.865, MBE: À0.165 to À0.122 for the LW 21) with the in situ local scale soil moisture measurements during Southern Great Plains Experiment 1997 (SGP97). The good correspondence of observed soil water characteristics (h) functions (from the soil core samples) and genetic algorithm (GA) searched soil parameters at the LW 13 and 21 sites demonstrated the robustness of the algorithm. Although the algorithm is tested under limited conditions at field scale, this approach improves the availability of remotely sensed soil moisture product at finer resolution for various land surface and hydrological model applications.Citation: Shin, Y., and B. P. Mohanty (2013), Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications, Water Resour. Res., 49,

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Applications of microwave remote sensing of soil moisture for water resources and agriculture

Cited by 193 publications

References 25 publications

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Passive Microwave Remote Sensing of Soil Moisture: A Step-ByStep Detailed Methodology using AMSR-E Data over Indian SubContinent

Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications

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