Satellite remote sensing applications for surface soil moisture monitoring: A review

Wang, Lingli; Qu, John J.

doi:10.1007/s11707-009-0023-7

Cited by 405 publications

(229 citation statements)

References 95 publications

(114 reference statements)

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“…Soil moisture is a crucial variable in controlling the hydrologic cycle between the land surface and the atmosphere through vegetation evaporation and transpiration [1][2][3][4]. Accurate soil moisture estimation at a site is of great importance in modeling the surface hydrologic circle and climate change.…”

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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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%

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

Zhang

Huang

et al. 2017

IJGI

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“…The results of this study showed a dramatic increase of agricultural land from 1985-2015 due to population growth. Rapid population growth and resource mismanagement can result in soil erosion and land degradation, causing imbalances in the environment [29].…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Analysis of Land Use and Land Cover Changes in Arid Region of Saudi Arabia

Albalawi¹

2018

GEOMATE

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Changes in land use/land cover (LU/LC) are a major factor driving global environmental change, especially in arid and semi-arid regions. As a result, expansion of agriculture and urbanization is occurring in ecologically fragile lands. This paper analyzed and assessed land use/land cover changes in an agricultural center Tabuk in Saudi Arabia, object-based classification technique (OBC) using Landsat images for seven years (1985, 1990, 1995, 2000, 2005, 2009, and 2015) were used for LU/LC analysis. The urban growth type for Tabuk was measured using Urban Growth Analysis Tool (UGAT) within a GIS. The study found an increase in urban areas from 48 km 2 in 1985 to 315 km 2 in 2015. A significant increase of 61% in agricultural land, from 1985 (112 km 2 ) to (577 km 2 ) in 2015. This expansion is occurring at the expense of the barren land with a loss of 768 km 2 . An overall classification accuracy ranged from 94% to 97% across all images. The urban growth type for Tabuk was determined as 72% to be of the extension-type over the study period, with urban areas expanding in a direction towards the northwest and west area. Assessing spatial and temporal LU/LC change has aided to understand the impacts of expanding agriculture and urban areas. The LU/LC maps could aid government planners and decision-makers to analyze and manage agricultural and urban growth.

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“…where CV s,k is the spatial coefficient of variation for all monitoring stations on measurement day k, σs is the mean spatial deviation on measurement day k and, and θ t is the mean temporal soil moisture content for the same measurement day k. The determination of the Number of Representative Stations (NRS for a unit area of 100 km 2 ) was based on prior studies [2,19,20]:…”

Section: Calculation Of Standard Errors Significance Levels and Optimentioning

confidence: 99%

“…The spatial distribution of soil moisture varies as a function of the spatial distribution of evapotranspiration and precipitation, ultimately influ-enced by topography, soil texture, and land use type [5][6][7][8]. Alternatives to collecting sufficient knowledge on its spatial distribution include high resolution in situ measurement and monitoring [17,18] or satellite remote sensing applications [19,20]. Soil moisture data sets could be correlated to other environmental variables to calculate more soil moisture data through correlation functions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

et al. 2015

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Between September 5, 2008 and September 5, 2009, near-surface soil moisture time series were collected in the northern part of a 1.7 km2 watershed in SW Hungary at 14 monitoring locations using a portable TDR-300 soil moisture sensor. The objectives of this study are to increase the accuracy of soil moisture measurement at watershed scale, to improve flood forecasting accuracy, and to optimize soil moisture sensor density. According to our results, in 10 of 13 cases, a strong correlation exists between the measured soil moisture data of Station 5 and all other monitoring stations; Station 5 is considered representative for the entire watershed. Logically, the selection of the location of the representative measurement point(s) is essential for obtaining representative and accurate soil moisture values for the given watershed. This could be done by (i) employing monitoring stations of higher number at the exploratory phase of the monitoring, (ii) mapping soil physical properties at watershed scale, and (iii) running cross-relational statistical analyses on the obtained data. Our findings indicate that increasing the number of soil moisture data points available for interpolation increases the accuracy of watershed-scale soil moisture estimation. The data set used for interpolation (and estimation of mean antecedent soil moisture values) could be improved (thus, having a higher number of data points) by selecting points of similar properties to the measurement points from the DEM and soil databases. By using a higher number of data points for interpolation, both interpolation accuracy and spatial resolution have increased for the measured soil moisture values for the Pósa Valley.

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Satellite remote sensing applications for surface soil moisture monitoring: A review

Cited by 405 publications

References 95 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

Spatio-Temporal Analysis of Land Use and Land Cover Changes in Arid Region of Saudi Arabia

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

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