Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

Hoehn, Dylan C.; Niemann, Jeffrey D.; Green, Timothy R.; Jones, Andrew; Grazaitis, Peter J.

doi:10.1016/j.rse.2017.07.021

Cited by 10 publications

(8 citation statements)

References 61 publications

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“…The use of multiple LR grids as input to disaggregation approaches has been proposed in Hoehn et al [60] and Merlin et al [26]. Hoehn et al [60] compared downscaling results obtained from single coarse resolution grid (using fixed window) and using multiple overlapping coarse resolution grids (by shifting windows). Shifting windows using multiple grids showed better performance as compared to the fixed window case with respect to error and smoothness.…”

Section: Inclusion Of Multiple Isr Gridsmentioning

confidence: 99%

“…In this paper, we propose to define multiple ISR grids as an input to DISPATCH Exp as in Merlin et al [26] and Hoehn et al [60]. However, one difference herein is that the multiple ISR grids are built from actual observations at the (higher) 1 km resolution and consequently, they are derived neither from the LR overlapped observations [26], nor from the oversampling of LR (SMAP) observations [60]. Figure 6 gives an illustration of the moving average window algorithm, over the 1 km resolution grid of the DISPATCH Lin output data.…”

Section: Inclusion Of Multiple Isr Gridsmentioning

confidence: 99%

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Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

et al. 2019

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Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) algorithm. The main objectives of this paper is (i) to implement DISPATCH algorithm at 100 m resolution using SMAP SM and Landsat land surface temperature and vegetation index data and (ii) to investigate the usefulness of an intermediate spatial resolution (ISR) between the SMAP 36 km resolution and the targeted 100 m resolution. The sequential disaggregation approach from 36 km to ISR (ranging from 1 km to 30 km) and from ISR to 100 m resolution is evaluated over 22 irrigated field crops in central Morocco using in-situ SM measurements collected from January to May 2016. The lowest root mean square difference (RMSD) between the 100 m resolution disaggregated and in-situ SM is obtained when the ISR is around 10 km. Therefore, the two-step disaggregation is more efficient than the direct disaggregation from SMAP to 100 m resolution. Moreover, we propose a moving average window algorithm to increase the accuracy in the 100 m resolution SM as well as to reduce the low-resolution boxy artifacts on disaggregated images. The correlation coefficient between 100 m resolution disaggregated and in situ SM ranges between 0.5–0.9 for four out of the six extensive sampling dates. This methodology relies solely on remote sensing data and can be easily implemented to monitor SM at a high spatial resolution over irrigated regions.

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Section: Inclusion Of Multiple Isr Gridsmentioning

confidence: 99%

Section: Inclusion Of Multiple Isr Gridsmentioning

confidence: 99%

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

et al. 2019

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“…Excellent reviews on the current downscaling methods have been provided by [29] and [28]. These methods can be classified into six types: radar-based [31][32][33], radiometer-based [34], optical-based [35,36], soil surface attributes-based [37], data-assimilation-based [38], and machine-learning-based [39] downscaling methods. Their strengths and weaknesses have been well-listed in [28,29].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

Zeng

Xiang

et al. 2019

Remote Sensing

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Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

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“…A large number of near-real-time on-site measurements (e.g. wireless sensor networks) is necessary in order to develop optimal prevention, mitigation, and adaptation strategies for soil erosion control, flood forecasting, afforestation programs after wildfires, and assessment of contaminant transport to the groundwater (Meyles et al, 2003;Cosh et al, 2004;Hoehn et al, 2017). However, such dense-information systems require excessive costs, time, and efforts for their installation and maintenance.…”

Section: Introductionmentioning

confidence: 99%

“…Coleman and Niemann (2013) proposed the Equilibrium Moisture from Topography (EMT) model to downscale spatial-average soil moisture values by exploiting information on topographic attributes only. The EMT model was subsequently upgraded by including spatial information about soil and vegetation characteristics (Ranney et al, 2015) across different spatial scales (Cowley et al, 2017;Hoehn et al, 2017). This physically-sound model provides metrics to define the dominant type of topographic indexes in explaining the spatial variation of soil moisture patterns.…”

Section: Introductionmentioning

confidence: 99%

Downscaling near-surface soil moisture from field to plot scale: A comparative analysis under different environmental conditions

Nasta

Penna

Brocca

et al. 2018

Journal of Hydrology

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a b s t r a c tIndirect measurements of field-scale (hectometer grid-size) spatial-average near-surface soil moisture are becoming increasingly available by exploiting new-generation ground-based and satellite sensors. Nonetheless, modeling applications for water resources management require knowledge of plot-scale (1-5 m grid-size) soil moisture by using measurements through spatially-distributed sensor network systems. Since efforts to fulfill such requirements are not always possible due to time and budget constraints, alternative approaches are desirable. In this study, we explore the feasibility of determining spatial-average soil moisture and soil moisture patterns given the knowledge of long-term records of climate forcing data and topographic attributes. A downscaling approach is proposed that couples two different models: the Eco-Hydrological Bucket and Equilibrium Moisture from Topography. This approach helps identify the relative importance of two compound topographic indexes in explaining the spatial variation of soil moisture patterns, indicating valley-and hillslope-dependence controlled by lateral flow and radiative processes, respectively. The integrated model also detects temporal instability if the dominant type of topographic dependence changes with spatial-average soil moisture. Model application was carried out at three sites in different parts of Italy, each characterized by different environmental conditions. Prior calibration was performed by using sparse and sporadic soil moisture values measured by portable time domain reflectometry devices. Cross-site comparisons offer different interpretations in the explained spatial variation of soil moisture patterns, with time-invariant valley-dependence (site in northern Italy) and hillslope-dependence (site in southern Italy). The sources of soil moisture spatial variation at the site in central Italy are time-variant within the year and the seasonal change of topographic dependence can be conveniently correlated to a climate indicator such as the aridity index.

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Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

Cited by 10 publications

References 61 publications

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution

Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

Downscaling near-surface soil moisture from field to plot scale: A comparative analysis under different environmental conditions

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