Generating a Danish raster-based topsoil property map combining choropleth maps and point information

Greve, Mogens Humlekrog; Greve, Mette B.; Bøcher, Peder Klith; Balstrøm, Thomas; Breuning‐Madsen, Henrik; Krogh, Lars Brian

doi:10.1080/00167223.2007.10649565

Cited by 80 publications

(60 citation statements)

References 11 publications

(11 reference statements)

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“…Soil texture data (sand-% vs. clay-% of 0-5 cm depth) per network station according to the composite classes. Discrimination of zones corresponding to the Danish soil grid (Greve et al, 2007) is also shown.…”

Section: Profile Soil Moisture and Temperature (All Depths)mentioning

confidence: 99%

“…Consequently, topographical effects were neglected in the successive analysis. Table 1 summarizes soil types with respective grain size distribution and organic matter content of the 0-20 cm topsoil layer (250 m Danish topsoil grid, Greve et al, 2007). Accordingly, Table 2 shows the subsoil composition below 30 cm depth (clay versus sand) with corresponding clay contents based on a map from Bornebusch and Milthers (1935), Smed (1979), Schou (1949), andMilthers (1939).…”

Section: Selection Of Theoretical Station Locationsmentioning

confidence: 99%

“…7 the 0-5 cm depth texture data (sand-% vs. clay-%) for the network are shown and compared to the composite classes used in the Danish soil grid (Greve et al, 2007). As the organic content was not measured it is not possible to classify the two stations representing class 292/AOS.…”

Section: Calibration and Soil Texture Checks (0-5 Cm)mentioning

confidence: 99%

“…The eastern margin of the catchment is situated at the rim of the ice sheet during the latest glacial advance with mainly loamy soils on undulating calcareous tills. The remaining part comprises the primal fluvioglacial outwash plain consisting of low-relief sandy soils and sediments, while poorly drained basins have been filled with organic material (Greve et al, 2007).…”

Section: Study Areamentioning

confidence: 99%

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A soil moisture and temperature network for SMOS validation in Western Denmark

Bircher

Skou

Jensen

et al. 2012

Hydrol. Earth Syst. Sci.

129

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Abstract. The Soil Moisture and Ocean Salinity Mission (SMOS) acquires surface soil moisture data of global coverage every three days. Product validation for a range of climate and environmental conditions across continents is a crucial step. For this purpose, a soil moisture and soil temperature sensor network was established in the Skjern River Catchment, Denmark. The objectives of this article are to describe a method to implement a network suited for SMOS validation, and to present sample data collected by the network to verify the approach. The design phase included (1) selection of a single SMOS pixel (44 × 44 km), which is representative of the land surface conditions of the catchment and with minimal impact from open water (2) arrangement of three network clusters along the precipitation gradient, and (3) distribution of the stations according to respective fractions of classes representing the prevailing environmental conditions. Overall, measured moisture and temperature patterns could be related to the respective land cover and soil conditions. Texture-dependency of the 0-5 cm soil moisture measurements was demonstrated. Regional differences in 0-5 cm soil moisture, temperature and precipitation between the north-east and south-west were found to be small. A first comparison between the 0-5 cm network averages and the SMOS soil moisture (level 2) product is in range with worldwide validation results, showing comparable trends for SMOS retrieved soil moisture (R 2 of 0.49) as well as initial soil moisture and temperature from ECMWF used in the retrieval algorithm (R 2 of 0.67 and 0.97, respectively). While retrieved/initial SMOS soil moisture indicate significant under-/overestimation of the network data (biases of −0.092/0.057 m 3 m −3 ), the initial temperature is in good agreement (bias of −0.2 • C). Based on these findings, the network performs according to expectations and proves to be well-suited for its purpose. The discrepancies between network and SMOS soil moisture will be subject of subsequent studies.

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Section: Profile Soil Moisture and Temperature (All Depths)mentioning

confidence: 99%

Section: Selection Of Theoretical Station Locationsmentioning

confidence: 99%

Section: Calibration and Soil Texture Checks (0-5 Cm)mentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

See 2 more Smart Citations

A soil moisture and temperature network for SMOS validation in Western Denmark

Bircher

Skou

Jensen

et al. 2012

Hydrol. Earth Syst. Sci.

129

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“…The parameterization of the unsaturated zone is spatially distributed and is based on texture data classified into nine soil types (Greve et al, 2007). These range from coarse sandy soil (soil type 1) to heavy clayey soil (soil type 8), and also includes organic soil (soil type 11).…”

Section: Model Parameterizationmentioning

confidence: 99%

Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance

Rasmussen

Madsen

Jensen

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members, an adaptive localization method is used. The performance of the adaptive localization method is compared to the more common distance-based localization. The relationship between filter performance in terms of hydraulic head and discharge error and the number of ensemble members is investigated for varying numbers and spatial distributions of groundwater head observations and with or without discharge assimilation and parameter estimation. The study shows that (1) more ensemble members are needed when fewer groundwater head observations are assimilated, and (2) assimilating discharge observations and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data only.

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Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark

Ridler

Madsen

Stisen

et al. 2014

Water Resources Research

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Real surface soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite is downscaled and assimilated in a fully integrated hydrological and soil-vegetation-atmosphere transfer (MIKE SHE SW-ET) model using a bias aware ensemble transform Kalman filter (Bias-ETKF). Satellite-derived soil moisture assimilation in a catchment scale model is typically restricted by two challenges: (1) passive microwave is too coarse for direct assimilation and (2) the data tend to be biased. The solution proposed in this study is to disaggregate the SMOS bias using a higher resolution land cover classification map that was derived from Landsat thermal images. Using known correlations between SMOS bias and vegetation type, the assimilation filter is adapted to calculate biases online, using an initial bias estimate. Real SMOS-derived soil moisture is assimilated in a precalibrated catchment model in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2) compare the efficacy of in situ versus SMOS assimilation, and (3) determine how soil moisture assimilation affects fluxes and discharge in the catchment. We find that assimilation of SMOS improved R 2 soil moisture correlations in the upper 5 cm compared to a network of 30 in situ sensors for most land cover classes. Assimilation also brought modest gains in R 2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks.

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Generating a Danish raster-based topsoil property map combining choropleth maps and point information

Cited by 80 publications

References 11 publications

A soil moisture and temperature network for SMOS validation in Western Denmark

A soil moisture and temperature network for SMOS validation in Western Denmark

Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance

Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark

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