Geostatistics for Engineers and Earth Scientists.

Webster, R.

doi:10.1046/j.1365-2389.2000.00334-9.x

Cited by 6 publications

(4 citation statements)

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“…The result implies the existence of a spatial autocorrelation between soil texture and terrain attributes. Thus, the result was consistent with the theory of catenary soil development [52]. In the catenary soil development approach, there is a sequence of soils down a slope, created by the balance of processes such as precipitation, infiltration, and runoff.…”

Section: Geo-statistical Interpolation Of Soil Texturesupporting

confidence: 85%

Land Use Evaluation over the Jema Watershed, in the Upper Blue Nile River Basin, Northwestern Highlands of Ethiopia

Taye¹,

Simane

Zaitchik

et al. 2019

Land

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Generating land capability class guidelines at a watershed scale has become a priority in sustainable agricultural land use. This study analyzed the area of cultivated land use situated on the non-arable land-capability class in the Jema watershed in the Upper Blue Nile River Basin. Soil surveys, meteorological ground observations, a digital elevation model (DEM) at 30 m, Meteosat at 10 km × 10 km and Landsat at 30 m were used to generate the sample soil texture class, average annual total rainfall (ATRF in mm), terrain, slope (%), elevation (m a.s.l) and land-use land cover (%). The land capability class was analyzed by considering raster layers of terrain, the average ATRF and soil texture. Geo-statistics was employed to fit a surface of soil texture and average ATRF estimates. An overlay technique was used to compute the proportion of cultivated land placed on non-arable land. As per the results of the terrain analysis, the elevation (m a.s.l) of the watershed is in the range of 1895 to 3518 m. The slope was found to be in the range of 0 to 45%. The amount of estimated rainfall ranged from 1640 to 131 mm with value declined from the lower to the higher elevation. Clay loam, clay and heavy clay were found to be the major soil texture classes. Four land capability classes, i.e., II, III, IV (arable) and V (non-arable), were identified with proportions of 28.56%, 45.74%, 22.16% and 3.54%, respectively. Seven land-use land covers were identified, i.e., annual crop land, grazing land, bush land, bare land, settlement land, forestland and water bodies, with proportions of 42.1, 35.9, 8.90, 8.3, 2.6, 2.1, and 0.2, respectively. Around 1707.7 ha of land in the watershed is categorized under non-arable land that cannot be used for annual crop cultivation at any level of intensity. Around 437 ha (3.5%) of land was cultivated on non-arable land. To conclude, the observed unsustainable crop land use could maximize soil loss in upstream regions and siltation and flooding downstream. The annual crop land use that was observed on non-arable land needs to be replaced with perennial crops, pasture and/or forest land uses.

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Section: Geo-statistical Interpolation Of Soil Texturesupporting

confidence: 85%

Land Use Evaluation over the Jema Watershed, in the Upper Blue Nile River Basin, Northwestern Highlands of Ethiopia

Taye¹,

Simane

Zaitchik

et al. 2019

Land

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“…Although the mapping can be performed at any spatial interval (e.g., once per 1 • × 1 • grid cell), the estimates remain representative of the variability at the scale of the observations. Here, we instead use block kriging (e.g., Webster, 2000), an approach that yields estimates that represent an average within a specified area. This makes it possible to disassociate the native footprint of the observations from the resolution of the mapped product, thereby making it possible to create contiguous maps at any desired spatial resolution equivalent to or greater than the size of the observation footprints.…”

Section: Mapping Using Moving Window Block Krigingmentioning

confidence: 99%

Mapping of satellite Earth observations using moving window block kriging

et al. 2015

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Abstract. Global gridded maps (a.k.a. Level 3 products) of Earth system properties observed by satellites are central to understanding the spatiotemporal variability of these properties. They also typically serve either as inputs into biogeochemical models or as independent data for evaluating such models. Spatial binning is a common method for generating contiguous maps, but this approach results in a loss of information, especially when the measurement noise is low relative to the degree of spatiotemporal variability. Such "binned" fields typically also lack a quantitative measure of uncertainty.Geostatistical mapping has previously been shown to make higher spatiotemporal resolution maps possible, and also provides a measure uncertainty associated with the gridded products. This study proposes a flexible moving window block kriging method that can be used as a tool for creating high spatiotemporal resolution maps from satellite data. It relies only on the assumption that the observed physical quantity exhibits spatial correlation that can be inferred from the observations. The method has several innovations relative to previously applied methods: (1) it provides flexibility in the spatial resolution of the contiguous maps, (2) it is applicable for physical quantities with varying spatiotemporal coverage (i.e., density of measurements) by utilizing a more general and versatile data sampling approach, and (3) it provides rigorous assessments of the uncertainty associated with the gridded products. The method is demonstrated by creating Level 3 products from observations of columnintegrated carbon dioxide (XCO 2 ) from the GOSAT (Greenhouse Gases Observing Satellite) satellite, and solar induced fluorescence (SIF) from the GOME-2 (Global Ozone Monitoring Experiment-2) instrument.

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“…Here, we instead use block kriging (e.g., Webster, 2000), an approach that yields estimates that represent an average within a specified area. This makes it possible to disassociate the native footprint of the observations from the resolution of the mapped product, thereby making it possible to create contiguous maps at any desired spatial resolution equivalent to or greater than the size of the observation footprints.…”

Section: Mapping Using Moving Window Block Krigingmentioning

confidence: 99%

Mapping of satellite Earth observations using moving window block kriging

Tadić¹,

Qiu²,

Yadav³

et al. 2014

Preprint

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Abstract. Global gridded maps (a.k.a. Level 3 products) of Earth system properties observed by satellites are central to understanding the spatiotemporal variability of these properties. They also typically serve either as inputs into biogeochemical models, or as independent data for evaluating such models. Spatial binning is a common method for generating contiguous maps, but this approach results in a loss of information, especially when the measurement noise is low relative to the degree of spatiotemporal variability. Such "binned" fields typically also lack a quantitative measure of uncertainty. Geostatistical mapping has previously been shown to make higher spatiotemporal resolution maps possible, and also provides a measure of the uncertainty associated with the gridded products. This study proposes a flexible moving window block kriging method that can be used as a tool for creating high spatiotemporal resolution maps from satellite data. It relies only on the assumption that the observed physical quantity exhibits spatial correlation that can be inferred from the observations. The method has several innovations relative to previously applied methods: (1) it provides flexibility in the spatial resolution of the contiguous maps (2) it is applicable for physical quantities with varying spatiotemporal coverage (i.e., density of measurements) by utilizing a more general and versatile data sampling approach, and (3) it provides rigorous assessments of the uncertainty associated with the gridded products. The method is demonstrated by creating Level 3 products from observations of column-integrated carbon dioxide (XCO2) from the GOSAT satellite, and solar induced fluorescence (SIF) from the GOME-2 instrument.

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Geostatistics for Engineers and Earth Scientists.

Cited by 6 publications

References 0 publications

Land Use Evaluation over the Jema Watershed, in the Upper Blue Nile River Basin, Northwestern Highlands of Ethiopia

Land Use Evaluation over the Jema Watershed, in the Upper Blue Nile River Basin, Northwestern Highlands of Ethiopia

Mapping of satellite Earth observations using moving window block kriging

Mapping of satellite Earth observations using moving window block kriging

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