Mapping within-field soil drainage using remote sensing, DEM and apparent soil electrical conductivity

Liu, Jiangui; Pattey, E.; Nolin, Michel C.; Miller, John R.; Ka, Oumar

doi:10.1016/j.geoderma.2007.11.011

Cited by 55 publications

(30 citation statements)

References 28 publications

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“…This supervised classification method has shown good performance in several works [17,52]. Two classification rules were created: the first one was used to predict the TFS-1 map and the second to predict the TFS-2 map.…”

Section: Digital Mapping Strategymentioning

confidence: 99%

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

Demattê¹,

López

Rizzo

et al. 2016

Remote Sensing

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There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in São Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400-2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R 2 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R 2 > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes (i.e., clay, iron and chemistry). We observed a close relationship between soil weathering index map and laboratory spectra + image spectra + relief parameters. The color composite of the 5R, 4G and 3B had great performance on the detection of soils along topossequences, since...

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Section: Digital Mapping Strategymentioning

confidence: 99%

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

Demattê¹,

López

Rizzo

et al. 2016

Remote Sensing

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“…The EM38 and Veris-3100 are commonly used in soil apparent electrical conductivity mapping (e.g. Bramley, 2001;Liu et al, 2008). These methods can map soil ECa at two different depths, the EM38 in horizontal and vertical dipole mode can measure soil ECa at roughly 0-75 cm and 0-150 cm, respectively, and the Veris-3100 usually measures at 0-30 cm and 0-90 cm.…”

Section: Geophysics -Gpr and Multi-frequency Emi Surveysmentioning

confidence: 99%

“…Different statistical techniques have been used to predict various soil properties from a variety of data sources (e.g. James et al, 2003;McBratney et al, 2003 and references therein;Liu et al, 2008;Grunwald, 2009). For example, logistic regression can be used to make continuous soil maps of soil groups influenced by topography (Debella-Gilo & Etzelmüller, 2009) and to quantify relationships between ancillary variables and soil maps (Kempen et al, 2009).…”

Section: Geophysics -Gpr and Multi-frequency Emi Surveysmentioning

confidence: 99%

Geophysics and Wine in New Zealand

Imre¹,

Mauk²

2011

Earth and Environmental Sciences

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“…To evaluate site-specific spatiotemporal variable soil properties at the field-scale, many functional soil mapping models and approaches based on digital elevation models (DEM), proximal and/or remote sensing (RS) data have been designed. Compared with geostatistical and spatial interpolation methods (e.g., Kriging procedures, fuzzy clustering algorithms) based on comprehensive, time-consuming, and costly field surveys and soil sampling, non-invasive remote and proximal sensors, combined with empirical-or physical-based data analysis, offer potentially more effective, quick, and cost-efficient continuous direct or indirect data on physiochemical soil characteristics, which are determined by spatial, temporal or spectral sensor resolutions [1,6,[11][12][13][14][15][16][17][18]. Due to the advantages of existing, easily accessible data archives, relatively low-cost and high-temporal, high-spatial resolution multispectral imagery and time series are available for qualitative and partly-quantitative soil information extraction, deduction of soil patterns, and mapping of SSM zones and soil surface units [7,15].…”

Section: Introductionmentioning

confidence: 99%

Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

et al. 2015

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This study proposes the development of a landscape-scale multitemporal soil pattern analysis (MSPA) method for organic matter (OM) estimation using RapidEye time series data analysis and GIS spatial data modeling, which is based on the methodology of Blasch et al. The results demonstrate (i) the potential of MSPA to predict OM for single fields and field composites with varying geomorphological, topographical, and pedological backgrounds and (ii) the method conversion of MSPA from the field scale to the multifield landscape scale. For single fields, as well as for field composites, significant correlations between OM and the soil pattern detecting first standardized principal components were found. Thus, high-quality functional OM soil maps could be produced after excluding temporal effects by applying modified MSPA analysis steps. A regional OM prediction model was developed using four representative calibration test sites. The MSPA-method conversion was realized applying the transformation parameters of the soilpattern detection algorithm used at the four calibration test sites and the developed regional prediction model to a multi-field, multitemporal, bare soil image mosaic of all agrarian fields of the Demmin study area in Northeast Germany. Results modeled at the landscape scale were validated at an independent test site with a resulting prediction error of 1.4 OM-% for the main OM value range of the Demmin study area.

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Mapping within-field soil drainage using remote sensing, DEM and apparent soil electrical conductivity

Cited by 55 publications

References 28 publications

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

Geophysics and Wine in New Zealand

Organic Matter Modeling at the Landscape Scale Based on Multitemporal Soil Pattern Analysis Using RapidEye Data

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