Sampling scheme optimization to map soil depth to petrocalcic horizon at field scale

Geoderma ◽  
2017 ◽  
Vol 290 ◽  
pp. 75-82 ◽  
Author(s):  
Marisa Beatriz Domenech ◽  
Mauricio Castro-Franco ◽  
José Luis Costa ◽  
Nilda Mabel Amiotti
Keyword(s):  
Soil Depth ◽  
Sampling Scheme ◽  
Field Scale ◽  
Scheme Optimization

scholarly journals Visible and near infrared reflectance spectroscopy for field-scale assessment of Stagnosols properties

2018 ◽  
Vol 64 (No. 6) ◽  
pp. 276-282 ◽  
Author(s):  
Šestak Ivana ◽  
Mesić Milan ◽  
Zgorelec Željka ◽  
Perčin Aleksandra ◽  
Stupnišek Ivan
Keyword(s):  
Soil Ph ◽  
Near Infrared ◽  
Soil Depth ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Infrared Reflectance ◽  
Field Scale ◽  
Near Infrared Reflectance ◽  
Vnir Spectroscopy

Spectral data contain information on soil organic and mineral composition, which can be useful for soil quality monitoring. The objective of research was to evaluate hyperspectral visible and near infrared reflectance (VNIR) spectroscopy for field-scale prediction of soil properties and assessment of factors affecting soil spectra. Two hundred soil samples taken from the experiment field (soil depth: 30 cm; sampling grid: 15 × 15 m) were scanned using portable spectroradiometer (350–1050 nm) to identify spectral differences of soil treated with ten different rates of mineral nitrogen (N) fertilizer (0–300 kg N/ha). Principal component analysis revealed distinction between higher- and lower-N level treatments conditioned by differences in soil pH, texture and soil organic matter (SOM) composition. Partial least square regression resulted in very strong correlation and low root mean square error (RMSE) between predicted and measured values for the calibration (C) and validation (V) dataset, respectively (SOM, %: R<sub>C</sub><sup>2</sup> = 0.75 and R<sub>V</sub><sup>2</sup> = 0.74; RMSE<sub>C</sub> = 0.334 and RMSE<sub>V</sub> = 0.346; soil pH: R<sub>C</sub><sup>2</sup> = 0.78 and R<sub>V</sub><sup>2</sup> = 0.62; RMSE<sub>C</sub> = 0.448 and RMSE<sub>V</sub> = 0.591). Results indicated that hyperspectral VNIR spectroscopy is an efficient method for measurement of soil functional attributes within precision farming framework.  

scholarly journals Sampling scheme optimization for diffuse optical tomography based on data and image space rankings

2016 ◽  
Vol 21 (10) ◽  
pp. 106004 ◽  
Author(s):  
Sohail Sabir ◽  
Changhwan Kim ◽  
Sanghoon Cho ◽  
Duchang Heo ◽  
Kee Hyun Kim ◽  
...  
Keyword(s):  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Image Space ◽  
Sampling Scheme ◽  
Scheme Optimization

A METHOD OF ESTIMATING YIELD FOR LARGE-SCALE FIELD RESEARCH PROJECTS

1990 ◽  
Vol 70 (1) ◽  
pp. 43-50
Author(s):  
JERELEEN BRYDON ◽  
D. A. RENNIE
Keyword(s):  
Grain Yield ◽  
Large Scale ◽  
Cropping Systems ◽  
Field Research ◽  
Farming Systems ◽  
Farming System ◽  
Sampling Procedure ◽  
Sampling Scheme ◽  
Field Scale ◽  
Intensive Sampling

The Innovative Acres field-scale project was designed to compare water-efficient farming systems with the more commonly used crop-fallow farming system in Saskatchewan. This project spanned the period between 1982 and 1987, and tested 40 locations each year. The present study was undertaken at two of these locations, to compare the sampling methodology used by the Innovative Acres (IA) project with a more intensive sampling scheme, and thereby assess the relative validity of productivity estimates developed from the IA sampling method. At both locations, grain yield estimates for the field based upon twelve IA benchmark sites were statistically similar (P > 0.05) to yield estimates from the more intensive sampling scheme (59 samples). Yield estimates from the IA transect more closely approximated the farmers' estimates of grain yield at both locations. Weighted grain yields, based on the distribution of topography along the transects, gave no better estimate of yield than did grouped mean data at both locations. The IA sampling procedure estimated to within 10% of the true mean grain yield, at the 90% probability level. It is concluded that this level of precision allows accurate comparisons to be made between two cropping systems. Key words: Field-scale research, transects, topography

scholarly journals Modelling effective soil depth at field scale from soil sensors and geomorphometric indices

2017 ◽  
Vol 66 (2) ◽  
Author(s):  
Mauricio Castro Franco ◽  
Marisa Domenech ◽  
José Luis Costa ◽  
Virginia Carolina Aparicio
Keyword(s):  
Electrical Conductivity ◽  
Random Forest ◽  
Buenos Aires ◽  
Soil Depth ◽  
Buenos Aires Province ◽  
Field Scale ◽  
Soil Sensors ◽  
Statistic Model ◽  
Apparent Electrical Conductivity

The effective soil depth (ESD) affects both dynamic of hydrology and plant growth. In the southeast of Buenos Aires province, the presence of petrocalcic horizon constitutes a limitation to ESD. The aim of this study was to develop a statistic model to predict spatial patterns of ESD using apparent electrical conductivity at two depths: 0-30 (ECa_30) and 0-90 (ECa_90) and geomorphometric indices. To do this, a Random Forest (RF) analysis was applied. RF was able to select those variables according to their predictive potential for ESD. In that order, ECa_90, catchment slope, elevation and ECa_30 had main prediction importance. For validating purposes, 3035 ESD measurements were carried out, in five fields. ECa and ESD values showed complex spatial pattern at short distances. RF parameters with lowest error (OOBerror) were calibrated. RF model simplified which uses main predictors had a similar predictive development to it uses all predictors. Furthermore, RF model simplified had the ability to delineate similar pattern to those obtained from in situ measure of ESD in all fields. In general, RF was an effective method and easy to work. However, further studies are needed which add other types of variables importance calculation, greater number of fields and test other predictors in order to improve these results.

CD and OCD sampling scheme optimization for HVM environment

2019 ◽  
Author(s):  
Chanha Park ◽  
Hongoo Lee ◽  
Dongyoung Lee ◽  
Ahlin Choi ◽  
Stefan Buhl ◽  
...  
Keyword(s):  
Sampling Scheme ◽  
Scheme Optimization

Progress in evaluating satellite soil moisture products in Great Britain against COSMOS-UK and in-situ soil moisture measurements

2020 ◽  
Author(s):  
Nevil Wyndham Quinn ◽  
Chris Newton ◽  
David Boorman ◽  
Michael Horswell ◽  
Harry West
Keyword(s):  
Soil Moisture ◽  
Soil Depth ◽  
Value Added ◽  
Cosmic Ray ◽  
Temporal Variations ◽  
Preliminary Evaluation ◽  
Field Scale ◽  
Satellite Measurements ◽  
Pixel Resolution ◽  
Soil Moisture Data

&lt;p&gt;The resolution of satellite of satellite-derived soil moisture data products has matured, notably in recent years due to the Soil Moisture Active Passive mission (SMAP) launched in 2015. Whilst spatial resolutions still fall short of those suitable for field-scale monitoring, there are several &amp;#8216;value-added&amp;#8217; RS soil moisture products available at the regional (e.g. SMAP: 36km) to meso-scale (e.g. SMAP: 9km) resolution. Although the intended 3km scale SMAP product did not materialise due to the failure of the L-Band radar, a potential substitute product has recently become available (Das et al. 2019). The SMAP-Sentinel1 product combines data from SMAP and C-Band Sentinel 1A/B SAR data to synthesise global soil moisture at a 3km and 1km resolution (~6 day revisit for Europe).&lt;/p&gt;&lt;p&gt;Evaluation of these products against ground-based measurements in the USA and elsewhere is encouraging, but only preliminary evaluation has been undertaken in the United Kingdom. Evaluation is always challenging because (i) rather than a direct measurement, satellite estimates are based on other measured properties (e.g. brightness temperature) with soil moisture algorithmically inferred, (ii) ground-based measurements are highly localised in comparison with the measurement averaged over the satellites much larger pixel resolution, and (iii) satellite sensors typically estimate only surface soil moisture (0-5cm).&lt;/p&gt;&lt;p&gt;The COSMOS-UK network, under development since 2013, provides high resolution soil moisture data at 51 sites in the UK, corresponding to a variety of climatic, soil and land cover settings. Sites typically contain soil moisture probes at a variety of depths (including 10cm) as well as a cosmic ray sensor. The latter integrates soil moisture over an area of ~12ha, and while not matching the spatial scale or soil depth of satellite measurements, it does avoid some of the field-scale heterogeneity issues associated with point-based measurements.&lt;/p&gt;&lt;p&gt;The 9km SMAP L3 product performs well against 10cm soil probe measurements at most sites (&gt;70% at ubRMSE &lt;0.04), and seasonal patterns in performance are evident. Satellite measurements performed less well in comparison with COSMOS-UK estimates (68% at ubRMSE &lt;0.06). Downscaling the SMAP L3 product based on hydroclimatology improves performance in some cases but worsens it in others. The SMAP-Sentinel 1 product generally performs worse than the 9km SMAP L3 product. &amp;#160;Reasons for spatio-temporal variations in correlations and performance are proposed including reference to soil profile characteristics and properties at each site, as well as vegetation and climatic setting.&lt;/p&gt;

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