Effect of sampling patterns and interpolation methods on prediction quality of soil variability mapping (original) (raw)
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Remote Sensing, 2019
With the increasing requirements of precision agriculture for massive and various kinds of data, remote sensing technology has become indispensable in acquiring the necessary data for precision agriculture. Understanding the spatial variability of a target soil variable (i.e., soil mapping) is a critical issue in solving many agricultural problems. Field sampling is one of the most commonly used technologies for soil mapping, but sample sizes are restricted by resources, such as field labor, soil physicochemical analysis, and funding. In this paper, we proposed a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes. The proposed method used the super-grid to achieve good spatial coverage, and it took advantage of remote sensing products that were highly correlated with the target soil property (SOM content) to achieve good feature space coverage...
Optimal Spatial Interpolation of Soil Properties to Assist Precision Agriculture
Site-specific crop management requires matching resource application and agronomic practices with soil and crop requirements, as they vary in space and time within a field. As such, information on the composition of soils at either farm or paddock scale is essential. Soil composition over an entire paddock might not be uniform, so, for instance, it may not be efficient to fertilise an entire paddock if only the northeast corner show deficiencies. Furthermore, it is not possible to sample every centimetre of the paddock, as this would be a very time consuming and costly procedure. Ideally, we should be able to collect enough sample points so that continuos maps of soil properties can be produced using accurate spatial interpolation techniques, and good judgements can be made about the soil composition of an entire paddock.
Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping
Geoderma, 2007
Legacy soil data form an important resource for digital soil mapping and are essential for calibration of models for predicting soil properties from environmental variables. Such data arise from traditional soil survey. Methods of soil survey are generally empirical and based on the mental development of the surveyor, correlating soil with underlying geology, landforms, vegetation and air-photo interpretation. There are no statistical criteria for traditional soil sampling, and this may lead to biases in the areas being sampled. The challenge is to test the use of legacy data for large-area mapping (e.g. national or continental extents) in order to limit the funds of field survey for large-area mapping. The problem is then to assess the reliability and quality of the legacy soil databases that have been mainly populated by traditional soil survey, and if there is a possibility of additional funding for sampling, to determine where new sampling units should be located. This additional sampling can be used to improve and validate the prediction model.Latin hypercube sampling (LHS) has been proposed as a sampling design for digital soil mapping when there is no prior sample. We use the principle of hypercube sampling to assess the quality of existing soil data and guide us to locations that need to be sampled.First an area is defined and the empirical environmental data layers or covariates are identified on a regular grid. The existing soil data are matched with the environmental variables. The HELS algorithm is used to check the occupancy of the legacy sampling units in the hypercube of the quantiles of the covarying environmental data. This is to determine whether legacy soil survey data occupy the hypercube uniformly or if there is over- or under-observation in the partitions of the hypercube. It also allows posterior estimation of the apparent probability of sample units being surveyed. From this information we can design further sampling. The methods are illustrated using legacy soil samples from Edgeroi, New South Wales, Australia, and from a large part of the Danube Basin. One third of the total number of sampling units are added to the original dataset. These new sampling units improve the representation of the feature space of the covariate. The standard deviation of the overall density is consequently smaller.
Optimization of soil sampling in sustainable agricultural systems
A traditional measurement of soil characteristics using soil sampling provides accurate information on current levels of soil conditions. However, with regard to cost and labour consumption, it does not allow the effective expression of spatial variability. At two different locations in the Czech Republic, were verified the use of soil electrical conductivity (EC) measurement and aerial imaging which gives rapid, inexpensive, and at the same time, precise description of spatial variability of pH value. EC and aerial imaging demonstrated a similar potential for detecting the change in soil characteristics because they are significantly affected by soil factors such as soil texture, moisture and organic matter. The results of indirect methods were used to optimize soil sampling designs and therefore they were compared to a regular sampling grid with different density. The results suggest that not only the sampling density but also the sampling design are of crucial importance for the desired accuracy of soil maps generated from soil sampling. The optimization of the sampling grid based on indirect methods enables the achievement of a considerable reduction in sample numbers (from 25 to 48 %) while keeping final accuracy of soil maps.
Agronomy, 2021
Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P2O5) and potassium oxide (K2O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of i...
Spatial interpolation quality assessment for soil sensor transect datasets
Near-ground geophysical soil sensors provide valuable information for precision agriculture applications. Indeed, their readings can be used as proxy for many soil parameters. On-the-go soil sensor surveys are, typically, carried out intensively (e.g., every 2 m) over many parallel transects. Two types of soil sensors measurements are considered in this paper: apparent electrical conductivity (4 fields in California, USA) and reflectance (1 field in Italy). Two types of spatial interpolations are carried out, universal kriging (model-based) and inverse distance weighting (deterministic). Interpolation quality assessment is usually carried out using leave-one-out (loo) resampling. We show that loo resampling on transect sampling datasets returns overly-optimistic, low interpolation errors, because the left-out data point has values very close to that of its neighbors in the training dataset. This bias in the map quality assessment can be reduced by removing the closest neighbors of the validation observation from the training dataset, in a (spatial) h-block (SHB) fashion. The results indicate that, for soil sensor data acquired along parallel transects: (i) the SHB resampling is a useful tool to test the performance of interpolation techniques and (ii) the optimal (i.e., rendering the same errors of un-sampled locations between transects) SHB threshold distance (h.dist) for neighbor-exclusion is proportional to the semi-variogram range and partial sill. This procedure provides research scientists with an improved means of understanding the error of soil maps made by interpolating soil sensor measurements. Published by Elsevier B.V.
The effect of short-range spatial variability on soil sampling uncertainty
Applied Radiation and Isotopes, 2008
This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.
Communications in Soil Science and Plant Analysis, 2019
Spatial interpolation methods are frequently used to characterize soil attributes' spatial variability. However, inconclusive results, about the comparative performance of these methods, have been reported in the literature. Therefore, the present study aimed to analyze the efficiency of ordinary kriging (OK) and inverse distance weighting (IDW) methods in estimating the soil penetration resistance (SPR), soil bulk density (SBD), and soil moisture content (SM) using two distinct sampling grids. The soil sampling was performed on a 5.7 ha area in Southeast Brazil. For data collection, a regular grid with 145 points (20 x 20 m) was created. Soil samples were taken at a 0.20 m layer depth. In order to compare the accuracy of OK and IDW, another grid was created from the initial grid (A), by eliminating one interspersed line, which resulted in a grid with 41 sampled points (40 x 40 m). Results showed that sampling grid A presented less errors than B, proving that the more sampling points, the lower the errors that are associated with both methods will be. Overall, the OK was less biased than IDW only for SBD (A) and SM (B) maps, whereas IDW outperformed OK for the other attributes for both sampling grids.