Conditioned Latin Hypercube Sampling: Optimal Sample Size for Digital Soil Mapping of Arid Rangelands in Utah, USA

Brungard, Colby; Boettinger, Janis L.

doi:10.1007/978-90-481-8863-5_6

Cited by 26 publications

(16 citation statements)

References 7 publications

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“…Results showed that optimized networks are representative of the whole study area and they well‐capture the overall seasonality of precipitation in the region according to the TRMM climatology. This result agrees with other studies which highlight the ability of cLHS to capture the variability of multiple input covariates with a limited number of samples (Brungard and Boettinger, ; Mulder et al ., ; Godinho Silva et al ., ; Levi and Rasmussen, ; Ramirez‐Lopez et al ., ; Yin et al ., ; ; Domenech et al ., ). Although a less representative sampling is expected under accessibility restrictions (Roudier et al ., ; Godinho Silva et al ., ; Yin et al ., ), no major difference were observed in the precipitation distribution captured by both networks.…”

Section: Discussionsupporting

confidence: 93%

“…Conditioned Latin hypercube sampling (cLHS) provides an approach for incorporating prior auxiliary information from remote sensing instruments as well as accessibility restrictions in a sample design. cLHS is a multivariate stratified random strategy (Minasny and McBratney, ) that has been proven to be an efficient sampling method because it captures the marginal variability of several variables using a relatively small sample (Brungard and Boettinger, ; Ramirez‐Lopez et al ., ; Stumpf et al ., ; Domenech et al ., ). Roudier et al .…”

Section: Introductionmentioning

confidence: 99%

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Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

Contreras

Ballari

Bruin

et al. 2018

Intl Journal of Climatology

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Rain gauge networks are crucial for enhancing the spatio-temporal characterization of precipitation. In tropical regions, scarcity of rain gauge data, climatic variability, and variable spatial accessibility make conventional approaches to design rain gauge networks inadequate and impractical. In this study, we propose the use of conditioned Latin hypercube sampling (cLHS) method with multi-temporal layers of remotely sensed precipitation measurements for capturing the spatio-temporal precipitation patterns in ungauged areas. The study was conducted in the Amazon region of Ecuador, for which monthly precipitation averages were derived based on a 16-year period of Tropical Rainfall Measuring Mission (TRMM 3B43 V7) data which were used as prior information to select representative sampling points through cLHS. Two scenarios for the sampling design were considered and evaluated, one without and one with restrictions on accessible sites according to the proximity to roads and settlements. Results showed that both optimized networks captured the variability of precipitation according to the TRMM climatology. Furthermore, evaluation against an independent satellite precipitation data set showed that the optimized networks support mapping precipitation based on ordinary kriging (OK). Comparison with regular and random sampling methods showed that particularly when a practical scenario is considered, the optimized network provided more reliable results over time, highlighting the suitability of the network to capture temporal changes and map precipitation with high accuracy. The proposed approach could be easily adopted in other ungauged and poorly accessible regions for rain gauge network design as well as to the design of multi-objective monitoring networks.

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Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

Contreras

Ballari

Bruin

et al. 2018

Intl Journal of Climatology

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“…We found that cLHS was efficient in capturing predictor variability of the soil properties. Our results support previous studies in this area (Brungard and Boettinger, 2010;Kidd et al, 2015). The curve density, mean, median, and S.D.…”

Section: Comparisons Of Soil-sampling Schemessupporting

confidence: 95%

“…Conditioned Latin hypercube sampling is a stratified random procedure that picks samples based on the distribution of predictors . Conditioned Latin hypercube sampling attempts to find a set of values from various soil predictors that satisfy the requirements of a Latin hypercube, which are that only one sample exists in each row and column in n dimensions (Brungard and Boettinger, 2010). The Latin hypercube is constructed by random sampling from the cumulative distribution of predictor data using a simulated annealing optimization approach, which additionally focuses on preserving the correlation between the predictors in the selected sample set .…”

Section: Model Developmentmentioning

confidence: 99%

“…The soil-sampling scheme is an important component of a DSM process (Brungard and Boettinger, 2010;Hengl et al, 2004) because it must ensure that soil-sampling sites are distributed through the full range of auxiliary information or predictors (feature space). Traditionally, the distribution of sampling locations is chosen on the basis of (i) subjective decisions of soil surveyors and/or (ii) a statistical approach such as judgmental sampling, simple random sampling, stratified sampling, systematic or grid sampling, ranked set sampling, adaptive cluster sampling, and composite sampling (Conklin, 2004;U.S.…”

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confidence: 99%

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Prediction of Soil Properties at Farm Scale Using a Model-Based Soil Sampling Scheme and Random Forest

et al. 2015

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Digital soil mapping techniques can reduce the costs associated with improving soil information at the farm scale. Many techniques exist for generating a digital soil mapping. In this study, we tested the use of a random forest (RF) and two model-based soil-sampling scheme, conditioned with Latin hypercube (cLHS) and fuzzy c-means sampling (FCMS) to predict soil properties for a 68-ha agricultural field. The predictor set included the use of inexpensive auxiliary information such as digital elevation models, yield maps, surface reflectance, and apparent soil electrical conductivity measurements. Our results support the underlying assumption that both cLHS and FCMS capture adequately the full predictor distribution in the Pampas conditions. Despite the complex local variation of soils and the instability of normalized difference vegetation index and yield maps, the RF models predicted soil properties at farm scale. The RF model with cLHS and FCMS explained about 65% (R 2 = 0.648) and 57% (R 2 = 0.571) of the variability of soil organic matter and about 71% (R 2 = 0.714) and 68% (R 2 = 0.686) of the variability of clay, respectively. Our results provide a significant improvement of information related to soil organic matter and clay content with respect to the soil survey available. Extrapolation and applicability of this study to other areas remain to be tested.

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Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping

Stumpf

Schmidt

Behrens

et al. 2016

J. Plant Nutr. Soil Sci.

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Most calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on the development and application of an efficiency improved DSM sampling design that (1) applies an optimized sample set size, (2) compensates for limited field accessibility, and (3) enables the integration of legacy soil samples. The proposed sampling design represents a modification of conditioned Latin Hypercube Sampling (cLHS), which originally returns distinct sample sites to optimally cover a soil related covariate space and to preserve the correlation of the covariates in the sample set. The sample set size was determined by comparing multiple sample set sizes of original cLHS sets according to their representation of the covariate space. Limited field accessibility and the integration of legacy samples were incorporated by providing alternative sample sites to replace the original cLHS sites. We applied the modified cLHS design (cLHS adapt ) in a small catchment (4.2 km 2 ) in Central China to model topsoil sand fractions using Random Forest regression (RF). For evaluating the proposed approach, we compared cLHS adapt with the original cLHS design (cLHS orig ). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHS adapt and cLHS orig , while the correlation between the covariates is preserved (r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHS adapt by adding available legacy samples (cLHS adapt+ ) and compared the prediction accuracies. Based on an external validation set cLHS val (n = 20), the coefficient of determination (R 2 ) of the cLHS adapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R 2 -values of the RF predictions based on cLHS adapt+ , using additional legacy samples, are marginally increased on average by 5%.

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Conditioned Latin Hypercube Sampling: Optimal Sample Size for Digital Soil Mapping of Arid Rangelands in Utah, USA

Cited by 26 publications

References 7 publications

Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

Prediction of Soil Properties at Farm Scale Using a Model-Based Soil Sampling Scheme and Random Forest

Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping

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