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

Domenech, Marisa; Castro-Franco, Mauricio; Costa, José Luís; Amiotti, Nilda Mabel

doi:10.1016/j.geoderma.2016.12.012

Cited by 21 publications

(12 citation statements)

References 33 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: Discussionmentioning

confidence: 97%

“…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: 97%

See 1 more Smart Citation

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: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

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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“…In our case studies, SRS and cLHS were equivalent in terms of map accuracy. Several studies (e.g., Castro‐Franco, Costa, Peralta, & Aparicio, 2015; Chu, Lin, Jang, & Chang, 2010; Contreras, Ballari, De Bruin, & Samaniego, 2019; Domenech, Castro‐Franco, Costa, & Amiotti, 2017; Schmidt et al, 2014) concluded that cLHS in combination with kriging or random forest for mapping gave the most accurate prediction. These studies promote the use of cLHS as an effective sampling design for mapping.…”

Section: Discussionmentioning

confidence: 99%

How to compare sampling designs for mapping?

Wadoux

Brus

2020

European J Soil Science

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If a map is constructed through prediction with a statistical or non‐statistical model, the sampling design used for selecting the sample on which the model is fitted plays a key role in the final map accuracy. Several sampling designs are available for selecting these calibration samples. Commonly, sampling designs for mapping are compared in real‐world case studies by selecting just one sample for each of the sampling designs under study. In this study, we show that sampling designs for mapping are better compared on the basis of the distribution of the map quality indices over repeated selection of the calibration sample. In practice this is only feasible by subsampling a large dataset representing the population of interest, or by selecting calibration samples from a map depicting the study variable. This is illustrated with two real‐world case studies. In the first case study a quantitative variable, soil organic carbon, is mapped by kriging with an external drift in France, whereas in the second case a categorical variable, land cover, is mapped by random forest in a region in France. The performance of two sampling designs for mapping are compared: simple random sampling and conditioned Latin hypercube sampling, at various sample sizes. We show that in both case studies the sampling distributions of map quality indices obtained with the two sampling design types, for a given sample size, show large variation and largely overlap. This shows that when comparing sampling designs for mapping on the basis of a single sample selected per design, there is a serious risk of an incidental result. Highlights We provide a method to compare sampling designs for mapping. Random designs for selecting calibration samples should be compared on the basis of the sampling distribution of the map quality indices.

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“…According to Doolittle and Brevik (2014), the relationships between the soil properties and ECa are complex and can vary over short distances, turning the interpretation of ECa into a challenge. Some studies have shown weak or inconsistent relationships between ECa and soil depth as determined by several types of impedance (Domenech et al, 2017;Doolittle et al, 2002;Paggi et al, 2013;Serrano et al, 2013;Sudduth et al, 2003). Most of these studies relied on linear associations between interpolated ECa measurements and soil depth estimates, failing to account for nonlinear patterns of the data (Sudduth et al, 2010;Tol et al, 2013).…”

Section: Core Ideasmentioning

confidence: 99%

“…Understanding the spatial distribution of soil depth to a restrictive layer, such as a petrocalcic horizon, is important for proper land evaluation, use, and management (Boettinger et al, 1997;Domenech et al, 2017;Taciara Zborowski, et al, 2021). On a landscape scale, knowledge of soil depth distribution facilitates the creation of useful soil survey information, such as accurate soil map unit descriptions and delineations (Boettinger et al, 1997;Dharumarajan et al, 2020;McBratney et al, 2003;Taciara Zborowski, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning

Peralta

Alesso

Costa

et al. 2021

Soil Science Soc of Amer J

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Restrictive layers such as hardpans limit the soil water and nutrients available for crops. In the southern Argentinean pampas, petrocalcic hardpans are found at variable depth within the field. Mapping the spatial distribution of depth to the petrocalcic hardpan is important for proper land evaluation, use, and management. Intensive grid sampling and spatial interpolation are labor-intensive and time-consuming mapping approaches for this soil property. In addition, the spatial distribution of this property is often difficult for interpolation techniques such as kriging. This study's objective was to evaluate the potential of soil electrical conductivity (ECa) and terrain attributes to map within-field spatial variation of soil depth using statistical learning techniques. Soil depth measurements up to 1-m depth were taken in eight fields at spatial sampling density ranging from 2 to 12 points ha -1 . Spatially dense data of elevation, terrain attributes, and ECa were migrated to soil depth sampling points and also spatially aggregated to include spatial information into the featured space. Then, random forest regression models were used to predict soil depth from colocated ECa and terrain data. Models were cross-validated using a k-fold approach using entire fields as folds. The overall model (using all data) resulted in out-of-the bag R 2 and RMSE of .66 and 22.8 cm respectively. Shallow (0-30 cm) and deep (0-90 cm) ECa values were the most important variables, accounting for variability at different ranges, but the importance varied between the soil types. These results suggested that field-scale ECa data and terrain attributes have potential to predict soil depth to hardpan. Further research is needed to improve the generalization of these models and improve the representation of spatial effects in order to implement site-specific management based on soil depth maps.

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

Cited by 21 publications

References 33 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

How to compare sampling designs for mapping?

Mapping soil depth in southern pampas Argentina using ancillary data and statistical learning

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