Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data

Mulder, Vera Leatitia; Bruin, S. de; Schaepman, Michael E.

doi:10.1016/j.jag.2012.07.004

Cited by 65 publications

(49 citation statements)

References 33 publications

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“…The case study in Hailuogou, Sichuan province, China showed that the CSS can represent nearly equal representativeness for NDVI to CLH, one of the most efficient sampling strategies proven by many documents [14,16,20,27]. As to slope, under-sampling was observed for large values, slightly lower than the representativeness, but this is reasonable considering the high cost to reach these points.…”

Section: Compromise Between Representativeness and Implementation Costmentioning

confidence: 97%

“…According to [42], a sample is maximally stratified when the number of strata equals the sample number n, and at the same time, the probability of falling in each of the strata is 1/n. CLH sampling aims at allocating individual plots to each of the strata while simultaneously imposing constraints to get a rational sample [14].…”

Section: The Cost-constrained Sampling Strategy (Css)mentioning

confidence: 99%

“…Currently, we took into account the influence of slope and distance to road on accessibility, and defined the cost-distance accordingly. However, other factors, e.g., river barrier, also influence accessibility [14,27,50], and should be considered in the future.…”

Section: Practicability Of the Proposed Sampling Strategymentioning

confidence: 99%

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A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

Yin

Zeng

et al. 2016

Remote Sensing

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Abstract:Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the validation of LAI in mountainous areas is still problematic. A cost-constrained sampling strategy (CSS) in support of LAI validation was presented in this study. To account for the influence of rugged terrain on implementation cost, a cost-objective function was incorporated to traditional conditioned Latin hypercube (CLH) sampling strategy. A case study in Hailuogou, Sichuan province, China was used to assess the efficiency of CSS. Normalized difference vegetation index (NDVI), land cover type, and slope were selected as auxiliary variables to present the variability of LAI in the study area. Results show that CSS can satisfactorily capture the variability across the site extent, while minimizing field efforts. One appealing feature of CSS is that the compromise between representativeness and implementation cost can be regulated according to actual surface heterogeneity and budget constraints, and this makes CSS flexible. Although the proposed method was only validated for the auxiliary variables rather than the LAI measurements, it serves as a starting point for establishing the locations of field plots and facilitates the preparation of field campaigns in mountainous areas.

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Section: Compromise Between Representativeness and Implementation Costmentioning

confidence: 97%

Section: The Cost-constrained Sampling Strategy (Css)mentioning

confidence: 99%

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A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

Yin

Zeng

et al. 2016

Remote Sensing

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“…To avoid being trapped in local optimum, the simulated annealing algorithm accepts some of the changes that worsen the OF, and the probability of accepting a worse group of ESUs is given by the following relation [22]:…”

Section: Sampling Strategy Based On Multi-temporal a Priori Knowledgementioning

confidence: 99%

“…The widely used random sampling or systematic sampling approaches in prior validation programs are simple and easy to carry out [16][17][18][19], but these approaches may not be appropriate over heterogeneous areas due to their low efficiency and the laborious and time-consuming nature of LAI field measurements [20]. To capture the surface heterogeneity, more efficient sampling strategies are designed with available a priori knowledge, such as vegetation types, vegetation index or soil types [21][22][23][24]. These methods are widely applied in the VALERI project field campaigns, the Ruokolahti forest observations in Finland and the Barrax cropland observations in Spain [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities

Zeng

Liu

et al. 2015

Remote Sensing

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Abstract:A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling OPEN ACCESS Remote Sens. 2015, 7 1301 strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R 2 , RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.

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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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Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data

Cited by 65 publications

References 33 publications

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

A Cost-Constrained Sampling Strategy in Support of LAI Product Validation in Mountainous Areas

An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities

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

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