Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temporal variations in surface spatial heterogeneity through field measurements. Since inaccessibility often coexists with heterogeneity, a cost-efficient sampling design is mandatory. We proposed a sampling strategy to generate spatiotemporally representative and cost-efficient ESUs based on the conditioned Latin hypercube sampling scheme. The proposed strategy was constrained by multi-temporal Normalized Difference Vegetation Index (NDVI) imagery, and the ESUs were limited within a sampling feasible region established based on accessibility criteria. A novel criterion based on the Overlapping Area (OA) between the NDVI frequency distribution hi...
Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. ...
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin...
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin...
Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temp...
Altres ajuts: EC Copernicus Global Land Service (CGLOPS-1, 199494-JRC.Spatiotemporally representativ...
A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer s...
A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer s...
Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountain...
Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountain...
Updated information on cultivated land is important for Chinese central and local governments. The d...
The development of efficient and systematic ground-based spatial sampling strategies is critical for...
Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have em...
Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. ...
Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. ...
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin...
Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. ...
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin...
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin...
Spatiotemporally representative Elementary Sampling Units (ESUs) are required for capturing the temp...
Altres ajuts: EC Copernicus Global Land Service (CGLOPS-1, 199494-JRC.Spatiotemporally representativ...
A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer s...
A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer s...
Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountain...
Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountain...
Updated information on cultivated land is important for Chinese central and local governments. The d...
The development of efficient and systematic ground-based spatial sampling strategies is critical for...
Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have em...
Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. ...
Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. ...
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin...
Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. ...
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin...
This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin...