The DOI matches its Crossref record.
Geostatistics and remote sensing
Abstract
In geostatistics, spatial autocorrelation is utilized to estimate optimally local values from data sampled elsewhere. The powerful synergy between geostatistics and remote sensing went unrealized until the 1980s. Today geostatistics are used to explore and describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data; and to increase the accuracy with which remotely sensed data can be used to classify land cover or estimate continuous variables. This article introduces these applications and uses two examples to highlight characteristics that are common to them all. The article concludes with a discussion of conditional simulation as a novel geostatistical technique for use in remote sensing.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Coops, N. C., Tompalski, P., Goodbody, T. R. H., Queinnec, M., Luther, J. E., Bolton, D. K., White, J. C., Wulder, M. A., van Lier, O. R., & Hermosilla, T. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112477 · Cited by 406
Wulder, M., Niemann, K. O., & Goodenough, D. G. (2000) · Remote Sensing of Environment · doi:10.1016/s0034-4257(00)00101-2 · Cited by 393
Vicente-Serrano, S., Saz-Sánchez, M., & Cuadrat, J. (2003) · Climate Research · doi:10.3354/cr024161 · Cited by 386
Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters
Wulder, M. (1998) · Progress in Physical Geography: Earth and Environment · doi:10.1177/030913339802200402 · Cited by 381
Super-resolution target identification from remotely sensed images using a Hopfield neural network
Tatem, A. J., Lewis, H. G., Atkinson, P. M., & Nixon, M. S. (2001) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.917895 · Cited by 310
Self-citation
Metadata
- Authors
- Curran, P. J., & Atkinson, P. M.
- Venue
- Progress in Physical Geography: Earth and Environment
- Publisher
- SAGE Publications
- Published
- 1998-03
- Volume
- 22
- Issue
- 1
- Pages
- 61-78
- ISSN
- 0309-1333 (ISSN-L)
- 1477-0296
- OpenAlex
- W2031456617
- Cited by
- 240
- References
- 0
- FWCI
- 12.34
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Geostatistics and remote sensing. https://app.cochat.ai/papers/10.1177/030913339802200103
- The paper
- Curran, P. J., & Atkinson, P. M. (1998). Geostatistics and remote sensing. Progress in Physical Geography: Earth and Environment. https://doi.org/10.1177/030913339802200103
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
