The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning
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
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Red and photographic infrared linear combinations for monitoring vegetation
Tucker, C. J. (1979) · Remote Sensing of Environment · doi:10.1016/0034-4257(79)90013-0 · Cited by 11,754
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002) · Remote Sensing of Environment · doi:10.1016/s0034-4257(02)00096-2 · Cited by 9,869
A soil-adjusted vegetation index (SAVI)
Huete, A. R. (1988) · Remote Sensing of Environment · doi:10.1016/0034-4257(88)90106-x · Cited by 7,948
NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space
Gao, B.-C. (1996) · Remote Sensing of Environment · doi:10.1016/s0034-4257(96)00067-3 · Cited by 6,951
Random forests for classification in ecology
Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007) · Ecology · doi:10.1890/07-0539.1 · Cited by 4,950
Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., et al. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.11.026 · Cited by 4,815
Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview
Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., & Morcette, J.-J. (1997) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.581987 · Cited by 3,178
Metadata
- Authors
- Houborg, R., & McCabe, M. F.
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 135
- Pages
- 173-188
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2771841295
- Cited by
- 285
- References
- 94
- FWCI
- 11.82
- 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: A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. https://app.cochat.ai/papers/10.1016/j.isprsjprs.2017.10.004
- The paper
- Houborg, R., & McCabe, M. F. (2018). A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2017.10.004
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.
