The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Geographically weighted regression based methods for merging satellite and gauge precipitation
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Cites
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., & Stocker, E. F. (2007) · Journal of Hydrometeorology · doi:10.1175/jhm560.1 · Cited by 6,767
Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
Fotheringham, A. S., Brunsdon, C., & Charlton, M. E. (2002) · doi:10.25405/ncl.34562610 · Cited by 3,938
Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996) · Geographical Analysis · doi:10.1111/j.1538-4632.1996.tb00936.x · Cited by 3,541
Joyce, R. J., Janowiak, J. E., Arkin, P. A., & Xie, P. (2004) · Journal of Hydrometeorology · doi:10.1175/1525-7541(2004)005<0487:camtpg>2.0.co;2 · Cited by 3,208
The Global Precipitation Measurement Mission
Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., & Iguchi, T. (2014) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-13-00164.1 · Cited by 2,766
Review article Multisensor image fusion in remote sensing: Concepts, methods and applications
Pohl, C., & Van Genderen, J. L. (1998) · International Journal of Remote Sensing · doi:10.1080/014311698215748 · Cited by 2,211
Geographically Weighted Regression
Brunsdon, C., Fotheringham, S., & Charlton, M. (1998) · Journal of the Royal Statistical Society: Series D (The Statistician) · doi:10.1111/1467-9884.00145 · Cited by 1,563
Fotheringham, A. S., Charlton, M. E., & Brunsdon, C. (1998) · Environment and Planning A: Economy and Space · doi:10.1068/a301905 · Cited by 1,152
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., & de Roo, A. (2017) · Hydrology and Earth System Sciences · doi:10.5194/hess-21-589-2017 · Cited by 1,108
Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
McMillen, D. P. (2004) · American Journal of Agricultural Economics · doi:10.1111/j.0002-9092.2004.600_2.x · Cited by 1,067
Cited by
Chen, T., Bao, A., Jiapaer, G., Guo, H., Zheng, G., Jiang, L., Chang, C., & Tuerhanjiang, L. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.11.058 · Cited by 253
Metadata
- Authors
- Chao, L., Zhang, K., Li, Z., Zhu, Y., Wang, J., & Yu, Z.
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 558
- Pages
- 275-289
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W2789329162
- Cited by
- 292
- References
- 44
- FWCI
- 15.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Geographically weighted regression based methods for merging satellite and gauge precipitation. https://app.cochat.ai/papers/10.1016/j.jhydrol.2018.01.042
- The paper
- Chao, L., Zhang, K., Li, Z., Zhu, Y., Wang, J., & Yu, Z. (2018). Geographically weighted regression based methods for merging satellite and gauge precipitation. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.01.042
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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.
