Spatial Statistical Data Fusion for Remote Sensing Applications
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DOI: 10.1080/01621459.2012.694717
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Cited by:
- Jun Wang & Yang Wang & Hui Zeng, 2016. "A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis," Journal of Geographical Systems, Springer, vol. 18(1), pages 45-66, January.
- Chen, Yenming J. & Chang, Kuo-Hao & Sheu, Jiuh-Biing & Liu, Chih-Hao & Chang, Chy-Chang & Chang, Chieh-Hsin & Wang, Guan-Xun, 2023. "Vulnerability-based regionalization for disaster management considering storms and earthquakes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
- Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
- Marchetti, Yuliya & Nguyen, Hai & Braverman, Amy & Cressie, Noel, 2018. "Spatial data compression via adaptive dispersion clustering," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 138-153.
- Brian J. Reich & Howard H. Chang & Kristen M. Foley, 2014. "A spectral method for spatial downscaling," Biometrics, The International Biometric Society, vol. 70(4), pages 932-942, December.
- Andrew M. Raim & Scott H. Holan & Jonathan R. Bradley & Christopher K. Wikle, 2021. "Spatio-temporal change of support modeling with R," Computational Statistics, Springer, vol. 36(1), pages 749-780, March.
- Si Cheng & Bledar A. Konomi & Georgios Karagiannis & Emily L. Kang, 2024. "Recursive nearest neighbor co‐kriging models for big multi‐fidelity spatial data sets," Environmetrics, John Wiley & Sons, Ltd., vol. 35(4), June.
- Esmail Yarali & Firoozeh Rivaz, 2020. "Incorporating covariate information in the covariance structure of misaligned spatial data," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
- Margaret C Johnson & Brian J Reich & Josh M Gray, 2021. "Multisensor fusion of remotely sensed vegetation indices using space‐time dynamic linear models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 793-812, June.
- Jialing Tao & Kaibo Wang & Bo Li & Liang Liu & Qi Cai, 2016. "Hierarchical models for the spatial–temporal carbon nanotube height variations," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6613-6632, November.
- Jonathan Bradley & Noel Cressie & Tao Shi, 2015. "Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 1-28, March.
- Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.
- Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
- Hang Zhang & Yong Liu & Dongyang Yang & Guanpeng Dong, 2022. "PM 2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
- Landrum, Carla & Castrignanò, Annamaria & Mueller, Tom & Zourarakis, Demetrio & Zhu, Junfeng & De Benedetto, Daniela, 2015. "An approach for delineating homogeneous within-field zones using proximal sensing and multivariate geostatistics," Agricultural Water Management, Elsevier, vol. 147(C), pages 144-153.
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