Cargando…
Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring
The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM)...
Autores principales: | Wu, Mingquan, Huang, Wenjiang, Niu, Zheng, Wang, Changyao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4555320/ https://www.ncbi.nlm.nih.gov/pubmed/26308017 http://dx.doi.org/10.3390/ijerph120809920 |
Ejemplares similares
-
Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data
por: Wu, Mingquan, et al.
Publicado: (2015) -
Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data
por: Kang, Yupeng, et al.
Publicado: (2021) -
A Learning-Enhanced Two-Pair Spatiotemporal Reflectance Fusion Model for GF-2 and GF-1 WFV Satellite Data
por: Ge, Yanqin, et al.
Publicado: (2020) -
Estimation of Daily Terrestrial Latent Heat Flux with High Spatial Resolution from MODIS and Chinese GF-1 Data
por: Bei, Xiangyi, et al.
Publicado: (2020) -
Long-term cross calibration of HJ-1A CCD1 and Terra MODIS reflective solar bands
por: Liu, Li, et al.
Publicado: (2021)