Jin Chen
Ecology · with Shen · 2004–2021
A researcher in the Science corpus: 8 papers; trusts 2 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Ecology — 29 citations into it; #4,789 of 10,717 by trust here, #3,630 by citations
- Global and Planetary Change — 16 citations into it; #8,861 of 10,225 by trust here, #4,309 by citations
- Environmental Engineering — 14 citations into it
Whom Jin Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Jin Chen overall
- Huanfeng Shen
- J. F. Hermance
- Chao Zeng
- Jennifer N. Hird
- B. Wilson
- Yang Shao
- Xinghua Li
- Qiang Zhang
- R. Hall
- Sivasathivel Kandasamy
Papers from Jin Chen's perspective
Ordered by how strongly the researchers Jin Chen trusts endorse each paper. Same corpus, a different order for every researcher.
- Seasonality extraction by function fitting to time-series of satellite sensor data (IEEE Transactions on Geoscience and Remote Sensing)
- On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance (IEEE Transactions on Geoscience and Remote Sensing)
- Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data
- Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection
- A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion
- Spatiotemporal Reflectance Fusion via Sparse Representation (IEEE Transactions on Geoscience and Remote Sensing)
- Comparative assessment of the measures of thematic classification accuracy
- Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain
- Theoretical noise sensitivity of BRDF and albedo retrieval from the EOS-MODIS and MISR sensors with respect to angular sampling
- Smoothing and differentiation of data by simplified least square procedure.
Their papers
- Improving the accuracy of spring phenology detection by optimally smoothing satellite vegetation index time series based on local cloud frequency (2021) — 53 citations
- A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter (2018) — 283 citations
- A flexible spatiotemporal method for fusing satellite images with different resolutions (2016) — 667 citations
- An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data (2015) — 130 citations
- Modeling tree root diameter and biomass by ground-penetrating radar (2011) — 81 citations
- A simple and effective method for filling gaps in Landsat ETM+ SLC-off images (2011) — 524 citations
- Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest (2007) — 751 citations
- A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter (2004) — 2,229 citations