Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection

By I. Emelyanova, T. McVicar, T. V. Niel, Lingtao Li, A. Dijk. 2013

356 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/dcc0b555715b25d499dcdc0be141455f2832ea18

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Francesca Bovolo
2. Lorenzo Bruzzone
3. Xiaolin Zhu
4. Jiaqi Tian
5. Fangyi Cai
6. Trecia Kay-Ann Williams
7. Jon Atli Benediktsson
8. Danfeng Hong

In Environmental Engineering

1. Caroline M. Gevaert
2. F. J. García-Haro
3. Peter M. Atkinson
4. Qunming Wang
5. Xiaolin Zhu
6. Desheng Liu
7. Michael Andrew Lefsky
8. Lorenzo Bruzzone

In Global and Planetary Change

1. Pedram Ghamisi
2. Jon Atli Benediktsson
3. Francesca Bovolo
4. Jocelyn Chanussot
5. Lorenzo Bruzzone
6. Desheng Liu
7. Jin Chen
8. Peter M. Atkinson

Citations in the corpus

• Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions (2018, methodology): “A study used MODIS and Landsat images in two sites with contrasting spatial and temporal variability to compare STARFM and ESTARFM, and they released the dataset for public use [105].”
• A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion (2015, methodology): “The spatial and temporal adaptive reflectance fusion model (STARFM) (Gao et al., 2006) is perhaps the most widely-used data fusion algorithms for Landsat and MODIS imagery (Emelyanova et al., 2013).”
• A flexible spatiotemporal method for fusing satellite images with different resolutions (2016, methodology): “Rice croplands are often irrigated in October–November (Emelyanova et al., 2013).”
• Spatio-temporal fusion for daily Sentinel-2 images (2018, background): “Based on the availability of two coarse-fine image pairs, ESTARFM estimates 86 the temporal change rate of each class separately and assumes the change rates to be stable during a period 87 (Emelyanova et al., 2013).”
• Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “ESTARFM requires two coarse/fine image pairs to estimate the temporal change rate of each class separately and assumes the change rates to be stable during the relevant period [85].”
• Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, background): “Landsat5/7, MODIS (Emelyanova et al., 2013) 14 and 17 pairs of Landsat-MODIS images from 2 dataset https://data.”