Global land cover mapping from MODIS: algorithms and early results

By Mark A. Friedl, D. McIver, J. Hodges, Xiaohe Zhang, D. Muchoney, Alan H. Strahler, Curtis E. Woodcock, S. Gopal, Annemarie Schneider, A. Cooper and 3 more. 2002

2,689 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/dc3e57dadea569feb567291be297fe7b69ab319d

How researchers see this paper

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

In Artificial Intelligence

1. Zhe Zhu
2. Curtis E. Woodcock
3. Warren B. Cohen
4. M. Wulder
5. David P. Roy
6. Joanne C. White
7. J. Soininen
8. Dara Entekhabi

In Ecology

1. Zhe Zhu
2. Curtis E. Woodcock
3. David P. Roy
4. M. Wulder
5. Warren B. Cohen
6. Annemarie Schneider
7. Robert E. Kennedy
8. Zhiqiang Yang

In Media Technology

1. Zhe Zhu
2. Annemarie Schneider
3. David P. Roy
4. Curtis E. Woodcock
5. Warren B. Cohen
6. Zhiqiang Yang
7. M. Wulder
8. Robert E. Kennedy

Citations in the corpus

• Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets (2008, methodology): “The classification combines prior and posterior probabilities to assign the most probable class for each location on the globe (Friedl et al., 2002) based on the IGBP classification scheme.”
• Land surface skin temperature climatology: benefitting from the strengths of satellite observations (2010, background): “, for sparsely vegetated areas with most of the net radiation balanced by sensible heat flux (Sun and Mahrt 1995, Jin et al 1997). Norman and Becker (1995) summarize the conceptual differences between thermodynamic and kinetic temperature that are here taken to be essentially equivalent.”
• An active-fire based burned area mapping algorithm for the MODIS sensor (2009, background): “, 2002), and the Level 3 MODIS “MOD12Q1” 96-day land cover product (Friedl et al., 2002).”
• Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service (2005, methodology): “2003) and the 1 km MOD12Q1 land cover data set (Friedl et al. 2002) were compared with forest–nonforest stratifications derived from NLCD-92 and NLCD-01.”
• Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance (2014, methodology): “The MODIS LAI retrieval algorithm uses the MODIS land cover type 3 (MOD12Q1) product [26] as a priori information with which to constrain the LAI space.”
• Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations (2014, neutral): “…albedo (MOD43C1: CMG, 0.05°) [Jin et al., 2003; Salomon et al., 2006], Collection 5 MODIS NDVI/EVI (MOD13C1: CMG, 0.05°) [Myneni et al., 2002], Collection 4 MODIS land cover (MOD12C1: CMG, 0.05°) [Friedl et al., 2002], and the Collection 5 MODIS FPAR/LAI (MOD15A2) with spatial resolution of 1 km.”
• Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations (2006, methodology): “The MODIS Collection 3 (C3) land cover classification [39] is used in the Collection 4 (C4) LAI/fPAR and Collection 4.”
• Toward More Integrated Ecosystem Research in Aquatic and Terrestrial Environments (2015, methodology): “They have been used by terrestrial ecologists for modeling variation in species richness (Nagendra 2001), in assessing ecological responses to environmental change (Pettorelli et al. 2005) and in studies of land cover patterns (Friedl et al. 2002).”
• Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation (2010, background): “At a global scale, characterizing changes in vegetation has become routine with the aid of remote sensing (DeFries et al., 2000; Friedl et al., 2002).”