Quantitative Remote Sensing of Land Surfaces

By Shunlin Liang. 2003

766 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/dcd615ad2a3afeb147a7ee4e868cc7796a20ceba

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Citations in the corpus

Unsupervised Deep Feature Extraction for Remote Sensing Image Classification (2015, background): “These myriad of sensors serve to particularly different objectives, focusing either on obtaining quantitative measurements and estimations of geo-bio-physical variables, or on the identiﬁcation of materials by the analysis of the acquired images [1]–[3].”
LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages (2008, methodology): “Another approach is to map LAI through the inversion of physical Canopy-Reflectance (CR) models using the optimization methods (Goel, 1988; Liang, 2004 ).”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “Remote sensing data are mainly used for environmental parameter monitoring based on physical models (Liang, 2005).”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, background): “The analysis can be done at local or global scales by looking at bio-geo-chemical cycles, atmospheric state and evolution, and vegetation dynamics [155], [156].”
Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop (2007, background): “Vegetation indices are mathematical transformations of the original spectral reflectance that are designed to reduce the additive and multiplicative errors associated with atmospheric effects, solar illumination, soil background effects, and sensor viewing geometry (Liang 2004).”
Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling (2009, background): “In general, up-scaling and down-scaling may also be called aggregation and disaggregation, respectively [18].”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, background): “Remote sensing exploits these physical facts and deals with the acquisition of information about a scene (or specific object) at a short, medium or long distance from the target [1]–[3].”
A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data (2009, methodology): “reflectance values in the bands used in the remote sensing [43], s R is determined by a combined”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, neutral): “…between LAI and NDSI calculated from all possible two-band combinations at jointing and flowering stages of wheat. analysis, data mining has been widely employed to associate hyperspectral information with physiological and biochemical crop parameters (Gong, 1999; Liang, 2005; Wang et al., 2008).”
Review on Estimation of Land Surface Radiation and Energy Budgets From Ground Measurement, Remote Sensing and Model Simulations (2010, methodology): “The physically based estimation methods typically include three steps [68], [73]:”