Advances in Quantitative Remote Sensing in China - In Memory of Prof. Xiaowen Li
Quantitative land remote sensing has recently advanced dramatically, particularly in China. It has been largely driven by vast governmental investment, the availability of a huge amount of Chinese satellite data, geospatial information requirements for addressing pressing environmental issues and ot...
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MDPI - Multidisciplinary Digital Publishing Institute
2019
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 08597naaaa2202449uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_40343 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03897-271-6 | ||
| 020 | |a 9783038972709 | ||
| 024 | 7 | |a 10.3390/books978-3-03897-271-6 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Shi, Jiancheng |4 auth | |
| 700 | 1 | |a Liang, Shunlin |4 auth | |
| 700 | 1 | |a Yan, Guangjian |4 auth | |
| 245 | 1 | 0 | |a Advances in Quantitative Remote Sensing in China - In Memory of Prof. Xiaowen Li |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (404 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Quantitative land remote sensing has recently advanced dramatically, particularly in China. It has been largely driven by vast governmental investment, the availability of a huge amount of Chinese satellite data, geospatial information requirements for addressing pressing environmental issues and other societal benefits. Many individuals have also fostered and made great contributions to its development, and Prof. Xiaowen Li was one of these leading figures. This book is published in memory of Prof. Li. The papers collected in this book cover topics from surface reflectance simulation, inversion algorithm and estimation of variables, to applications in optical, thermal, Lidar and microwave remote sensing. The wide range of variables include directional reflectance, chlorophyll fluorescence, aerosol optical depth, incident solar radiation, albedo, surface temperature, upward longwave radiation, leaf area index, fractional vegetation cover, forest biomass, precipitation, evapotranspiration, freeze/thaw snow cover, vegetation productivity, phenology and biodiversity indicators. They clearly reflect the current level of research in this area. This book constitutes an excellent reference suitable for upper-level undergraduate students, graduate students and professionals in remote sensing. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a gross primary production (GPP) | ||
| 653 | |a interference filter | ||
| 653 | |a Visible Infrared Imaging Radiometer Suite (VIIRS) | ||
| 653 | |a cost-efficient | ||
| 653 | |a precipitation | ||
| 653 | |a topographic effects | ||
| 653 | |a land surface temperature | ||
| 653 | |a Land surface emissivity | ||
| 653 | |a scale effects | ||
| 653 | |a spatial-temporal variations | ||
| 653 | |a statistics methods | ||
| 653 | |a inter-annual variation | ||
| 653 | |a spatial representativeness | ||
| 653 | |a FY-3C/MERSI | ||
| 653 | |a sunphotometer | ||
| 653 | |a PROSPECT | ||
| 653 | |a passive microwave | ||
| 653 | |a flux measurements | ||
| 653 | |a urban scale | ||
| 653 | |a vegetation dust-retention | ||
| 653 | |a multiple ecological factors | ||
| 653 | |a leaf age | ||
| 653 | |a standard error of the mean | ||
| 653 | |a LUT method | ||
| 653 | |a spectra | ||
| 653 | |a SURFRAD | ||
| 653 | |a Land surface temperature | ||
| 653 | |a aboveground biomass | ||
| 653 | |a uncertainty | ||
| 653 | |a land surface variables | ||
| 653 | |a copper | ||
| 653 | |a Northeast China | ||
| 653 | |a forest disturbance | ||
| 653 | |a end of growing season (EOS) | ||
| 653 | |a random forest model | ||
| 653 | |a probability density function | ||
| 653 | |a downward shortwave radiation | ||
| 653 | |a machine learning | ||
| 653 | |a MODIS products | ||
| 653 | |a composite slope | ||
| 653 | |a daily average value | ||
| 653 | |a canopy reflectance | ||
| 653 | |a spatiotemporal representative | ||
| 653 | |a light use efficiency | ||
| 653 | |a hybrid method | ||
| 653 | |a disturbance index | ||
| 653 | |a quantitative remote sensing inversion | ||
| 653 | |a SCOPE | ||
| 653 | |a GPP | ||
| 653 | |a South China's | ||
| 653 | |a anisotropic reflectance | ||
| 653 | |a vertical structure | ||
| 653 | |a snow cover | ||
| 653 | |a land cover change | ||
| 653 | |a start of growing season (SOS) | ||
| 653 | |a MS-PT algorithm | ||
| 653 | |a aerosol | ||
| 653 | |a pixel unmixing | ||
| 653 | |a HiWATER | ||
| 653 | |a algorithmic assessment | ||
| 653 | |a surface radiation budget | ||
| 653 | |a latitudinal pattern | ||
| 653 | |a ICESat GLAS | ||
| 653 | |a vegetation phenology | ||
| 653 | |a SIF | ||
| 653 | |a metric comparison | ||
| 653 | |a Antarctica | ||
| 653 | |a spatial heterogeneity | ||
| 653 | |a comprehensive field experiment | ||
| 653 | |a reflectance model | ||
| 653 | |a sinusoidal method | ||
| 653 | |a NDVI | ||
| 653 | |a BRDF | ||
| 653 | |a cloud fraction | ||
| 653 | |a NPP | ||
| 653 | |a VPM | ||
| 653 | |a China | ||
| 653 | |a dense forest | ||
| 653 | |a vegetation remote sensing | ||
| 653 | |a <i>Cunninghamia</i> | ||
| 653 | |a high resolution | ||
| 653 | |a geometric-optical model | ||
| 653 | |a phenology | ||
| 653 | |a LiDAR | ||
| 653 | |a ZY-3 MUX | ||
| 653 | |a point cloud | ||
| 653 | |a multi-scale validation | ||
| 653 | |a Fraunhofer Line Discrimination (FLD) | ||
| 653 | |a rice | ||
| 653 | |a fractional vegetation cover (FVC) | ||
| 653 | |a interpolation | ||
| 653 | |a high-resolution freeze/thaw | ||
| 653 | |a drought | ||
| 653 | |a Synthetic Aperture Radar (SAR) | ||
| 653 | |a controlling factors | ||
| 653 | |a sampling design | ||
| 653 | |a downscaling | ||
| 653 | |a n/a | ||
| 653 | |a Chinese fir | ||
| 653 | |a MRT-based model | ||
| 653 | |a RADARSAT-2 | ||
| 653 | |a northern China | ||
| 653 | |a leaf area density | ||
| 653 | |a potential evapotranspiration | ||
| 653 | |a black-sky albedo (BSA) | ||
| 653 | |a decision tree | ||
| 653 | |a CMA | ||
| 653 | |a fluorescence quantum efficiency in dark-adapted conditions (FQE) | ||
| 653 | |a surface solar irradiance | ||
| 653 | |a validation | ||
| 653 | |a geographical detector model | ||
| 653 | |a vertical vegetation stratification | ||
| 653 | |a spatiotemporal distribution and variation | ||
| 653 | |a gap fraction | ||
| 653 | |a phenological parameters | ||
| 653 | |a spatio-temporal | ||
| 653 | |a albedometer | ||
| 653 | |a variability | ||
| 653 | |a GLASS | ||
| 653 | |a gross primary productivity (GPP) | ||
| 653 | |a EVI2 | ||
| 653 | |a machine learning algorithms | ||
| 653 | |a latent heat | ||
| 653 | |a GLASS LAI time series | ||
| 653 | |a boreal forest | ||
| 653 | |a leaf | ||
| 653 | |a maize | ||
| 653 | |a heterogeneity | ||
| 653 | |a temperature profiles | ||
| 653 | |a crop-growing regions | ||
| 653 | |a satellite observations | ||
| 653 | |a rugged terrain | ||
| 653 | |a species richness | ||
| 653 | |a voxel | ||
| 653 | |a LAI | ||
| 653 | |a TMI data | ||
| 653 | |a GF-1 WFV | ||
| 653 | |a spectral | ||
| 653 | |a HJ-1 CCD | ||
| 653 | |a leaf area index | ||
| 653 | |a evapotranspiration | ||
| 653 | |a land-surface temperature products (LSTs) | ||
| 653 | |a SPI | ||
| 653 | |a AVHRR | ||
| 653 | |a Tibetan Plateau | ||
| 653 | |a snow-free albedo | ||
| 653 | |a PROSPECT-5B+SAILH (PROSAIL) model | ||
| 653 | |a MCD43A3 C6 | ||
| 653 | |a 3D reconstruction | ||
| 653 | |a photoelectric detector | ||
| 653 | |a multi-data set | ||
| 653 | |a BEPS | ||
| 653 | |a aerosol retrieval | ||
| 653 | |a plant functional type | ||
| 653 | |a multisource data fusion | ||
| 653 | |a remote sensing | ||
| 653 | |a leaf spectral properties | ||
| 653 | |a solo slope | ||
| 653 | |a land surface albedo | ||
| 653 | |a longwave upwelling radiation (LWUP) | ||
| 653 | |a terrestrial LiDAR | ||
| 653 | |a AMSR2 | ||
| 653 | |a geometric optical radiative transfer (GORT) model | ||
| 653 | |a MuSyQ-GPP algorithm | ||
| 653 | |a tree canopy | ||
| 653 | |a FY-3C/MWRI | ||
| 653 | |a meteorological factors | ||
| 653 | |a solar-induced chlorophyll fluorescence | ||
| 653 | |a metric integration | ||
| 653 | |a observations | ||
| 653 | |a polar orbiting satellite | ||
| 653 | |a arid/semiarid | ||
| 653 | |a homogeneous and pure pixel filter | ||
| 653 | |a thermal radiation directionality | ||
| 653 | |a biodiversity | ||
| 653 | |a gradient boosting regression tree | ||
| 653 | |a forest canopy height | ||
| 653 | |a Landsat | ||
| 653 | |a subpixel information | ||
| 653 | |a MODIS | ||
| 653 | |a humidity profiles | ||
| 653 | |a NIR | ||
| 653 | |a geostationary satellite | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/1158 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/40343 |7 0 |z DOAB: description of the publication |