Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8
暂无分享,去创建一个
Yelu Zeng | Xuanlong Ma | Ngoc Nguyen Tran | Jian Bi | Alfredo Huete | Sicong Gao | A. Huete | J. Bi | Xuanlong Ma | Yelu Zeng | N. N. Tran | Sicong Gao
[1] Alfredo Huete,et al. Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS , 2019, Remote. Sens..
[2] A. Huete,et al. Amazon rainforests green‐up with sunlight in dry season , 2006 .
[3] Hideki Kobayashi,et al. In Situ Observations Reveal How Spectral Reflectance Responds to Growing Season Phenology of an Open Evergreen Forest in Alaska , 2018, Remote. Sens..
[4] S. Nagai,et al. Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN) , 2015, Ecological Research.
[5] Alfredo Huete,et al. Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect , 2013 .
[6] C. Woodcock,et al. Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation , 2003 .
[7] A. Huete,et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .
[8] José A. Sobrino,et al. Phenology Estimation From Meteosat Second Generation Data , 2013, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens..
[9] Rui Sun,et al. Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data , 2016, Remote. Sens..
[10] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .
[11] Michael Ghil,et al. Spatio-temporal filling of missing points in geophysical data sets , 2006 .
[12] Alan H. Strahler,et al. Quality assessment of BRDF/albedo retrievals in MODIS operational system , 2008 .
[13] Zhuosen Wang,et al. Angular Effects and Correction for Medium Resolution Sensors to Support Crop Monitoring , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[14] Ray D. Jackson,et al. Diurnal Patterns of Wheat Spectral Reflectances , 1983, IEEE Transactions on Geoscience and Remote Sensing.
[15] J. Colwell. Vegetation canopy reflectance , 1974 .
[16] Dailiang Peng,et al. Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites , 2017 .
[17] Alfredo Huete,et al. Soil and Sun angle interactions on partial canopy spectra , 1987 .
[18] Quansheng Ge,et al. The spatial pattern of leaf phenology and its response to climate change in China , 2014, International Journal of Biometeorology.
[19] Xiangqian Wu,et al. Overview of Intercalibration of Satellite Instruments , 2013, IEEE Transactions on Geoscience and Remote Sensing.
[20] C. Justice,et al. A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .
[21] J. Roujean,et al. A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .
[22] Larry Biehl,et al. Measuring vegetation spectral properties , 1990 .
[23] Ranga B. Myneni,et al. Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data , 2019, Remote. Sens..
[24] Zoltán Barcza,et al. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices , 2018, Agricultural and Forest Meteorology.
[25] Wenping Yuan,et al. Estimating crop yield using a satellite-based light use efficiency model , 2016 .
[26] P. J. Pinter,et al. Influence of topography and sensor view angles on NIR/red ratio and greenness vegetation indices of wheat , 1987 .
[27] Alan H. Strahler,et al. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing , 1992, IEEE Trans. Geosci. Remote. Sens..
[28] Lei Ji,et al. Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition , 2015, Remote. Sens..
[29] Shi Li,et al. Deriving Vegetation Phenological Time and Trajectory Information Over Africa Using SEVIRI Daily LAI , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[30] Alfredo Huete,et al. Extreme precipitation patterns and reductions of terrestrial ecosystem production across biomes , 2013 .
[31] Hideki Kobayashi,et al. Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index , 2005 .
[32] S. Phinn,et al. Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland, Australia , 2011 .
[33] M. Bindi,et al. A simple model of regional wheat yield based on NDVI data , 2007 .
[34] A. Arneth,et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .
[35] Luiz E. O. C. Aragão,et al. Can MODIS EVI monitor ecosystem productivity in the Amazon rainforest? , 2014 .
[36] C. Tucker,et al. Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor , 2006 .
[37] Dong Yan,et al. Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[38] P. Ciais,et al. Variations in satellite‐derived phenology in China's temperate vegetation , 2006 .
[39] Matthew O. Jones,et al. Sunlight mediated seasonality in canopy structure and photosynthetic activity of Amazonian rainforests , 2015 .
[40] David J. Harding,et al. Amazon forests maintain consistent canopy structure and greenness during the dry season , 2014, Nature.
[41] Alan H. Strahler,et al. Topographic effects on bidirectional and hemispherical reflectances calculated with a geometric-optical canopy model , 1994, IEEE Trans. Geosci. Remote. Sens..
[42] A. Strahler,et al. On the derivation of kernels for kernel‐driven models of bidirectional reflectance , 1995 .
[43] Tim R. McVicar,et al. Spectral band unification and inter-calibration of Himawari AHI with MODIS and VIIRS: Constructing virtual dual-view remote sensors from geostationary and low-Earth-orbiting sensors , 2018 .
[44] A. Strahler,et al. Monitoring vegetation phenology using MODIS , 2003 .
[45] W. Oechel,et al. FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .
[46] E. Nikinmaa,et al. Interpreting canopy development and physiology using the EUROPhen camera network at flux sites , 2015 .
[47] R. Leuning,et al. Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates , 2005 .
[48] Lênio Soares Galvão,et al. Sensitivity of Seven MODIS Vegetation Indices to BRDF Effects during the Amazonian Dry Season , 2019, Remote. Sens..
[49] Qi Jing,et al. Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada , 2019, Remote. Sens..
[50] C. Tucker,et al. Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction , 2012 .
[51] Xingwang Fan,et al. Multisensor Normalized Difference Vegetation Index Intercalibration: A Comprehensive Overview of the Causes of and Solutions for Multisensor Differences , 2018, IEEE Geoscience and Remote Sensing Magazine.
[52] Jerry Y. Pan,et al. Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network , 2012 .
[53] C. Justice,et al. Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .
[54] Jeffrey P. Walker,et al. An introduction to the Australian and New Zealand flux tower network - OzFlux , 2016 .
[55] João Roberto dos Santos,et al. View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest , 2013, Int. J. Appl. Earth Obs. Geoinformation.
[56] Alan H. Strahler,et al. Solar zenith angle effects on forest canopy hemispherical reflectances calculated with a geometric-optical bidirectional reflectance model , 1993, IEEE Trans. Geosci. Remote. Sens..
[57] P. Beck,et al. Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .
[58] A. Okuyama,et al. An Introduction to Himawari-8/9— Japan’s New-Generation Geostationary Meteorological Satellites , 2016 .
[59] Compton J. Tucker,et al. Recent Declines in Warming and Vegetation Greening Trends over Pan-Arctic Tundra , 2013, Remote. Sens..
[60] Kenta Obata,et al. Relative Azimuthal-Angle Matching (RAM): A Screening Method for GEO-LEO Reflectance Comparison in Middle Latitude Forests , 2019, Remote. Sens..
[61] Didier Tanré,et al. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..
[62] Dong Yan,et al. A Comparison of Tropical Rainforest Phenology Retrieved From Geostationary (SEVIRI) and Polar-Orbiting (MODIS) Sensors Across the Congo Basin , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[63] J. Peñuelas,et al. Matching the phenology of Net Ecosystem Exchange and vegetation indices estimated with MODIS and FLUXNET in-situ observations , 2016 .
[64] M. Migliavacca,et al. Angle matters: Bidirectional effects impact the slope of relationship between gross primary productivity and sun‐induced chlorophyll fluorescence from Orbiting Carbon Observatory‐2 across biomes , 2018, Global change biology.
[65] W. Lucht,et al. Considerations in the parametric modeling of BRDF and albedo from multiangular satellite sensor observations , 2000 .
[66] Li Fang,et al. Evapotranspiration Data Product from NESDIS GET-D System Upgraded for GOES-16 ABI Observations , 2019, Remote. Sens..
[67] Yingying Dong,et al. Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[68] Alan H. Strahler,et al. A Comparison of Satellite-Derived Spectral Albedos to Ground-Based Broadband Albedo Measurements Modeled to Satellite Spatial Scale for a Semidesert Landscape , 2000 .
[69] E. Middleton. Solar zenith angle effects on vegetation indices in tallgrass prairie , 1991 .
[70] Alfredo R. Huete,et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests , 2016, Science.
[71] S. Los,et al. A method to convert AVHRR Normalized Difference Vegetation Index time series to a standard viewing and illumination geometry , 2005 .
[72] Changyong Cao,et al. Suomi NPP VIIRS reflective solar band on-orbit radiometric stability and accuracy assessment using desert and Antarctica Dome C sites , 2015 .
[73] Piero Toscano,et al. Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data , 2015 .
[74] N. C. Strugnell,et al. First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .
[75] A. Huete,et al. MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data , 1999 .
[76] Jing M. Chen,et al. Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS , 2014 .
[77] Feng Zhang,et al. Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands. , 2018 .
[78] D. Hollinger,et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. , 2009, Tree physiology.
[79] M. S. Moran,et al. Ecosystem resilience despite large-scale altered hydroclimatic conditions , 2013, Nature.
[80] Alan H. Strahler,et al. An algorithm for the retrieval of albedo from space using semiempirical BRDF models , 2000, IEEE Trans. Geosci. Remote. Sens..
[81] C. Schaaf,et al. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products , 2018 .
[82] Alfredo Huete,et al. Abrupt shifts in phenology and vegetation productivity under climate extremes , 2015 .
[83] Christopher C. Schmidt,et al. Near-Real-Time Global Biomass Burning Emissions Product from Geostationary Satellite Constellation , 2012 .
[84] Jinwei Dong,et al. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016 , 2017, Scientific Data.
[85] A. Huete,et al. MODIS vegetation products as proxies of photosynthetic potential along a gradient of meteorologically and biologically driven ecosystem productivity , 2016 .
[86] Santiago R. Verón,et al. Comparison of different BRDF correction methods to generate daily normalized MODIS 250 m time series , 2014 .
[87] Rasmus Fensholt,et al. The Normalization of Surface Anisotropy Effects Present in SEVIRI Reflectances by Using the MODIS BRDF Method , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[88] Jing M. Chen,et al. Land surface phenology from optical satellite measurement and CO2 eddy covariance technique , 2012 .
[89] Edwin W. Pak,et al. An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .
[90] Alfredo Huete,et al. Dry-season greening of Amazon forests , 2016, Nature.
[91] Kazuhito Ichii,et al. Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data , 2019, Scientific Reports.
[92] R. Myneni,et al. The interpretation of spectral vegetation indexes , 1995 .
[93] Michael C. Dietze,et al. A Statistical Model for Estimating Midday NDVI from the Geostationary Operational Environmental Satellite (GOES) 16 and 17 , 2019, Remote. Sens..