Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review

[1]  F. Aires,et al.  Global inundation dynamics inferred from multiple satellite observations, 1993–2000 , 2007 .

[2]  Emanuele Santi,et al.  Modeling the Multifrequency Emission of Broadleaf Forests and Their Components , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Yi Y. Liu,et al.  Annual South American forest loss estimates based on passive microwave remote sensing (1990–2010) , 2015 .

[4]  Andrew E. Suyker,et al.  Satellite L–band vegetation optical depth is directly proportional to crop water in the US Corn Belt , 2019, Remote Sensing of Environment.

[5]  A. Al Bitar,et al.  An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa , 2018, Biogeosciences.

[6]  Jean-Pierre Wigneron,et al.  First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields , 2019, Remote. Sens..

[7]  G. Guyot,et al.  Estimating surface soil moisture and leaf area index of a wheat canopy using a dual-frequency (C and X bands) scatterometer , 1993 .

[8]  Jakob van Zyl,et al.  Estimation of canopy water content in Konza Prairie grasslands using synthetic aperture radar measurements during FIFE , 1995 .

[9]  C. Field,et al.  Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types , 1995 .

[10]  Yann Kerr,et al.  Impact of Direct Solar Radiations Seen by the Back-Lobes Antenna Patterns of SMOS on the Retrieved Images , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Imen Gherboudj,et al.  Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data , 2011 .

[12]  Pierre Borderies,et al.  Radar altimetry backscattering signatures at Ka, Ku, C, and S bands over West Africa , 2015 .

[13]  Matthew F. McCabe,et al.  Recent reversal in loss of global terrestrial biomass , 2015 .

[14]  A. Al Bitar,et al.  The global SMOS Level 3 daily soil moisture and brightness temperature maps , 2017 .

[15]  Irene E. Teubner,et al.  A carbon sink-driven approach to estimate gross primary production from microwave satellite observations , 2019, Remote Sensing of Environment.

[16]  Jean-Pierre Wigneron,et al.  Simulating L-band emission of coniferous forests using a discrete model and a detailed geometrical representation , 2006, IEEE Geoscience and Remote Sensing Letters.

[17]  E. Njoku,et al.  Passive microwave remote sensing of soil moisture , 1996 .

[18]  M. Raupach,et al.  Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series , 2003 .

[19]  Mariette Vreugdenhil,et al.  Assessing Vegetation Dynamics Over Mainland Australia With Metop ASCAT , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Simonetta Paloscia,et al.  Airborne multifrequency L- to Ka-band radiometric measurements over forests , 2001, IEEE Trans. Geosci. Remote. Sens..

[21]  S. Higgins,et al.  Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally , 2012, Nature.

[22]  J. Calvet,et al.  Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France , 2017 .

[23]  Ramakrishna R. Nemani,et al.  Numerical Terradynamic Simulation Group 12-2014 Asynchronous Amazon forest canopy phenology indicates adaptation to both water and light availability , 2018 .

[24]  D. Walker,et al.  Vegetation greening in the Canadian Arctic related to decadal warming. , 2009, Journal of environmental monitoring : JEM.

[25]  Nate G. McDowell,et al.  Interacting Effects of Leaf Water Potential and Biomass on Vegetation Optical Depth , 2017 .

[26]  Niko E. C. Verhoest,et al.  Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates , 2017 .

[27]  Philippe Richaume,et al.  SMOS Retrieval Results Over Forests: Comparisons With Independent Measurements , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Dara Entekhabi,et al.  L-band vegetation optical depth and effective scattering albedo estimation from SMAP. , 2017 .

[29]  T. Schmugge,et al.  Vegetation effects on the microwave emission of soils , 1991 .

[30]  Y. Inoue,et al.  Inferring the effect of plant and soil variables on C- and L-band SAR backscatter over agricultural fields, based on model analysis , 2007 .

[31]  Thierry Amiot,et al.  SWIM: The First Spaceborne Wave Scatterometer , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Richard de Jeu,et al.  Analyzing the Vegetation Parameterization in the TU-Wien ASCAT Soil Moisture Retrieval , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Frédéric Frappart,et al.  Estimating surface soil moisture over Sahel using ENVISAT radar altimetry , 2012 .

[34]  Christopher Ruf,et al.  Radio-Frequency Interference Mitigation for the Soil Moisture Active Passive Microwave Radiometer , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Pierre Gentine,et al.  Global variations in ecosystem‐scale isohydricity , 2017, Global change biology.

[36]  Mahta Moghaddam,et al.  Estimation of crown and stem water content and biomass of boreal forest using polarimetric SAR imagery , 2000, IEEE Trans. Geosci. Remote. Sens..

[37]  Jeffrey P. Walker,et al.  A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index , 2001, IEEE Trans. Geosci. Remote. Sens..

[38]  P. Ciais,et al.  Leaf onset in the northern hemisphere triggered by daytime temperature , 2015, Nature Communications.

[39]  C. Justice,et al.  Development of vegetation and soil indices for MODIS-EOS , 1994 .

[40]  R. Jeu,et al.  Land surface temperature from Ka band (37 GHz) passive microwave observations , 2009 .

[41]  Christian Mätzler,et al.  Microwave transmissivity of a forest canopy: Experiments made with a beech , 1994 .

[42]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[43]  J. Paris,et al.  The effect of leaf size on the microwave backscattering by corn , 1986 .

[44]  R. Jeu,et al.  Multisensor historical climatology of satellite‐derived global land surface moisture , 2008 .

[45]  Paolo Ferrazzoli,et al.  Passive microwave remote sensing of forests: a model investigation , 1996, IEEE Trans. Geosci. Remote. Sens..

[46]  Yann Kerr,et al.  Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product. , 2018, Remote sensing of environment.

[47]  Ahmad Al Bitar,et al.  Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region. , 2018 .

[48]  Arnaud Mialon,et al.  Comparison between SMOS Vegetation Optical Depth products and MODIS vegetation indices over crop zones of the USA , 2014 .

[49]  Susan C. Steele-Dunne,et al.  Using Diurnal Variation in Backscatter to Detect Vegetation Water Stress , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Manfred Owe,et al.  Determination of microwave vegetation optical depth and single scattering albedo from large scale soil moisture and Nimbus/SMMR satellite observations , 1993 .

[51]  Xiaoping Zhou,et al.  Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: A cross-border study on the Mongolian Plateau. , 2018, The Science of the total environment.

[52]  Ahmad Al Bitar,et al.  High resolution mapping of inundation area in the Amazon basin from a combination of L-band passive microwave, optical and radar datasets , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[53]  T. Holmes,et al.  An analysis of spatiotemporal variations of soil and vegetation moisture from a 29‐year satellite‐derived data set over mainland Australia , 2009 .

[54]  S. Paloscia,et al.  Microwave Emission and Plant Water Content: A Comparison between Field Measurements and Theory , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[55]  Paolo Ferrazzoli,et al.  Vegetation optical depth at L-band and above ground biomass in the tropical range: Evaluating their relationships at continental and regional scales , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[56]  Andrew E. Suyker,et al.  SMOS Optical Thickness Changes in Response to the Growth and Development of Crops, Crop Management, and Weather , 2016 .

[57]  A. Al Bitar,et al.  Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures , 2017 .

[58]  R. Neilson,et al.  Response of vegetation distribution, ecosystem productivity, and fire to climate change scenarios for California , 2008 .

[59]  Yann Kerr,et al.  Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[60]  P. Ciais,et al.  Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite , 2018, Nature Ecology & Evolution.

[61]  W. Cohen,et al.  Evaluation of MODIS NPP and GPP products across multiple biomes. , 2006 .

[62]  Yi Y. Liu,et al.  Global long‐term passive microwave satellite‐based retrievals of vegetation optical depth , 2011 .

[63]  Jean-Pierre Wigneron,et al.  Monitoring coniferous forest characteristics using a multifrequency (5–90 GHz) microwave radiometer☆ , 1997 .

[64]  John S. Kimball,et al.  Passive Microwave Remote Sensing of Soil Moisture Based on Dynamic Vegetation Scattering Properties for AMSR-E , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[65]  O. Sonnentag,et al.  Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .

[66]  Pierre Prandi,et al.  The Benefits of the Ka-Band as Evidenced from the SARAL/AltiKa Altimetric Mission: Quality Assessment and Unique Characteristics of AltiKa Data , 2018, Remote. Sens..

[67]  Paolo Ferrazzoli,et al.  Retrieving soil moisture and agricultural variables by microwave radiometry using neural networks , 2003 .

[68]  Thomas Jagdhuber,et al.  Estimation of active-passive microwave covariation using SMAP and Sentinel-1 data , 2019, Remote Sensing of Environment.

[69]  P. Ciais,et al.  Tropical forests did not recover from the strong 2015–2016 El Niño event , 2020, Science Advances.

[70]  F. Aires,et al.  Changes in land surface water dynamics since the 1990s and relation to population pressure , 2012 .

[71]  Li Li,et al.  Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz , 1999, IEEE Trans. Geosci. Remote. Sens..

[72]  Matthew O. Jones,et al.  Satellite passive microwave remote sensing for monitoring global land surface phenology , 2011 .

[73]  Yann Kerr,et al.  Inversion of surface parameters from passive microwave measurements over a soybean field , 1993 .

[74]  Alfredo Huete,et al.  Abrupt shifts in phenology and vegetation productivity under climate extremes , 2015 .

[75]  F. Frappart,et al.  Compared performances of SMOS-IC soil moisture and vegetation optical depth retrievals based on Tau-Omega and Two-Stream microwave emission models , 2020 .

[76]  J. Calvet,et al.  Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop , 2017 .

[77]  Dara Entekhabi,et al.  Improved SMAP Dual-Channel Algorithm for the Retrieval of Soil Moisture , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[78]  Eleanor J. Burke,et al.  Using a modeling approach to predict soil hydraulic properties from passive microwave measurements , 1998, IEEE Trans. Geosci. Remote. Sens..

[79]  Jiancheng Shi,et al.  Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E , 2008 .

[80]  Compton J. Tucker,et al.  Monitoring vegetation using Nimbus-7 scanning multichannel microwave radiometer's data , 1987 .

[81]  Arnaud Mialon,et al.  Comparison of SMOS and AMSR-E vegetation optical depth to four MODIS-based vegetation indices , 2016 .

[82]  Mike Schwank,et al.  "Tau-Omega"- and Two-Stream Emission Models Used for Passive L-Band Retrievals: Application to Close-Range Measurements over a Forest , 2018, Remote. Sens..

[83]  Mehrez Zribi,et al.  Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas , 2017, Remote. Sens..

[84]  B. Choudhury,et al.  Simulated and observed 37 GHz emission over Africa , 1990 .

[85]  Fernando Niño,et al.  An ERS-2 altimetry reprocessing compatible with ENVISAT for long-term land and ice sheets studies , 2016 .

[86]  Arnaud Mialon,et al.  Evaluating the Semiempirical $H$– $Q$ Model Used to Calculate the L-Band Emissivity of a Rough Bare Soil , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[87]  O. Phillips,et al.  The 2010 Amazon Drought , 2011, Science.

[88]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[89]  Lun Gao,et al.  Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite , 2020 .

[90]  M. Piles,et al.  Simultaneous retrieval of global scale Vegetation Optical Depth, surface roughness, and soil moisture using X-band AMSR-E observations , 2019 .

[91]  Yi Y. Liu,et al.  ESA CCI Soil Moisture for improved Earth system understanding : State-of-the art and future directions , 2017 .

[92]  Jean-Pierre Wigneron,et al.  Simulating L-band emission of forests in view of future satellite applications , 2002, IEEE Trans. Geosci. Remote. Sens..

[93]  Robert M. Parinussa,et al.  Spatio-temporal evaluation of resolution enhancement for passive microwave soil moisture and vegetation optical depth , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[94]  Brian K. Hornbuckle,et al.  Initial Validation of SMOS Vegetation Optical Thickness in Iowa , 2013, IEEE Geosci. Remote. Sens. Lett..

[95]  G. Asner,et al.  An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR , 2018 .

[96]  J. Terborgh,et al.  Drought Sensitivity of the Amazon Rainforest , 2009, Science.

[97]  W. Mauser,et al.  Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany , 2012 .

[98]  Philippe Richaume,et al.  Mitigation of RFIS for SMOS: A Distributed Approach , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[99]  Yann Kerr,et al.  Assessment of the SMAP Passive Soil Moisture Product , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[100]  Irene E. Teubner,et al.  The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA , 2019, Earth System Science Data.

[101]  F. Aires,et al.  Expected Performances of the Copernicus Imaging Microwave Radiometer (CIMR) for an All‐Weather and High Spatial Resolution Estimation of Ocean and Sea Ice Parameters , 2018, Journal of Geophysical Research: Oceans.

[102]  W. Salas,et al.  Benchmark map of forest carbon stocks in tropical regions across three continents , 2011, Proceedings of the National Academy of Sciences.

[103]  Steven J. Phillips,et al.  Shifts in Arctic vegetation and associated feedbacks under climate change , 2013 .

[104]  Jean-Pierre Wigneron,et al.  Modeling Forest Emissivity at L-Band and a Comparison With Multitemporal Measurements , 2007, IEEE Geoscience and Remote Sensing Letters.

[105]  A. Chehbouni,et al.  Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation , 2011 .

[106]  JoBea Way,et al.  Radar estimates of aboveground biomass in boreal forests of interior Alaska , 1994, IEEE Trans. Geosci. Remote. Sens..

[107]  Jan Vanderborght,et al.  FOSMEX: Forest Soil Moisture Experiments With Microwave Radiometry , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[108]  Simonetta Paloscia,et al.  Remote monitoring of soil moisture using passive microwave-based techniques — Theoretical basis and overview of selected algorithms for AMSR-E , 2014 .

[109]  Robert M. Parinussa,et al.  A Methodology to Determine Radio-Frequency Interference in AMSR2 Observations , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[110]  Arnaud Mialon,et al.  The SMOS Soil Moisture Retrieval Algorithm , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[111]  R. Neilson,et al.  Climate Change Effects on Vegetation Distribution and Carbon Budget in the United States , 2001, Ecosystems.

[112]  Arnaud Mialon,et al.  SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product , 2017, Remote. Sens..

[113]  Matthew O. Jones,et al.  Satellite microwave detection of boreal forest recovery from the extreme 2004 wildfires in Alaska and Canada , 2013, Global change biology.

[114]  R. Neilson.  A Model for Predicting Continental‐Scale Vegetation Distribution and Water Balance , 1995 .

[115]  S. Steele‐Dunne,et al.  Macro to micro: microwave remote sensing of plant water content for physiology and ecology. , 2019, The New phytologist.

[116]  Yi Y. Liu,et al.  Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers , 2013 .

[117]  Ke Zhang,et al.  Numerical Terradynamic Simulation Group 9-2008 Satellite-based model detection of recent climate-driven changes in northern high-latitude vegetation productivity , 2018 .

[118]  Emanuele Santi,et al.  Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[119]  S. Goetz,et al.  Vegetation productivity patterns at high northern latitudes: a multi-sensor satellite data assessment , 2014, Global change biology.

[120]  M. Piles,et al.  Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices , 2019, Remote Sensing of Environment.

[121]  N. Verhoest,et al.  GLEAM v3: satellite-based land evaporation and root-zone soil moisture , 2016 .

[122]  Arnaud Mialon,et al.  Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands , 2018, Nature Ecology & Evolution.

[123]  Ranga B. Myneni,et al.  Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the terra MODIS sensor: 2000-2005 , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[124]  Philippe Richaume,et al.  SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates , 2016 .

[125]  A. Al Bitar,et al.  Modelling the Passive Microwave Signature from Land Surfaces: A Review of Recent Results and Application to the L-Band SMOS SMAP Soil Moisture Retrieval Algorithms , 2017 .

[126]  Pierre Borderies,et al.  Spaceborne altimetry and scatterometry backscattering signatures at C- and Ku-bands over West Africa , 2015 .

[127]  Manabu Watanabe,et al.  Operational performance of the ALOS global systematic acquisition strategy and observation plans for ALOS-2 PALSAR-2 , 2014 .

[128]  Marianne E. Porter,et al.  Differential tree mortality in response to severe drought: evidence for long‐term vegetation shifts , 2005 .

[129]  Tim R. McVicar,et al.  Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data , 2013 .

[130]  Fawwaz T. Ulaby,et al.  Relating the microwave backscattering coefficient to leaf area index , 1984 .

[131]  Christian Mätzler,et al.  Relating the X-band opacity of a tropical tree canopy to sapflow, rain interception and dew formation , 2011 .

[132]  Arief Wijaya,et al.  An integrated pan‐tropical biomass map using multiple reference datasets , 2016, Global change biology.

[133]  C. Tucker,et al.  Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 , 2001 .

[134]  John S. Kimball,et al.  An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing , 2016 .

[135]  Mehrez Zribi,et al.  Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands , 2017, Remote. Sens..

[136]  S. Goetz,et al.  Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps , 2012 .

[137]  Susan C. Steele-Dunne,et al.  Impact of Diurnal Variation in Vegetation Water Content on Radar Backscatter From Maize During Water Stress , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[138]  Gian-Reto Walther,et al.  Plants in a warmer world , 2003 .

[139]  Arnaud Mialon,et al.  Satellite-observed pantropical carbon dynamics , 2019, Nature Plants.

[140]  D. Fuller,et al.  Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987-1993 , 1998 .

[141]  K. Price,et al.  Response of seasonal vegetation development to climatic variations in eastern central Asia , 2003 .

[142]  S. Higgins,et al.  Three decades of multi-dimensional change in global leaf phenology , 2015 .

[143]  N. Stephenson.  Climatic Control of Vegetation Distribution: The Role of the Water Balance , 1990, The American Naturalist.

[144]  K. S. Hari Prasad,et al.  Estimation of water cloud model vegetation parameters using a genetic algorithm , 2012 .

[145]  R. Stöckli,et al.  European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset , 2004 .

[146]  J. Tenhunen,et al.  On the relationship of NDVI with leaf area index in a deciduous forest site , 2005 .

[147]  F. Ulaby,et al.  Vegetation modeled as a water cloud , 1978 .

[148]  Fawwaz Ulaby,et al.  Microwave Dielectric Spectrum of Vegetation - Part II: Dual-Dispersion Model , 1987, IEEE Transactions on Geoscience and Remote Sensing.

[149]  J. Pulliainen,et al.  Radar-based forest biomass estimation , 1994 .

[150]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[151]  Kevin P. Price,et al.  Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains , 2001 .

[152]  Clément Albergel,et al.  Interpretation of ASCAT Radar Scatterometer Observations Over Land: A Case Study Over Southwestern France , 2019, Remote. Sens..

[153]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[154]  T. Schmugge,et al.  Passive microwave sensing of soil moisture under vegetation canopies , 1982 .

[155]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[156]  T. Carlson,et al.  A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .

[157]  Ronald P. Neilson,et al.  Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. , 2010 .

[158]  Yi Y. Liu,et al.  Changing Climate and Overgrazing Are Decimating Mongolian Steppes , 2013, PloS one.

[159]  Malcolm Davidson,et al.  GMES Sentinel-1 mission , 2012 .

[160]  C. Tucker,et al.  A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data , 2000 .

[161]  M. Owe,et al.  Further validation of a new methodology for surface moisture and vegetation optical depth retrieval , 2003 .

[162]  Dara Entekhabi,et al.  Characterization of higher-order scattering from vegetation with SMAP measurements , 2018, Remote Sensing of Environment.

[163]  R. Koster,et al.  Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation , 2004 .

[164]  Thomas J. Jackson,et al.  A dielectric model of the vegetation effects on the microwave emission from soils , 1992, IEEE Trans. Geosci. Remote. Sens..

[165]  Robert M. Parinussa,et al.  Assessing the relationship between microwave vegetation optical depth and gross primary production , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[166]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[167]  Mehrez Zribi,et al.  Evaluation of ALOS/PALSAR L-Band Data for the Estimation of Eucalyptus Plantations Aboveground Biomass in Brazil , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[168]  Dara Entekhabi,et al.  Vegetation optical depth and scattering albedo retrieval using time series of dual-polarized L-band radiometer observations , 2016 .

[169]  Frédéric Baup,et al.  Backscattering signatures at Ka, Ku, C and S bands from low resolution radar altimetry over land , 2020, Advances in Space Research.

[170]  Peter S. Eagleson,et al.  Climate, soil, and vegetation: 1. Introduction to water balance dynamics , 1978 .

[171]  Thuy Le Toan,et al.  Dependence of radar backscatter on coniferous forest biomass , 1992, IEEE Trans. Geosci. Remote. Sens..

[172]  Adriano Camps,et al.  L-band vegetation optical depth seasonal metrics for crop yield assessment , 2018, Remote Sensing of Environment.

[173]  T. Mo,et al.  A model for microwave emission from vegetation‐covered fields , 1982 .

[174]  Fawwaz T. Ulaby,et al.  Effects of Vegetation Cover on the Radar Sensitivity to Soil Moisture , 1982, IEEE Transactions on Geoscience and Remote Sensing.

[175]  Simonetta Paloscia,et al.  Microwave vegetation indexes for detecting biomass and water conditions of agricultural crops , 1992 .

[176]  M. Goulden,et al.  Rapid shifts in plant distribution with recent climate change , 2008, Proceedings of the National Academy of Sciences.

[177]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[178]  J. Watts,et al.  A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations , 2017 .

[179]  Martin Brandt,et al.  Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel , 2016 .

[180]  Kevin P. Price,et al.  Relations between NDVI and tree productivity in the central Great Plains , 2004 .

[181]  Kelly K. Caylor,et al.  Terrestrial hydrological controls on land surface phenology of African savannas and woodlands , 2014 .

[182]  Yann Kerr,et al.  Two-Dimensional Microwave Interferometer Retrieval Capabilities over Land Surfaces (SMOS Mission) , 2000 .

[183]  John S. Kimball,et al.  Satellite passive microwave detection of North America start of season , 2012 .

[184]  Mike Schwank,et al.  L-band radiometer measurements of soil water under growing clover grass , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[185]  Y. Kerr,et al.  L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields , 2007 .