Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images
暂无分享,去创建一个
P. Starks | Xiangming Xiao | Jie Wang | R. Bajgain | J. Steiner | R. Doughty | Q. Chang
[1] Management of water resources for grasslands Research Laboratory – USDA-ARS, USA , 2019, Improving grassland and pasture management in temperate agriculture.
[2] Renaud Mathieu,et al. Estimating above ground biomass as an indicator of carbon storage in vegetated wetlands of the grassland biome of South Africa , 2019, Int. J. Appl. Earth Obs. Geoinformation.
[3] Jiaguo Qi,et al. Grassland canopy cover and aboveground biomass in Mongolia and Inner Mongolia: Spatiotemporal estimates and controlling factors , 2018, Remote Sensing of Environment.
[4] R. Collins,et al. Improving grassland and pasture management in temperate agriculture , 2018 .
[5] P. Gowda,et al. Carbon dioxide and water vapor fluxes in winter wheat and tallgrass prairie in central Oklahoma. , 2018, The Science of the total environment.
[6] A. Evans,et al. Quantifying grazing patterns using a new growth function based on MODIS Leaf Area Index , 2018 .
[7] Yanlian Zhou,et al. Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes , 2018 .
[8] Jinwei Dong,et al. Characterizing the encroachment of juniper forests into sub-humid and semi-arid prairies from 1984 to 2010 using PALSAR and Landsat data , 2018 .
[9] Dario Papale,et al. Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data , 2018 .
[10] Severino G. Salmo,et al. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery , 2017 .
[11] Alexandre Bouvet,et al. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications , 2017 .
[12] Gang Fu,et al. Validation of MODIS collection 6 FPAR/LAI in the alpine grassland of the Northern Tibetan Plateau , 2017 .
[13] Prasanna H. Gowda,et al. Examining the short-term impacts of diverse management practices on plant phenology and carbon fluxes of Old World bluestems pasture , 2017 .
[14] George L. Geissler,et al. Mapping the dynamics of eastern redcedar encroachment into grasslands during 1984–2010 through PALSAR and time series Landsat images , 2017 .
[15] Hongjie Xie,et al. Multi-factor modeling of above-ground biomass in alpine grassland: A case study in the Three-River Headwaters Region, China , 2016 .
[16] E. Dwyer,et al. Satellite remote sensing of grasslands: from observation to management—a review , 2016 .
[17] Scott J. Goetz,et al. Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data , 2016 .
[18] Onisimo Mutanga,et al. Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space , 2016 .
[19] Xu Wang,et al. Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods , 2016, Remote. Sens..
[20] Maxim Shoshany,et al. Mediterranean shrublands biomass estimation using Sentinel-1 and Sentinel-2 , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
[21] M. Claverie,et al. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.
[22] R. Nemani,et al. Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements , 2016, Remote. Sens..
[23] P. Poschlod,et al. Grazing vs. mowing: A meta-analysis of biodiversity benefits for grassland management , 2016 .
[24] Mariana Belgiu,et al. Random forest in remote sensing: A review of applications and future directions , 2016 .
[25] Christopher O. Justice,et al. A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation , 2016, Remote. Sens..
[26] Kees de Bie,et al. Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists , 2016 .
[27] Mehrez Zribi,et al. Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[28] M. Feng,et al. Impact of spectral saturation on leaf area index and aboveground biomass estimation of winter wheat , 2016 .
[29] Lijuan Liu,et al. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems , 2016, Int. J. Digit. Earth.
[30] Yingxin Gu,et al. Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations , 2015 .
[31] Ruben Van De Kerchove,et al. Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data , 2015, Int. J. Appl. Earth Obs. Geoinformation.
[32] M. Schlerf,et al. Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy , 2015, Int. J. Appl. Earth Obs. Geoinformation.
[33] Nitin K. Tripathi,et al. Grassland Growth in Response to Climate Variability in the Upper Indus Basin, Pakistan , 2015 .
[34] L. Bruzzone,et al. Retrieval of Leaf Area Index in mountain grasslands in the Alps from MODIS satellite imagery , 2015 .
[35] Cyrus Samimi,et al. Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna , 2015, Remote. Sens..
[36] C. Woodcock,et al. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .
[37] Dandan Xu,et al. Some Insights on Grassland Health Assessment Based on Remote Sensing , 2015, Sensors.
[38] Inci Güneralp,et al. Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling , 2014, Int. J. Appl. Earth Obs. Geoinformation.
[39] Stuart M. Wilson,et al. Experimental evidence that invasive grasses use allelopathic biochemicals as a potential mechanism for invasion: chemical warfare in nature , 2014, Plant and Soil.
[40] E. Nkonya,et al. Biomass Productivity-Based Mapping of Global Land Degradation Hotspots , 2014 .
[41] Samuel Corgne,et al. Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring , 2014, Remote. Sens..
[42] A. Challinor,et al. Climate variability and vulnerability to climate change: a review , 2014, Global change biology.
[43] B. Wylie,et al. Linking Phenology and Biomass Productivity in South Dakota Mixed-Grass Prairie , 2013 .
[44] Peter M. Atkinson,et al. Ecological sustainability in rangelands: the contribution of remote sensing , 2013 .
[45] A. Gonsamo,et al. Deriving land surface phenology indicators from CO2 eddy covariance measurements , 2013 .
[46] V. Klemas. Remote Sensing of Coastal Wetland Biomass: An Overview , 2013 .
[47] D. Peddle,et al. Quantifying biomass production on rangeland in southern Alberta using SPOT imagery , 2013 .
[48] David P. Billesbach,et al. Carbon, water, and heat flux responses to experimental burning and drought in a tallgrass prairie , 2012 .
[49] Andrew K. Skidmore,et al. Estimation of grassland biomass and nitrogen using MERIS data , 2012, Int. J. Appl. Earth Obs. Geoinformation.
[50] Michael E. Schaepman,et al. Sentinels for science: potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land , 2012 .
[51] Mark A. Friedl,et al. Long-Term Detection of Global Vegetation Phenology from Satellite Instruments , 2012 .
[52] Luis Alonso,et al. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 , 2012 .
[53] G. A. B. Yiran,et al. A synthesis of remote sensing and local knowledge approaches in land degradation assessment in the Bawku East District, Ghana , 2012, Int. J. Appl. Earth Obs. Geoinformation.
[54] A. Skidmore,et al. Mapping grassland leaf area index with airborne hyperspectral imagery : a comparison study of statistical approaches and inversion of radiative transfer models , 2011 .
[55] Jungho Im,et al. Support vector machines in remote sensing: A review , 2011 .
[56] Joseph R. Buckley,et al. Monitoring grasslands with radarsat 2 quad-pol imagery , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.
[57] Yanhong Tang,et al. Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai‐Tibetan grasslands , 2010 .
[58] J. Dash,et al. Estimating the relative abundance of C3 and C4 grasses in the Great Plains from multi-temporal MTCI data: issues of compositing period and spatial generalizability , 2010 .
[59] Martha C. Anderson,et al. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale , 2009 .
[60] A. Skidmore,et al. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .
[61] J. Derner,et al. CARBON SEQUESTRATION AND RANGELANDS: A SYNTHESIS OF LAND MANAGEMENT AND PRECIPITATION EFFECTS , 2007 .
[62] Lan Xu,et al. Using Weather Data to Explain Herbage Yield on Three Great Plains Plant Communities , 2007 .
[63] F. Baret,et al. Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data : Principles and validation , 2006 .
[64] Christopher B. Field,et al. Diverse responses of phenology to global changes in a grassland ecosystem , 2006, Proceedings of the National Academy of Sciences.
[65] D. Lu. The potential and challenge of remote sensing‐based biomass estimation , 2006 .
[66] J. Mustard,et al. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .
[67] Yanhong Tang,et al. Alpine grassland degradation and its control in the source region of the Yangtze and Yellow Rivers, China , 2005 .
[68] Changsheng Li,et al. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .
[69] J. Ellis,et al. Vulnerability of the Asian Typical Steppe to Grazing and Climate Change , 2004 .
[70] D. Engle,et al. Predicting juniper encroachment and CRP effects on avian community dynamics in southern mixed-grass prairie, USA , 2004 .
[71] R. Colombo,et al. Retrieval of leaf area index in different vegetation types using high resolution satellite data , 2003 .
[72] A. Strahler,et al. Monitoring vegetation phenology using MODIS , 2003 .
[73] A. Huete,et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .
[74] S. Running,et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .
[75] S. Leblanc,et al. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements , 2002 .
[76] Peter Poschlod,et al. Challenges for the conservation of calcareous grasslands in northwestern Europe: integrating the requirements of flora and fauna , 2002 .
[77] B. Wylie,et al. Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands a case study , 2002 .
[78] Karin S. Fassnacht,et al. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites , 1999 .
[79] Simonetta Paloscia,et al. The potential of C- and L-band SAR in estimating vegetation biomass: the ERS-1 and JERS-1 experiments , 1999, IEEE Trans. Geosci. Remote. Sens..
[80] D. O. Hall,et al. The global carbon sink: a grassland perspective , 1998 .
[81] Andrew D. Friend,et al. A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0) , 1997 .
[82] I. C. Prentice,et al. An integrated biosphere model of land surface processes , 1996 .
[83] I. C. Prentice,et al. BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types , 1996 .
[84] J. Chen,et al. Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images , 1996 .
[85] Jesslyn F. Brown,et al. Measuring phenological variability from satellite imagery , 1994 .
[86] J. Michaelsen,et al. Estimating grassland biomass and leaf area index using ground and satellite data , 1994 .
[87] C. Tucker. Red and photographic infrared linear combinations for monitoring vegetation , 1979 .
[88] Hannah M. Cooper,et al. Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data , 2018 .
[89] Sepideh Karimi,et al. Generalizability of gene expression programming and random forest methodologies in estimating cropland and grassland leaf area index , 2018, Comput. Electron. Agric..
[90] H. Xie,et al. Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region , 2018 .
[91] K. Swing. Challenges to Conservation , 2017 .
[92] E. Nkonya,et al. Global Estimates of the Impacts of Grassland Degradation on Livestock Productivity from 2001 to 2011 , 2016 .
[93] Min Liu,et al. Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches , 2016 .
[94] A. Franzluebbers,et al. Enhancing soil and landscape quality in smallholder grazing systems. , 2014 .
[95] M. Zortea,et al. OBJECT-BASED CLOUD AND CLOUD SHADOW DETECTION IN LANDSAT IMAGES FOR TROPICAL FOREST MONITORING , 2012 .
[96] G. Yiran,et al. A Synthesis of Remote Sensing and Local Knowledge Approaches in Assessing Land Degradation in the Bawku East District – Ghana , 2011 .
[97] M. Sahebi,et al. A review on biomass estimation methods using synthetic aperture radar data. , 2011 .
[98] Jingyun Fang,et al. Aboveground biomass in Tibetan grasslands , 2009 .
[99] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[100] Yunqian Ma,et al. Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.