Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
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[1] D. Guo,et al. Estimation of Cotton Yield Using the Reconstructed Time-Series Vegetation Index of Landsat Data , 2017 .
[2] S. Taghvaeian,et al. Impacts of Irrigation Termination Date on Cotton Yield and Irrigation Requirement , 2019, Agriculture.
[3] James F. Reynolds,et al. Modelling photosynthesis of cotton grown in elevated CO2 , 1992 .
[4] Weimin Ju,et al. Remote sensing-based ecosystem–atmosphere simulation scheme (EASS)—Model formulation and test with multiple-year data , 2007 .
[5] Ahmad Al Bitar,et al. Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data , 2016 .
[6] M. Tamura,et al. Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia , 2002 .
[7] Isabelle Piccard,et al. Towards Predictive Modeling of Sorghum Biomass Yields Using Fraction of Absorbed Photosynthetically Active Radiation Derived from Sentinel-2 Satellite Imagery and Supervised Machine Learning Techniques , 2019, Agronomy.
[8] Jonathan M. Adams,et al. Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate , 2009 .
[9] T. Cheng,et al. Predicting wheat productivity: Integrating time series of vegetation indices into crop modeling via sequential assimilation , 2019, Agricultural and Forest Meteorology.
[10] Improved soil physical properties and cotton root parameters under sub-soiling enhance yield of Cotton-Wheat cropping system , 2019, Data in brief.
[11] Jing M. Chen,et al. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[12] Quevedo Amaya,et al. Caracterización fisiológica y bioquímica de cuatro genotipos de algodón (Gossypium hirsutum L.) en condiciones de déficit hídrico , 2020 .
[13] Philip Lewis,et al. Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST–PROSAIL model , 2019, European Journal of Agronomy.
[14] David B. Lobell,et al. Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa , 2017, Remote. Sens..
[15] Yi Peng,et al. Remote prediction of yield based on LAI estimation in oilseed rape under different planting methods and nitrogen fertilizer applications , 2019, Agricultural and Forest Meteorology.
[16] X. Zeng,et al. Evaluation of Greenland near surface air temperature datasets , 2017 .
[17] M. Janat,et al. Assessment of yield and water use efficiency of drip-irrigated cotton (Gossypium hirsutum L.) as affected by deficit irrigation , 2011, Turkish Journal of Agriculture and Forestry.
[18] John J. Read,et al. Canopy reflectance in cotton for growth assessment and lint yield prediction , 2007 .
[19] Jiahua Zhang,et al. Estimation of Rice Yield with a Process-Based Model and Remote Sensing Data in the Middle and Lower Reaches of Yangtze River of China , 2017, Journal of the Indian Society of Remote Sensing.
[20] Piers J. Sellers,et al. Relations between surface conductance and spectral vegetation indices at intermediate (100 m2 to 15 km2) length scales , 1992 .
[21] Pramod K. Aggarwal,et al. Predicting cotton production using Infocrop-cotton simulation model, remote sensing and spatial agro-climatic data , 2008 .
[22] H. Herren,et al. Deconstructing Indian cotton: weather, yields, and suicides , 2015, Environmental Sciences Europe.
[23] D. Baldocchi,et al. What is global photosynthesis? History, uncertainties and opportunities , 2019, Remote Sensing of Environment.
[24] W. Ju,et al. Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing. , 2007, Journal of environmental management.
[25] P. C. Struik,et al. Leaf photosynthesis and respiration of three bioenergy crops in relation to temperature and leaf nitrogen: how conserved are biochemical model parameters among crop species? , 2011, Journal of experimental botany.
[26] D. Meshesha,et al. Developing crop yield forecasting models for four major Ethiopian agricultural commodities , 2018, Remote Sensing Applications: Society and Environment.
[27] James W. Jones,et al. The DSSAT cropping system model , 2003 .
[28] C. Rosolem,et al. Shading and Nitrogen Effects on Cotton Earliness Assessed by Boll Yield Distribution , 2019, Crop Science.
[29] Anil Kumar Singh,et al. Evaluation of CERES-Wheat and CropSyst models for water-nitrogen interactions in wheat crop , 2008 .
[30] E. Bautista,et al. Integrating geospatial data and cropping system simulation within a geographic information system to analyze spatial seed cotton yield, water use, and irrigation requirements , 2015, Precision Agriculture.
[31] Craig S. T. Daughtry,et al. Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery , 2018, Remote. Sens..
[32] Li Ren,et al. Evaluating the effects of limited irrigation on crop water productivity and reducing deep groundwater exploitation in the North China Plain using an agro-hydrological model: I. Parameter sensitivity analysis, calibration and model validation , 2019, Journal of Hydrology.
[33] Xiaoliang Lu,et al. Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS) , 2016 .
[34] Jing M. Chen,et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications , 1999 .
[35] Martha C. Anderson,et al. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery , 2017 .
[36] Marvin N. Wright,et al. SoilGrids250m: Global gridded soil information based on machine learning , 2017, PloS one.
[37] David B. Lobell,et al. Smallholder maize area and yield mapping at national scales with Google Earth Engine , 2019, Remote Sensing of Environment.
[38] T. A. Black,et al. Modelling multi-year coupled carbon and water fluxes in a boreal aspen forest , 2006 .
[39] M. Kirschbaum,et al. Does Enhanced Photosynthesis Enhance Growth? Lessons Learned from CO2 Enrichment Studies[W] , 2010, Plant Physiology.
[40] J. Monteith. Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.
[41] Yanlian Zhou,et al. Modeling the impact of drought on canopy carbon and water fluxes for a subtropical evergreen coniferous plantation in southern China through parameter optimization using an ensemble Kalman filter , 2010 .
[42] Ray Leuning,et al. Temperature dependence of two parameters in a photosynthesis model , 2002 .
[43] Randal D. Koster,et al. Assessment of MERRA-2 Land Surface Energy Flux Estimates , 2018 .
[44] Bin Chen,et al. Optimization of water uptake and photosynthetic parameters in an ecosystem model using tower flux data , 2014 .
[45] C. Domenikiotis,et al. Early cotton yield assessment by the use of the NOAA/AVHRR derived Vegetation Condition Index (VCI) in Greece , 2004 .
[46] Jianxi Huang,et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation , 2016 .
[47] Gordon B. Bonan,et al. Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model , 1995 .
[48] W. Verhoef,et al. PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .
[49] Gordon B. Bonan,et al. A biophysical surface energy budget analysis of soil temperature in the boreal forests of interior Alaska , 1991 .
[50] Yanghui Kang,et al. Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach , 2019, Remote Sensing of Environment.
[51] D. Timlin,et al. Effect of Phosphorus Nutrition on Growth and Physiology of Cotton Under Ambient and Elevated Carbon Dioxide , 2013 .
[52] Peijuan Wang,et al. Yield estimation of winter wheat in the North China Plain using the remote-sensing–photosynthesis–yield estimation for crops (RS–P–YEC) model , 2011 .
[53] Randal D. Koster,et al. Assessment of MERRA-2 Land Surface Hydrology Estimates , 2017 .
[54] Sha Zhang,et al. A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops , 2018, Remote Sensing of Environment.
[55] Jane Liu,et al. Assessment of SMAP soil moisture for global simulation of gross primary production , 2017 .
[56] B. Zierl,et al. A water balance model to simulate drought in forested ecosystems and its application to the entire forested area in Switzerland , 2001 .
[57] A. B. Hearn,et al. OZCOT: A simulation model for cotton crop management , 1994 .
[58] Ronggao Liu,et al. Changes in the Shadow: The Shifting Role of Shaded Leaves in Global Carbon and Water Cycles Under Climate Change , 2018 .
[59] I. E. Woodrow,et al. A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .
[60] R. Leuning,et al. A two-leaf model for canopy conductance, photosynthesis and partitioning of available energy I:: Model description and comparison with a multi-layered model , 1998 .
[61] Jiahua Zhang,et al. Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain , 2015 .
[62] Kaihui Song,et al. Estimation methods developing with remote sensing information for energy crop biomass: A comparative review , 2019, Biomass and Bioenergy.
[63] Sha Zhang,et al. Evaluation and improvement of the daily boreal ecosystem productivity simulator in simulating gross primary productivity at 41 flux sites across Europe , 2018 .
[64] Christopher Conrad,et al. Modeling of Cotton Yields in the Amu Darya River Floodplains of Uzbekistan Integrating Multitemporal Remote Sensing and Minimum Field Data , 2007 .
[65] Bunkei Matsushita,et al. Estimation of regional net primary productivity (NPP) using a process-based ecosystem model: How important is the accuracy of climate data? , 2004 .
[66] W. Rawls,et al. Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions , 2006 .
[67] T. Quaife,et al. Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model , 2018, Remote Sensing of Environment.
[68] K. Boote,et al. An assessment of yield gains under climate change due to genetic modification of pearl millet , 2017, The Science of the total environment.
[69] T. Vesala,et al. Simulation and scaling of temporal variation in gross primary production for coniferous and deciduous temperate forests , 2004 .
[70] W. Cao,et al. Effects of planting pattern on growth and yield and economic benefits of cotton in a wheat-cotton double cropping system versus monoculture cotton , 2017 .
[71] Jiali Shang,et al. Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean. , 2019, The Science of the total environment.
[72] Jing M. Chen,et al. Improved assessment of gross and net primary productivity of Canada's landmass , 2013 .
[73] Bin Chen,et al. Assessment of foliage clumping effects on evapotranspiration estimates in forested ecosystems , 2016 .
[74] Brian G. Leib,et al. Prediction of cotton lint yield from phenology of crop indices using artificial neural networks , 2018, Comput. Electron. Agric..
[75] Wenqing Zhao,et al. Effect of cropping system on cotton biomass accumulation and yield formation in double–cropped wheat–cotton , 2016 .
[76] Robert E. Dickinson,et al. A Two-Big-Leaf Model for Canopy Temperature, Photosynthesis, and Stomatal Conductance , 2004 .
[77] J. Chen,et al. A process-based boreal ecosystem productivity simulator using remote sensing inputs , 1997 .
[78] M. Reichstein,et al. Global variability of carbon use efficiency in terrestrial ecosystems , 2019 .
[79] Mingquan Wu,et al. Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion , 2018, Scientific Reports.
[80] E. Sertel,et al. Estimating maize and cotton yield in southeastern Turkey with integrated use of satellite images, meteorological data and digital photographs , 2014 .
[81] Chenghai Yang,et al. Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability , 2004, Precision Agriculture.
[82] Jing M. Chen,et al. Evaluation of leaf-to-canopy upscaling methodologies against carbon flux data in North America , 2012 .
[83] T. A. Black,et al. Comparison of Big‐Leaf, Two‐Big‐Leaf, and Two‐Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon‐Water Modeling , 2018 .
[84] W. Chow,et al. Two distinct strategies of cotton and soybean differing in leaf movement to perform photosynthesis under drought in the field. , 2011, Functional plant biology : FPB.
[85] S. Schubert,et al. MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .
[86] J. Randerson,et al. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity , 2016, Proceedings of the National Academy of Sciences.
[87] Chris Murphy,et al. APSIM - Evolution towards a new generation of agricultural systems simulation , 2014, Environ. Model. Softw..
[88] I. C. Prentice,et al. Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) , 2008 .
[89] Benoît Duchemin,et al. A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index , 2008, Environ. Model. Softw..
[90] Niklaus E. Zimmermann,et al. Water-use efficiency and transpiration across European forests during the Anthropocene , 2015 .
[91] Sander Janssen,et al. 25 years of the WOFOST cropping systems model , 2019, Agricultural Systems.
[92] Mathew R. Schwaller,et al. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[93] Stephan J. Maas,et al. Relationship between cotton yield and soil electrical conductivity, topography, and Landsat imagery , 2012, Precision Agriculture.
[94] R. Koster,et al. Land Surface Precipitation in MERRA-2 , 2017 .
[95] Susan L. Ustin,et al. Assessment of the effectiveness of spatiotemporal fusion of multi-source satellite images for cotton yield estimation , 2019, Comput. Electron. Agric..
[96] H. P. Maheswarappa,et al. Carbon Footprint and Sustainability of Agricultural Production Systems in India , 2011 .
[97] A cotton yield estimation model based on agrometeorological and high resolution remote sensing data , 2019, Precision agriculture ’19.
[98] P. De Angelis,et al. Effects of elevated (CO2) on photosynthesis in European forest species: a meta-analysis of model parameters , 1999 .
[99] J. Thepaut,et al. A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets , 2017 .
[100] J. Chen,et al. Defining leaf area index for non‐flat leaves , 1992 .
[101] Xavier Blaes,et al. Estimating smallholder crops production at village level from Sentinel-2 time series in Mali's cotton belt , 2018, Remote Sensing of Environment.
[102] Bruno Basso,et al. Predicting spatial patterns of within-field crop yield variability , 2018 .
[103] Luis Alonso,et al. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 , 2012 .
[104] J. Pisek,et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity , 2012 .
[105] Philip Lewis,et al. Assimilation of remote sensing into crop growth models: Current status and perspectives , 2019, Agricultural and Forest Meteorology.
[106] Susan L. Ustin,et al. Derivation of phenological metrics by function fitting to time-series of Spectral Shape Indexes AS1 and AS2: Mapping cotton phenological stages using MODIS time series , 2012 .
[107] David J. Beerling,et al. Maximum leaf conductance driven by CO2 effects on stomatal size and density over geologic time , 2009, Proceedings of the National Academy of Sciences.
[108] J. Norman. SIMULATION OF MICROCLIMATES , 1982 .
[109] David M. Johnson,et al. Measuring land-use and land-cover change using the U.S. department of agriculture's cropland data layer: Cautions and recommendations , 2017, Int. J. Appl. Earth Obs. Geoinformation.
[110] J. Berry,et al. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.
[111] D. Pury,et al. Simple scaling of photosynthesis from leaves to canopies without the errors of big‐leaf models , 1997 .
[112] D. Baldocchi. An analytical solution for coupled leaf photosynthesis and stomatal conductance models. , 1994, Tree physiology.
[113] Pablo J. Zarco-Tejada,et al. Temporal and Spatial Relationships between within-field Yield variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery , 2005 .
[114] Jian-xiong Huang. Effects of Meteorological Parameters Created by Different Sowing Dates on Drip Irrigated Cotton Yield and Yield Components in Arid Regions in China , 2015 .