The Ability of Sun-Induced Chlorophyll Fluorescence From OCO-2 and MODIS-EVI to Monitor Spatial Variations of Soybean and Maize Yields in the Midwestern USA

[1]  Martha C. Anderson,et al.  An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications , 2013 .

[2]  Nguyen Hieu Trung,et al.  A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation , 2014 .

[3]  Carol X. Song,et al.  Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications , 2016 .

[4]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[5]  C. Frankenberg,et al.  Prospects for Chlorophyll Fluorescence Remote Sensing from the Orbiting Carbon Observatory-2 , 2014 .

[6]  C. Frankenberg,et al.  Effect of environmental conditions on the relationship between solar‐induced fluorescence and gross primary productivity at an OzFlux grassland site , 2017 .

[7]  Douglas K. Bolton,et al.  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics , 2013 .

[8]  C. Frankenberg,et al.  Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. , 2014, Journal of experimental botany.

[9]  A. Gitelson,et al.  Near real-time prediction of U.S. corn yields based on time-series MODIS data , 2014 .

[10]  C. Frankenberg,et al.  Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison, and global monitoring for GPP , 2018 .

[11]  P. Gentine,et al.  Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence , 2018, Geophysical research letters.

[12]  Alex J. Cannon,et al.  Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods , 2016 .

[13]  E. Middleton,et al.  First observations of global and seasonal terrestrial chlorophyll fluorescence from space , 2010 .

[14]  V. Gond,et al.  Vegetation structure and greenness in Central Africa from Modis multi-temporal data , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  D. Lobell.  Changes in diurnal temperature range and national cereal yields , 2007 .

[16]  M. Mkhabela,et al.  Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA's-AVHRR , 2005 .

[17]  Achim Zeileis,et al.  BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests , 2008 .

[18]  Ralf Wieland,et al.  YIELDSTAT – A spatial yield model for agricultural crops , 2014 .

[19]  Nathaniel P. Robinson,et al.  Terrestrial primary production for the conterminous United States derived from Landsat 30 m and MODIS 250 m , 2018 .

[20]  Jian Peng,et al.  Toward building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the U.S , 2019, Field Crops Research.

[21]  Ming Pan,et al.  Benefits of Seasonal Climate Prediction and Satellite Data for Forecasting U.S. Maize Yield , 2018, Geophysical Research Letters.

[22]  M. Rossini,et al.  Solar‐induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest , 2015 .

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

[24]  D. Lobell,et al.  The effects of extremely wet planting conditions on maize and soybean yields , 2015, Climatic Change.

[25]  A. Gonsamo,et al.  Deriving land surface phenology indicators from CO2 eddy covariance measurements , 2013 .

[26]  W. Schlenker,et al.  Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change , 2009, Proceedings of the National Academy of Sciences.

[27]  D. Lobell,et al.  Improving the monitoring of crop productivity using spaceborne solar‐induced fluorescence , 2016, Global change biology.

[28]  ZhangGuangquan,et al.  Transfer learning using computational intelligence , 2015 .

[29]  A. Challinor,et al.  Impacts of El Niño Southern Oscillation on the global yields of major crops , 2014, Nature Communications.

[30]  Gidon Eshel,et al.  Forecasting Zimbabwean maize yield using eastern equatorial Pacific sea surface temperature , 1994, Nature.

[31]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[32]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Juraj Balkovic,et al.  Consistent negative response of US crops to high temperatures in observations and crop models , 2017, Nature Communications.

[34]  N. Ramankutty,et al.  Influence of extreme weather disasters on global crop production , 2016, Nature.

[35]  J. Joiner,et al.  Retrieval of sun-induced chlorophyll fluorescence from space , 2014 .

[36]  D. Lobell,et al.  Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest , 2014, Science.

[37]  Alemu Gonsamo,et al.  Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems , 2018, Global change biology.

[38]  Philip Lewis,et al.  Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements , 2012 .

[39]  Min Liu,et al.  Urban−rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons , 2019, Nature Ecology & Evolution.

[40]  M. S. Moran,et al.  Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence , 2014, Proceedings of the National Academy of Sciences.

[41]  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.

[42]  D. Lobell,et al.  The critical role of extreme heat for maize production in the United States , 2013 .

[43]  A. Huete,et al.  Estimation of vegetation photosynthetic capacity from space‐based measurements of chlorophyll fluorescence for terrestrial biosphere models , 2014, Global change biology.

[44]  M. Svoboda,et al.  Response of crop yield to different time-scales of drought in the United States: Spatio-temporal patterns and climatic and environmental drivers , 2019, Agricultural and Forest Meteorology.

[45]  Wout Verhoef,et al.  The FLuorescence EXplorer Mission Concept—ESA’s Earth Explorer 8 , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Gerrit Hoogenboom,et al.  Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment , 2007 .

[47]  James E. Specht,et al.  High-yield maize–soybean cropping systems in the US Corn Belt , 2015 .

[48]  Peng Hao,et al.  Transfer learning using computational intelligence: A survey , 2015, Knowl. Based Syst..

[49]  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.

[50]  Luis Alonso,et al.  Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications , 2009 .

[51]  D. Lobell,et al.  On the use of statistical models to predict crop yield responses to climate change , 2010 .

[52]  Nathaniel K. Newlands,et al.  An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty , 2014, Front. Environ. Sci..

[53]  Liangyun Liu,et al.  Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence , 2017 .

[54]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[55]  Xanthoula Eirini Pantazi,et al.  Wheat yield prediction using machine learning and advanced sensing techniques , 2016, Comput. Electron. Agric..

[56]  P. Gentine,et al.  A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks , 2018, Biogeosciences.

[57]  C. Frankenberg,et al.  Multiscale analyses of solar‐induced florescence and gross primary production , 2017 .

[58]  J. Olden,et al.  A new R2-based metric to shed greater insight on variable importance in artificial neural networks , 2015 .

[59]  Frank Wechsung,et al.  Statistical regression models for assessing climate impacts on crop yields: A validation study for winter wheat and silage maize in Germany , 2016 .

[60]  P. Burgess,et al.  Yield-SAFE: A parameter-sparse process-based dynamic model for predicting resource capture, growth and production in agroforestry systems , 2007 .

[61]  T. Iizumi,et al.  Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach , 2009 .

[62]  James W. Jones,et al.  How do various maize crop models vary in their responses to climate change factors? , 2014, Global change biology.

[63]  J. Hesketh,et al.  A vapor pressure deficit effect on crop canopy photosynthesis , 1990, Photosynthesis Research.

[64]  Jesse B. Tack,et al.  Effect of warming temperatures on US wheat yields , 2015, Proceedings of the National Academy of Sciences.

[65]  Andrew E. Suyker,et al.  Improving maize growth processes in the community land model: Implementation and evaluation , 2018 .

[66]  J. Tukey.  Comparing individual means in the analysis of variance. , 1949, Biometrics.

[67]  D. G. Rao,et al.  Consequences of future climate change and changing climate variability on maize yields in the midwestern United States , 2000 .

[68]  L. Gu,et al.  Sun-induced Chl fluorescence and its importance for biophysical modeling of photosynthesis based on light reactions. , 2019, The New phytologist.

[69]  C. Frankenberg,et al.  New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity , 2011, Geophysical Research Letters.

[70]  Feng Gao,et al.  The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts , 2016 .

[71]  Michael K Adjemian.  Using Usda Forecasts to Estimate the Price Flexibility of Demand for Agricultural Commodities , 2012 .

[72]  Senthold Asseng,et al.  Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches , 2018, Agricultural and Forest Meteorology.

[73]  Stefano Ermon,et al.  Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data , 2018, COMPASS.

[74]  Robert P. Sheridan,et al.  Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships , 2015, J. Chem. Inf. Model..

[75]  Martha C. Anderson,et al.  The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. , 2016 .

[76]  Mohsen Azadbakht,et al.  Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[77]  C. Frankenberg,et al.  Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2 , 2013 .

[78]  N. Baker.  Chlorophyll fluorescence: a probe of photosynthesis in vivo. , 2008, Annual review of plant biology.

[79]  Vimal Mishra,et al.  Retrospective droughts in the crop growing season: Implications to corn and soybean yield in the Midwestern United States , 2010 .

[80]  Andrew J. Challinor,et al.  Agriculture: Forecasting food , 2011 .

[81]  Lorenzo Bruzzone,et al.  Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[82]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[83]  Fulu Tao,et al.  A review on statistical models for identifying climate contributions to crop yields , 2013, Journal of Geographical Sciences.

[84]  S. Irwin,et al.  Forecast performance of WASDE price projections for U.S. corn , 2015 .

[85]  J. Landgraf,et al.  Global Retrievals of Solar‐Induced Chlorophyll Fluorescence With TROPOMI: First Results and Intersensor Comparison to OCO‐2 , 2018, Geophysical research letters.

[86]  Evgeny Putin,et al.  Deep biomarkers of human aging: Application of deep neural networks to biomarker development , 2016, Aging.

[87]  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.

[88]  Ying Sun,et al.  High‐Resolution Global Contiguous SIF of OCO‐2 , 2019, Geophysical Research Letters.

[89]  William J. Sacks,et al.  Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance , 2011 .

[90]  L. Guanter,et al.  Satellite chlorophyll fluorescence measurements reveal large‐scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests , 2016, Global change biology.

[91]  Kelly K. Caylor,et al.  Photosynthetic seasonality of global tropical forests constrained by hydroclimate , 2015 .