Mapping wheat nitrogen uptake from RapidEye vegetation indices

[1]  E. Benzel,et al.  Too Much of a Good Thing? , 2018, World Neurosurgery.

[2]  Jan U.H. Eitel,et al.  Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality , 2016 .

[3]  Jan U.H. Eitel,et al.  Response of high frequency Photochemical Reflectance Index (PRI) measurements to environmental conditions in wheat , 2016 .

[4]  Bruno Basso,et al.  Variable rate nitrogen fertilizer response in wheat using remote sensing , 2015, Precision Agriculture.

[5]  Jan G. P. W. Clevers,et al.  Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .

[6]  Jan G. P. W. Clevers,et al.  Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison , 2015 .

[7]  G. Brester,et al.  Net Returns from Terrain-Based Variable-Rate Nitrogen Management on Dryland Spring Wheat in Northern Montana , 2015 .

[8]  Bastian Siegmann,et al.  The Tasseled Cap Transformation for RapidEye data and the estimation of vital and senescent crop parameters , 2015 .

[9]  Heather McNairn,et al.  International Journal of Applied Earth Observation and Geoinformation , 2014 .

[10]  J. Eitel,et al.  Assessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology , 2014 .

[11]  Y. Kurucu,et al.  Crop Type Classification Using Vegetation Indices of RapidEye Imagery , 2014 .

[12]  O. Mutanga,et al.  Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels , 2014 .

[13]  Muhammad Farooq,et al.  Drought Stress in Wheat during Flowering and Grain-filling Periods , 2014 .

[14]  A. Gitelson,et al.  Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production , 2014 .

[15]  J. Eitel,et al.  LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status , 2014 .

[16]  Geoffrey M. Henebry,et al.  A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method , 2013, Remote. Sens..

[17]  Andrew E. Suyker,et al.  A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US , 2013, Remote. Sens..

[18]  D. Long,et al.  Optical-Mechanical System for On-Combine Segregation of Wheat by Grain Protein Concentration , 2013 .

[19]  H. Tian,et al.  Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR , 2013, PloS one.

[20]  Johannes Breidenbach,et al.  Early Detection of Bark Beetle Green Attack Using TerraSAR-X and RapidEye Data , 2013, Remote. Sens..

[21]  D. Mulla Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[22]  Georg Bareth,et al.  Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain , 2013 .

[23]  Dongrong Xu,et al.  Association of Cerebral Networks in Resting State with Sexual Preference of Homosexual Men: A Study of Regional Homogeneity and Functional Connectivity , 2013, PloS one.

[24]  Philip Lewis,et al.  Hyperspectral remote sensing of foliar nitrogen content , 2012, Proceedings of the National Academy of Sciences.

[25]  Yoshio Inoue,et al.  Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements , 2012 .

[26]  N. Ramankutty,et al.  Closing yield gaps through nutrient and water management , 2012, Nature.

[27]  B. Kleinschmit,et al.  Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data , 2012 .

[28]  G. Fitzgerald,et al.  Rapid estimation of canopy nitrogen of cereal crops at paddock scale using a Canopy Chlorophyll Content Index , 2012 .

[29]  E. Pattey,et al.  Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons , 2012 .

[30]  A. Gitelson,et al.  Remote estimation of crop gross primary production with Landsat data , 2012 .

[31]  Francesco Montemurro,et al.  Precision nitrogen management of wheat. A review , 2012, Agronomy for Sustainable Development.

[32]  Alan A. Ager,et al.  Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland , 2011 .

[33]  Deli Chen,et al.  Use of the Canopy Chlorophyl Content Index (CCCI) for remote estimation of wheat nitrogen content in rainfed environments , 2011 .

[34]  J. Palta,et al.  Heat Stress in Wheat during Reproductive and Grain-Filling Phases , 2011 .

[35]  Luis Alonso,et al.  Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content , 2011, Sensors.

[36]  C. Daughtry,et al.  Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index , 2011 .

[37]  S. Vincenzi,et al.  Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy , 2011 .

[38]  D. Gray,et al.  Towards a More Sustainable Agriculture , 2011 .

[39]  M. Vincini,et al.  Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies , 2011, Precision Agriculture.

[40]  Christopher O. Justice,et al.  Estimating Global Cropland Extent with Multi-year MODIS Data , 2010, Remote. Sens..

[41]  Prediction of protein content in malting barley using proximal and remote sensing , 2010, Precision Agriculture.

[42]  F. Daniel-Vedele,et al.  REVIEW: PART OF A SPECIAL ISSUE ON PLANT NUTRITION Nitrogen uptake, assimilation and remobilization in plants: challenges for sustainable and productive agriculture , 2010 .

[43]  G. Fitzgerald,et al.  Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI) , 2010 .

[44]  Dan S. Long,et al.  Active Ground Optical Remote Sensing for Improved Monitoring of Seedling Stress in Nurseries , 2010, Sensors.

[45]  Wenjiang Huang,et al.  [New index for crop canopy fresh biomass estimation]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

[46]  D. Huggins,et al.  Yield, Protein and Nitrogen Use Efficiency of Spring Wheat: Evaluating Field-Scale Performance , 2010 .

[47]  G. Robertson,et al.  Nitrogen in Agriculture: Balancing the Cost of an Essential Resource , 2009 .

[48]  Paul E. Gessler,et al.  Sensitivity of Ground‐Based Remote Sensing Estimates of Wheat Chlorophyll Content to Variation in Soil Reflectance , 2009 .

[49]  J. Keppler,et al.  Using satellite remote sensing to estimate winter cover crop nutrient uptake efficiency , 2009, Journal of Soil and Water Conservation.

[50]  E. Hunt,et al.  Combined Spectral Index to Improve Ground‐Based Estimates of Nitrogen Status in Dryland Wheat , 2008 .

[51]  Jessica Bertheloot,et al.  Dynamics of Light and Nitrogen Distribution during Grain Filling within Wheat Canopy1[OA] , 2008, Plant Physiology.

[52]  M. Jeuffroy,et al.  Diagnosis tool for plant and crop N status in vegetative stage Theory and practices for crop N management , 2008 .

[53]  Simon D. Jones,et al.  Remote sensing of nitrogen and water stress in wheat , 2007 .

[54]  J. Eitel,et al.  Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status , 2007 .

[55]  Nicholas L. Crookston,et al.  Partitioning error components for accuracy-assessment of near-neighbor methods of imputation , 2007 .

[56]  F. Baret,et al.  Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. , 2006, Journal of experimental botany.

[57]  Daniel Rodriguez,et al.  Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts , 2006 .

[58]  Molly E. Brown,et al.  Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[59]  Hiroyuki Oguma,et al.  Seasonal changes in the relationship between photochemical reflectance index and photosynthetic light use efficiency of Japanese larch needles , 2006 .

[60]  R. G. Evans,et al.  Opportunities for conservation with precision irrigation , 2005 .

[61]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[62]  A. Viña,et al.  New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops , 2005 .

[63]  A. Viña,et al.  Remote estimation of canopy chlorophyll content in crops , 2005 .

[64]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[65]  D. Long,et al.  Method for Precision Nitrogen Management in Spring Wheat: II. Implementation , 2000, Precision Agriculture.

[66]  D. Long,et al.  Method for Precision Nitrogen Management in Spring Wheat: I Fundamental Relationships , 1999, Precision Agriculture.

[67]  J. R. Evans Photosynthesis and nitrogen relationships in leaves of C3 plants , 2004, Oecologia.

[68]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[69]  D. Mulla,et al.  Spatial and temporal variation in economically optimum nitrogen rate for corn , 2003 .

[70]  Prasad S. Thenkabail,et al.  Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images , 2003 .

[71]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[72]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[73]  P. Scharf,et al.  Remote sensing for nitrogen management , 2002 .

[74]  Jennifer L. Dungan,et al.  A balanced view of scale in spatial statistical analysis , 2002 .

[75]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[76]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[77]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[78]  G. Carter,et al.  Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. , 2001, American journal of botany.

[79]  M. Louhaichi,et al.  Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat , 2001 .

[80]  Jonathan Cheung-Wai Chan,et al.  Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data , 2000 .

[81]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[82]  Janet Franklin,et al.  A Neural Network Method for Efficient Vegetation Mapping , 1999 .

[83]  Frédéric Baret,et al.  Radiometric Estimates of Nitrogen Status of Leaves and Canopies , 1997 .

[84]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[85]  Josep Peñuelas,et al.  Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis , 1995 .

[86]  G. Carter,et al.  Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands , 1994 .

[87]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[88]  A. Gitelson,et al.  Quantitative estimation of chlorophyll-a using reflectance spectra : experiments with autumn chestnut and maple leaves , 1994 .

[89]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[90]  D. Huggins,et al.  Nitrogen efficiency component analysis: an evaluation of cropping system differences in productivity , 1993 .

[91]  David J. Mulla,et al.  Comparing landscape-scale estimation of soil erosion in the palouse using Cs-137 and RUSLE , 1993 .

[92]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[93]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

[94]  Alan H. Strahler,et al.  On the nature of models in remote sensing , 1986 .

[95]  J. R. Evans,et al.  Nitrogen and Photosynthesis in the Flag Leaf of Wheat (Triticum aestivum L.). , 1983, Plant physiology.

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

[97]  R. B. Cate,et al.  A Simple Statistical Procedure for Partitioning Soil Test Correlation Data Into Two Classes1 , 1971 .

[98]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[99]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[100]  D. M. Gates,et al.  Spectral Properties of Plants , 1965 .