Mapping wheat nitrogen uptake from RapidEye vegetation indices
暂无分享,去创建一个
[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 .