Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat

Addabbo, 2016, Contribution of Sentinel-2 data for applications in vegetation monitoring, Acta Imeko, 5, 44, 10.21014/acta_imeko.v5i2.352 Atzberger, 2013, Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs, Rem. Sens., 5, 949, 10.3390/rs5020949 Babar, 2006, Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation, Crop Sci., 46, 578, 10.2135/cropsci2005.0059 Babar, 2006, The potential of using spectral reflectance indices to estimate yield in wheat grown under reduced irrigation, Euphytica, 150, 155, 10.1007/s10681-006-9104-9 Babar, 2007, Heritability, correlated response, and indirect selection involving spectral reflectance indices and grain yield in wheat, Aust. J. Agric. Res., 58, 432, 10.1071/AR06270 Backer, 2005, Band selection for hyperspectral remote sensing band selection for hyperspectral remote sensing, Pattern Recognit., 2, 319 Barmeier, 2017, Mid-season prediction of grain yield and protein content of spring barley cultivars using high-throughput spectral sensing, Eur. J. Agron., 90, 108, 10.1016/j.eja.2017.07.005 Becker, 2017, Evaluation of yield and drought using active and passive spectral sensing systems at the reproductive stage in wheat, Front. Plant Sci., 8, 10.3389/fpls.2017.00379 Berdugo, 2013, Sensors and imaging techniques for the assessment of the delay of wheat senescence induced by fungicides, Funct. Plant Biol., 40, 677, 10.1071/FP12351 Berntsen, 2006, Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat, Precis. Agric., 7, 65, 10.1007/s11119-006-9000-2 Bogard, 2010, Deviation from the grain protein concentration-grain yield negative relationship is highly correlated to post-anthesis N uptake in winter wheat, J. Exp. Bot., 61, 4303, 10.1093/jxb/erq238 Clevers, 2013, Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and-3, Int. J. Appl. Earth Obs. Geoinf., 23, 344, 10.1016/j.jag.2012.10.008 Dahms, 2016, Important variables of a rapideye time series for modelling biophysical parameters of winter wheat, Photogramm. - Fernerkundung - Geoinf., 2016, 285, 10.1127/pfg/2016/0303 Delegido, 2011, Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content, Sensors, 11, 7063, 10.3390/s110707063 Diacono, 2013, Precision nitrogen management of wheat. A review, Agron. Sustain. Dev., 33, 219, 10.1007/s13593-012-0111-z Distelfeld, 2014, Senescence, nutrient remobilization, and yield in wheat and barley, J. Exp. Bot., 65, 3783, 10.1093/jxb/ert477 Erdle, 2013, Spectral high-throughput assessments of phenotypic differences in biomass and nitrogen partitioning during grain filling of wheat under high yielding Western European conditions, F. Crop. Res., 141, 16, 10.1016/j.fcr.2012.10.018 Erdle, 2013, Spectral assessments of phenotypic differences in spike development during grain filling affected by varying N supply in wheat, J. Plant Nutr. Soil Sci., 176, 952, 10.1002/jpln.201300247 Fiorani, 2013, Future scenarios for plant phenotyping, Annu. Rev. Plant Biol., 64, 267, 10.1146/annurev-arplant-050312-120137 Frampton, 2013, Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation, ISPRS J. Photogramm. Rem. Sens., 82, 83, 10.1016/j.isprsjprs.2013.04.007 Frels, 2018, Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat, F. Crop. Res., 217, 82, 10.1016/j.fcr.2017.12.004 Furbank, 2011, Phenomics – technologies to relieve the phenotyping bottleneck, Trends Plant Sci., 16, 635, 10.1016/j.tplants.2011.09.005 Geesing, 2014, Site-specific effects of variable water supply and nitrogen fertilisation on winter wheat, J. Plant Nutr. Soil Sci., 177, 509, 10.1002/jpln.201300215 Gerighausen, 2015, Evaluation of leaf area index and dry matter predictions for crop growth modelling and yield estimation, EARSeL eProceedings, 71–90 Gitelson, 1996, Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll, J. Plant Physiol., 148, 494, 10.1016/S0176-1617(96)80284-7 Guo, 2017, Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption, Eur. J. Agron., 82, 113, 10.1016/j.eja.2016.10.009 Gutierrez, 2015, Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat, Int. J. Rem. Sens., 36, 701, 10.1080/01431161.2014.999878 Gutierrez, 2010, Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes, J. Exp. Bot., 61, 3291, 10.1093/jxb/erq156 Hansen, 2003, Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression, Rem. Sens. Environ., 86, 542, 10.1016/S0034-4257(03)00131-7 Hatfield, 2010, Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices, Rem. Sens., 2, 562, 10.3390/rs2020562 Herrmann, 2011, LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands, Rem. Sens. Environ., 115, 2141, 10.1016/j.rse.2011.04.018 Huang, 2017, Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages, Rem. Sens., 9 Inoue, 2016, Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation, Plant. Cell Environ., 39, 2609, 10.1111/pce.12815 Jacquemoud, 2009, PROSPECT + SAIL models: a review of use for vegetation characterization, Remote Sens. Environ., 113, S56, 10.1016/j.rse.2008.01.026 Jay, 2017, Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: comparison of vegetation indices and PROSAIL inversion for field phenotyping, F. Crop. Res. accepted, 33–46 Kipp, 2014, Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques, Funct. Plant Biol., 41, 227, 10.1071/FP13221 Kross, 2015, Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops, Int. J. Appl. Earth Obs. Geoinf., 34, 235, 10.1016/j.jag.2014.08.002 Kusnierek, 2015, Simultaneous identification of spring wheat nitrogen and water status using visible and near infrared spectra and Powered Partial Least Squares Regression, Comput. Electron. Agric., 117, 200, 10.1016/j.compag.2015.08.001 Latshaw, 2016, Genotypic differences for nitrogen use efficiency and grain protein deviation in hard winter wheat, Agron. J., 108, 2201, 10.2134/agronj2016.02.0070 Lee, 2004, Hyperspectral versus multispectral data for estimating leaf area index in four different biomes, Rem. Sens. Environ., 91, 508, 10.1016/j.rse.2004.04.010 Lehnert, L.W., Meyer, H., Bendix, J., 2016. hsdar: Manage, analyse and simulate hyperspectral data. R Packag. version 0.6.0. Lelong, 2008, Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots, Sensors, 8, 3557, 10.3390/s8053557 Li, 2012, Remotely estimating aerial N status of phenologically differing winter wheat cultivars grown in contrasting climatic and geographic zones in China and Germany, F. Crop. Res., 138, 21, 10.1016/j.fcr.2012.09.002 Magney, 2016, Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality, Agric. For. Meteorol., 217, 46, 10.1016/j.agrformet.2015.11.009 Magney, 2017, Mapping wheat nitrogen uptake from RapidEye vegetation indices, Precis. Agric., 18, 429, 10.1007/s11119-016-9463-8 Mariotto, 2013, Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission, Rem. Sens. Environ., 139, 291, 10.1016/j.rse.2013.08.002 Marshall, 2015, Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation, ISPRS J. Photogramm. Remote Sens., 108, 205, 10.1016/j.isprsjprs.2015.08.001 Mistele, 2010, Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat, Agron. J., 102, 499, 10.2134/agronj2009.0282 Moharana, 2016, Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery, ISPRS J. Photogramm. Rem. Sens., 122, 17, 10.1016/j.isprsjprs.2016.09.002 Mulla, 2013, Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps, Biosyst. Eng., 114, 358, 10.1016/j.biosystemseng.2012.08.009 Mutanga, 2015, Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset, Int. J. Appl. Earth Obs. Geoinf., 34, 178, 10.1016/j.jag.2014.08.008 Pask, 2012, Quantifying how winter wheat crops accumulate and use nitrogen reserves during growth, F. Crop. Res., 126, 104, 10.1016/j.fcr.2011.09.021 Prasad, 2007, Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices, Crop Sci., 47, 1416, 10.2135/cropsci2006.08.0546 Prasad, 2007, Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions, Crop Sci., 47, 1426, 10.2135/cropsci2006.07.0492 Reyniers, 2006, Measuring wheat nitrogen status from space and ground-based platform, Int. J. Rem. Sens., 27, 549, 10.1080/01431160500117907 Rischbeck, 2016, Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley, Eur. J. Agron., 78, 44, 10.1016/j.eja.2016.04.013 Schmidhalter, U., Maidl, F.-X., Heuwinkel, H., Demmel, M., Auernhammer, H., Noack, P.O., Rothmund, M., 2008. Precision farming – adaptation of land use management to small scale heterogeneity. In: Perspectives for Agroecosystem Management. Elsevier, pp. 121–199. https://doi.org/10.1016/B978-044451905-4.50007-6. Segl, 2012, End-to-end sensor simulation for spectral band selection and optimization with application to the Sentinel-2 mission, Appl. Opt., 51, 439, 10.1364/AO.51.000439 Shoko, 2017, Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species, ISPRS J. Photogramm. Remote Sens., 129, 32, 10.1016/j.isprsjprs.2017.04.016 Shou, 2007, Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat, J. Plant Nutr., 30, 1669, 10.1080/01904160701615533 Sibanda, 2015, Exploring the potential of in situ hyperspectral data and multivariate techniques in discriminating different fertilizer treatments in grasslands, J. Appl. Rem. Sens., 9, 96033, 10.1117/1.JRS.9.096033 Tardieu, 2017, Plant phenomics, from sensors to knowledge, Curr. Biol., 27, R770, 10.1016/j.cub.2017.05.055 Tattaris, 2016, A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding, Front. Plant Sci., 7, 1, 10.3389/fpls.2016.01131 Thenkabail, 2000, Hyperspectral vegetation indices and their relationships with agricultural crop characteristics, Rem. Sens. Environ., 71, 158, 10.1016/S0034-4257(99)00067-X White, 2012, Field crops research field-based phenomics for plant genetics research, F. Crop. Res., 133, 101, 10.1016/j.fcr.2012.04.003