Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Calderini, 1997, Consequences of breeding on biomass, radiation interception and radiation-use efficiency in wheat, Field Crops Res., 52, 271, 10.1016/S0378-4290(96)03465-X Garcia, 1988, Interception and use efficiency of light in winter wheat under different nitrogen regimes, Agric. For. Meteorol., 44, 175, 10.1016/0168-1923(88)90016-0 D. Raes, P. Steduto, T.C. Hsiao, E. Fereres, AquaCrop-the FAO crop model to simulate yield response to water: II. Main algorithms and software description, Agron. J. 101 (2008) 438–447. Jin, 2015, Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAR-2 data, Remote Sens., 7, 13251, 10.3390/rs71013251 Jin, 2016, Estimation of winter wheat biomass and yield by combining the AquaCrop model and field hyperspectral data, Remote Sens., 8, 972, 10.3390/rs8120972 Claverie, 2012, Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data, Remote Sens. Environ., 124, 844, 10.1016/j.rse.2012.04.005 Hunsaker, 2005, Cotton irrigation scheduling using remotely sensed and FAO-56 basal crop coefficients, Trans. ASAE, 48, 1395, 10.13031/2013.19197 Bastiaanssen, 2000, Remote sensing for irrigated agriculture examples from research and possible applications, Agric. Water Manag., 46, 137, 10.1016/S0378-3774(00)00080-9 Scharf, 2002, Calibrating corn color from aerial photographs to predict side-dress nitrogen need, Agron. J., 94, 397, 10.2134/agronj2002.3970 Mahlein, 2012, Recent advances in sensing plant diseases for precision crop protection, Eur. J. Plant Pathol., 133, 197, 10.1007/s10658-011-9878-z Luedeling, 2009, Remote sensing of spider mite damage in California peach orchards, Int. J. Appl. Earth Obs., 11, 244, 10.1016/j.jag.2009.03.002 Groten, 1993, NDVI-crop monitoring and early yield assessment of Burkina Faso, Int. J. Remote Sens., 14, 1495, 10.1080/01431169308953983 Mkhabela, 2011, Crop yield forecasting on the Canadian Prairies using MODIS NDVI data, Agric. For. Meteorol., 151, 385, 10.1016/j.agrformet.2010.11.012 Damisch, 1991, Biomass yield-a topical issue in modern wheat breeding programmes, Plant Breed., 107, 11, 10.1111/j.1439-0523.1991.tb00523.x Donald, 1976, Biological yield and harvest index of cereals as agronomic and plant breeding criteria, Adv. Agron., 28, 361, 10.1016/S0065-2113(08)60559-3 Richards, 2002, Breeding opportunities for increasing the efficiency of water use and crop yield in temperate cereal, Crop Sci., 42, 111, 10.2135/cropsci2002.1110 Reynolds, 2012, G, Slafer, Achieving yield gains in wheat, Plant Cell Environ., 35, 1799, 10.1111/j.1365-3040.2012.02588.x Chen, 2016, Climate change-associated trends in net biomass change are age dependent in western boreal forests of Canada, Ecol. Lett., 19, 1150, 10.1111/ele.12653 A. Houghton, F. Hall, J. Goetz, Importance of biomass in the global carbon cycle, J. Geophys. Res. Biogeosci. 114 (2009) G00E03. Gitelson, 2003, Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophys. Res. Lett., 30, 1248, 10.1029/2002GL016450 Gitelson, 2014, Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: implications for remote sensing of primary production, Remote Sens. Environ., 144, 65, 10.1016/j.rse.2014.01.004 A.D. Bosch Serra, D. Casanova, Estimation of onion (Allium cepa L.) biomass and light interception from reflectance measurements at field level, ISHS Acta Hortic. 519 (1998) 53–64. Jin, 2013, Estimation of wheat agronomic parameters using new spectral indices, PLoS One, 8, 10.1371/journal.pone.0072736 W. Koppe, M.L. Gnyp, S.D. Hennig, Fei. Li, Y.X. Miao, X.P. Chen, L.L. Jia, G. Bareth, Multi-temporal hyperspectral and radar remote sensing for estimating winter wheat biomass in the North China Plain, Photogrammetrie Fernerkundung Geoinformation 3 (2012) 281–298. Liu, 2010, Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model, Remote Sens. Environ., 114, 1167, 10.1016/j.rse.2010.01.004 Swain, 2010, Adoption of an unmanned helicopter for low altitude remote sensing to estimate yield and total biomass of a rice crop, Trans. ASABE, 53, 21, 10.13031/2013.29493 Chen, 2010, New index for crop canopy fresh biomass estimation, Spectrosc. Spect. Anal., 30, 512 Gao, 2013, Estimating the leaf area index, height and biomass of maize sing HJ-1 and RADARSAT-2, Int. J. Appl. Earth Obs., 24, 1, 10.1016/j.jag.2013.02.002 Tilly, 2014, Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice, J. Appl. Remote. Sens., 8, 83671, 10.1117/1.JRS.8.083671 Hofle, 2014, Radiometric correction of terrestrial LiDAR point cloud data for individual maize plant detection, IEEE Geosci. Remote Sens. Lett., 11, 94, 10.1109/LGRS.2013.2247022 R. Ballesteros, J.F. Ortega, D. Hernández, M.A. Moreno, Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing, Precis. Agric. 15 (2014) 579–592. J.V. Bendig, Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling—a new method for plant height and biomass estimation based on RGB-imaging, 2015, Retrieved from http://kups.ub.uni-koeln.de/6018/1/Bendi g_PhD_2014_Ort&DatumfinalnoCV.pdf. Geipel, 2014, Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system, Remote Sens., 6, 10335, 10.3390/rs61110335 Yue, 2017, Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models, Remote Sens., 9, 708, 10.3390/rs9070708 Ballesteros, 2018, Onion biomass monitoring using UAV-based RGB imaging, Precis. Agric., 19, 840, 10.1007/s11119-018-9560-y Qiu, 2014, 1 Dalto, 2015, 1657 Ryu, 2017, Deep neural network based demand side short term load forecasting, Energies, 10, 3, 10.3390/en10010003 Chen, 2018, Short-term load forecasting with deep residual networks, IEEE Trans, Smart Grid, 3943 Penuelas, 1993, The reflectance at the 950–970 nm region as an indicator of plant water status, Int. J. Remote Sens., 14, 1887, 10.1080/01431169308954010 Seelig, 2008, The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared, Int. J. Remote Sens., 29, 3701, 10.1080/01431160701772500 Hunt, 1989, Detection of changes in leaf water content using near-and middle-infrared reflectances, Remote Sens. Environ., 30, 43, 10.1016/0034-4257(89)90046-1 Wang, 2011, Estimating dry matter content from spectral reflectance for green leaves of different species, Int. J. Remote Sens., 32, 7097, 10.1080/01431161.2010.494641 Jiang, 2008, Development of a two-band enhanced vegetation index without a blue band, Remote Sens. Environ., 112, 3833, 10.1016/j.rse.2008.06.006 Haboudane, 2004, Hyper-spectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture, Remote Sens. Environ., 90, 337, 10.1016/j.rse.2003.12.013 Rondeaux, 1996, Optimization of soil-adjusted vegetation indices, Remote Sens. Environ., 55, 95, 10.1016/0034-4257(95)00186-7 A.A. Gitelson, A. Viña, V. Ciganda, D.C. Rundquist, Remote estimation of canopy chlorophyll content in crops, Geophys. Res. Lett. 32 (2005) L08 403. Dash, 2004, The MERIS terrestrial chlorophyll index, Int. J. Remote Sens., 25, 5403, 10.1080/0143116042000274015 Rouse, 1974 Jin, 2014, New combined spectral indices to improve estimation of total leaf chlorophyll content in cotton, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 4589, 10.1109/JSTARS.2014.2360069 Jin, 2013, Estimation of leaf water content in winter wheat using grey relational analysis-partial least squares modeling with hyperspectral data, Agron. J., 105, 1385, 10.2134/agronj2013.0088 Gitelson, 2004, Wide dynamic range vegetation index for remote quantification of characteristics of vegetation, J. Plant Physiol., 161, 165, 10.1078/0176-1617-01176 Szegedy, 2015, 1 Krizhevsky, 2012, 1097 Girshick, 2014, 580 Längkvist, 2014, A review of unsupervised feature learning and deep learning for time-series modeling, Pattern Recogn. Lett., 42, 11, 10.1016/j.patrec.2014.01.008 Hinton, 2012, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE Signal. Proc. Mag., 29, 82, 10.1109/MSP.2012.2205597 Merkel, 2008, Short-term load forecasting of natural gas with deep neural network regression, Energies, 11, 2008, 10.3390/en11082008 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, 34, 235, 10.1016/j.jag.2014.08.002