Data fusion of spectral,thermal and canopy height parameters for improved yield prediction of drought stressed spring barley |
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| Institution: | 1. Chair of Plant Nutrition, Technical University of Munich, Emil-Ramann-Straße 2, D-85350 Freising-Weihenstephan, Germany;2. Evaluation of Natural Resources Department, Environmental Studies and Research Institute, Sadat City University, Egypt;3. Department of Agricultural Engineering, Fachhochschule Südwestfalen, Soest, Germany;1. Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, 11451 Riyadh, Saudi Arabia;2. Department of Agronomy, Faculty of Agriculture, Suez Canal University, 41522 Ismailia, Egypt;3. Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, 41522 Ismailia, Egypt;4. Department of Biology, College of Science and Humanities at Quwayiah, Shaqra University, Saudi Arabia;5. Horticulture Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El Sheikh 33516, Egypt;6. Evaluation of Natural Resources Department, Environmental Studies and Research Institute, Sadat City University, Egypt;7. Agronomy Department, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt;8. Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Freising-Weihenstephan, Germany;1. Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Science (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy;2. European Commission, DG-JRC, Institute for Environment and Sustainability, Monitoring Agricultural Resources Unit – H04, Ispra, VA, Italy;3. European Commission, DG-JRC, Institute for Environment and Sustainability, Forest Resources and Climate Unit – H03, Ispra, VA, Italy;4. Istituto di agronomia generale e coltivazioni erbacee, Università Cattolica del Sacro Cuore, Piacenza, Italy;5. Remote Sensing Department – IREA – National Research Council (CNR), Milano, Italy;6. Consiglio per la Ricerca e la Sperimentazione in Agricoltura (CRA), Research Unit of Food Technology, Milano, Italy;7. Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich, Leo-Brandt-Straße, 52425 Jülich, Germany;8. Department of Architecture, Built Environment and Construction Engineering (ABC), University Politecnico di Milano, Italy;1. University of Thessaly, Dept. of Agriculture Crop Production and Rural Environment, Fytokou Str., 38446, Volos, Greece;2. Centre for Research and Technology Hellas, Institute for Research and Technology of Thessaly, Dimitriados 95 & P. Mela, 38333, Volos, Greece;1. Institute of Geography, GIS & RS Group, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany;2. Research Centre Hanninghof, Yara International ASA, Hanninghof 35, 48249 Dülmen, Germany;3. ICASD-International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China;1. Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, 11451 Riyadh, Saudi Arabia;2. Department of Agronomy, Faculty of Agriculture, Suez Canal University, 41522 Ismailia, Egypt;3. Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, 41522 Ismailia, Egypt;4. Department of Biology, College of Science and Humanities at Quwayiah, Shaqra University, Saudi Arabia;5. Department of Plant Sciences, Technische Universität München, Emil-Ramann-Str. 2, D-85350 Freising, Germany |
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| Abstract: | Yield modelling based on visible and near infrared spectral information is extensively used in proximal and remote sensing for yield prediction of crops. Distance and thermal information contain independent information on canopy growth, plant structure and the physiological status. In a four-years′ study hyperspectral, distance and thermal high-throughput measurements were obtained from different sets of drought stressed spring barley cultivars. All possible binary, normalized spectral indices as well as thirteen spectral indices found by others to be related to biomass, tissue chlorophyll content, water status or chlorophyll fluorescence were calculated from hyperspectral data and tested for their correlation with grain yield. Data were analysed by multiple linear regression and partial least square regression models, that were calibrated and cross-validated for yield prediction. Overall partial least square models improved yield prediction (R2 = 0.57; RMSEC = 0.63) compared to multiple linear regression models (R2 = 0.46; RMSEC = 0.74) in the model calibration. In cross-validation, both methods yielded similar results (PLSR: R2 = 0.41, RMSEV = 0.74; MLR: R2 = 0.40, RMSEV = 0.78). The spectral indices R780/R550, R760/R730, R780/R700, the spectral water index R900/R970 and laser and ultrasonic distance parameters contributed favourably to grain yield prediction, whereas the thermal based crop water stress index and the red edge inflection point contributed little to the improvement of yield models. Using only more uniform modern cultivars decreased the model performance compared to calibrations done with a set of more diverse cultivars. The partial least square models based on data fusion improved yield prediction (R2 = 0.62; RMSEC = 0.59) compared to the partial least square models based only on hyperspectral data (R2 = 0.48; RMSEC = 0.69) in the model calibration. This improvement was confirmed by cross-validation (data fusion: R2 = 0.39, RMSEV = 0.76; hyperspectral data only: R2 = 0.32, RMSEV = 0.79). Thus, a combination of spectral multiband and distance sensing improved the performance in yield prediction compared to using only hyperspectral sensing. |
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| Keywords: | Abiotic stress Data fusion High throughput phenotyping Multi-annual Multiple linear regression Partial least square Phenomics Yield modelling |
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