Developing and validating novel hyperspectral indices for leaf area index estimation: Effect of canopy vertical heterogeneity |
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| Affiliation: | 1. Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China;2. Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan;1. Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy;2. School of Agricultural, Forest, Food and Environmental Science, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100, Potenza, Italy;3. Département de Sciences Fondamentales, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC, G7H2B1, Canada;4. Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China;1. High Institute of Agronomy of Chott-Mariem, University of Sousse, Sousse, Tunisia;2. Laboratory of Plant Genetics and Molecular Biology, Institute of Plant Genetics, National Research Council (CNR), 70126 Bari, Italy;1. Agricultural College, Shanxi Agricultural University, Jinzhong 030600, China;2. College of Resources and Environment, Shanxi Agricultural University, Jinzhong 030600, China;3. Institute of Molecular Biology and Biotechnology, The University of Lahore, Pakistan;1. Chair for Forest Growth and Yield Science, Technische Universität München, Germany;2. INIA-Forest Research Centre and Sustainable Forest Management Research Institute, University of Valladolid & INIA, Ctra de A Coruña km 7.5, 28040 Madrid, Spain;3. Abteilung Waldbau und Waldökologie der gemäßigten Zonen, Georg-August-Universität Göttingen, Germany;4. Faculty of Forestry, University Sarajevo, Bosnia-Herzegovina;5. LERFoB, AgroParisTech, INRA, F-54000 Nancy, France;6. Department of Silviculture, Warsaw University of Life Sciences, Warsaw, Poland;7. Institute of Forest Biology and Silviculture, Aleksandras Stulginskis University, Kaunas, Lithuania;8. Dpt of Agriculture and Forest Engineering (EAGROF), University of Lleida, Lleida, Spain;9. Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Alnarp, Sweden;10. Department of Forest Management and Geodesy, Faculty of Forestry, Technical University in Zvolen, Slovakia;11. Chair of Silviculture, Albert-Ludwigs-Universität Freiburg, Germany;12. Forestry Academy of Sciences of Ukraine, Lviv, Ukraine;13. Department of Agraria, Mediterranean University of Reggio Calabria, loc. Feo di Vito, I-89060 Reggio Calabria, Italy;14. Institute of Lowland Forestry and Environment, University of Novi Sad, Novi Sad, Serbia;15. Forest Ecology and Forest Management Group, Wageningen University & Research, Wageningen, The Netherlands;p. Dept. of Agricultural, Forest and Food Sciences DISAFA, University of Turin, Italy;q. Department of Silviculture, Institute of Forest Ecology and Silviculture, University of Agriculture, Krakow, Poland;r. Universite Catholique de Louvain, Faculty of Bioscience Engineering & Earth and Life Institute, Louvain-la-Neuve, Belgium;s. Forestry and Game Management Research Institute, Strnady, Czech Republic;t. Department of Forest and Soil Science, BOKU University of Natural Resources and Life Sciences, Vienna, Austria;u. Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Kamýcká 129, Praha 6 Suchdol 16521, Czech Republic;v. Forest & Nature Lab, Ghent University, Melle-Gontrode, Belgium;w. Department of Silviculture, Forest Research Institute, Sofia, Bulgaria;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands;2. School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia;3. Department of Land Surface, German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany;4. Bavarian Forest National Park, Freyunger Straße 2, 94481 Grafenau, Germany;5. Department of Remote Sensing in Cooperation with German Aerospace Center, University of Würzburg, Oswald-Külpe-Weg 86, 97074 Würzburg, Germany |
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| Abstract: | Leaf area index (LAI) is one of the key biophysical parameters for understanding land surface photosynthesis, transpiration, and energy balance processes. Estimation of LAI from remote sensing data has been a premier method for a large scale in recent years. Recent studies have revealed that the within-canopy vertical variations in LAI and biochemical properties greatly affect canopy reflectance and significantly complicate the retrieval of LAI inversely from reflectance based vegetation indices, which has yet been explicitly addressed. In this study, we have used both simulated datasets (dataset I with constant vertical profiles of LAI and biochemical properties, dataset II with varied vertical profile of LAI but constant vertical biochemical properties, and dataset III with both varied vertical profiles) generated from the multiple-layer canopy radiative transfer model (MRTM) and a ground-measured dataset to identify robust spectral indices that are insensitive to such within canopy vertical variations for LAI prediction. The results clearly indicated that published indices such as normalized difference vegetation index (NDVI) had obvious discrepancies when applied to canopies with different vertical variations, while the new indices identified in this study performed much better. The best index for estimating canopy LAI under various conditions was D(920,1080), with overall RMSEs of 0.62–0.96 m2/m2 and biases of 0.42–0.55 m2/m2 for all three simulated datasets and an RMSE of 1.22 m2/m2 with the field-measured dataset, although it was not the most conservative one among all new indices identified. This index responded mostly to the quantity of LAI but was insensitive to within-canopy variations, allowing it to aid the retrieval LAI from remote sensing data without prior information of within-canopy vertical variations of LAI and biochemical properties. |
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| Keywords: | Spectral indices LAI Vertical variation Canopy reflectance RTM |
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