文献类型： 会议论文

第一作者： Potgieter A. B

作者： Potgieter A. B ¹ ; Camino C ² ; Poblete T ³ ; Zhi X ⁴ ; Reynolds-Massey-Reed S ⁵ ; Zhao Y ¹ ; Belwalkar A ⁶ ; Ruizhu J ¹ ; George-Jaeggli B ⁷ ; Chapman S ⁸ ; Jordan D ⁵ ; Wu A ¹ ; Hammer G. L ¹ ; Zarco-Tejada P. J ⁹ ;

作者机构： 1.The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, St Lucia, Queensland, Australia

2.European Commission, Joint Research Centre, Ispra, VA, Italy

3.Faculty of Science, School of Agriculture, Food and Ecosystem Sciences (SAFES), The University of Melbourne, Melbourne, Victoria, Australia|Faculty of Engineering and Information Technology (FEIT), The University of Melbourne, Melbourne, Victoria, Australia

4.Hermitage Research Facility, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Warwick, Queensland, Australia|State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, China

5.Hermitage Research Facility, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Warwick, Queensland, Australia

6.Faculty of Engineering and Information Technology (FEIT), The University of Melbourne, Melbourne, Victoria, Australia

7.Hermitage Research Facility, The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Warwick, Queensland, Australia|Department of Agriculture and Fisheries, Agri-Science Queensland, Hermitage Research Facility, Warwick, Australia

8.School of Agriculture and Food Sciences, The University of Queensland, St Lucia, Queensland, Australia

9.Faculty of Science, School of Agriculture, Food and Ecosystem Sciences (SAFES), The University of Melbourne, Melbourne, Victoria, Australia|Faculty of Engineering and Information Technology (FEIT), The University of Melbourne, Melbourne, Victoria, Australia|Consejo Superior de Investigaciones Científicas (CSIC), Instituto de Agricultura Sostenible (IAS), Córdoba, Spain

关键词： Biological system modeling;Crops;Fluorescence;Pigments;Physiology;Sensors;Australia

会议名称： IEEE International Geoscience and Remote Sensing Symposium

主办单位：

页码： 1952-1955

摘要： In this paper, we discuss the integration of systems such as multi-dimensional radiative transfer models (RTM) with deep learning (DL) algorithms to estimate plant biochemical, physiological, and morphological traits at canopy level using high-resolution hyperspectral imagery (361 bands in the 400-1000 nm spectral range). We applied the approaches to two case studies for dryland cropping in Australia (i.e., wheat and sorghum). Crop type averages for the early flight for leaf area index (LAI) varied between 2, for Canola, to as high as 4.3 for Lentils. Wheat and Barley had LAI of 4.1 and 3.8 (m2/m2), respectively. Chlorophyll a+b (Ca+b) averages for emerged crops were 18, 41, 44, 51 and 59 μg/cm2 for Faba beans, Wheat, Canola, Barley and Oats, respectively. The pigment Anthocyanin varied from 4.9 to 15.9 μg/cm2 for Lentils and Canola, respectively. Similar patterns were observed in the Carotenoid (Cx+c) levels (as high as 16.5 μg/cm2 for Oats). For sorghum plots, the integrated DL approaches showed significant high correlation in predicting sorghum LAI (R2 = 0.84, RMSE = 0.65 m2/m2) and Ca+b (R2 = 0.94, RMSE = 4.94 μgcm-2). The maximum velocity carboxylation rates (Vcmax) varied between 45-75 μmol m-2s-1. For both studied periods, we yielded a R2 > 0.78 and RMSE -2s-1, being the RMSE lower when using the modelled fluorescence emission for retrieving the Vcmax. In addition, we derived the solar induced fluorescence emission hyperspectral narrowband (5.8 nm) sensing and radiative transfer models (RTM).

分类号： tp7-53