Sino-EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources
文献类型： 外文期刊
作者： Pignatti, Stefano 1 ; Casa, Raffaele 2 ; Laneve, Giovanni 3 ; Li, Zhenhai 4 ; Liu, Linyi 5 ; Marzialetti, Pablo 3 ; Mz 1 ;
作者机构： 1.Natl Council Res CNR, Inst Methodol Environm Anal IMAA, I-85050 Tito, Italy
2.Univ Tuscia, Dept Agr Forests Nat & Energy DAFNE, Via San Camillo Lellis, I-01100 Viterbo, Italy
3.Univ Roma La Sapienza, Sch Aerosp Engn SIA, SIA, Via Salaria,851, I-00138 Rome, Italy
4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
5.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
6.ESRIN, European Space Agcy, Via Galileo Galilei 1, I-00044 Frascati, Italy
7.Maynooth Univ, Dept Comp Sci, Dublin W23 F2H6, Ireland
关键词： multispectral data analysis; satellite data assimilation; crop variables estimation; modeling; crop pest and disease
期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）
ISSN：
年卷期： 2021 年 13 卷 15 期
页码：
收录情况： SCI
摘要： Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t center dot ha(-1) vs 4.42 t center dot ha(-1) RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.
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