Sino-EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

文献类型： 外文期刊

第一作者： Pignatti, Stefano

作者： Pignatti, Stefano;Pascucci, Simone;Casa, Raffaele;Tolomio, Massimo;Laneve, Giovanni;Marzialetti, Pablo;Li, Zhenhai;Yang, Hao;Yang, Guijun;Liu, Linyi;Huang, Wenjiang;Silvestro, Paolo Cosmo;Upreti, Deepak

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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 ）

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年卷期： 2021 年 13 卷 15 期

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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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