Abstract
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·ha−1 vs 4.42 t·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%.
Highlights
IntroductionAn extraordinary amount of medium and high spatial resolution remote sensing data can be obtained and applied for agricultural applications when combining European and Chinese satellite acquisitions [1,2,3,4,5,6]
The activity aims to test the performances of the Earth Observation (EO) data assimilation into biophysical The activity aims durum to test the performances of the EO data assimilation into biophysicrop models to improve wheat predictions
Season aggregated by field, preprocessed, andand used for data assimilation
Summary
An extraordinary amount of medium and high spatial resolution remote sensing data can be obtained and applied for agricultural applications when combining European and Chinese satellite acquisitions [1,2,3,4,5,6]. This can produce a strong impact if a common targeting and operational policy is applied. In both European and Asian continents, agricultural system monitoring activities face very similar observational requirements, both in terms of satellite repetition frequencies and of spectral and spatial resolutions. Decametric satellite spatial resolution is the best available option to map and monitor crop biophysical parameters, crop yield, crop phenology and crop 4.0/).
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