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APPLICATION OF SINO-EU OPTICAL DATA INTO AGRONOMIC MODELS TO PREDICT CROP PERFORMANCE AND TO MONITOR AND FORECAST CROP PESTS AND DISEASES
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Starting from the outmost results of the previous Dragon4 (32275) initiative this new proposal intends to explore the application of Sino-EU optical data into agronomic models to predict crop performance and to monitor and forecast crop pests [...] |
Dr. Stefano Pignatti Morano, Istituto di metodologie per l'analisi ambientale, CNR IMAA, ITALY |
Prof.. Wenjiang Huang, Aerospace Information Research Institute, CHINA |
Sustainable Agriculture and Water Resources |
Starting from the outmost results of the previous Dragon4 (32275) initiative this new proposal intends to explore the application of Sino-EU optical data into agronomic models to predict crop performance and to monitor and forecast crop pests and diseases.The team, as in the previous project, is composed by the Italian team with the National Research Council and two Universities, while the Chinese one with the Chinese Academy of Science and the Beijing Research center for Information Technology in Agriculture. The project will explore and verify pre-operative algorithms and processing chains using ESA/Chinese multi-frequency EO data to (a) develop innovative and advanced methods for crop plant key parameters retrieval at different growth stages (plant pigment, equivalent water thickness, dry matter, nitrogen and biomass); (b) estimate crop yields and grain quality using agronomic models by integrating multi-source information and by different assimilation techniques; (c) identify early crop stress both at the leaf and at the canopy level also by inferring the agricultural soil properties; (d) develop new dynamic methods and models to monitor and forecast crop pests and disease. This will be achieved by using ESA Products (Sentinel-2), TPM (Landsat-8, SPOT 7, WorldView) and Chinese product (GF-6, GF-1), taking also advantage of the spectral resolution of the ASI PRISMA hyperspectral imagery and the next generation ESA FLEX and Sentinel-5 missions. Moreover, the project foresees to explore the possibility offered by the DIAS systems.The Sino-EU synergy, beside the use of the EO mission data, will be exploited on jointly selected agricultural test sites in China and in Italy. Firstly, the Team will jointly explore the capabilities of the operational mapping of vegetation variables through RTMs, which is a challenging task due to the ill-posedness of the inversion and to the influence of several hampering factors (e.g., the canopy structure, the influence of the atmosphere, the illumination conditions and the sun-sensor geometries). For these reasons, the use of regularization strategies (cost functions, multiple solution) to reduce the uncertainties in the quantitative estimation of vegetation variables. To this aim, approaches exploiting physically-based radiative transfer models (RTM), will be compared with other methods. Secondly, data assimilation of multivariate and multi-scale remotely sensed variables into agricultural models (i.e. crop growing, pest e disease) will be explored. The project proposes to advance those approaches that tackle multiple scales and multiple variables, i.e. employing concurrently two or more variables for the assimilation. Agricultural model uncertainties will be assessed using global sensitivity analysis methods. Different assimilation algorithms (deterministic and stochastic) based on the EnKF and PSO methods. These methods will update the state variables and/or parameters of the crop models, to estimate variables of agronomic interests, such as crop yield and grain protein quality.Finally, the integration of multi-source data to retrieve crop physicochemical parameters, monitor crop pests and diseases habitat and then forecast damaged areas and levels at both farm and national scales will be explored. Machine learning methods, such as decision tree, SVM and deep learning algorithms will be applied to learn from field samples and devise complex models to detect the relationship between pest/disease and features. New dynamic methods and models will be compared with traditional methods using ground samples. Cross cutting validation activities will provide the data and for the retrieval algorithms validation and for the data assimilation approaches. Field experiments will be implemented. Ground hyperspectral data, agronomic management data, agrometeorological data, and soil data will be collected. Crop yield and quality variables (e.g. protein content) will be measured at harvest.
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MONITORING WATER PRODUCTIVITY IN CROP PRODUCTION AREAS FROM FOOD SECURITY PERSPECTIVES
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Climate change and its subsequent implication of water availability is having a major impact on the crop production globally. Feed a growing population while minimizing water consummation for agriculture are twin challenges directly related to [...] |
Dr. Qinghan Dong, Flemish Institute for Technological Research (VITO), Belgium |
Dr. LIANG ZHU, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, CHINA |
Sustainable Agriculture and Water Resources |
Climate change and its subsequent implication of water availability is having a major impact on the crop production globally. Feed a growing population while minimizing water consummation for agriculture are twin challenges directly related to food security in many parts of the world. Water productivity is considered as a robust measure of the ability of agricultural systems to convert water to food. Focusing on two test sites, one in Europe and one in China, the proposed project will try to explore the methodology using remote sensing to generate water productivity and to examine variability of this measure in two different geographic locations, for different crop types or different cropping conditions. The research is planned in four steps involving the crop distribution mapping, crop productivity prediction, estimation of water use through evapotranspiration and water productivity mapping. The output of this proposed project would contribute to elucidate the driving factors for water productivity and ultimately improve the agricultural water management in drought threatening regions. The proposed research takes advantage of outcome from Dragon 3 (crop yield estimation) and Dragon 2&4 (crop distribution mapping) programmes, adding a dimension of water use efficiency in the theme of food security, contributing hopefully the development of the current ESA’s Food Security Thematic Exploitation Platform (FS-TEP, https://foodsecurity-tep.net/). Finally, it is worth to underline the multidisciplinary character of this proposal, which encompasses several the Dragon 5 program’s topics or sub-topics including food security, sustainable water use, crop monitoring as well as climate change. The deliverables consist of the databases for crop productivity maps, water use maps and water productivity maps relating to two study regions, as well as the presentations at the Dragon symposia and related publications acknowledging the contribution of Dragon Programmes. The proposed project would be partially supported by H2020 SIEUSOIL project co-funded by EU and Chinese MOST.
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RETRIEVING THE CROP GROWTH INFORMATION FROM MULTIPLE SOURCE SATELLITE DATA TO SUPPORT SUSTAINABLE AGRICULTURE
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Remote sensing community has entered into a new era with the huge volume of satellite images at around 10 to 30 meter resolution fully and open available, including the sentinel series satellite in Europe and GF series satellite in China. These [...] |
Prof.. Pierre Defourny, Université catholique de Louvain, BELGIUM |
Dr. Jinlong Fan, National Satellite Meteorological Center, CHINA |
Sustainable Agriculture and Water Resources |
Remote sensing community has entered into a new era with the huge volume of satellite images at around 10 to 30 meter resolution fully and open available, including the sentinel series satellite in Europe and GF series satellite in China. These satellites brought more data options for the application in agricultural monitoring. The capability of agricultural monitoring in general is expected to be enhanced and improved with these satellite data in term of the monitoring spatial extent and the quality of the retrieved crop growth information. However, the agricultural cultivation is diverse in the world. There are existing large fields with mono crop and small fields with multiple strips of various crop types. This fact is impacting on the application of satellite data for agricultural monitoring. In order to support sustainable agriculture practices, it is important to find the best trade-off between the field size which can be assessed and the quality of the EO-derived information. Indeed EO-derived information for small fields should not be misleading when monitoring cropping practices towards a more sustainable agriculture.In general, the field size is quite small in many parts of farm land in China in comparison with that in Europe. The fine resolution satellite data are always expected to be used in the agricultural monitoring in China. Taking advantage of the operational availability of Sentinel and GF, how or to what fine extent can the remote sensing information support the framer in the agricultural management? In this project, 8 study sites are selected representing the major cropping systems, 3 sites for winter wheat and maize and another 3 for rice. These sites also will be representing the agricultural systems in the flat area or in hilly area, irrigated or rainfed, in the north or south. The Sentinal1/2 and GF1/3/5/6, CBERS data will be mainly data sources to support this study. The remote sensing parameters, like LAI/FPAR/FCOVER/NDVI will be retrieved with the adapted algorithm. The crop classification algorithm will be applied to make crop type maps. Crop specific N retrieval algorithm will be developed. Finally, the retrieved information in the field level will be communicated with the farmers and jointly come up with the management suggestions for the nutritional application, irrigation and other practices. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm will be exchanged and the objective of this project will be fulfilled as the task team brings a step forwards to support agricultural monitoring at fine scale.
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SATELLITE OBSERVATIONS FOR IMPROVING IRRIGATION WATER MANAGEMENT – SAT4IRRIWATER
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The project objective is to assess high resolution irrigation water needs and crop water productivity based on the integrated use of satellite data, ground-hydro meteorological data and numerical modelling suitable for agricultural farms as well [...] |
Prof.. Marco Mancini, Politecnico di Milano, ITALY |
Dr. Li Jia, Aerospace Information Research Institute, Chinese Academy of Sciences, CHINA |
Sustainable Agriculture and Water Resources |
The project objective is to assess high resolution irrigation water needs and crop water productivity based on the integrated use of satellite data, ground-hydro meteorological data and numerical modelling suitable for agricultural farms as well as large un-gauged agricultural areas. The project responds to the call Topic 5. Sustainable Agriculture and Water Resources – 5.3 Water resources and its utilization. This satellite data driven integrated approach is a necessary support to improve water management in intensive irrigated areas. In fact, agriculture is the largest consumer of water worldwide and at the same time irrigation is one of the sectors with the hugest differences between modern technology and ancient practices. Improving water use efficiency and water productivity is an immediate requirement of society for sustaining global food security, to preserve quality and quantity of water and to reduce causes of poverties, migrations and conflicts among states. Climate changes and increasing human pressure together with traditional wasteful irrigation practices are enhancing the conflictual problems in water use also in countries traditionally rich in water.The assessment of the main objective will imply the achievement of the following sub-objectives:i) Retrieval of Earth Observation (EO) products at different temporal and spatial scales combining ESA, Chinese, Copernicus and NASA information: land use and land cover, or crop classification map (sentinel 2, GF), soil moisture (SM) by SMOS or FY3 and high resolution SM by downscaling method, land surface temperature byFY3, Sentinel 3, MODIS, Landsat; ii) Calibration/validation/assimilation of hydrological models (the Italian model FEST-EWB and the Chinese model ETMonitor) using EO data of land surface temperature (LST) and soil moisture (SM); iii) Assessing high resolution soil hydraulic parameters using EO data and hydrological models; iv) Irrigation water needs and crop water productivity maps through the combined use of EO data and hydrological models; v) Product Comparison with Copernicus services, vi) Exportability to un-gauged sites simplified approaches based mainly or totally on satellite information.This will be achieved in the Work Packages (WP): WP1: Land surface variables from satellite observations; WP2: Development and improvement of hydrological models to estimate crop water and irrigation needs; WP3: Assessment/prediction of Irrigation water needs; WP4: Crop water productivity.The project partnership between Chinese partner the Aerospace Information Research Institute of Chinese Academy of Sciences (AIR-CAS) and the italian group of Politecnico di Milano is based on a consolidated collaboration experience since 2000 that was also reinforced thanks to the previous Dragon projects.The project activities will be based mainly on partnersÔÇÖ case studies in Italy (Chiese and Capitanata irrigation consortia) and China (agricultural areas in Shiyang River basin and Shandian River basin in Inner-Mongolia), and will be also supported by two other case studies in Spain (Barrax) and in Morocco (Sidi Bennour irrigation district) where common previous activities were developed. These intense cultivated and irrigated areas have been chosen for differences in climatic conditions, water volume availability, crop types, irrigation schemes and water distribution rules.The project deliverables will provide pixel wise irrigation water volumes and a series of ancillary products that will match the project objectives as described in the follows .The present project will be supported by a significant partners heritage projects as : EU PRIMA SMARTIES, EU ERANETMED RET- SIF, China DBAR program (Digital Belt and Road), China NBS-SY project (Nature Based Solution for Desertification Risk by MOST), led by the two present project partners in Europe and In China.
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UTILIZING SINO-EUROPEAN EARTH OBSERVATION DATA TOWARDS AGRO-ECOSYSTEM HEALTH DIAGNOSIS AND SUSTAINABLE AGRICULTURE
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Agriculture production systems are facing unprecedented challenges from increasing demand for food for a growing population, but an intensified agriculture bares also risks for environmental pollution and unsustainable use of water resources. In [...] |
Dr. Carsten Montzka, Forschungszentrum Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), GERMANY |
Dr. Liang Liang, Jiangsu Normal University, CHINA |
Sustainable Agriculture and Water Resources |
Agriculture production systems are facing unprecedented challenges from increasing demand for food for a growing population, but an intensified agriculture bares also risks for environmental pollution and unsustainable use of water resources. In addition, climatic change and soil degradation affect food production in the long term. The ÔÇÿZero HungerÔÇÖ goal was highly stressed as one of the major Sustainable Development Goals by the United Nations, which obviously calls for sustainable agricultural practices and wise management.Therefore, monitoring and prediction of agricultural systems has been a strong motivation for scientists and remains challenging where gaps in our understanding and capability of monitoring systems are limited. Earth Observation (EO) is already used to estimate land surface variables, but the step to a full process understanding of agricultural systems has not yet been taken. Adequate mitigation strategies during droughts, nitrogen pollution events, and pests cannot be adequately employed without this understanding. Integrated agricultural system studies at local, regional and national levels are needed to achieve synergies and adequately address trade-offs among the food-water-energy nexus during land and climate change.Objective The aim of this project is to monitor essential variables in agriculture based on various in situ and remote observations to investigate agricultural processes and to carry out a full agro-ecosystem health diagnosis by data assimilation. This synoptical perspective allows us to conserve, protect, and improve the efficiency of the use of natural resources to facilitate sustainable agricultural development. A near-real-time prototype will enable informing stakeholders about timely management actions. With this proposal, we plan to prepare for agricultural applications of future missions, such as BIOMASS, EnMAP.Research contents and methodsDuring Dragon 5, we will develop a multi-variable EO strategy for agricultural areas. Essential variables such as crop type, LAI, biomass, soil moisture, evapotranspiration, and soil carbon content are monitored by remote sensing methods to characterize the current state of agro-ecosystems. Machine learning, process-based radiative transfer, hybrid inversion, and Bayesian scaling approaches will be applied to timely retrieve the variables in focus. To bring together the different variable types, multi-source ensemble Kalman filter methods will be applied to improve the performance in crop growth and hydrological simulation, where the links between these two aspects will be investigated especially for extreme weather conditions. This will pave the way for simulating near-future states for the implementation of a sound and sustainable soil, land, water, nutrient and pest management, and appropriate use of fertilizers.DeliverablesWe will provide essential agro-ecosystem variable products in the hydrological (soil moisture, evapotranspiration) and plant physical (crop type, LAI, biomass, chlorophyll, and nitrogen content, fire occurrence) domain. It will make use of European, Chinese and TPM EO data in different wavelengths, if it is in visual, infrared, thermal or microwave region of the electromagnetic spectrum. We deliver a strategy how an early warning system can be implemented for crop health diagnosis and investigate the effects of different environmental threads. The transferability of the approaches will be tested at European and Asian sites to assure global applicability. A vital deliverable is also the education of young scientists in the field of remote sensing of agriculture to multiply their EO expertise in their further career at different stakeholders.FundingThe Dragon 5 project will be supported by the National Natural Science Foundation of China (NSFC) (Grant No. 41971305, 41701371, 41807001, 41701236), and the European Commission Horizon 2020 Program ERA-PLANET/GEOEssential (Grant Agreement no. 689443).
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