Regional crop monitoring and assessment with quantitative remote sensing and data assimilation
Lead Research Organisation:
University College London
Department Name: Geography
Abstract
China has only 10% of the world arable land and water resources, but has to feed 20% of the world population. Moreover, the population continues to increase, while the amount of arable land is shrinking due to pollution, urban sprawl, groundwater depletion, and other stresses. With future climate change expected to only worsen these pressures, the accurate monitoring of agricultural productivity is essential to China's future food security, in addition to the economic development of low-income rural regions. In no part of the country is this more essential than China's north plain. This has historically been the breadbasket of China. Today, however, it faces an exceptionally challenging combination of very high population densities and ecological stresses, and low levels of household income.
Traditionally, researchers have used two general methods for monitoring agricultural productivity. The first, which has long been used by Chinese government agencies, is to combine field surveys of crop growth, with mathematical models of crop growth processes, to construct estimates of changing harvest yields over time. The second, which has risen to prominence more recently, is to use satellite imagery to continuously assess agricultural productivity. Each of these techniques has its own notable strengths and weaknesses. Survey-calibrated models of crop growth are able to produce highly accurate estimates of yields in the limited areas where survey data has been collected; however, their accuracy drops off significantly outside of these areas. On the other hand, satellite remote sensing data offers universal geographic coverage; however, the resolution of this data is extremely coarse over either time or space. MODIS data, for example, provides near-daily data that can be used to assess the productivity of every single farm in China. However, the spatial resolution of pixels is only 500-1000 meters, an area which will invariably be contaminated, in densely populated China, by a mixture of roads, villages, and other non-agricultural land uses in addition to the farmland actually being studied. Other satellites (e.g. LandSat TM and forthcoming Sentinel) provide finer scale pixel resolution than MODIS; however, they do not cover the same sites as often, making it harder to smoothly track agricultural production over time.
Reflecting the wider explosion of the field of "big data" analysis, rapid strides have been made recent years in the development of so-called "data assimilation" techniques. These can be broadly described as statistical methodologies that allow for otherwise incompatible datasets to be combined together, in order to produce hybrid datasets that are superior to any of their predecessors. The basic objective of the proposed project is to apply advanced data assimilation techniques to multiple types of crop data-from both survey-calibrated crop growth models and satellite imagery-to produce superior estimates of Chinese agricultural productivity than would be possible using any of these data sources by itself. In addition to making use of more advanced statistical methods than previous studies, this analysis will be among the first to make use of data from the forthcoming Sentinel and the Chinese GF satellites. Taken together, we expect that the result will be the most accurate portrait created to date of changing agricultural production in the North China Plain. Moreover, having created this data, we will be able to apply it predictively in conjunction with modelled scenarios of future climate change, in order to map and assess the likely geographies of agricultural stress that this will create. Ultimately, the findings of this project will directly inform work by academic researchers, national and regional Chinese governmental authorities, agritech companies in both China and the UK, and extension workers directly advising farmers in China.
Traditionally, researchers have used two general methods for monitoring agricultural productivity. The first, which has long been used by Chinese government agencies, is to combine field surveys of crop growth, with mathematical models of crop growth processes, to construct estimates of changing harvest yields over time. The second, which has risen to prominence more recently, is to use satellite imagery to continuously assess agricultural productivity. Each of these techniques has its own notable strengths and weaknesses. Survey-calibrated models of crop growth are able to produce highly accurate estimates of yields in the limited areas where survey data has been collected; however, their accuracy drops off significantly outside of these areas. On the other hand, satellite remote sensing data offers universal geographic coverage; however, the resolution of this data is extremely coarse over either time or space. MODIS data, for example, provides near-daily data that can be used to assess the productivity of every single farm in China. However, the spatial resolution of pixels is only 500-1000 meters, an area which will invariably be contaminated, in densely populated China, by a mixture of roads, villages, and other non-agricultural land uses in addition to the farmland actually being studied. Other satellites (e.g. LandSat TM and forthcoming Sentinel) provide finer scale pixel resolution than MODIS; however, they do not cover the same sites as often, making it harder to smoothly track agricultural production over time.
Reflecting the wider explosion of the field of "big data" analysis, rapid strides have been made recent years in the development of so-called "data assimilation" techniques. These can be broadly described as statistical methodologies that allow for otherwise incompatible datasets to be combined together, in order to produce hybrid datasets that are superior to any of their predecessors. The basic objective of the proposed project is to apply advanced data assimilation techniques to multiple types of crop data-from both survey-calibrated crop growth models and satellite imagery-to produce superior estimates of Chinese agricultural productivity than would be possible using any of these data sources by itself. In addition to making use of more advanced statistical methods than previous studies, this analysis will be among the first to make use of data from the forthcoming Sentinel and the Chinese GF satellites. Taken together, we expect that the result will be the most accurate portrait created to date of changing agricultural production in the North China Plain. Moreover, having created this data, we will be able to apply it predictively in conjunction with modelled scenarios of future climate change, in order to map and assess the likely geographies of agricultural stress that this will create. Ultimately, the findings of this project will directly inform work by academic researchers, national and regional Chinese governmental authorities, agritech companies in both China and the UK, and extension workers directly advising farmers in China.
Planned Impact
The outputs of the proposed research will feed directly into agricultural production planning in China. With ever increasing demand for staple foods, decreasing availability of agricultural land, and a requirement to control the use of fertilizers, pesticides and water for irrigation, accurate and timely management information is essential. Our research will deliver improved simulations and predictions of crop performance that will assist in strategic planning, optimizing regional production and managing national food supply and security. The lead partner in China, CAAS-IARRP runs the China Agriculture Remote Sensing Monitoring System (CHARMS) which monitors crop acreage change, yield, production, drought and other agriculture-related information for 7 main crops in China. CHARMS provides information directly to the Ministry of Agriculture (MoA) and related agriculture management sectors in the form of detailed reports to MoA through the growing season. The optimizations being undertaken in the project will allow the use of advanced DA techniques on regional/national scale which had previously been impossible due to the high computational demand of the algorithms. This will feed directly into improving the quality and usefulness of CHARMS outputs. Understanding seasonal crop production and yield outcomes for each growing season helps with national scale planning of food production and gives advanced warning of any significant imbalance food balance between production and demand. Our research to improve the information content and quality of crop predictions will therefore feed directly into the key Chinese agricultural management stakeholders.
In addition to the primary seasonal production applications, the research will also contribute to improve long term planning. Physically based crop yield simulation using EO data will allow crop responses to different climate scenarios to be modelled more accurately, giving simulations that are more robust to changes in climate than the currently widely used NDVI curve matching approaches. The results of the project will also be disseminated via the Group on Earth Observations GEOGLAM and GECAM initiatives to which the CHARMS contributes. At the National scale our research will support analysis of yield performance at local/regional scale and will contribute to development of adaptation strategies to optimize agricultural production under different climate scenarios.
The impacts of our research will initially be focused on regional and national scale monitoring, also have a longer term application in farm scale and precision agriculture applications Increasing the resolution at which simulations can be run, and improving the information content of products through the use of crop models will allow more regionally specific information on crop performance to be derived. The optimization techniques being developed in this project will allow simulations and forecasts to be run at higher spatial resolutions, taking advantage of new satellite data such as that from Sentinel-2. The use of crop growth models will provide more precise analytics of crop development and responses to different stresses which will allow more suitable management practices to be deployed. We will work proactively with the Newton Agritech Network+ which is currently being established, to promote the results of the project amongst commercial precision farming operators in China.
In addition to the specific applications and users described here the techniques and tools developed in the project will be disseminated within the science community to promote wider uptake. Both BNU and PKU operate summer schools in advanced remote sensing techniques and these schools will be used as method for dissemination to graduate students and early-career researchers. In the UK UCL is already making extensive use of these notebooks for training purposes and will promote these tools through the wider NCEO to maximize uptake.
In addition to the primary seasonal production applications, the research will also contribute to improve long term planning. Physically based crop yield simulation using EO data will allow crop responses to different climate scenarios to be modelled more accurately, giving simulations that are more robust to changes in climate than the currently widely used NDVI curve matching approaches. The results of the project will also be disseminated via the Group on Earth Observations GEOGLAM and GECAM initiatives to which the CHARMS contributes. At the National scale our research will support analysis of yield performance at local/regional scale and will contribute to development of adaptation strategies to optimize agricultural production under different climate scenarios.
The impacts of our research will initially be focused on regional and national scale monitoring, also have a longer term application in farm scale and precision agriculture applications Increasing the resolution at which simulations can be run, and improving the information content of products through the use of crop models will allow more regionally specific information on crop performance to be derived. The optimization techniques being developed in this project will allow simulations and forecasts to be run at higher spatial resolutions, taking advantage of new satellite data such as that from Sentinel-2. The use of crop growth models will provide more precise analytics of crop development and responses to different stresses which will allow more suitable management practices to be deployed. We will work proactively with the Newton Agritech Network+ which is currently being established, to promote the results of the project amongst commercial precision farming operators in China.
In addition to the specific applications and users described here the techniques and tools developed in the project will be disseminated within the science community to promote wider uptake. Both BNU and PKU operate summer schools in advanced remote sensing techniques and these schools will be used as method for dissemination to graduate students and early-career researchers. In the UK UCL is already making extensive use of these notebooks for training purposes and will promote these tools through the wider NCEO to maximize uptake.
Organisations
- University College London (Lead Research Organisation)
- Ghana Space Science and Technology Institute (Collaboration)
- University of South Dakota (Collaboration)
- European Space Agency (Collaboration)
- University of Valencia (Collaboration)
- China Agricultural University (CAU) (Collaboration)
- Chinese Academy of Agricultural Sciences (Collaboration)
- Chinese Academy of Agricultural Sciences (Project Partner)
- Assimila Ltd (Project Partner)
- Peking University (Project Partner)
- Beijing Normal University (Project Partner)
People |
ORCID iD |
Philip Lewis (Principal Investigator) | |
Qingling Wu (Researcher) |
Publications
Roy D
(2016)
A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance
in Remote Sensing of Environment
Hao P
(2018)
A sampling workflow based on unsupervised clusters and multi-temporal sample interpretation (UCMT) for cropland mapping
in Remote Sensing Letters
Wu S
(2018)
An Improved Subpixel Mapping Algorithm Based on a Combination of the Spatial Attraction and Pixel Swapping Models for Multispectral Remote Sensing Imagery
in IEEE Geoscience and Remote Sensing Letters
Liu C
(2019)
Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification
in Remote Sensing
Huang J
(2019)
Assimilation of remote sensing into crop growth models: Current status and perspectives
in Agricultural and Forest Meteorology
Meng S
(2017)
Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags
in Journal of Hydrology
Huang J
(2018)
Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield
in International Journal of Digital Earth
Sun Y
(2018)
Crop Leaf Area Index Retrieval Based on Inverted Difference Vegetation Index and NDVI
in IEEE Geoscience and Remote Sensing Letters
Description | Progress was made in developing approaches to mix satellite observations with crop models to predict and map crop yield. We had some issues with data quality with limited the validation of the work, but methodological advances were made. |
Exploitation Route | The work was carried forward in an stfc project, applying this work to maize crops in Ghana. |
Sectors | Agriculture Food and Drink Education |
Description | The research outputs feed directly into agricultural production planning in China. Our research delivers significantly improved simulations and predictions of crop performance that assist in the strategic planning, optimization of regional production and management of national food supply and security. The lead partner in China, IARRP-CAAS runs the China Agriculture Remote Sensing Monitoring System (CHARMS) which monitors crop acreage change, yield, production, drought and other agriculture-related information for 7 main crops in China. CHARMS provides information directly to the Ministry of Agriculture and Rural Affairs (MARA), and related agriculture management sectors, in the form of detailed bi-weekly reports submitted to MARA throughout the growing season. The optimizations being undertaken in this project allows the use of advanced Data Assimilation (DA) techniques on a regional/national scale which had previously been impossible due to the high computational demand of the algorithms. This feeds directly into improving the quality and usefulness of CHARMS outputs. Understanding seasonal crop production and yield outcomes for each growing season helps with national scale planning of food production, and gives advanced warning of any significant imbalance between food production and demand. For example, our research has achieved a 10% improvement in prediction accuracy (reduced RMSE by ~150kg/ha) for winter wheat in the NCP, where the planted area covers approximately 16 million hectares. In addition to the primary seasonal production applications, the research also contributes to the improvement of long-term planning. Physically-based crop yield simulations using Earth Observation (EO) data allow crop responses to different climate scenarios to be modelled more accurately, leading to simulations whose results are more robust to changes in climate than the currently widely used NDVI curve matching approaches. The results of the project will also be disseminated via the Group on Earth Observations GEOGLAM initiatives, to which the CHARMS contributes. At the national scale, our research supports analysis of yield performance at local and regional scales, and contributes to the development of adaptation strategies to optimize agricultural production under different climate scenarios. The impacts of our research also include longer term farm-scale and precision agricultural applications. Increasing the simulation resolution and improving the information content of products allows for more regionally specific information on crop performance to be derived. The optimization techniques being developed allow for simulations and forecasts to be run at 10-meter resolution, taking advantage of new satellite data such as that from Sentinel-1 and -2. The use of growth models provides more precise analytics of crop development and responses to different stresses which allow more suitable management practices to be deployed. We have also established business connections to promote the results amongst commercial operators and agricultural insurance services in China. In addition, the techniques and tools developed have been disseminated throughout the scientific community to promote wider uptake. The project has hosted workshops in advanced remote sensing and DA techniques for graduate students and early-career researchers. Over 500 attendees have participated in these training events, and more have benefitted from UCL's freely distributed software packages and notebooks through the wider NCEO. |
First Year Of Impact | 2021 |
Sector | Agriculture, Food and Drink,Education |
Impact Types | Economic |
Description | AMAZING- Advancing MAiZe INformation for Ghana |
Amount | £404,373 (GBP) |
Funding ID | ST/V001388/1 |
Organisation | Science and Technologies Facilities Council (STFC) |
Sector | Public |
Country | United Kingdom |
Start | 02/2020 |
End | 02/2022 |
Description | CSC PhD studentship |
Amount | ¥32,390 (CNY) |
Funding ID | 201506400010 |
Organisation | University of Leeds |
Department | China Scholarship Council |
Sector | Academic/University |
Country | United Kingdom |
Start | 08/2015 |
End | 09/2016 |
Description | H2020 (MULTIPLY) |
Amount | € 519,329 (EUR) |
Funding ID | 713694 |
Organisation | European Commission |
Sector | Public |
Country | European Union (EU) |
Start | 01/2016 |
End | 01/2020 |
Description | Newton Agritech Fund |
Amount | £166,600 (GBP) |
Funding ID | 179677333109 |
Organisation | Science and Technologies Facilities Council (STFC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2015 |
End | 03/2016 |
Description | Newton Prize |
Amount | £459,985 (GBP) |
Organisation | Department for Business, Energy & Industrial Strategy |
Sector | Public |
Country | United Kingdom |
Start | 07/2020 |
End | 03/2022 |
Title | Location, biophysical and agronomic parameters for croplands in Northern Ghana |
Description | We present a dataset describing (i) crop locations, (ii) biophysical parameters and (iii) crop yield and biomass was collected in 2020 and 2021 in Ghana, mostly focusing on maize in northern Ghana. The dataset contains repeated multiple measurements of leaf area index (LAI), leaf chlorophyll concentration over a large number of maize fields, as well as associated grain yield, biomass and polygons that delineate the fields. |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
Impact | Partners in Ghana have continued gathering data for the 2022 growing season. |
URL | https://zenodo.org/record/6632083 |
Title | New data assimilation technique for crop monitoring |
Description | A rapid high-resolution remote sensing data assimilation method was newly implemented with Python and Cloud-based Google Earth Engine (GEE) for monitoring crop growth. This method is based on a pre-generated large ensemble of crop simulations, calculating the optimal growth trajectory of crop growth in a very effective way, achieving yield/other variables estimation in high resolution. This allows 10-m resolution yield estimation/forecasting over province scale in China to be conducted in just few hours. |
Type Of Material | Data analysis technique |
Year Produced | 2019 |
Provided To Others? | No |
Impact | This technique allows 10-m resolution yield estimation or forecasting over province scale in China to be conducted in just a few hours. |
Description | BRDF-normalisation of Landsat data |
Organisation | University of South Dakota |
Country | United States |
Sector | Academic/University |
PI Contribution | We have worked together with American colleagues on providing BRDF-normalised Landsat data. |
Collaborator Contribution | Our American partners have implemented a simple algorithm to correct for angular (e.g. solar illumination) effects on Landsat data. |
Impact | A paper has been produced. |
Start Year | 2015 |
Description | Collaboration on radiative transfer/data assimilation with the ESA FLEX team |
Organisation | University of Valencia |
Country | Spain |
Sector | Academic/University |
PI Contribution | I have been in discussions with the FLEX team to use our work on emulation of RT models to speed up their scene simulator, a major part of the effort to assess the capabilities of the instrument. |
Collaborator Contribution | The FLEX team are investigating methods to correct for atmospheric effects in the retrieved signal. This requires running costly radiative transfer models, which makes the whole endeavour impractical. We are currently investigating extending the use of emulators to other areas of the FLEX processing flow. |
Impact | We have produced two papers in collaboration with the Valencia group (one accepted, another one in press). |
Start Year | 2015 |
Description | Crop model calibration with China Agricultural University |
Organisation | China Agricultural University (CAU) |
Country | China |
Sector | Academic/University |
PI Contribution | Cooperate with China Agricultural University (CAU) partners in crop model calibration approaches and validation dataset collection. 2 workshops were held in CAU on 10th January and 16th June to discuss the technical route and further cooperation. |
Collaborator Contribution | CAU provided data and model calibration data for both winter wheat and summer maize from their test sites for several years. |
Impact | Huang J, Gómez-Dans JL, Huang H, Ma H, Wu Q, Lewis PE, Liang S, Chen Z, et al. (2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, 276-277: 107609-107609. Huang, J., Ma, H., Sedano, F., Lewis, P., Liang, S., Wu, Q., . . . Zhu, D. (2019). Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST-PROSAIL model. European Journal of Agronomy, 102, 1-13. |
Start Year | 2016 |
Description | Crop modelling and vegetation monitoring with Chinese Academy of Agricultural Sciences |
Organisation | Chinese Academy of Agricultural Sciences |
Country | China |
Sector | Academic/University |
PI Contribution | UCL advised on the data assimilation techniques for regional-scale crop monitoring with remote sensing to CAAS |
Collaborator Contribution | CAAS has provided their insights on crop monitoring in North China Plain, their data assimilation strategy for national crop monitoring and organized multiple field campaigns for collecting calibration data for our joint modeling work. |
Impact | Huang J, Gómez-Dans JL, Huang H, Ma H, Wu Q, Lewis PE, Liang S, Chen Z, et al. (2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, 276-277: 107609-107609. |
Start Year | 2015 |
Description | Crop monitoring training to GSSTI |
Organisation | Ghana Space Science and Technology Institute |
Country | Ghana |
Sector | Public |
PI Contribution | We have been providing field survey training and advice on crop monitoring technologies to Ghanaian partners, to help them conduct ground-based survey of crop variables. |
Collaborator Contribution | GSSTI is working on a series of monthly field surveys to collect ground-based crop measurement data |
Impact | Delayed dut to COVID |
Start Year | 2020 |
Description | Sentinel-2 Level 2 product assessment |
Organisation | European Space Agency |
Country | France |
Sector | Public |
PI Contribution | The UCL group is part of an international experts team that will assess different approaches to produce a level 2 (surface reflectance) product from the Sentinel-2 sensors. |
Collaborator Contribution | Partners will use different methodologies for the estimation of surface reflectance, which will then be used by the UCL/NCEO team within the EOLDAS framework to critique these different approaches. |
Impact | None yet |
Start Year | 2015 |
Description | Submission of a proposal to the Lacuna Fund Announcement of Opportunity |
Organisation | Ghana Space Science and Technology Institute |
Country | Ghana |
Sector | Public |
PI Contribution | Supported GSSTI to bid for a funding proposal to extend data collection underpinning agricultural monitoring in Ghana |
Collaborator Contribution | Supported proposal writing. |
Impact | Unfortunately, the proposal was not funded at the end. |
Start Year | 2021 |
Title | Data assimilation online demonstrator for Ghana |
Description | In order to support in-country activities as well as to provide our partners in Ghana with a capability to continue the work started with this collaboration, we prepared some intuitive materials to demonstrate (i) typical EO signals used to understand crop development, (ii) simplified crop modelling efforts and (iii) data assimilation techniques to combine EO observations with crop models. |
Type Of Technology | Webtool/Application |
Year Produced | 2022 |
Open Source License? | Yes |
Impact | The software was installed in GSSTI's servers, and partners were trained to use it. New field data has been collected, and partners in Ghana will use it to further validate the prototype software. |
Description | GEOGLAM activity presentation |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | We have provided regular activity updates to the GEO organisation GEOGLAM, as part of UK contributions to that organisation. 2021 contribution https://www.youtube.com/watch?v=6UQPfXNICyE |
Year(s) Of Engagement Activity | 2021,2022 |
URL | https://earthobservations.org/geoglam.php |
Description | Presentation at COP26 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Public/other audiences |
Results and Impact | Presented the project collaboration to participants at the COP26 in Glasgow and online streamed to the general public. |
Year(s) Of Engagement Activity | 2021 |
URL | https://ukcop26.org/wp-content/uploads/2021/10/Earth-Information-Day-Event-Plan.pdf |
Description | Research Competition |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | "Sentinels of Wheat" cup: Competition on the use of Sentinels remote sensing image for crop study in China. The submitted works included topics of crop classification, retrieval of water stress, crop disease monitoring, crop growth monitoring, meteorological impact on crop growth, yield estimate and retrieval of grain quality, from 29 institutes from both mainland China and Hongkong. We selected eight final winners. |
Year(s) Of Engagement Activity | 2018 |
Description | The 2nd Winter School on Second International Symposium on Agricultural and Ecological Remote Sensing |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | 150 students from CAU and other universities in Beijing attended the 2nd Winter School on Second International Symposium on Agricultural and Ecological Remote Sensing, a series of biennial workshops we co-organize with Chinese partners to promote the frontier of research in agricultural remote sensing. Due to pandemic travel restrictions, only local students were able to attend on-site, and many others have watched the live stream of the event. |
Year(s) Of Engagement Activity | 2018,2020 |
Description | Using Earth observation for Crop Monitoring (iEOCM) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | We organised a workshop in the city of Tamale, and invited local community representatives, government officials as well as a group of farmers that had been involved in the study. The workshop aimed to inform these stakeholders of the possibilities of Earth Observation and crop modelling to monitor agricultural lands. The discussion sparked interest from Ghana's statistics collecting body, as well as interest from local farmers. |
Year(s) Of Engagement Activity | 2022 |
URL | https://ucl-eo.github.io/Workshop2022/tamale |
Description | Using Earth observation for Crop Monitoring (iEOCM) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | We organised a well-attended hybrid workshop in Ghana (and online) to showcase the application of Earth Observation to agriculture in a smallholder subsistence farmer context. The workshop was attended by Ghanaian academics, government officials from the Ghanaian Ministry of Agriculture (MoFA), delegates from agricultural insurance companies and delegates from FAO. |
Year(s) Of Engagement Activity | 2022 |
URL | https://ucl-eo.github.io/Workshop2022/accra |
Description | Winter School (Beijing) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Postgraduate students |
Results and Impact | The first International Symposium on Agricultural & Ecological Remote Sensing and Postgraduate Training Conference in Beijing from 24th to 26th Nov 2018. With the STFC impact fund, we were able to host a three-day workshop without charge for postgraduate students in China to attend. The Symposium and Conference were hosted at the China Agricultural University (CAU), and it was a multi-institution event, including inputs from UCL, Newcastle University, NERCITA, CAAS, CMA, BNU and Peking University. Over 500 students attended the event. Among topics addressed by 27 UK and China-based speakers were optical remote sensing, crop modelling, radar, hyperspectral remote sensing, vegetation florescence, data assimilation, and land management. The UCL team also ran a practical session for 150 students on data assimilation with the support of Assimila Ltd. Feedback was very positive, and it is hoped to continue such workshop in future years. |
Year(s) Of Engagement Activity | 2018 |
URL | https://www.geog.ucl.ac.uk/news-events/news/the-first-international-symposium-on-agricultural-ecolog... |