Conference Agenda

With an increasingly warming climate globally, hydro-climate extremes such as heatwaves and droughts have surged, challenging the stability and resilience of social-ecological systems. In particular, the frequency, intensity, and duration of compound heatwave and drought extremes (compound hot-dry extremes) largely increased in recent decades, which intensifies the spatio-temporal mismatch between water supply and demand in the agricultural water community. Furthermore, compound hot-dry extremes are foreseen to be more frequent and intense in the coming decades under a projected warming scenario. This underscores the imperative need for well-informed and effective adaptation strategies within the agricultural water system to address the disasters induced by compound hot-dry extremes. The AgriWATER project encompasses three key aspects: (1) detection and mechanism analysis of compound hot-dry extremes in both Europe and China for the past two decades; (2) quantifying impacts of compound hot-dry extremes on agriculture and water resources across the major crop areas of Europe and China; and (3) developing adaptative strategies to reduce the vulnerability of agricultural water system to compound hot-dry extremes. Using multi-source remote sensing retrievals, machine learning methods, and advanced hydrological and crop models, the AgriWATER project aims to improve the understanding of interactions between compound hot-dry extremes and agricultural water systems over the core food production areas in Europe and China. This provides an important reference into water resources management and food security for both Europe and China, and delivers valuable insights for other agricultural areas globally.

Agriculture is the largest water user in the world and irrigation water management is facing major challenges in the sustainable development of food production and water use. Improving irrigation water efficiency is a must in our changing world and requires extensive, comprehensive and accurate (physically based) tools.

The objective of the project is to evaluate the role of satellite earth observation (EO), and in particular of optical-thermal-microwave data, in support of water management at different spatial scales (field, regional, global). Satellite data can play a major role in assessing water availability and crop water needs and stress conditions, as both stand alone source of information or in combination with hydro-meteorological modelling tools. This project aims at addressing some of the open research questions in the topic ‘5. Sustainable Agriculture and Water Resources’ and sub-topic ‘5.3 Water resources and its utilization’. The increasing treat on available water posed by climate change, and the increasing frequency of drought events in many regions of the world, suggests how accurate estimations on the inter-annual fluctuation of crop water needs can significantly contribute also to sub-topic ‘5.4 Drought and flood disaster’.

Despite of a large satellite system data base of products useful for water balance modelling at multiple spatial scales characteristic of climate, meteorological models, basin-scale water management model and field scale precision irrigation, there is a need to understand how the accuracy of these data products affects the reliability of hydrological model simulations when used for operational application at multiple spatial scales as those of climate and meteorological models, river basin water management models, field scale precision irrigation models. These needs are exacerbated by the increasing expansion of areas devoted to water-intensive crops and simultaneous impact of climate change on water availability.

This project specific aims are: 1) investigating the applicability of satellite observations at different spatial (i.e. global, regional and farmland scales) and temporal (daily, monthly and annual) scales for more effective water/land governance and sustainable agricultural water management by quantifying water flows/fluxes, consumptive use, and use efficiency in the selected study areas; 2) investigating to which extent the integration of EO data and hydrological modelling may overcome the limitations mentioned above, by evaluating these issues over a set of different case studies characterized by various degree of spatial heterogeneity, field size, meteorological conditions, etc; 3) assess water availability and water use efficiency for sustainable water resource management.

Rapid and effective monitoring of crop growth in terms of biophysical variables and the occurrence of diseases and insect pests can provide a scientific basis for optimal farmland management in terms of water, fertilizer, and proper timing of pesticides, which is of great importance for reducing environmental pollution and ensuring food safety and quality. The project aims at analyzing the factors, commonly neglected, that directly reflect crop growth and environmental conditions. Factors that are related to the potential of remote sensing for the monitoring of crop status and crop growing and the early identification of pests and diseases issues. EO variable that, when integrated/assimilated with non EO data are useful to define predicting models for defining better Sustainable Agriculture practices. The project will consider the archived and newly developed GF series, ZY series, Copernicus series including specific Hyperspectral resources i.e. PRISMA/ENMAP and jointly carried out field plot control experiments and regional survey experiments. Among the variables to potentially retrieved from remote sensing to be fully explored are:

i) for vegetation the leaf angle distribution, pigments content (i.e. anthocyanins, carotenoids, chlorophyll, and Nitrogen);

ii) for soil the estimation of the soil constituencies (N, P and K), moisture and texture;

iii) for meteo-climatic condition they are LST and evapotranspiration (ET) taking advantage of the current (ECOSTRESS) and the forthcoming mission (i.e. LSTM, THRISNA and SBG-TIR);

iv) for pest and diseases the biochemical and biophysical traits combined with meteo-climatic to derive a pest and disease predicting model.

The project combines the Italian team's advanced methodologies in multi-source satellite data processing and the Chinese expertise in building multi-parametric prediction models for pest e disease, and by the academic/scientific research exchange on multi-satellite remote sensing data fusion processing and scale conversion to:

i) retrieve the physical and chemical parameters of crop traits (e.g. leaf area index and chlorophyll, Nitrogen content) by using optical and radar data optimizing the different RTM's nonlinear inversion model. The availability of different biophysical RTM models (e.g. integrating optical and thermal data) and the hybrid retrieval approaches are still to be explored. Moreover, the use of active learning (AL) methods to reduce training sample sizes so to optimize models effectively for an operational continuity scenario, is far to be completed. The key challenge lies in finding the trade-off between site-specific accuracy and operational continuity to generate both at leaf and canopy level (e.g. WC, MA, NC) products with an associated uncertainty.

ii) Monitoring of crop diseases and insect pests, at present, ignore the physical and chemical parameters of the cultivars and the habitat conditions. This negatively reflects on the accuracy of monitoring and predicting diseases and insect pests and, moreover, in a poor universality of the method. This project aims to conduct research on the key issues of the retrieval of high-precision crop (e.g. cereal) physical and chemical parameters suitable to monitor crop pest/diseases under the background of climate change scenario. The temporal and spatial changes of wheat growth period in different damaged regions of China and in EU, will be analyzed to establish pest monitoring models suitable to set up a general monitoring model (e.g. for an operational continuity scenario) to be tested within experimental applications in China, in EU and in specific test areas in some African countries

Both Entities will support this project activity with co-funding derived by 3rd part research projects. In particularly the Italian team at present is involved in the following ongoing projects that intends to use for supporting this proposal. ESA CHEES – Chime End to End Mission Performance Simulator, ESA "EO AFRICA Explorers”- PRISMA 4 AFRICA and AFRI4CAST , and ASI PRIS4VEG.

Glaciers are sensitive indicators of climate change and affect regional and global water circulation. High Mountain Asia (HMA) has the largest volume of glacier ice in mid-latitude regions and is considered as the water tower of Asia. Most glaciers have been losing mass at an accelerated rate. However, there is strong heterogeneity in mass loss and its drivers are still not well understood. Detailed information of the accumulation and ablation characteristics along with climate data can provide important insights to better understand the climate response of glaciers. The major aims of this project are therefore to investigate the potential of obtaining seasonal, annual, and long-term geodetic mass balance information from high-resolution optical and SAR satellite data, complemented by satellite altimetry data. Furthermore, we aim to calibrate and validate a glacier mass balance model using the remote sensing-derived results and then model the future glacier changes until the end of this century. We aim to obtain seasonal mass changes for the period 2016 until 2026 in order to make inferences about glacier accumulation regimes and analyse the patterns of seasonal accumulation and their influences on annual or multi-year mass change. We will focus on five study areas across High Mountain Asia with different climate regimes: Muztag Ata in Eastern Pamir, Western Kunlun in North-Western Tibetan Plateau, Purogangri Ice Cap in Central Tibet, Mt. Everest region in Central Himalaya, and Parlung Zangbo River catchment in South-East Tibet.

The main foreseen deliverables of this project include the annual and seasonal evolution of the snow line and the glacier mass balance for the period 2016 - 2026, and the expected mass changes until 2100 of the glaciers in HMA.