Conference Agenda

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. It is recognized that satellite data can play an important role in supporting agricultural models, particularly for determining crop water requirements or phenological crop status. While the use of satellite data to support agriculture may seem intuitive and straightforward, there is a strong need for accuracy in retrieving agricultural model parameters and state variables, especially when it comes to high resolution for precision agriculture, a key approach to food production and irrigation water management. In this respect, the current DRAGON 5 project (Dr5. 59061) focuses on the use of visible, thermal and microwave satellite data for water resource management and operational precision agriculture.

The Chinese and Italian research groups have been using satellite data for soil moisture assessment and precision agriculture modelling for many years in several test sites, characterized by different crop cover and heterogeneity, different climates and irrigation practices. Indeed, satellite data together with field data and soil water balance models contribute to the accuracy needed for precision agriculture. Over the past four years, the project carried out case studies in China, Italy, Africa and globally. The main progresses of the project are as follows:

• Soil moisture retrieval algorithm and product. Soil moisture is a critical variable for irrigation assessment and scheduling. An innovative algorithm was developed and applied to retrieve the surface soil moisture based on multi-temporal and multi-angular L-band microwave observation from the Soil Moisture and Ocean Salinity (SMOS) mission (Bai et al., 2022). In order to reduce the uncertainties in soil moisture retrieval caused by the topography influences, a new methodology using the first brightness Stokes parameter observed by the SMOS mission was also proposed to improve soil moisture retrieval under complex topographic conditions (Bai et al., 2023). Various gap-filling and downscaling methods were developed to fill the gaps in the soil moisture dataset and download the coarse-resolution (0.25°) soil moisture data to fine resolution (1-km), and a global gap-free 1-km daily soil moisture dataset was published (Zheng et al., 2023; Mi et al., 2023).
• Crop classification, crop yield and crop water use estimation based on Sentinel-2 data. In order to assess the agricultural water use for better water resource management, a series of data-driven methods were developed for crop mapping (Yi et al., 2020), early-season crop identification (Yi et al., 2022), evapotranspiration (ET) and crop yield estimation (Chen et al., 2023), crop water consumption and irrigation efficiency estimation (Pani et al., 2020; Zheng et al., 2024), based mainly on Sentinel satellite with high spatial resolution. The developed methods were applied for different case studies, e.g., crop mapping and crop yield prediction in the fragmented desert-oasis agroecosystem in the Shiyang river basin with highly heterogeneous land surface and severe water scarcity, evaluation of cropland water productivity in the irrigation district of Morocco, assessment of irrigation efficiency in the Inner Mongolia autonomous region of China with modern sprinkler irrigation equipment.
• Assessing the performance of different satellite data products for irrigation detection. The potential of satellite soil moisture datasets to detect irrigation signals was also analyzed in two irrigation consortia in Italy. We tested several products at different spatial resolutions, including Sentinel-1, SMOS, SMAP, and ESA-CCI products, and found some inconsistencies. No single soil moisture dataset showed consistent hydrological coherence with precipitation. Hybrid datasets perform better (+15-20%) than single technology measurements, with active data providing slightly better results (+5-10%) than passive data. These inconsistencies are discussed in relation to several factors that may affect the data, such as field dimensions, vegetation cover, and soil moisture status.
• Combining satellite LST with an energy and mass balance hydrological model for better irrigation management. To overcome the inconsistency of relying solely on satellite soil moisture data, we utilized satellite land surface temperature (LST) in conjunction with an energy and mass balance hydrological model (FEST-EWB). The combined approach of satellite data and energy and water balance model have been also investigated for irrigation management to overcome the limitations of the commonly used remotely-sensed based evapotranspiration models based on LST data, which provides high quality estimates of ET but could not provide continuous estimates in time due to satellite overpass time and the presence of clouds.
• Assimilation of LST and soil moisture into hydrological model to improve the model performance. The satellite LST and soil moisture have been separately and jointly assimilated into the hydrological model for the Capitanata consortium to demonstrate the potential for accounting unknown processes (e.g. irrigation). The modelled total evapotranspiration of crop trees with the water-energy balance scheme FEST-EWB seems to be slightly affected by spatial resolution. Therefore, in the crop trees field, the two-source modelling approach of water and energy with FEST-EWB better explains the evapotranspiration from the vegetated pixel and soil components. In this particular case study, where the LST is not distinguishable between trees and grass covering the interrow, both the two-source and one-source energy water balance models produce similar values of latent heat.
• Improving hydrological modelling through calibration with satellite-based evapotranspiration data. In order to improve the water and land management, especially for the ungauged basins, a robust framework has been developed by integrating the satellite remote sensing and hydrological model. The method simulates water balance and investigates its regulation mechanism by calibrating hydrological model (Soil and Water Assessment Tool, SWAT) by satellite remote sensing evapotranspiration dataset (e.g., ET data from ETMonitor) (Bennour et al., 2022). The developed method was applied in three basins in the Sahel region to investigate the impacts of land use/land cover change (LULC) and climate variability on the water balance components from 1990 to 2020 (Bennour et al., 2023).

The results reinforced the synergistic use of satellite data in water and energy balance models is a robust approach for irrigation.

Forecasts concerning agroecosystem functions, hydrological cycles, and biogeochemical processes, in response to climate change and human interventions, are imperative both at broad continental scales and within management-relevant contexts. Addressing this scientific imperative demands a comprehensive exploration of inter-compartmental dynamics and scale-specific relationships to anticipate how agricultural systems will react to evolving environmental conditions. Of particular concern is the current role of agriculture as a carbon emitter, necessitating critical assessment and the formulation of strategies to transition farming practices into carbon sinks.

In pursuit of this goal, during the Dragon 5 Cooperation, we proposed Project No. 59197 to conduct agroecosystem health assessments and examine agricultural processes using diverse in situ and earth observation data. This initiative aims to conserve, safeguard, and enhance the judicious utilization of natural resources, thereby fostering sustainable agricultural development. Here, we aimed at comprehensively understanding agroecosystem conditions and processes via remote sensing, with a focus on regions in Europe and China. The presentation outlines remote sensing methodologies for crop identification, monitoring of biophysical parameters such as leaf area index and biomass, assessment of hydrological conditions including soil moisture, evapotranspiration, and drought stress, and the calculation of carbon budgets for agricultural fields. This schema represents a structured workflow for integrating crucial variables to address multifaceted challenges, whether they pertain to informing policy decisions at the continental level or providing actionable insights for directly involved farmers at the local level.