Conference Agenda

Agriculture is the largest water user worldwide and irrigation water management is facing important challenges in 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 tools (physically based). Satellite data, as largely recognized, may play an important role in supporting data for agricultural models, especially to determine crop water needs or phenological crop status. While using 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 the object is high resolution for precise agriculture, a key approach to food production and irrigation water management. In this respect the present DRAGON 5 project, thanks to ESA and the Ministry of Science and Technology (MOST), focuses on the exploitation of visible, thermal and microwave satellite data for operative agriculture.

The Chinese and Italian research groups since many years use satellite data for soil moisture assessment and precise agriculture modelling on several test sites in China and Italy, as well as in other places of the world, characterized by different crop cover and heterogeneity, different climates, irrigation practices. Indeed, satellite data together with field data and soil water balance models contribute to the accuracy needed in precision agriculture. In the past two years, the project work examined data from case studies in China, Italy, Africa and global scale.

In China, over agricultural fields in Shiyang River Basin (northwestern China) the present work supports the development of tools for crop type characterization, evapotranspiration estimation and irrigation water need: 1) Early-Season Crop Identification Using a Deep Learning Algorithm and Time-Series Sentinel-2 (S2) Data in Shiyang River Basin in China Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. 2) A data-driven high spatial resolution model to estimate biomass accumulation and crop yield using S2 and other satellite data was developed and applied in the Shiyang River Basin in northwestern China. For highly heterogeneous desert-oasis agroecosystem characterized by dominant crops, i.e., spring wheat, maize, sunflower, and melon, the developed model relies on three major innovations: i) the identification of start/end of the growing season of crops is done using NDVI from the S2 MSI (Multi-Spectral Instrument) in combination with limited local phenological information; ii) ETMonitor ET at 1km resolution was downscaled to 10m resolution to monitor crop water stress indicator in the biomass/yield model; iii) the air temperature stress indicator in the biomass/yield model was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system.

Taking the Sahel as an example, we investigated the impacts of land use/land cover change (LULC) and climate variability on the water balance components in 1990-2020 in three typical basins in the Sahel (Senegal, Niger rivers and Lake Chad) by using satellite-observation-based evapotranspiration derived from our model ETMonitor and ESA CCI soil moisture. The outcomes give useful hydrological insights into water and land management, emphasizing the crucial role of water recycling. This study has been published in Journal of Hydrology: Regional Studies and will be presented as a poster by a young scientist at the Dragon 5 symposium.

Soil moisture (SM) derived from microwave remote sensing is very useful, although the spatial resolution is not favorable for agricultural water use monitoring in farmland scale. The topography influences the emitted brightness temperature observed by a satellite microwave radiometer, leading to uncertainties in SM retrieval. A new methodology using the first brightness Stokes parameter observed by the Soil Moisture and Ocean Salinity (SMOS) was proposed to improve SM retrieval under complex topographic conditions. This work has been published in IEEE JSTARS and will be presented as a poster by a young scientist at the Dragon 5 symposium.

In Italy irrigated fields within the domain of irrigation consortia have been used as test area for SM and irrigation water demand estimates using satellite data and pixel-wise water-energy balance model (FEST-EWB) for different soil types and land cover heterogeneity.

Satellite data were used by FEST-EWB model: 1) for control model state variable (LST) and relative SM over large areas pixel-wise computed by the FEST-EWB model, solving the energy and water balances (Corbari-Mancini, 2014); ii) for definition of input parameter maps (e.g., leaf area Index, vegetational fraction cover).

The first approach analyses different scheme of soil water energy balance equations in consideration of remote sensing data crop or arboreal land cover heterogeneity comparing simulated energy, mass fluxes and relative surface temperature with fluxes observed at ground station and surface temperature from satellite. Using this approach a crop trees total evapotranspiration modelled with the water-energy balance scheme FEST-EWB seems to be slightly affected by the spatial resolution. For this reason, in the crop trees field the two-source modelling approach of the water and energy FEST-EWB seems to better explain the evapotranspiration from the vegetated pixel and soil components. Indeed, in the specific case study where LST are not different between trees and grass covering the interrow, similar values of latent heat are computed using both two-source and one-source energy water balance models.

Pixelwise land surface temperature computed by the hydrologic model have been compared with Satellite LST (Sentinel 3, Landsat 7, 8) showing the possibility to quantitative control pixel wise soil water balance model with the satellite data on large extension.

The second approach uses a coupled vegetation growth model with soil water and energy balance FEST-EWB-SAFY showing consistent estimates of LAI against satellite image information. This is also confirmed by modelled crop yields on the entire irrigation season respect to the observed yields for tomatoes and maize crop.

The project results obtained for the different case studies strengthen the idea that a synergic use of satellite data in water and energy balance models is a robust approach for irrigation engineer controlling crop water use of large irrigation district at high spatial resolution.

Predictions of agroecosystem processes as well as the hydrological and biogeochemical cycles in response to climate change and human interventions are needed both at continental level and at management-relevant scales. Obtaining such information is challenging since a multitude of essential variables need to be monitored to evaluate all relevant processes and to generate an agricultural digital twin. Natural hydrological and biogeochemical processes are additionally altered by anthropogenic drivers. The research community has to face this scientific challenge by a comprehensive consideration of multi-compartment interactions and scale-dependent relationships to enable the prediction of the response of agricultural systems to changing environmental conditions. Especially the current role of agriculture as a carbon source needs to be critically evaluated and strategies developed to transform farming systems into sinks for carbon.

To this end, in the fifth phase of the Dragon Cooperation (Dragon 5), we propose a project (No. 59197) to carry out agroecosystem health diagnosis and investigate agricultural processes based on various in situ and earth observation data, allowing to conserve, protect and improve the efficiency in the use of natural resources to facilitate sustainable agriculture development. At the mid-term of the Dragon 5, this paper summarizes individual steps of our project to gain knowledge about full agroecosystem states and processes by remote sensing, exemplarily for regions in Europe and China, in order to present our understanding of linking agroecosystem monitoring with carbon farming through multi-source remote sensing observations. The current study provides remote sensing approaches to identify crops such as object extraction based on SAR observations and individual plant detection by UAV; to monitor crop biophysical parameters such as leaf area index and biomass; to record hydrological states such as soil moisture, evapotranspiration, drought stress; as well as to finally provide a carbon budget (e.g., soil organic carbon content, gross primary productivity and net primary productivity) for agricultural fields. This can be seen as a workflow scheme of combining essential variables in the agricultural domain to meet the multiple challenges for providing a basis for mitigation measures, if they are at the continental level for policy advisory or at the local level to inform directly involved farmers to support sustainable agriculture development.