Conference Agenda

In recent years, 10 to 30 meter resolution optical multispectral and hyperspectral satellites data and SAR data from Europe and China became available and is encouraging the Remote sensing community to explore the new technology to harness all advantages of all these satellites to achieve best crop monitoring at various scale. The capability of agricultural monitoring in general is being enhanced and improved with these diverse 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 China and the rest of the world. There are existing large fields with mono crop and small fields with multiple strips of various crop types. It has to take this situation into account when the crop monitoring is being conducted. What are the performance differences between multispectral time series or hyperspectral imagery from both side for crop type mapping, in particular between similar crop types? What is the transferability from one year to another for crop type classification models based on multispectral time series on one hand, and hyperspectral data on the other hand? In this project, 5 study sites, 4 from China and 1 from Belgium, are selected representing the major cropping systems, including winter wheat, maize, rice, sugarcane in China and winter cereals, potato, sprint cereals and maize in Europe. These sites also will be representing the agricultural systems in the flat area or in hilly area, irrigated or rainfed, in the North and the South. The field campaigns will be organized and the data collection will be following the JECAM guideline. The Sentinal1/2, EnMAP, from Europe and GF1/6, Obita and BNUE from China as well as other satellite data will be mainly investigated to support this study. The crop classification algorithm will be evaluated with various satellite data, like optical, SAR and hyperspectral data, either alone or combination to make best crop type maps, but in particular with a focus on hyperspectral data. 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 forward to support agricultural monitoring at fine scale. The collaboration will also make great technological contributions to GEO Global Agricultural Monitoring Imitative, GEOGLAM.

Climate change and anthropogenic pressures increasingly threaten food security and quality, necessitating advanced monitoring and analytical tools. The activity conducted since now in the project ID# 95250 aims at evaluating the effectiveness of synergistically combining EU and Chinese EO data for retrieving key parameters for soil and crop conditions.

Specifically, for soil topic, our objective is to assess the efficacy of a combined use of visible and thermal sensor data to retrieve soil texture and properties, such as Soil Organic Carbon (SOC), Calcium Carbonate (CaCO3), and pH. The primary objective is to evaluate the potential of an approach combining visible and thermal data in view of the future ESA missions (i.e. CHIME and LST) as well as the NASA missions (i.e. SBG and SBG-TIR).

While current satellite missions such as PRISMA and EnMAP, have demonstrated their capabilities in soil monitoring, the TIR spectra range has not been fully investigated, Moreover, PRISMA and EnMAP acquire images only on demand, while HYTES, being an airborne mission, covers only limited and localized areas. This makes it difficult to obtain simultaneous VSWIR and TIR images on the same area, hindering the study of a combined approach. However, with the new ESA missions CHIME and LSTM, featuring larger swaths of 130 km and 670 km, respectively, and high revisit times of 11 and 2 days, the likelihood of obtaining simultaneous thermal and visible images increases significantly.

This study investigates the Jolanda di Savoia site, located in the agriculturally significant Po Valley of Northern Italy (centered44.87°N, 11.97°E). This region, a very fertile land, is a prominent producer of high-quality rice. The soil composition exhibits considerable variability, primarily attributed to the area's historical landscape evolution. Notably, the presence of buried paleo-channels, remnants of former marshlands, contributes to the observed soil heterogeneity. A time series of PRISMA and EnMAP imagery, from 2020 to 2025 has been used as well as a HYTES airborne survey was performed in 2023 and data, acquired within a joint ESA-ASI campaign, were obtained for this site.

The ongoing research, at present, has integrated in-situ soil wet lab measurements with 0.4-2.5 µm (VSWIR) and 7.5-11.6 µm (LWIR). Various preprocessing techniques and machine learning algorithms, including Gaussian Process Regression (GPR) and Partial Least Squares Regression (PLSR), have been employed to develop robust models for retrieving soil properties. The efficacy of these models will be assessed based on their accuracy and their capacity to effectively take advantage of combined visible and thermal data.

Results indicate that SOC, by using VSWIR time series, could be predicted within an RMSE of 2.79, an R² of 0.54, and an RPD of 1.47, while with LWIR spectral range, an RMSE of 1.22, an R² of 0.54, and an RPD of 1.42 has been obtained. Future research will be concentrated to the combined use of VSWIR and LWIR mimic the future CHIME-LSTM tandem.

In China, the demand for accurate and up to date information on wheat Grain Protein Content (GPC) has gained increased urgency, driven by the rising demands in the food consumption market and intensifying international market competition. To address this challenge and the data gap, the first 500-meter spatial resolution of GPC, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500), was created by integrating multi-source data from ERA5/MODIS and Hierarchical Linear Model (HLM), established by deciphering the spatial hierarchical relationships among temporal phenological, meteorological, and remote sensing data. CNWheatGPC-500 provides valuable insights for improving wheat production, enhancing quality control, and supporting decision-making in the agricultural sector. The CNWheatGPC-500 product is freely accessible at https://doi.org/10.5281/zenodo.10066544.

For what concerns crop threats, the core of the system aiming at detecting yellow rust outbreaks in maize and wheat crops, will be built on PRISMA satellite. Several VIs (NDVI, SIPI, PRI, PSRI, MSR) computed by using hyperspectral images will be used to implement a Diseases Infection Index. The impact of spatial resolution on the capability to detect yellow rust in crops will be one of the results of the activity. For desert locust presence risk forecasting, we have proposed a dynamic prediction method of desert locust presence risk at Somalia-Ethiopia-Kenya. Monthly prediction experiments until now were conducted, extracting high, medium and low risk areas of desert locust occurrence in the study area. Results demonstrated that the overall accuracy was 77.46%, and the model enables daily dynamic forecasting of desert locust risk up to 16 days in advance, providing early warning and decision support for preventive ground control measures for the desert locust.

In addition, we have established the remote sensing-based monitoring and risk assessment methods for biological disaster of forest and grassland. For pine wilt disease monitoring, we proposed a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The results showed that the proposed method effectively distinguished wilted pine trees from other easily confused objects. For grasshopper, we took the two steppe types as the research object and established a remote sensing monitoring model for grasshopper potential habitat. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. We also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes.