Conference Agenda

Monitoring of agricultural crops is of vital importance for efficient food production and sustainable development of agricultural sector. The main objective of the present study was to evaluate the possibilities for crop recognition, using Sentinel-2 data in Parvomay test area in Bulgaria. For that purpose, the classification methods Support Vector Machines (SVM) and Random Forest (RF) were evaluated. These methods were applied to satellite multispectral data acquired by the Sentinel-2 satellites, for the growing season 2020-2021. Main crops grown in the research area are winter wheat, rapeseed, sunflower and maize. In accordance with their development cycles, we developed temporal image composites for the suitable moments of time when each crop is most accurately distinguished from other crops. Ground truth data was available from the integrated administration and control system (IACS) - a vector database containing information about crops sown in individual agricultural parcels for the territory of Bulgaria. The IACS data was used for both training the classifiers and accuracy assessments of the final maps.

The sentinel series satellite in Europe and GF series satellite in China are providing the data options for agricultural monitoring as well as enhancing the capability of agricultural monitoring in general. This project has made the great progress since the inception of this project. In Heilongjiang Sanjiang site, the 15 farms in the plain were selected as the study area. The sentinel 2 time series images during the growing season of 2020 to 2022 were downloaded from the official website https://scihub.copernicus.eu. The samples were collected from field by the queries with farmers and field survey. The Random Forest algorithm was applied to implement the classification. Finally, the field preparation, different rice planting methods and the harvested fields were identified from the images. In Jinzhong site, a heave flood happened in early Oct. 2021. In the middle Oct., the research team was sent to the field and collected the crop types in the field, crop types in the flooded water and others harvested already in the field. The 3 good images in September were available to retrieved the crop types before the flood. The image on Oct 17, 2021 was used to retrieved the crop types and flood crops. Then the results were layered and used to analysis the flood situation. The field situation in winter also was investigated. The bare fields, fruit tree fields, vegetated fields and others were classified with a Random Forest classifier. In Shanxi site, the apple fields and winter wheat/maize fields were also identified from the satellite image. The Venus satellite images are agreed by the team to provide this year. But it seems that there are some issues and the data are not made available right now. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm is being exchanged and it is expected to bring a step forwards to support agricultural monitoring at fine scale.

Agriculture is the largest consumer of water worldwide, accounting for about 70% of the global fresh water withdrawals. Irrigation efficiency and crop water use efficiency are key concerns for agricultural water management. The objective of the project is to assess irrigation water needs and crop water productivity based on the integrated use of satellite data with high resolution, ground hydro-meteorological data and numerical modelling, which is particularly significant for large un gauged agricultural areas. In such studies, satellite observation-based products or information with high accuracy and continuously spatial and temporal coverage are essential to support monitoring and modelling of agricultural water use and efficiency at farm and basin scales.

The following progresses have been made in the two years of project implementation:

1) FEST-EWB model improvement in evapotranspiration estimates over crop trees areas for optimizing crop irrigation efficiency.

Remotely- sensed data at different temporal and spatial resolution of vegetation parameters (leaf area index (LAI), fractional coverage of vegetation, albedo) which are used as inputs to hydrological model are obtained at high spatial and temporal resolution merging Sentinel 2 data with Landsat 8 and 9. Satellite LST is further retrieved from Landsat 8 and 9 at 30 m spatial resolution to be used for the hydrological model calibration.

Indeed, the energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations.

The model can work both in the single-source version (Corbari et al., 2011) and in its double-source one (Paciolla et al., 2022). The former uses a single balance equation for the pixel, while the latter, although requiring the same amount of input data, distinguishes between the vegetated and non-vegetated areas in the pixel. This improvement may result crucial in analysing heterogeneous agricultural areas such as those of fruit tree crops, where arboreal canopy is interspersed with bare soil or low-cut grass land cover.

The FEST-EWB model uses as input data the meteorological information, soil type data and satellite vegetation information (as LAI, fv and albedo from Sentinel-2 data). The comparison between modelled and observed LST was used to calibrate the model soil parameters with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration.

Preliminary results of the amount of precision irrigation water supply and the Evapotranspiration deficit at pixel scale will also be shown.

The FEST-EWB modelling approach has been applied, both in its single- and two-source structure, over field sites featuring walnut (Italy, 2019-21), vineyard (Spain, 2012 and Italy, 2008) and pear trees (Italy, 2022).

2) Estimation of Cropland Gross Primary Production by Integrating Water Availability Variable in Light-Use-Efficiency Model. A light-use-efficiency (LUE) model for cropland Gross Primary Production (GPP) estimation, named EF-LUE, driven by remote sensing data, was developed by integrating evaporative fraction (EF) as limiting factor accounting for soil water availability. Model parameters were optimized using CO2 flux measurements by eddy covariance system from flux tower sites, and the optimized parameters were spatially extrapolated according to climate zones for global cropland GPP estimation in 2001–2019. According to the site-level evaluation, the proposed EF-LUE model explained 82% of the temporal variations of GPP across crop sites, which is much better than original LUE model. The fraction of absorbed photosynthetically active radiation (FAPAR) data from the Copernicus Global Land Service System (CGLS) GEOV2 dataset, EF from the ETMonitor model, and meteorological forcing variables from ERA5 data were applied to EF-LUE model For the global cropland GPP estimation. The results showed overall better accuracy than other existing global GPP products, and it could capture the significant negative GPP anomalies during drought or heat-wave events, indicating its ability to express the impacts of the soil water stress on cropland GPP. This work was published in Remote Sensing.

3) Calibration and validation of SWAT model in ungauged basins. To meet the challenge of model calibration and validation in ungauged basins, we developed a new approach to calibrate SWAT hydrological model using remote sensing evapotranspiration data. This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. Different remote sensing ET datasets were tested in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR, and the calibration results based on ETMonitor ET showed the best performance with R² > 0.9 and Nash–Sutcliffe Efficiency (NSE) > 0.8. The calibrated SWAT model simulation was validated against remote sensing data on total water storage change with acceptable performance (R² = 0.57, NSE = 0.55), and remote sensing soil moisture data from ESA CCI product with R² and NSE greater than 0.85. Based on the proposed procedure, a case study focused on Lake Chad Basin in Africa was carried out and the paper was published in Remote Sensing.

4) A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data. Microwave retrieval of soil moisture is an underdetermined issue, as microwave emission from the land surface is affected by various surface parameters. Increasing observation information is an effective means to make retrievals more robust. We developed a multi-temporal and multi-angular (MTMA) approach using SMOS (Soil Moisture and Ocean Salinity) satellite L-band data for systematically retrieving vegetation optical depth (VOD_p , p indicates the polarization ─ H: horizontal, V: vertical), effective scattering albedo (ω_{p_eff}), soil surface roughness (Z_{p_s}), and soil moisture (SM_p). The retrieved by MTMA shows generally good agreement with in-situ measurements with overall correlation coefficients of larger than 0.75, and the overall ubRMSE of (0.050 m³/m³) and (0.054 m³/m³) which are lower than that of SMOS-IC Version 2 (V2) (referred to as SMOS-IC) (0.058 m³/m³) and SMOS-L3 (SMOS Level 3) (0.066 m³/m³) products. This work was published in Remote Sensing of Environment.

Net primary productivity (NPP) and Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this study, based on the long-time series NPP data products, the global NPP spatiotemporal dynamic change analysis is realized by using the methods of trend analysis, catastrophe analysis and wavelet analysis. And then, the change trend and law of NPP in different regions are analyzed, which can provide a reference for the study of global carbon cycle. In addition, on this basis, by coupling the improved CASA model, geoscience statistical model (GSMSR) and the soil respiration–soil heterotrophic respiration (R_s-R_h) relationship model, the NEP of terrestrial ecosystems at intercontinental, national and regional scales is estimated, which provides scientific basis and technical conditions for carbon balance assessment and carbon neutralization policy formulation at different scales.