New needs have arisen from member states and paying agencies (PA) to achieve the compliance assessment from farmers in the frame of the European Common Agricultural Policy (CAP). Traditional field inspection (on-the-spot checks) and computer-aided photointerpretation (CAPI) carried out by each PA over a sample of 5% of the applicants are being replaced by a 100% sample Copernicus satellite-based system (checks by monitoring, CbM). This new approach will be an integral part of the Area Monitoring System that will be part of the Integrated Administrative Control System (IACS) in the post-2020 CAP. Among all the aid schemes having to be analyzed, there are some specific aids in which the detection of irrigation of certain crops can result in a no-compliance resolution. Apart from that, the knowledge of the truly irrigated area in each campaign has always been data of great interest in irrigation planning, crop yield statistics, and water management, and now more than ever. Although several sources of information exist, there is no consensual methodology for estimating the actual irrigated area. The objective of this study is to propose a methodological approach based mainly on Copernicus Sentinel and IACS data not only to detect the surface of herbaceous crops that have been actually irrigated but also to derive a product suitable to be incorporated into the CAP monitoring process system. This methodology is already being used operationally during the ongoing campaign 2020 by Castile and León PA.

This paper investigates the use of high resolution (~100 m) surface soil moisture (SSM) maps to detect irrigation occurrences, in time and space. The SSM maps have been derived from time series of Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) observations. The analysis focused on the Riaza irrigation district in the Castilla y León region (Spain), where detailed information on land use, irrigation scheduling, water withdrawal, meteorology and parcel borders is available from 2017 to 2021. The well-documented data basis has supported a solid characterization of the sources of uncertainties affecting the use of SSM to map and monitor irrigation events. The main factors affecting the irrigation detection are meteo-climatic condition, crop type, water supply and spatial and temporal resolution of Earth observation data. Results indicate that approximately three-quarters of the fields irrigated within three days of the S-1 acquisition can be detected. The specific contribution of SSM to irrigation monitoring consists of (i) an early detection, well before vegetation indexes can even detect the presence of a crop, and (ii) the identification of the irrigation event in time, which remains unfeasible for vegetation indexes. Therefore, SSM can integrate vegetation indexes to resolve the irrigation occurrences in time and space.

This study presents an operational and robust method for detecting and dating cereal harvest events using temporal stacks of Copernicus Sentinel-2 imagery and crop and fields border information from ancillary records. The proposed approach is exempt from training data, thereby enabling its application across diverse geographical contexts. The method was used to generate 10 m resolution maps of harvest dates for all wheat and barley fields in 2021, 2022, and 2023 in Castilla y León, a major cereal-producing region of Spain. This work also investigates the use of a reference dataset derived from real time kinematic records (RTK) in agricultural machinery as an alternative source of large-scale in situ data reference as for Earth observation-based agricultural products. The initial comparison of annual harvest date maps with the RTK-based reference datasets revealed that the temporal lag in the detection of harvest events between Earth observation-derived maps and reference harvest dates was less than 10 days for 65.7% of fields, while the temporal lag was between 10 and 30 days for 26.1% of the fields. The 3-year average root mean square error of the lag between harvest dates in the reference dataset and maps was 16.1 days. An in-depth visual analysis of the Sentinel-2 temporal series was carried out to understand and evaluate the potential and limitations of the RTK-based reference dataset. The visual inspection of a representative sample of 668 fields with large temporal lags revealed that the date of harvest of 41.11% of these fields had been correctly identified in the Sentinel-2 based maps and 16.43% of them had been incorrectly identified. The visual inspection could not find evidence of harvest in 10.52% of the analyzed fields. Monte Carlo simulations were parameterized using the findings of the visual inspection to build a series of synthetic reference datasets. Accuracy metrics calculated from synthetic datasets revealed that the quality of the harvest maps was higher than what the initial comparison against the RTK-based reference dataset suggested. The date of harvest was registered within 10 days in both the maps and the synthetic reference datasets for 90.5% of the fields, the root mean squared error of the comparison was 9.5 days, and harvest dates were registered in the Sentinel-2 based maps 2 days (median) after the dates registered in the reference dataset. These results highlight the feasibility of mapping harvest dates in cereal fields with time series of high-resolution satellite imagery and expose the potential use of alternative sources of calibration and validation datasets for Earth observation products. More generally, these results contribute to defining plausible targets for monitoring of agricultural practices with Earth observation data.

Accurate and harmonized soil information is fundamental for sustainable agricultural management, environmental assessment, and land-use planning. This study developed a national Digital Soil Mapping (DSM) framework for mainland Spain and the Balearic Islands using a harmonized database of more than 50,000 georeferenced legacy soil observations. Seven key soil properties (clay, silt, sand, pH, organic matter, available phosphorus, and exchangeable potassium) were modelled using environmental covariates derived from Sentinel-2 imagery, terrain attributes, climate, and land-use data. Three machine-learning algorithms were evaluated, with Cubist providing the best predictive performance and therefore selected for mapping all soil properties. Model validation showed the highest accuracy for soil pH (R² = 0.60), sand (R² = 0.51), and organic matter (R² = 0.50), whereas moderate performance was obtained for silt and clay. Available phosphorus and exchangeable potassium showed lower predictive accuracy, reflecting their strong dependence on management practices. The resulting maps were generated at 20 m spatial resolution and generalized to 200 m for dissemination, providing a harmonized national soil property dataset for Spain. This operational and reproducible framework supports agricultural and environmental decision-making and can be adapted for soil mapping in other regions using open-access data and open-source tools.

The purpose of this work is to investigate the use of high-resolution ($~100$ m) surface soil moisture (SSM) maps derived from Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify irrigation events occurring in the Riaza irrigation district (Castilla y León region, Spain) from 2017 to 2021. The proposed method is based on the application of the Constant False Alarm Rate (CFAR) algorithm, which is an adaptive and unsupervised thresholding algorithm traditionally used for target detection in SAR images. This algorithm uses a sliding window approach that allows an adaptive threshold estimate for each pixel of the image, depending on the distribution of the surrounding pixels. The analysis was carried out on fields cultivated with maize, sugar beet and sunflower. Results show that the Overall Accuracy (OA) of the detection mainly depends on the time span (TS) between the S-1 passage and the irrigation event, the acquisition timing and the development stage of the vegetation. Indeed, the OA reaches a mean of $78\%$ and $70\%$, respectively, for the $6$ a.m. and $6$ p.m. acquisitions, when the irrigation events occur within $36$ h before the S-1 passage, and it follows a downward trend as the TS increases. On the other hand, when the vegetation reaches the mature stage, the mean OA decreases respectively to $56\%$ and $52\%$. Stemming from the event detection, the study explored the estimation of the total irrigated area in the early growing season, showing promising agreement with in situ data, as evidenced by the low Relative Error ($E_r\approx 5.6\%$). Additionally, the analysis revealed a significant correlation between field-scale mean SSM and irrigation depths ($R=0.89$).

Following the need of winter cereal farmers from the main producing region (Castilla y León) in Spain to estimate crop yield with at least one season of anticipation, we have developed a climate service based essentially on current and historical meteorological observations, on spring seasonal forecasts from ECMWF System 5 and on the crop growth model AquaCrop. Different experiments have been designed to produce both a synthetic yield database serving as observed truth and three different seasonal forecasting strategies. Calculation of objective verification scores for deterministic and probabilistic crop yield forecasts -including an assessment of their potential economic value- in hindcast mode determines the quality of this service and the differences among forecasting strategies. We demonstrate that the three compared strategies show good skill of wheat yield forecasts at the beginning of July, although the meteorological forcing for Aquacrop simulations between 1st April and 30th June is very different for the three compared strategies. The important role of the memory from previous (autumn and winter) climate conditions carried by the crop growth model is analysed and discussed.  A yearly assessment also allows some preliminary estimation of the value and possible benefits of the service for final users. Finally, we conclude that the simulation synthetically producing the observed truth compares rather well –especially the interannual variability- with other yield data based on surveys and experts estimations although it overestimates yield. Users have played a decisive role in co-design and co-development phases of this climate service. They have also actively intervened in the analysis and evaluation of results.