Vanessa Paredes

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.

Aim of study: This study aimed to determine the distribution of the fungal pathogen Gremmeniella abietina in forest stands close to the first detection zone in Spain. Additionally, we aimed to identify stand characteristics associated with the presence of the pathogen and evaluate the use of remote sensing methods, such as the Normalized Difference Vegetation Index (NDVI), for early disease detection. Area of study: Province of Palencia, northern Spain. Material and methods: We surveyed 36 forest stands to assess the distribution of G. abietina. Stand inventories, NDVI values derived from Deimos-1 satellite imagery and DNA amplification techniques were used to characterize each stand and determine the presence or absence of the pathogen. Finally, a combination of a random forest algorithm and a permutational multivariate analysis of variance (PERMANOVA) was applied to identify the most significant predictors of the pathogen presence. Main results: The presence of G. abietina was confirmed in 13 plots and was found to be correlated with lower NDVI values during the summer months and with greater competition (i.e., high basal area) on steeper slopes. The algorithm had limited predictive power but it was sufficiently reliable for descriptive purposes. Infected stands also showed higher levels of defoliation, distortion of terminal twigs and dry needles. Research highlights: The results indicate that although the pathogen has spread beyond the initial detection zone, it currently causes only moderate damage. The absence of epidemic outbreaks in the region may be due to environmental conditions that are not conducive to disease development. Nevertheless, the presence of G. abietina could negatively affect forest productivity, particularly in stands located on steep slopes and characterized by a high level of competition. Use of the NDVI may be a useful tool for detecting trees affected by the pathogen at an early stage, but confirmation through field diagnosis surveys and molecular diagnostics remains essential.

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.

Desde su creación, el ITACyL, apuesta firmemente por impulsar el sector agrario mediante la digitalización y la incursión de las nuevas tecnologías en la agricultura, fomentando el uso de herramientas y soluciones digitales que ayuden a la toma de decisiones. Estas soluciones van encaminadas no sólo a los responsables de políticas agrarias, sino también a los propios agricultores y comunidades de regantes ayudándoles a optimizar la gestión de su actividad. Así mismo, en el nuevo marco regulatorio de la reforma de la PAC para el periodo 2023‐2027, articulado por el Real Decreto 1048/2022, de 27 de diciembre, se expresan los nuevos requerimientos para las tierras de regadío que soliciten alguno de los nueve eco‐regímenes (EERR) que complementarán la ayuda de renta básica de la PAC. Acogerse a determinados EERR, puede generar la necesidad de aportar información extra en el cuaderno digital oficial que permita gestionar correctamente dicha ayuda. Cabe recordar que, ya desde la campaña 2023, todos los beneficiarios de los EERR que incluyen entre sus requisitos de subvencionabilidad una gestión sostenible de insumos (Rotación con cultivos mejorantes, Siembra directa en tierras de cultivo de regadío, Espacios de biodiversidad en cultivos bajo agua), o soliciten una ayuda asociada, deben contar con un plan de abonado. Aunque no es obligatorio rellenar los campos pertinentes en el CUE digital oficial hasta el 1 de septiembre de 2024 en caso de explotaciones grandes (o con más de 5 ha de regadío) y septiembre de 2025 para las pequeñas con superficie de regadío entre 1 y 5 ha. Las explotaciones con menos de 5 ha en su totalidad o menos de 1 ha de regadío quedarán exentas de la obligación de emplear el cuaderno digital. En este contexto, disponer de una herramienta digital que permita satisfacer todos los cumplimientos de manera efectiva y con un cierto rigor científico es un aspecto clave para la competencia del sector. Es el caso de SATIVUM, un herramienta abierta y gratuita desarrollada por el ITACyL para ayudar a agricultores y asesores en la toma de decisiones de una explotación. Permite acceder y gestionar información de parcelas agrícolas, monitorizar los cultivos presentes a través de imágenes de satélite durante toda la campaña y desde fechas anteriores, consultar datos climáticos, de suelo y usar algoritmos para estimar las necesidades de nutrientes del cultivo y elaborar un plan de abonado. También servirá para enviar la información oportuna al cuaderno oficial de explotación de cada comunidad autónoma, previa autorización del agricultor, asistiéndole a cumplimentar y poder cumplir con la normativa del RD 1054/2022 que regula el SIEX, REA y CUE. Es de especial interés, la información relativa al módulo de nutrientes que incorpora SATIVUM, que además de ser reconocido por las autoridades para elaborar planes de abonado sofisticados, incorpora al plan datos de obligado cumplimiento en el cuaderno digital como son, el contenido en N y K de las aguas de riego procedente de aguas subterráneas. Además, se ha desarrollado una API, para que cualquier servicio externo pueda consultar el contenido en nutrientes del agua de riego. Todas estas funcionalidades que ofrece SATIVUM operan a nivel nacional, demostrando ser una herramienta muy útil no sólo para agricultores y asesores, sino para avanzar en la digitalización del campo.

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.

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.

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.

In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately ~100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three European agricultural areas located in Italy, Spain and France. Results will be further reported in the paper.