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.

Zucchini (Cucurbita pepo L.) is a cucurbitaceous plant ranking high in economic importance among vegetable crops worldwide. Pathogen infections cause alterations in plants primary and secondary metabolism that lead to a significant decrease in crop quality and yield. Such changes can be monitored by remote and proximal sensing, providing spatial and temporal information about the infection process. Remote sensing can also provide specific signatures of disease that could be used in phenotyping and to detect a pest, forecast its evolution and predict crop yield. In this work, metabolic changes triggered by soft rot (caused by Dickeya dadantii) and powdery mildew (caused by Podosphaera fusca) on zucchini leaves have been studied by multicolour fluorescence imaging and by thermography. The fluorescence parameter F520/F680 showed statistically significant differences between infected (with D. dadantii or P. fusca) and mock-control leaves during the whole period of study. Artificial neural networks, logistic regression analyses and support vector machines trained with a set of features characterising the histograms of F520/F680 images could be used as classifiers, discriminating between healthy and infected leaves. These results show the applicability of multicolour fluorescence imaging on plant phenotyping.

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.

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.

Castile and Leon crops and natural land map is a highly detailed land cover layer, obtained through satellite imagery, which distinguishes between 122 land cover classes and includes 50 specific crop types. The project began in 2013 by using several satellites, with the production cost greatly reduced since 2016 when Sentinel-2 imagery became freely available, and is updated annually. The classification is performed using a machine learning algorithm trained with data retrieved from Integrated Administration and Control System and some other land use databases available in Spain. This map is also proposed as an advanced crop map, within SENSAGRI project (Sentinels Synergy for Agriculture) drafting in response of the EO Work programme “EO-3-2016: Evolution of Copernicus Services”, as one of the four advanced proof-of-concept services. The algorithm will be validated in others European agricultural test areas which, along with Castile and León, are representative enough to show an overview of the European crop diversity.

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.