The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

The launch of Sentinel-2A and B satellites has boosted the development of many applications that could benefit from the fine resolution of the supplied information, both in time and in space. Crop classification is a necessary task for efficient land management. We evaluated the benefits of combining Landsat-8 and Sentinel-2A information for irrigated crop classification. We also assessed the robustness and efficiency of 22 nonparametric classification algorithms for classifying irrigated crops in a semiarid region in the southeast of Spain. A parcel-based approach was proposed calculating the mean normalized difference vegetation index (NDVI) of each plot and the standard deviation to generate a calibration-testing set of data. More than 2000 visited plots for 12 different crops along the study site were utilized as ground truth. Ensemble classifiers were the most robust algorithms but not the most efficient because of their low prediction rate. Nearest neighbor methods and support vector machines have the best balance between robustness and efficiency as methods for classification. Although the F1 score is close to 90%, some misclassifications were found for spring crops (e.g., barley, wheat and peas). However, crops with quite similar cycles could be differentiated, such as purple garlic and white garlic, showing the powerfulness of the developed tool.

There is a strong need for long term observations of land surface fluxes such as actual evapotranspiration (ETa). Eddy covariance (EC) method is widely used to provide ETa measurements, and several gap-filling methods have been proposed to complete inherent missing data. However, implementing gap-filling methods is questionable for EC time series collected within hilly agricultural areas at the watershed extent. Indeed, changes in wind direction induce changes in airflow inclination and footprint, and therefore possibly induce changes in the relationships on which rely gap-filling methods. This study aimed to obtain continuous ETa time series by adapting gap-filling methods to the particular conditions abovementioned. The experiment took place within an agricultural watershed in north-eastern Tunisia. A 9.6-m-high EC flux tower has been operating close to the watershed center since 2010. The sensible and latent heat fluxes data collected from 2010 to 2013 were quality controlled, and the REddyProc software was used to fill gaps at the hourly timescale. Adapting REddyProc method consisted of splitting the dataset according to wind direction, which improved the flux data at the hourly timescale, but not at the daily and monthly timescales. Finally, complete time series permitted to analyze seasonal and inter-annual variability of ETa.

Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of images in terms of different types of sensors, spatial and spectral resolutions generates limitations due to the heterogeneity and complexity of the data, making it difficult to exploit the full potential of satellite imagery. Addressing this issue requires new approaches to organize, manage, and analyse remote-sensing imagery. This paper focuses on the growing trend based on satellite EO and the analysis-ready data (ARD) to integrate two public optical satellite missions: Landsat 8 (L8) and Sentinel 2 (S2). This paper proposes a new way to combine S2 and L8 imagery based on a Local Nested Grid (LNG). The LNG designed plays a key role in the development of new products within the European EO downstream sector, which must incorporate assimilation techniques and interoperability best practices, automatization, systemization, and integrated web-based services that will potentially lead to pre-operational downstream services. The approach was tested in the Duero river basin (78,859 km²) and in the groundwater Mancha Oriental (7279 km²) in the Jucar river basin, Spain. In addition, a viewer based on Geoserver was prepared for visualizing the LNG of S2 and L8, and the Normalized Difference Vegetation Index (NDVI) values in points. Thanks to the LNG presented in this paper, the processing, storage, and publication tasks are optimal for the combined use of images from two different satellite sensors when the relationship between spatial resolutions is an integer (3 in the case of L8 and S2).

In arid and semi-arid regions, irrigation is crucial to mitigate water stress and yield loss. However, the overexploitation of water resources by the agricultural sector together with the climate change effects can lead to water scarcity. Effective regional water management depends on estimating irrigation demand using maps of irrigable areas or national and regional statistics of irrigated areas. These statistical data are not always of reliable quality because they generally do not reflect the updated spatial distribution of irrigated and rainfed fields. In this context, remote sensing provides reliable methods for gathering useful agricultural information from derived records. The combined use of optical and radar Earth Observation data enhances the probability of detecting irrigation events, which can improve the accuracy of irrigation mapping. Hence, we aimed to utilize Sentinel-1 (VV and VH) and Sentinel-2 (NDVI) data to classify irrigated fruit trees and rainfed ones in a study area located in the Castilla La-Mancha region in Spain. To obtain these time-series data from Sentinel-1 and Sentinel-2, which constitute the input data for the classification algorithms, a tool has been developed for automating the download from the Sentinel Hub. This tool downloads products organized by tiles for the region of interest and for the entire required time-series, ensuring the spatial repeatability of each pixel across all products and dates. The classification of irrigated plots was carried out by SVM Support Vector Machine. The employed methodology displayed promising results, with an overall accuracy of 88.4%, indicating the methodology’s ability to detect irrigation over orchards that were declared as non-irrigated. These results were evaluated by applying the change detection method of the ${\sigma }_p^0$ backscattering coefficient at plot scale.