Grasslands encompass vast and diverse ecosystems that provide food, wildlife habitat and carbon storage. Their large range in land use intensity significantly impacts their ecological value and the balance between these goods and services. Mowing dates and frequencies are major aspects of grassland use intensity, which have an impact on their ecological value as habitats. Previous studies highlighted the feasibility of detecting mowing events based on remote sensing time series, a few of which using synthetic aperture radar (SAR) imagery. Although providing encouraging results, research on grassland mowing detection often lacks sufficient precise reference data for corroboration. The goal of the present study is to quantitatively and statistically assess the potential of Sentinel-1 C-band SAR for detecting mowing events in various agricultural grasslands, using a large and diverse reference data set collected in situ. Several mowing detection methods, based on SAR backscattering and interferometric coherence time series, were thoroughly evaluated. Results show that 54% of mowing events could be detected in hay meadows, based on coherence jumps. Grazing events were identified as a major confounding factor, as most false detections were made in pastures. Parcels with one mowing event in the summer were identified with the highest accuracy (71%). Overall, this study demonstrates that mowing events can be detected through Sentinel-1 coherence. However, the performances could probably be further enhanced by discriminating pastures beforehand and combining Sentinel-1 and Sentinel-2 data for mowing detection.

Ecosystem accounting is based on the definition of the extent and the status of an ecosystem. Land cover maps extents are representative of several ecosystems and can therefore be used to support ecosystem accounting if reliable change information is available. The dataset described in this paper aims to provide land cover information (13 classes) for biodiversity monitoring, which has driven two key features. On one hand, open areas were described in more details (5 classes) than in the other maps available in the study area in order to increase their relevance for biodiversity models. On the other hand, monitoring means that the time series must consist of comparable layers. The time series integrate information from existing high quality land cover maps that are not fully comparable, as well as thematic products (crop type, road network and forest type) and remote sensing data (25 cm orthophotos, 0.8 pts/m² LIDAR and Sentinel-1&2 data). Because of the high spatial resolution of the data and the fragmented landscape, boundary errors could cause a large proportion of false change detection if the maps are classified independently. Buildings and forests were therefore consolidated across time in order to build a time series where these changes can be trusted. Based on an independent validation, the overall accuracy was 93.1%, 92.6%, 94.8% and 93.9% +/−1.3% for the years 2006, 2015, 2018 and 2019, respectively. The specific assessment of forest patch change highlighted a 98% +/−2.7% user accuracy across the 4 years and 85% of forest cut detection. This time series will be completed and further consolidated with other dates using the same protocol and legend.

Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as observed in the Walloon region (Belgium), very high spatial resolution is required to depict all the infrastructures, buildings and most of the structural elements of the semi-natural landscapes (like hedges and small water bodies). Because of the resolution, the vertical dimension needs explicit handling to avoid discontinuities incompatible with many applications. For example, how to map a river flowing under a bridge? The particularity of our data is to provide a two-digit land cover code to label all the overlapping items. The identification of all the overlaps resulted from the combination of remote sensing image analysis and decision rules involving ancillary data. The final product is therefore semantically precise and accurate in terms of land cover description thanks to the addition of 24 classes on top of the 11 pure land cover classes. The quality of the map has been assessed using a state-of-the-art validation scheme. Its overall accuracy is as high as 91.5%, with an average producer’s accuracy of 86% and an average user’s accuracy of 91%.

Grassland management practices and intensities are key factors influencing the quality and balance of their provisioning and regulating ecosystem services. Most European temperate grasslands are exploited through mowing, grazing, or a combination of both in relatively small management units. Grazing and mowing can however not be considered equivalent because the first is gradual and selective and the second is not. In this study, the aim is to differentiate grasslands in terms of management practices and to retrieve homogeneous management units. Grasslands are classified hierarchically, first through a pixel-based supervised classification to differentiate grazed pastures from mown hay meadows and then through an object-based mowing detection method to retrieve the timing and frequency of mowing events. A large field dataset was used to calibrate and validate the method. For the classification, 18 different input feature combinations derived from Sentinel-1 and Sentinel-2 were tested for a random forest classifier through a cross-validation scheme. The best results were obtained based on the Leaf Area Index (LAI) times series with cubic spline interpolation. The classification differentiated pastures (grazed) from hay meadows (mown) with an overall accuracy of 88%. The classification is then combined with the existing parcel delineation and high-resolution ancillary data to retrieve the homogeneous management units, which are used for the object-based mowing detection based on the Sentinel-1 coherence and Sentinel-2 NDVI. The mowing detection performances were increased thanks to the grassland mask, the management unit delineation, and the exclusion of pastures, reaching a precision of 93% and a detection rate of 82%. This hierarchical grassland classification approach allowed to differentiate three types of grasslands, namely pastures, and meadows (including mixed practices) with an early first mowing event and with a late first mowing event, with an overall accuracy of 79%. The grasslands could be further differentiated by mowing frequency, resulting in five final classes.

Adapting grassland management to meet the rising demand in food production, while sustaining their ecological value, is a challenge. This highlights the need to monitor grassland use intensity to support sustainable agricultural policies. The objectives of this study are to (1) retrieve grassland yield and nitrogen content from satellite imagery and (2) link these to management practices and pedo-climatic conditions. A stepwise linear regression performed best out of 7 different methods to retrieve grasslands dry matter yield (DM, t/ha), nitrogen concentration (NC, g/kg) and canopy nitrogen content (CNC, kg/ha) from Sentinel-2 reflectances and indices. The models are calibrated using field measurements made during three growing seasons. They are validated on an independent field dataset with a normalized RMSE of 9.5%, 15.6%, and 6.7% for DM, NC, and CNC respectively. In addition, the nitrogen nutrition index (NNI) is computed from the estimated DM and NC to evaluate grasslands nitrogen nutrition status. The retrieval models are then combined with grassland classification and mowing detection methods developed in previous studies to evaluate grasslands vegetation status in spring and harvested forage yield at regional scale. These large-scale experiments consistently showed the impacts of management practices and pedo-climatic conditions on grassland yield and nitrogen content.