Semi-natural grasslands are perennial ecosystems and an important part of agricultural landscapes that are threatened by urbanization and agricultural intensification. However, implementing national grassland conservation policies remains challenging because their inventory, based on short-term observation, rarely discriminate semi-natural permanent from temporary grasslands. This study aims to map grassland frequency at a national scale over a long period using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m satellite time-series. A three-step method was applied to the entire area of metropolitan France (543,940 km²). First, land-use and land-cover maps—including grasslands—were produced for each year from 2006–2017 using the random forest classification of MOD13Q1 and MYD13Q1 products, which were calibrated and validated using field observations. Second, grassland frequency from 2006–2017 was calculated by combining the 12 annual maps. Third, sub-pixel analysis was performed using a reference layer with 20 m spatial resolution to quantify percentages of land-use and land-cover classes within MODIS pixels classified as grassland. Results indicate that grasslands were accurately modeled from 2006–2017 (F1-score 0.89–0.93). Nonetheless, modeling accuracy varied among biogeographical regions, with F1-score values that were very high for Continental (0.94 ± 0.01) and Atlantic (0.90 ± 0.02) regions, high for Alpine regions (0.86 ± 0.04) but moderate for Mediterranean regions (0.62 ± 0.10). The grassland frequency map for 2006–2017 at 250 m spatial resolution provides an unprecedented view of stable grassland patterns in agricultural areas compared to existing national and European GIS layers. Sub-pixel analysis showed that areas modeled as grasslands corresponded to grassland-dominant areas (60%–94%). This unique long-term and national monitoring of grasslands generates new opportunities for semi-natural grassland inventorying and agro-ecological management.
Decadal time-series derived from satellite observations are useful for discriminating crops and identifying crop succession at national and regional scales. However, use of these data for crop modeling is challenged by the presence of mixed pixels due to the coarse spatial resolution of these data, which influences model accuracy, and the scarcity of field data over the decadal period necessary to calibrate and validate the model. For this data article, cloud-free satellite “Vegetation Indices 16-Day Global 250 m” Terra (MOD13Q1) and Aqua (MYD13Q1) products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as the Land Parcel Information System (LPIS) vector field data, were collected throughout France for the 12-year period from 2006 to the end of 2017. A GIS workflow was developed using R software to combine the MOD13Q1 and MYD13Q1 products, and then to select “pure” MODIS pixels located within single-crop parcels over the entire period. As a result, a dataset for 21,129 reference plots (corresponding to “pure” pixels) was generated that contained a spectral time-series (red band, near-infrared band, Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI)) and the associated annual crop type with an 8-day time step over the period. This dataset can be used to develop new classification methods based on time-series analysis using deep learning, and to monitor and predict crop succession.
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