An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series

Abstract

Cropland mapping relies heavily on field data for algorithm calibration, making it, in many cases, applicable only at the field campaign scale. While the recently launched Sentinel-2 satellite will be able to deliver time series over large regions, it will not really be compatible with the current mapping approach or the available in situ data. This research introduces a generic methodology for mapping annual cropland along the season at high spatial resolution with the use of globally available baseline land cover and no need for field data. The methodology is based on cropland-specific temporal features, which are able to cope with the diversity of agricultural systems, prior information from which mislabeled pixels have been removed and a cost-effective classifier. Thanks to the JECAM network, eight sites across the world were selected for global cropland mapping benchmarking. Accurate cropland maps were produced at the end of the season, showing an overall accuracy Remote Sens. 2015, 7 13209 of more than 85%. Early cropland maps were also obtained at three-month intervals after the beginning of the growing season, and these showed reasonable accuracy at the three-month stage (>70% overall accuracy) and progressive improvement along the season. The trimming-based method was found to be key for using spatially coarse baseline land cover information and, thus, avoiding costly field campaigns for prior information retrieval. The accuracy and timeliness of the proposed approach shows that it has substantial potential for operational agriculture monitoring programs.

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

“…Sentinel-2 data become available for Africa and Europe in the middle of 2015 [60] and its capabilities to map crop types and tree species have been assessed [34,[61][62][63][64] and its 10-m and 20-m data provides much more details than Landsat 30-m data. The easy and simultaneous access to entire archive of Sentinel-2 and Landsat-8 products through GEE, as well as the fast and scalable computational tools that it offers, makes GEE an essential and powerful tool for this project.…”

“…Alternate procedures have consisted of unsupervised approaches [13,[21][22][23] and supervised methods in small regional areas with different classifiers including decision trees [12], Support Vector Machine [24,25], Random Forest [12], neural networks [26][27][28], data mining [29], and hybrid methods [30]. In order to improve classification results, the following issues were investigated in literature which include the selection of the dates [31], temporal windows derivation [32], input features selection [33] and automated classification methods [34]. Object-based approaches of crop identification have also been explored [35].…”

Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2

“…These ambitions could only be ensured since the TDS is representative of the diverse agricultural practices and climate types over the world and is made of both EO and in situ data acquired in enough quantity during the same season. The site selection and benchmarking approach adopted in this project has allowed algorithms and methods to be compared across sites [23][24][25]. So far, very few examples of this kind of across-sites study exist.…”

“…Specific attention was paid to methodologies that will benefit most from the spatial, temporal and spectral properties of Sentinel-2. The studies carried out for the crop mask and crop type products are described in detail in three papers of this special issue [23][24][25]. Project reports also document these analyses, including the ones related to the cloud-free composite and the vegetation status.…”

Multi-Temporal Land Cover Classification With Long Short-Term Memory Neural Networks

“…Thus, classical land-cover classification approaches have concentrated on multi-or hyperspectral sensors at one single observation time. Matton et al (2015) propose a generic methodology for global cropland mapping and statistical temporal features derived from LANDSAT-7 and SPOT images for K-means and maximum likelihood classifiers on eight test regions on the entire world. Following this approach, Valero et al (2016) use SEN-TINEL 2A images to create a binary cropland/non-cropland mask by using randomized decision forests (RDF) classifiers on statistical temporal features extracted from spectro-temporal profiles.…”