Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth observation (EO) data. This letter introduces an open dataset for training and benchmarking machine learning methods to delineate agricultural field boundaries in polygon format. The large-scale dataset consists of 439 001 field polygons divided into 62 tiles of approximately 5× 5 km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized from satellite images, following a rigorous multistep quality control process and topological consistency checks. Multitemporal composites of Sentinel-2 (S2) images are provided to ensure cloud-free data. We conducted an experimental analysis testing a state-of-the-art deep learning (DL) workflow based on fully convolutional networks (FCNs), contour closing, and polygonization. We anticipate that this large-scale dataset will enable researchers to further enhance the delineation of agricultural fields in smallholder farms and to support the achievement of the Sustainable Development Goals (SDGs). The dataset can be downloaded from https://doi.org/10.17026/dans-xy6-ngg6.

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The IR absorbance of adsorbed CO is a suitable probe for evaluating the extent of oxidation of small supported copper particles. The residual absorbance of the main band at about 2120 cm-1 after evacuation of the gas phase and the presence of additional CO absorbance bands indicate the extent of oxidation. Exposure of a reduced catalyst to NO brings about the oxidation of the copper particles. With increasing extent of oxidation the intensity of bands due to adsorbed NO and to adsorbed nitrito and nitrato like species increases. Adsorbed NO already desorbs upon room-temperature evacuation, the nitrito and nitrato like species decompose at temperatures between 423 and 473 K. The formation of N2O (during exposure of NO to a catalyst which is not completely oxidized) and the formation of isocyanate (during subsequent exposure to CO) indicate the removal of weakly adsorbed nitrogen, originating from dissociated NO, from the surface of the copper particles. Upon simultaneous exposure of the reduced catalyst to equimolar amounts of NO and CO at room temperature, a surface reaction proceeds: the copper particles are not severely oxidized and the formation of CO2, N2O and isocyanate is observed. The agreement of the results from IR spectroscopy with those from XPS and anticipated from single-crystal results is good.@en