A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of

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Rice husk (RH) biomass is a massive byproduct from rice milling. Applications of RHs have been very limited. Therefore, RHs are often considered as a biowaste. RHs are mainly composed of lignocellulose (ca. 72–85 wt %) and silica (ca. 15–28 wt %). The majority of previous explorations focused on the preparation of silica or other silicon based materials from RHs, while the lignocellulose in RHs was usually burnt and thus wasted. Herein, an approach for comprehensive utilization of RHs has been developed to obtain both lignocellulose and high quality porous silica nanoparticles from RHs. Most of the lignocellulose in RHs was first extracted by dissolving in ionic liquids. The dissolved lignocellulose was subsequently separated and collected. The remaining RH residue after extraction that contains a high concentration of silica was thermally treated to synthesize amorphous porous silica nanoparticles with a high purity and surface area. It was also found that, during the extraction of lignocellulose using ionic liquids, some metal cations (e.g., K+) that generate negative effect for the synthesis of silica can be removed simultaneously, which generates a synergy for this comprehensive approach to make full use of RH biomass. The implication of the present findings is discussed.