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 RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

The first NOAA/NASA Join Polar Satellite System (JPSS-1) satellite was successfully launched on November 18, 2017, becoming NOAA-20. Instruments on-board NOAA-20 satellite include the Visible Infrared Imaging Radiometer Suite (VIIRS). This instrument is the second build of VIIRS, with the first flight instrument on-board NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite operating since October 2011. The purpose of these VIIRS instruments is to continue the long-term measurements of biogeophysical variables for multiple applications including weather forecasting, rapid response and climate research. The geometric performance of VIIRS is essential to retrieving accurate biogeophysical variables. This paper describes the early on-orbit geometric performance of the JPSS-1/NOAA-20 VIIRS. It first discusses the on-orbit orbit and attitude performance, a key input needed for accurate geolocation. It then discusses the on-orbit geometric characterization and calibration of VIIRS geometry and an initial assessment of the geometric accuracy. This section includes a discussion of an improvement in the geometric model that corrects small geometrical artifacts that appear in the along-scan direction. Finally, this paper discusses on-orbit measurements of the focal length and the impact of this on the scan-to-scan underlap/overlap.

A new spectral-spatial method for hyperspectral data classification is proposed. For a given hyperspectral image, probabilistic pixelwise classification is first applied. Then, hierarchical step-wise optimization algorithm is performed, by iteratively merging neighboring regions with the smallest Dissimilarity Criterion (DC) and recomputing class labels for new regions. The DC is computed by comparing region mean vectors, class labels and a number of pixels in the two regions under consideration. The algorithm is converged when all the pixels get involved in the region merging procedure. Experimental results are presented on two hyperspectral remote sensing images acquired by the AVIRIS and ROSIS sensors. The proposed approach improves classification accuracies and provides maps with more homogeneous regions, when compared to previously proposed classification techniques.

The visible infrared imaging radiometer suite (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on October 28, 2011. The VIIRS instrument is a whiskbroom system with 22 spectral and thermal bands split between 16 moderate resolution bands (M-bands), 5 imagery resolution bands (I-bands), and a day-night band. In this study, we measure the along-scan and along-track band-to-band registration between the I-bands and M-bands from on-orbit data. This measurement is performed by computing the normalized mutual information (NMI) between shifted image band pairs and finding the amount of shift required (if any) to produce the peak in NMI value. Subpixel accuracy is obtained by utilizing a bicubic interpolation. The product of the NMI peak slope and the NMI peak value is shown to be a better criterion for evaluating the quality of the NMI result than just the NMI peak value. Registration shifts are found to be similar to prelaunch measurements and stable (within measurement error) over the instrument's first four years in an orbit.

We have developed an approach for the measurement of the effective focal length (EFL) and band-to-band registration (BBR) of selected spectral bands of satellite-borne whiskbroom imaging sensors from on-orbit data. Our approach is based on simulating the coarser spatial resolution whiskbroom sensor data with finer spatial resolution Landsat 7 Enhanced Thematic Mapper Plus or Landsat 8 Operational Land Imager data using the geolocation (Earth location) information from each sensor and computing the correlation between the simulated and original data. For each scan of a selected spectral band of the whiskbroom dataset, various subsets of the data are examined to find the subset with the highest spatial correlation between the original and simulated data using the nominal geolocation information. Then, for this best subset, the focal length value and the spatial shift are varied to find the values that produce the highest spatial correlation between the original and simulated data. This best focal length value is taken to be the measured instrument EFL, and the best spatial shift is taken to be the registration of the whiskbroom data relative to the Landsat data, from which the BBR is inferred. Best results are obtained with cloud-free subsets with contrasting land features. This measurement is repeated over other scans with cloud-free subsets. We demonstrate our approach with on-orbit data from the Aqua and Terra MODIS instruments and SNPP and J1 VIIRS instruments.