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The Visible/Infrared Imager/Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite was launched in 2011, in part to provide continuity with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard National Aeronautics and Space Administration’s (NASA) Terra and Aqua remote-sensing satellites. The VIIRS will eventually replace Aqua MODIS for both land science and applications and add to the coarse-resolution, long-term data record. It is, therefore, important to provide the user community with an assessment of the consistency of equivalent products from the two sensors. For this study, we do this in the context of example agricultural monitoring applications. Surface reflectance that is routinely delivered within the M{O,Y}D09 and VNP09 series of products provides critical input for generating downstream products. Given the range of applications utilizing the normalized difference vegetation index (NDVI) generated from the M{O,Y}D09 and VNP09 products and the inherent differences between MODIS and VIIRS sensors in calibration, spatial sampling, and spectral bands, the main objective of this study is to quantify uncertainties associated with transitioning from using MODIS to VIIRS-based NDVIs. In particular, we compare NDVIs derived from two sets of Level 3 MYD09 and VNP09 products with various spatial-temporal characteristics, namely 8-day composites at 500 m spatial resolution and daily climate modelling grid images at 0.05° spatial resolution. Spectral adjustment of VIIRS I1 (red) and I2 (near infra-red – NIR) bands to match MODIS/Aqua b1 (red) and b2 (NIR) bands is performed to remove a bias between MODIS and VIIRS-based red, NIR, and NDVI estimates. Overall, red reflectance, NIR reflectance, and NDVI uncertainties were 0.014, 0.029, and 0.056, respectively, for the 500 m product and 0.013, 0.016, and 0.032 for the 0.05° product. The study shows that MODIS and VIIRS NDVI data can be used interchangeably for applications with an uncertainty of less than 0.02–0.05, depending on the scale of spatial aggregation, which is typically the uncertainty of the individual data sets. [ABSTRACT FROM PUBLISHER]

Reducing the Number of Rabbits in the Draize Eye Irritancy Test: A Statistical Analysis of 155 Studies Conducted over 6 Years. TALSMA, D. M, LEACH, C. L., HATOUM, N. S., GIBBONS, R. D., ROGER, J.-C, AND GARVIN, P. J. (1988). . 10, 146–153. The Draize eye irritancy test in rabbits has been the focus of recent efforts to reduce the use of live animals in toxicity testing. A suitable alternative is not yet available; therefore, we studied the adequacy of reducing the number of rabbits used per test. Data generated from 6-rabbit eye irritation tests of 155 various materials were used to determine the ability of irritation scores from all possible combinations of 5-, 4-, 3-, or 2-rabbit subsets to predict the Draize score derived from six rabbits. There are 930, 2325, 3100, and 2325 possible combinations of 155 studies for the 5-, 4-, 3-, and 2-rabbit subsets, respectively. We classify materials using a four-level adjectival rating system based on (among other factors) the Draize score. Comparisons indicated that 5-, 4-, 3-, and 2-rabbit scores were in 98,96,94, and 91% agreement, respectively, with the classification assigned on the basis of the 6-rabbit score. The correlation coefficients for randomly selected subsets of 5-, 4-, 3-, and 2-rabbit scores versus the Draize score for six rabbits were 0.998,0.996, 0.992, and 0.984, respectively. This study confirms the findings of an earlier report by De Sousa et al (1984), and indicates that a high level of accuracy can be obtained with reduced numbers of rabbits per test. [ABSTRACT FROM PUBLISHER]

This study aims at analyzing sub-pixel misregistration between multi-spectral images acquired by the Multi-Spectral Instrument (MSI) aboard Sentinel-2A remote sensing satellite, and exploring its potential for moving target and cloud detection. By virtue of its hardware design, MSI's detectors exhibit a parallax angle that leads to sub-pixel shifts that are corrected with special pre-processing routines. However, these routines do not correct shifts for moving and/or high altitude objects. In this letter, we apply a phase correlation approach to detect sub-pixel shifts between B2 (blue), B3 (green) and B4 (red) Sentinel-2A/MSI images. We show that shifts of more than 1.1 pixels can be observed for moving targets, such as airplanes and clouds, and can be used for cloud detection. We demonstrate that the proposed approach can detect clouds that are not identified in the built-in cloud mask provided within the Sentinel-2A Level-1C (L1C) product.

Timely and accurate information on crop yield is critical to many applications within agriculture monitoring. Thanks to its coverage and temporal resolution, coarse spatial resolution satellite imagery has always been a source of valuable information for yield forecasting and assessment at national and regional scales. With availability of free images acquired by Landsat-8 and Sentinel-2 remote sensing satellites, it becomes possible to enable temporal resolution of an image every 3-5 days, and therefore, to develop next generation agriculture products at higher spatial resolution (30 m). This paper explores the combined use of Landsat-8 and Sentinel-2A for winter crop mapping and winter wheat assessment at regional scale. For the former, we adapt a previously developed approach for Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution that allows automatic mapping of winter crops taking into account knowledge on crop calendar and without ground truth data. For the latter, we use a generalized winter wheat yield model that is based on NDVI-peak estimation and MODIS data, and further downscaled to be applicable at 30 m resolution. We show that integration of Landsat-8 and Sentinel-2A has a positive impact both for winter crop mapping and winter wheat yield assessment. In particular, the error of winter wheat yield estimates can be reduced up to 1.8 times comparing to the single satellite usage.