REMOTE SENSING

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ISciences develops and uses remote sensing techniques to improve understanding of the Earth. We develop models of optical properties, algorithms for extracting information from remotely sensed data, and conduct studies to solve specific user problems.

In response to an article by Lester Brown titled “Who Will Feed China?” that argued China had insufficient agricultural land to feed its population, ISciences undertook a study of China’s arable land using multi-resolution satellite data. We developed a novel multi-stage sampling technique that allowed high-confidence assessment of all of China. The results of this study, which showed that China had significantly more arable land than was publicly reported, altered the entire dialog on this subject. The multi-stage sampling technique is embedded in a commercial remote sensing software package.

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ISciences developed a physics-based model for accurately predicting the polarimetric Bi-directional Reflectance Distribution Function (BRDF) for arbitrary wavelength and source /sensor geometry, using a limited number of BRDF measurements to characterize the material of interest. Use of this model allows correction of collected data for the effects of illumination or view angle. This is particularly useful if an overhead sensor is not looking straight down, such is often the case with wide-swath or aircraft-based sensors. The model supports collection planning and optimization, allowing assessment of the optimal time and geometry for collecting data to maximize detectability or identification.

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ISciences develops methods for reducing complex multi- or hyper-spectral datasets through invariant subspace transformations. These transformations reduce the dimensionality of the data, placing the data in a context that both has physical relatability and is independent of the specifics of the scene content. Examples include the Tasselled Cap transform - created for Landsat Thematic Mapper data by Ric Cicone (et al), now part of ISciences' team - that separates vegetation, soil, and water characteristics of the scene, and the Thermal Spiral which helps separate temperature and emissivity effects in Long-Wave Infrared (LWIR) data.

For more information contact info@isciences.com.

Many analyses reported in ISciences-authored blog posts are based on data generated by the ISciences Water Security Indicator Model (WSIM). Other sources, if used, are referenced in footnotes accompanying individual posts. WSIM is a validated capability that produces monthly reports on current and forecast global freshwater surpluses and deficits with lead times of 1-9 months at 0.25°x0.25° resolution. This capability has been in continuous operation since April 2011 and has proven to provide reliable forecasts of emerging water security concerns in that time-frame. WSIM has the ability to assess the impacts of water anomalies on people, agriculture, and electricity generation. Detailed data, customized visualizations, and reports are available for purchase.

For more information contact info@isciences.com.

Copyright 2022 ISCIENCES, L.L.C. Global Water Monitor & Forecast Watch List is the property of ISCIENCES, L.L.C. It is protected by U.S. copyright laws and may not be reproduced in any way without the written permission of ISCIENCES, L.L.C. The user assumes the entire risk related to its use of information on ISCIENCES, L.L.C. Web pages, including information derived from Water Security Indicators Model (WSIM). This information may include forecasts, projections and other predictive statements that represent ISCIENCES, L.L.C.’s assumptions and expectations in light of currently available information and using the highest professional standards. Actual results may differ from those projected. Consequently, no guarantee is presented or implied as to the accuracy of specific forecasts, projections or predictive statements contained herein. ISCIENCES, L.L.C. provides such information "as is," and disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose. In no event will ISCIENCES, L.L.C. be liable to you or to any third party for any direct, indirect, incidental, consequential, special or exemplary damages or lost profit resulting from any use or misuse of this data.