Starting in 2023, the Carbon Mapper public-private partnership will launch two imaging spectrometers into low earth orbit as the first demonstration satellites for a larger, emerging constellation. This mission is a critical collaboration between several partners including Planet, Carbon Mapper, Arizona State University, NASA’s Jet Propulsion Laboratory, the University of Arizona, the High Tide Foundation, California Air Resources Board, and the Rocky Mountain Institute. This hyperspectral constellation will complement Planet’s existing high-spatial and high-temporal mission lines and increase the ability to measure and monitor the impacts of climate change on our planet and tackle dynamic, wide-ranging and complex challenges such as sustainability. Each satellite is equipped with a 400 - 2500 nm hyperspectral imaging system capable of addressing a wide range of applications. The core mission for the Carbon Mapper Mission is to monitor climate risks (methane, CO2) but it has capacity to collect data for other sectors such as Defense, Intelligence, Agriculture, Mining, and others. The Carbon Mapper Mission is a tasked system and is designed to be responsive to dynamic events where analysis in a matter of days or hours may be important. In this paper, we provide an overview of the anticipated technical capabilities of the system and discuss applications for the Defense and Intelligence communities. We will also outline how the Carbon Mapper Mission can work in conjunction with the rest of the Planet constellations to enable unique fusion products.
It is well known that disturbed grass covered surfaces show variability with view and illumination conditions. A good example is a grass field in a soccer stadium that shows stripes indicating in which direction the grass was mowed. These spatial variations are due to a complex interplay of spectral characteristics of grass blades, density, their length and orientations. Viewing a grass surface from nadir or near horizontal directions results in observing different components. Views from a vertical direction show more variations due to reflections from the randomly oriented grass blades and their shadows. Views from near horizontal show a mixture of reflected and transmitted light from grass blades. An experiment was performed on a mowed grass surface which had paths of simulated heavy foot traffic laid down in different directions. High spatial resolution hyperspectral data cubes were taken by an imaging spectrometer covering the visible through near infrared over a period of time covering several hours. Ground truth grass reflectance spectra with a hand held spectrometer were obtained of undisturbed and disturbed areas. Close range images were taken of selected areas with a hand held camera which were then used to reconstruct the 3D geometry of the grass using structure-from-motion algorithms. Computer graphics rendering using raytracing of reconstructed and procedurally created grass surfaces were used to compute BRDF models. In this paper, we discuss differences between observed and simulated spectral and spatial variability. Based on the measurements and/or simulations, we derive simple spectral index methods to detect spatial disturbances and apply scattering models.
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