Since 1970, increased plastic use and waste mismanagement have contributed to environmental pollution leading to the proliferation of microplastics and ocean contamination. Understanding the plastic presence on-land is crucial as most plastic pollution originates from land-based sources. From its vantage point-of-view on the International Space Station, NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer can measure surface reflectance spectra across a wavelength range of 380 nm to 2500 nm. We present a study using this instrument to detect distinctive absorption features of High-density polyethylene (HDPE) and Polyvinyl Chloride (PVC) plastics in the shortwave infrared. By applying a column-wise adaptive matched filter and using a library of plastic reflectance spectra, we successfully identified signatures of these two types of plastics across multiple continents between +/- 52 degrees, primarily related to agricultural practices. Our study underscores the importance of monitoring land-based sources of plastic pollution to guide future mitigation strategies.

Snow and ice melt processes are a key in Earth’s energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth’s ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 μg/g_ice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.