Search for: All records

Abstract. Environmental science is increasingly reliant on remotely sensedobservations of the Earth's surface and atmosphere. Observations frompolar-orbiting satellites have long supported investigations on land coverchange, ecosystem productivity, hydrology, climate, the impacts ofdisturbance, and more and are critical for extrapolating (upscaling)ground-based measurements to larger areas. However, the limited temporalfrequency at which polar-orbiting satellites observe the Earth limits ourunderstanding of rapidly evolving ecosystem processes, especially in areaswith frequent cloud cover. Geostationary satellites have observed theEarth's surface and atmosphere at high temporal frequency for decades, andtheir imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 min. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solar radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential to expand ABI observations to estimate vegetation greenness, moisture, and productivity at a high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarseresolution of ABI observations through multisensor fusion to resolvelandscape heterogeneity and to leverage observations from ABI to study thecarbon cycle and ecosystem function at unprecedented temporal frequency. 

Determining whether a cloud is composed of spherical water droplets of polyhedral ice crystals (i.e., the thermodynamic phase) from a passive remote sensing instrument is very difficult because of the immense variety of clouds and their highly variable microphysical properties. To improve upon the popular method of radiance ratios, we enhance the classification ability by adding polarimetric sensitivity to an instrument that measures radiance in three short-wave infrared bands. Clouds typically induce a polarization signature on the order of a percent, and so sensitive optics are required for accurate classification. In this paper, we present the combination of spectral and polarimetric sensitivity for cloud thermodynamic phase classification using data from a ground-based, 3-band, short-wave infrared polarimeter and cloud-phase validation from a dual-polarization lidar. We then analyze the classification quality of various methods using surface-fitting techniques to show that the addition of polarimetry is advantageous for cloud classification.