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The Earth Observing One (EO-1) satellite has completed 16 years of Earth observations in early 2017. What started as a technology mission to test various new advancements turned into a science and application mission that extended many years beyond the satellite’s planned life expectancy. EO-1’s primary instruments are spectral imagers: Hyperion, the only civilian full spectrum spectrometer (430–2400 nm) in orbit, and the Advanced Land Imager (ALI), the prototype for Landsat-8’s pushbroom imaging technology. Both Hyperion and ALI instruments have continued to perform well, but in February 2011, the satellite ran out of the fuel necessary to maintain orbit, which initiated a change in precession rate that led to increasingly earlier equatorial crossing times during its last five years. The change from EO-1’s original orbit, when it was formation flying with Landsat-7 at a 10:01 a.m. equatorial overpass time, to earlier overpass times results in image acquisitions with increasing solar zenith angles (SZAs). This study takes several approaches to characterize data quality as SZAs increased. The results show that for both EO-1 sensors, atmospherically corrected reflectance products, are within 5 to 10% of mean pre-drift products. No marked trend in decreasing quality in ALI or Hyperion is apparent through 2016, and these data remain a high quality resource through the end of the mission. [ABSTRACT FROM AUTHOR]

The Earth Observing One (EO-1) satellite was launched in November 2000 as a technology demonstration mission with an estimated 1-year lifespan. It has now successfully completed 12 years of high spatial resolution imaging operations from low Earth orbit. EO-1's two main instruments, Hyperion and the Advanced Land Imager (ALI), have both served as prototypes for new generation satellite missions. ALI, an innovative multispectral instrument, is the forerunner of the Operational Land Imager (OLI) onboard the Landsat Data Continuity Mission's (LDCM) Landsat-8 satellite, recently launched in Feb. 2013. Hyperion, a hyperspectral instrument, serves as the heritage orbital spectrometer for future global platforms, including the proposed NASA Hyperspectral Infrared Imager (HyspIRI) and the forthcoming (in 2017) German satellite, EnMAP. [ABSTRACT FROM PUBLISHER]

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy \sim 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350–2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using \sim 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was \sim 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. [ABSTRACT FROM PUBLISHER]

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends and their stability consistently is within 2.5–5 percent throughout most of the spectral range spanning the 12+ year data record. Using three vegetated sites instrumented with eddy covariance towers, the Hyperion reflectance time series were evaluated for their ability to determine important variables of ecosystem function. A number of narrowband and derivative vegetation indices (VI) closely described the seasonal profiles in vegetation function and ecosystem carbon exchange (e.g., net and gross ecosystem productivity) in three very different ecosystems, including a hardwood forest and tallgrass prairie in North America, and a Miombo woodland in Africa. Our results demonstrate the potential for scaling the carbon flux tower measurements to local and regional landscape levels. The VIs with stronger relationships to the CO2 parameters were derived using continuous reflectance spectra and included wavelengths associated with chlorophyll content and/or chlorophyll fluorescence. Since these indices cannot be calculated from broadband multispectral instrument data, the opportunity to exploit these spectrometer-based VIs in the future will depend on the launch of satellites such as EnMAP and HyspIRI. This study highlights the practical utility of space-borne spectrometers for characterization of the spectral stability and uniformity of the calibration sites in support of sensor cross-comparisons, and demonstrates the potential of narrowband VIs to track and spatially extend ecosystem functional status as well as carbon processes measured at flux towers. [ABSTRACT FROM PUBLISHER]

Satellite observations for the Arctic and boreal region may contain information of vegetation, soil, snow, snowmelt, and/or other surface water bodies. We investigated the impacts of vegetation, soil, snow and surface water on empirical vegetation/snow indices on a tundra ecosystem area located around Utqiaġvik (formerly Barrow) of Alaska with the Moderate Resolution Imaging Spectrometer (MODIS) images in 2001–2014. Empirical vegetation indices, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), the index of near infrared of vegetation (NIR v), and modified EVI (EVI2), have been used to monitor vegetation. Normalized difference snow index (NDSI) has been widely applied to monitor snow. The vegetation cover fraction (VGCF), the soil cover fraction (SOILCF), the snow cover fraction (SNOWCF), the surface water body cover fraction (WaterBodyCF), the fractional absorption of photosynthetically active radiation (PAR) by vegetation chlorophyll (fAPAR chl), the fractional absorption of PAR by non-chlorophyll components of the vegetation (fAPAR non-chl), and the fractional absorption of PAR by the entire canopy (fAPAR canopy) are retrieved with the MODIS images and a coupled Leaf-Vegetation-Soil-Snow-Surface water body radiative transfer model, LVS3. The vegetation indices (NDVI, EVI, EVI2 and NIR v) differ from VGCF, fAPAR chl , fAPAR non-chl , and fAPAR canopy. In addition to vegetation, we find that soil, snow and surface water also have impacts on vegetation indices NDVI, EVI (EVI2), and NIR v. Presence of snow makes lower the observed values of NDVI, EVI2 and NIR v. After snowmelt is gone, the vegetation indices (NDVI, EVI, EVI2 and NIR v) linearly decrease with SOILCF and WaterBodyCF, and WaterBodyCF has stronger impacts on these vegetation indices than SOILCF. The relationship between EVI and snow is complicated. NDSI non-linearly increases with SNOWCF, but linearly increases with sum of SNOWCF and WaterBodyCF (sum = 0.5893 × NDSI +0.4342, R2 = 0.976). NDSI linearly decreases with VGCF, and the relationship between NDSI and SOILCF is complex. Retrievals of VGCF, fAPAR chl , fAPAR non-chl and fAPAR canopy with the LVS3 model provide alternatives for vegetation monitoring and ecological modeling. • Presence of snow nonlinearly decreases vegetation indices NDVI, EVI2 and NIR v. • NDVI, EVI2 and NIR v linearly increase with VGCF. • NDSI linearly decreases with VGCF. • NDSI non-linearly increases with snow cover fraction (SNOWCF). • NDSI linearly increases with sum of SNOWCF and WaterBodyCF (R2 = 0.976). [ABSTRACT FROM AUTHOR]