Jeffrey A. Pedelty

An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series

Ecological and climate models require high-quality consistent biophysical parameters as inputs and validation sources. NASAs moderate resolution imaging spectroradiometer (MODIS) biophysical products provide such data and have been used to improve our understanding of climate and ecosystem changes. However, the MODIS time series contains occasional lower quality data, gaps from persistent clouds, cloud contamination, and other gaps. Many modeling efforts, such as those used in the North American Carbon Program, that use MODIS data as inputs require gap-free data. This letter presents the algorithm used within the MODIS production facility to produce temporally smoothed and spatially continuous biophysical data for such modeling applications. We demonstrate the algorithm with an example from the MODIS-leaf-area-index (LAI) product. Results show that the smoothed LAI agrees with high-quality MODIS LAI very well. Higher R-squares and better linear relationships have been observed when high-quality retrieval in each individual tile reaches 40% or more. These smoothed products show similar data quality to MODIS high-quality data and, therefore, can be substituted for low-quality retrievals or data gaps.

An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data

An enhanced TIMESAT algorithm was developed for retrieving vegetation phenology metrics from 250 m and 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indexes (VI) over North America. MODIS VI data were pre-processed using snow-cover and land surface temperature data, and temporally smoothed with the enhanced TIMESAT algorithm. An objective third derivative test was applied to define key phenology dates and retrieve a set of phenology metrics. This algorithm has been applied to two MODIS VIs: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In this paper, we describe the algorithm and use EVI as an example to compare three sets of TIMESAT algorithm/MODIS VI combinations: (a) original TIMESAT algorithm with original MODIS VI, (b) original TIMESAT algorithm with pre-processed MODIS VI, and (c) enhanced TIMESAT and pre-processed MODIS VI. All retrievals were compared with ground phenology observations, some made available through the National Phenology Network. Our results show that for MODIS data in middle to high latitude regions, snow and land surface temperature information is critical in retrieving phenology metrics from satellite observations. The results also show that the enhanced TIMESAT algorithm can better accommodate growing season start and end dates that vary significantly from year to year. The TIMESAT algorithm improvements contribute to more spatial coverage and more accurate retrievals of the phenology metrics. Among three sets of TIMESAT/MODIS VI combinations, the start of the growing season metric predicted by the enhanced TIMESAT algorithm using pre-processed MODIS VIs has the best associations with ground observed vegetation greenup dates.

A tamarisk habitat suitability map for the continental United States

This paper presents a national-scale map of habitat suitability for tamarisk (Tamarix spp, salt cedar), a high-priority invasive species. We successfully integrate satellite data and tens of thousands of field sampling points through logistic regression modeling to create a habitat suitability map that is 90% accurate. This interagency effort uses field data collected and coordinated through the US Geological Survey and nationwide environmental data layers derived from NASAs MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrate the use of the map by ranking the 48 continental US states (and the District of Columbia) based on their absolute, as well as proportional, areas of “highly likely” and “moderately likely” habitat for Tamarix. The interagency effort and modeling approach presented here could be used to map other harmful species, in the US and globally.

Generating a long-term land data record from the AVHRR and MODIS Instruments

The goal of NASAs land long term data record (LTDR) project is to produce a consistent long term data set from the AVHRR and MODIS instruments for land climate studies. The project will create daily surface reflectance and normalized difference vegetation index (NDVI) products at a resolution of 0.05deg, which is identical to the climate modeling grid (CMG) used for MODIS products from EOS Terra and Aqua. Higher order products such as burned area, land surface temperature, albedo, bidirectional reflectance distribution function (BRDF) correction, leaf area index (LAI), and fraction of photosynthetically active radiation absorbed by vegetation (fPAR), will be created. The LTDR project will reprocess global area coverage (GAC) data from AVHRR sensors onboard NOAA satellites by applying the preprocessing improvements identified in the AVHRR Pathfinder II project and atmospheric and BRDF corrections used in MODIS processing. The preprocessing improvements include radiometric in-flight vicarious calibration for the visible and near infrared channels and inverse navigation to relate an Earth location to each sensor instantaneous field of view (IFOV). Atmospheric corrections for Rayleigh scattering, ozone, and water vapor are undertaken, with aerosol correction being implemented. The LTDR also produces a surface reflectance product for channel 3 (3.75 mum). Quality assessment (QA) is an integral part of the LTDR production system, which is monitoring temporal trends in the AVHRR products using time-series approaches developed for MODIS land product quality assessment. The land surface reflectance products have been evaluated at AERONET sites. The AVHRR data record from LTDR is also being compared to products from the PAL (pathfinder AVHRR land) and GIMMS (global inventory modeling and mapping studies) systems to assess the relative merits of this reprocessing vis-a-vis these existing data products. The LTDR products and associated information can be found at http://ltdr.nascom.nasa.gov/ltdr/ ltdr.html.

The Spectral Response of the Landsat-8 Operational Land Imager

Abstract: This paper discusses the pre-launch spectral characterization of the Operational Land Imager (OLI) at the component, assembly and instrument levels and relates results of those measurements to artifacts observed in the on-orbit imagery. It concludes that the types of artifacts observed and their magnitudes are consistent with the results of the pre-launch characterizations. The OLI in-band response was characterized both at the integrated instrument level for a sampling of detectors and by an analytical stack-up of component measurements. The out-of-band response was characterized using a combination of Focal Plane Module (FPM) level measurements and optical component level measurements due to better sensitivity. One of the challenges of a pushbroom design is to match the spectral responses for all detectors so that images can be flat-fielded regardless of the spectral nature of the targets in the imagery. Spectral variability can induce striping (detector-to-detector variation), banding (FPM-to-FPM variation) and other artifacts in the final data products. Analyses of the measured spectral response showed that the maximum discontinuity between FPMs due to spectral filter differences is 0.35% for selected targets for all bands except for Cirrus, where there is almost no signal. The average discontinuity between FPMs is 0.12% for the same targets. These results were expected and are in accordance with the OLI requirements. Pre-launch testing identified low levels (within requirements) of spectral crosstalk amongst the three HgCdTe (Cirrus, SWIR1 and SWIR2) bands of the OLI and on-orbit data confirms this crosstalk in the imagery. Further post-launch analyses and simulations revealed that the strongest crosstalk effect is from the SWIR1 band to the Cirrus band; about 0.2% of SWIR1 signal leaks into the Cirrus. Though the total crosstalk signal is only a few counts, it is evident in some scenes when the in-band cirrus signal is very weak. In moist cirrus-free atmospheres and over typical land surfaces, at least 30% of the cirrus signal was due to the SWIR1 band. In the SWIR1 and SWIR2 bands, crosstalk accounts for no more than 0.15% of the total signal.

Vegetation Phenology Metrics Derived from Temporally Smoothed and Gap-Filled MODIS Data

A set of phenology metrics have been estimated based on temporally smoothed and spatially gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VI) over the North American continent. The phenology algorithm has been applied to three MODIS vegetation indices: Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI). The spatial coverage of this phenology data is more complete than other remotely sensed data based phenology products. This is because of the quality of the smoothed and gap-filled MODIS data that was produced using an enhanced version of the TIMESAT algorithm. In this paper, we review the enhanced TIMESAT algorithm and related smoothing, gap filling and phenology algorithm, and compare the phenology metrics estimated from NDVI and EVI. Our results show differences in phenology inferred from EVI versus NDVI. The magnitude of the difference depends on the land cover type and could be used to improve the land cover classification accuracy.

Effect of three-dimensional canopy architecture on thermal infrared exitance

We present a theoretical study of the effects of three- dimensional canopy structure on directional thermal infrared exitance. A physics-based model employing steady-state energy budget formula- tions is used to compute scene element temperatures. Two approaches are then used to combine soil and vegetation contributions to the com- posite scene response. One method uses a plane-parallel abstraction of canopy architecture to estimate canopy view factors for weighting of soil and vegetation emission terms. The second approach employs computer graphics and rendering techniques to estimate 3-D canopy view factors and scene shadows. Both approaches are applied to a test agricultural scene and compared with available measurements. The models cor- rectly estimate hemispherically averaged thermal infrared exitance to within experimental error with root-mean-square errors of 15.3 W m 22 for the 1-D model and 12.5 W m 22 for the 3-D model. However, the 1-D model systematically underestimates exitance at high sun angles. Ex- plicit modeling of canopy 3-D row structure indicates potential directional anisotropy in brightness temperature of up to 14°C.

The operational land imager: spectral response and spectral uniformity

The Landsat Data Continuity Mission (LDCM) will carry the Operational Land Imager (OLI) as one of its payloads. This instrument is a derivative of the Advanced Land Imager (ALI), flown on Earth Observing-1 (EO1) though its mission is to continue the operational land imaging of the Landsat program. The OLI follows the highly successful Landsat-5 and Landsat-7 missions in continuing to populate an archive of earth images that dates back to 1972. The OLI has significant changes from the Landsat Thematic Mapper instruments, given that is the first pushbroom instrument in the program. However, it is intended to be a continuity mission, so the spatial coverage and spectral bands are similar. The suite of OLIs multispectral bands cover the same bandpasses but the panchromatic band is narrower than that of the ETM+. The OLI also has a shorter wavelength blue band for better resolution of coastal waters and a new band to aid in the detection of Cirrus clouds in the atmosphere. The thermal bands traditionally carried on the TM instruments have been moved to a separate instrument, also onboard the LDCM spacecraft. With the pushbroom design, each OLI multi-spectral band consists of nearly 7000 detectors. The OLI underwent prelaunch to verify a host of requirements on its spectral performance, where was characterized at three different points during development. This paper will cover the results of the tests that attempt to validate the spectral uniformity requirements, including in-band response, out-of-band response, and spectral uniformity across the focal plane.

The landsat data continuity mission operational land imager (OLI) radiometric calibration

The Operational Land Imager (OLI) on the Landsat Data Continuity Mission (LDCM) has a comprehensive radiometric characterization and calibration program beginning with the instrument design, and extending through integration and test, on-orbit operations and science data processing. Key instrument design features for radiometric calibration include dual solar diffusers and multi-lamped on-board calibrators. The radiometric calibration transfer procedure from NIST standards has multiple checks on the radiometric scale throughout the process and uses a heliostat as part of the transfer to orbit of the radiometric calibration. On-orbit lunar imaging will be used to track the instruments stability and side slither maneuvers will be used in addition to the solar diffuser to flat field across the thousands of detectors per band. A Calibration Validation Team is continuously involved in the process from design to operations. This team uses an Image Assessment System (IAS), part of the ground system to characterize and calibrate the on-orbit data.

The Landsat Data Continuity Mission Operational Land Imager (OLI) sensor

The Landsat Data Continuity Mission (LDCM) is being developed by NASA and USGS and is currently planned for launch in January 2013 [1]. Once on-orbit and checked out, it will be operated by USGS and officially named Landsat-8. Two sensors will be on LDCM: the Operational Land Imager (OLI), which has been built and delivered by Ball Aerospace & Technology Corp (BATC) and the Thermal Infrared Sensor (TIRS)[2], which was built and delivered by Goddard Space Flight Center (GSFC). The OLI covers the Visible, Near-IR (NIR) and Short-Wave Infrared (SWIR) parts of the spectrum; TIRS covers the Thermal Infrared (TIR). This paper discusses only the OLI instrument and its pre-launch characterization; a companion paper covers TIRS.