Jicheng Liu

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.

Modeling the View Angle Dependence of Gap Fractions in Forest Canopies: Implications for Mapping Fractional Snow Cover Using Optical Remote Sensing

Abstract Forest canopies influence the proportion of the land surface that is visible from above, or the viewable gap fraction (VGF). The VGF limits the amount of information available in satellite data about the land surface, such as snow cover in forests. Efforts to recover fractional snow cover from the spectral mixture analysis model Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area and grain size (MODSCAG) indicate the importance of view angle effects in forested landscapes. The VGF can be estimated using both hemispherical photos and forest canopy models. For a set of stands in the Cold Land Field Processes Experiment (CLPX) sites in the Fraser Experimental Forest in Colorado, the convergence of both measurements and models of the VGF as a function of view angle supports the idea that VGF can be characterized as a function of forest properties. A simple geometric optical (GO) model that includes only between-crown gaps can capture the basic shape of the VGF as a function of vie...

Improving MODIS land cover classification by combining MODIS spectral and angular signatures in a Canadian boreal forest

This study explores the use of reflectance anisotropy as described by the Bidirectional Reflectance Distribution Function (BRDF) as an additional source of information to improve land surface classification accuracies in a Canadian boreal forest region through the use of a decision tree classifier (C4.5). This effort primarily uses a daily rolling version of the operational algorithm developed for Direct Broadcast to generate 500 m 16-day daily rolling data sets in the study region. Descriptive statistic and statistically rigorous techniques are used to assess classification accuracies based on confusion matrices and a 10-fold cross-validation method. The results show that the inclusion of additional 7-band model anisotropic parameter group (volumetric (VOL) plus geometric (GEO)) with spectral feature group (nadir BRDF-adjusted reflectance (NABR) plus Enhanced Vegetation Index (EVI)) is most useful in classification, increasing overall accuracies by 5.68%. The most improvements of per-class accuracies are seen for Wetland shrub class with users and producers accuracies increasing by up to 17.7% and 11.3%, respectively. Increases on the order of 5% to 15% are encountered for the classes of Wetland herb, Wetland tree, Coniferous dense, and Coniferous open with no detriments to other candidate classes. The inclusion of the 2-band BRDF shape indicator group in the classification is, however, not as useful as inclusion of the 7-band model anisotropic parameter group in improving the classification accuracies. A further investigation of the classification accuracies regarding reflectance anisotropy for the sampling pixels within each class shows that land cover types that are dominated by geometric-optical scattering type or a mixture of scattering types are relatively difficult to be classified with spectral feature group alone, and the inclusion of additional BRDF features can significantly improve classification accuracies for these land cover types. However, despite their use as ancillary data, this study also confirms that the spectral feature group provided with NBAR and EVI captures the major information content regarding land cover types, exceeding the information content contained in the model anisotropic parameter group provided with the 7-band VOL and GEO parameters of RossThick-LiSparse-Reciprocal (RTLSR) models.

Monitoring vegetation phenology using improved MODIS products

Land surface vegetation phenology is an important process for the real-time monitoring and detecting inter-annual variability in terrestrial ecosystem carbon exchange and climate-biosphere interactions. Crop phenology is an important factor that influences crop growth and yield estimation models. Since the mid-1980s, coarse-resolution, temporally-composited satellite data have been used to study vegetation phenology. View-angle corrected nadir reflectances from the 16-day, 1km operational MODIS BRDF/Albedo product are currently used to monitor global land cover dynamics. In this paper, we developed an improved methodology for using the new 500-m MODIS BRDF/Albedo Version 005 product to monitor global vegetation phenology by utilizing time series of the Normalized Difference Vegetation Index (NDVI). The method adopts a rolling strategy for the continuous updating of the underlying anisotropy (or BRDF shape), so that the latest land surface BRDF information can be used as prior-knowledge for next retrieval. Using this approach, transition dates for vegetation phenology in time series of NDVI can be determined from MODIS data at finer temporal and spatial resolution. Preliminary results based on monitoring crops in northern China demonstrate the effectiveness of our rolling retrievals coupled with the improved spatial resolution of the new MODIS product.