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Reliable information about the geographic distribution and abundance of major plant functional types (PFTs) around the world is increasingly needed for global change research. Using remote sensing techniques to map PFTs is a relatively recent field of research. This paper presents a method to map PFTs from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a multisource evidential reasoning (ER) algorithm. The method first utilizes a suite of improved and standard MODIS products to generate evidence measures for each PFT class. The multiple lines of evidence computed from input data are then combined using Dempster''s Rule of combination. Finally, a decision rule based on maximum support is used to make classification decisions. The proposed method was tested over the states of Illinois, Indiana, Iowa, and North Dakota, USA where crops dominate. The Cropland Data Layer (CDL) data provided by the United States Department of Agriculture were employed to validate our new PFT maps and the current MODIS PFT product. Our preliminary results suggest that multisource data fusion is a promising approach to improve the mapping of PFTs. For several major PFT classes such as crop, trees, and grass and shrub, the PFT maps generated with the ER method provide greater spatial details compared to the MODIS PFT. The overall accuracies increased for all the four states, with the biggest improvement occurring in Iowa from 51% (MODIS) to 64% (ER). The overall kappa statistic also increased for all the four states, with the biggest improvement occurring in Iowa from 0.03 (MODIS) to 0.38 (ER). The paper concludes with a discussion of several methodological issues pertaining to the further improvement of the ER approach. [Copyright &y& Elsevier]

Abstract: Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by ecosystems and is an important component of the terrestrial carbon cycle. Empirical light use efficiency (LUE) models and process-based Farquhar, von Caemmerer, and Berry (FvCB) photosynthetic models are widely used for GPP estimation. In this paper, the MODIS GPP algorithm using the LUE approach and the Boreal Ecosystem Productivity Simulator (BEPS) based on the FvCB model in which a sunlit and shaded leaf separation scheme is evaluated against GPP values derived from eddy-covariance (EC) measurements in a variety of ecosystems. Although the total GPP values simulated using these two models agree within 89% when they are averaged for the conterminous U.S., there are systematic differences between them in terms of their spatial and temporal distribution patterns. The spatial distribution of MODIS GPP therefore differs substantially from that produced by BEPS. These differences may be due to an inherent problem of the LUE modeling approach. When a constant maximum LUE value is used for a biome type, this simplification cannot properly handle the contribution of shaded leaves to the total canopy-level GPP. When GPP is modeled by BEPS as the sum of sunlit and shaded leaf GPP, the problem is minimized, i.e., at the low end, the relative contribution of shaded leaves to GPP is small and at the high end, the relative contribution of shaded leaves is large. Compared with monthly and annual GPP derived from eddy covariance data at 40 tower sites in North America, BEPS performed better than the MODIS GPP algorithm. The difference between MODIS and BEPS GPP widens as with the fraction of shaded leaves increases. The simpler LUE modeling approach should therefore be further improved to reduce this bias issue for effective estimation of regional and temporal GPP distributions. [Copyright &y& Elsevier]

Abstract: NOAA will soon use the new Visible Infrared Imager Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) as its primary polar-orbiting satellite imager. Employing a near real-time processing system, NOAA will generate a series of Environmental Data Records (EDRs) from VIIRS data. For example, the VIIRS Land Surface Temperature (LST) EDR will estimate the surface skin temperature over all global land areas and provide key information for monitoring Earth surface energy and water fluxes. Because both VIIRS and its processing algorithms are new, NOAA is conducting a rigorous calibration and validation program to understand and improve product quality. This paper presents a new validation methodology to estimate the quantitative uncertainty in the LST EDR, and contribute to improving the retrieval algorithm. It employs a physically-based approach to scaling up point LST measurements currently made operationally at many field and weather stations around the world. The scaling method consists of the merging information collected at different spatial resolutions within a land surface model to fully characterize large area (km×km scale) satellite products. The approach can be used to explore scaling issues over terrestrial surfaces spanning a large range of climate regimes and land cover types, including forests and mixed vegetated areas. The methodology was tested successfully with NASA/MODIS data, indicating an absolute error for MODIS LST products of 2.0K at a mixed agricultural site (Bondville, IL) when accounting for scaling, and higher than 3K without scaling. The VIIRS LST EDR requires a 1.5K measurement accuracy and 2.5K measurement precision. Ultimately, this validation approach should lead to an accurate and continuously-assessed VIIRS LST product suitable to support weather forecast, hydrological applications, or climate studies. It is readily adaptable to other moderate resolution satellite systems. [Copyright &y& Elsevier]

Abstract: Validation is an important task in the development of satellite remote sensing products. Strategies for validation vary depending on the nature of the products. The validation process of the flood and standing water product (FSW) for the Geostationary Operational Environmental Satellite - R series (GOES-R) is presented in this paper. A major challenge in the validation of the FSW product is the lack of ground truth flood maps and similar reference products from other satellite systems and other sources. To overcome this limitation, a two-level validation scheme for the FSW product is developed using the Moderate-resolution Imaging Spectroradiometer (MODIS) data as a proxy. In the first level, gauging station data collected by the U.S. Geological Survey (USGS) are employed as ground truth flood point information on local scales to verify the effectiveness of the proposed algorithm for flood detection. Gauging station data collected during 34 flood cases that occurred in 2010 and 2011 in the continental US were validated and assessed according to the rate of correct detection. Results showed that 79.71% of flooding stations were accurately detected from the MODIS 1km images by the proposed FSW algorithm. In the second level of the validation, FSW detection results using the proposed algorithm were compared to the reference flood maps, which were generated by a supervised support vector machine (SVM) classification followed by human interpretation and editing. Flood detection accuracies for three major flood events occurred in Asia and Australia in 2010 were evaluated. Confusion matrices were employed as the accuracy measurement for the second level of the validation. Commission errors for the three flood cases were 6.75%, 13.45% and 21.45%, respectively. Omission errors of flood pixels varied between 9.58% and 19.61%. The validation results suggest that the employed FSW algorithm is capable of producing flood and standing water maps in an operational environment, and it meets the required accuracy and execution time of the product. [Copyright &y& Elsevier]

Abstract: Time series of the Normalized Difference Vegetation Index (NDVI) derived from satellite observations provide important information on the state of terrestrial vegetation over a wide range of spatiotemporal scales. For understanding long-term changes in terrestrial ecosystems (post-1981), data collected by the Advanced Very High Resolution Radiometer (AVHRR) on board the satellites of National Oceanic and Atmospheric Administration (NOAA) series is a unique source of information. In this paper, we describe a new processing methodology for a comprehensive AVHRR data set at 1km spatial resolution acquired over Canada, the northern United States and Greenland post-1981. The methodology incorporates a pre-processing algorithm, Canadian AVHRR Processing System (CAPS), recently developed by the Canada Centre of Remote Sensing (CCRS), which enables highly accurate geolocation and ortho-rectification at efficiency rates of >90%. Once image navigation is completed, our approach consists of five key steps: first, two clear-sky composites for each 10day interval are generated from the forward or backward scattering hemisphere; second, AVHRR Channel 1 and 2 reflectances are normalized to the AVHRR/3 on board NOAA-17 to account for differences in the spectral response function among the AVHRR sensors; third, atmospheric correction is performed using the Simplified Method for Atmospheric correction (SMAC) algorithm, using standard meteorological data sets (water vapor, surface level air pressure, ozone); fourth, NDVI is calculated based on atmospherically corrected Channel 1 and 2 reflectances; and finally, the NDVI is adjusted for directional effects based on the Ross-Thick Li-Sparse Bidirectional Reflectance Distribution Function (BRDF) model. The processed NDVI data are compared to an equivalent spatially and temporally overlapping MODIS NDVI data set from 2001 to 2005 for validation. Results at continental scale indicate that time series of MODIS and AVHRR were similar for a wide range of biomes and generalized ecoregions. Analysis stratified by land cover indicated that the correlation was strongest for homogeneous land cover types, such as cropland, when compared to structurally more diverse classes, such as deciduous broadleaf forests. The comparison of the NDVI at the local scale at seven sites of the Fluxnet Canada Research Network resulted in the correlation coefficient r=0.95. Given confidence in the processing approach, this NDVI data set can be a valuable source of information for climate and vegetation-related studies over Canada and the northern United States. [Copyright &y& Elsevier]