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During the summer of 2018, the upward-pointing Wyoming Cloud Lidar (WCL) was deployed on board the University of Wyoming King Air (UWKA) research aircraft for the Biomass Burning Flux Measurements of Trace Gases and Aerosols (BB-FLUX) field campaign. This paper describes the generation of calibrated attenuated backscatter coefficients and aerosol extinction coefficients from the WCL measurements. The retrieved aerosol extinction coefficients at the flight level strongly correlate (correlation coefficient, rr > 0.8) with in situ aerosol concentration and carbon monoxide (CO) concentration, providing a first-order estimate for converting WCL extinction coefficients into vertically resolved CO and aerosol concentration within wildfire smoke plumes. The integrated CO column concentrations from the WCL data in nonextinguished profiles also correlate (rr = 0.7) with column measurements by the University of Colorado Airborne Solar Occultation Flux instrument, indicating the validity of WCL-derived extinction coefficients. During BB-FLUX, the UWKA sampled smoke plumes from more than 20 wildfires during 35 flights over the western United States. Seventy percent of flight time was spent below 3 km above ground level (AGL) altitude, although the UWKA ascended up to 6 km AGL to sample the top of some deep smoke plumes. The upward-pointing WCL observed a nearly equal amount of thin and dense smoke below 2 km and above 5 km due to the flight purpose of targeted fresh fire smoke. Between 2 and 5 km, where most of the wildfire smoke resided, the WCL observed slightly more thin smoke than dense smoke due to smoke spreading. Extinction coefficients in dense smoke were 2–10 times stronger, and dense smoke tended to have larger depolarization ratio, associated with irregular aerosol particles. [ABSTRACT FROM AUTHOR]

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]

The National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite series R (GOES-R) will greatly expand the ability to observe the earth from geostationary orbit compared to the current-generation GOES, with more than 3 times as many spectral bands and a 75% reduction in footprint size. These enhanced capabilities are beneficial to rainfall rate estimation since they provide sensitivity to cloud-top properties such as phase and particle size that cannot be achieved using the limited channel selection of current GOES. The GOES-R rainfall rate algorithm, which is an infrared-based algorithm calibrated in real time against passive microwave rain rates, has been previously described in an algorithm theoretical basis document (ATBD); this paper describes modifications since the release of the ATBD, including a correction for evaporation of precipitation in dry regions and improved calibration updates. These improvements have been evaluated using a simplified version applicable to current-generation GOES to take advantage of the high-resolution ground validation data routinely available over the conterminous United States. Correcting for subcloud evaporation using relative humidity from a numerical model reduced false alarm rainfall by half and reduced the overall error by 35% for hourly accumulations validated against the National Centers for Environmental Prediction stage IV radar-gauge field; however, the number of missed events did increase slightly. Reducing the size of the calibration regions and increasing the training data requirements improved the consistency of the retrieved rates in time and space and reduced the overall error by an additional 4%. [ABSTRACT FROM AUTHOR]

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: 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]

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 \m^3/\m^3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation. [ABSTRACT FROM AUTHOR]

Summary: Typically, flash floods are studied ex post facto in response to a major impact event. A complement to field investigations is developing a detailed database of flash flood events, including minor events and null reports (i.e., where heavy rain occurred but there was no flash flooding), based on public survey questions conducted in near-real time. The Severe hazards analysis and verification experiment (SHAVE) has been in operation at the National Severe Storms Laboratory (NSSL) in Norman, OK, USA during the summers since 2006. The experiment employs undergraduate students to analyse real-time products from weather radars, target specific regions within the conterminous US, and poll public residences and businesses regarding the occurrence and severity of hail, wind, tornadoes, and now flash floods. In addition to providing a rich learning experience for students, SHAVE has also been successful in creating high-resolution datasets of severe hazards used for algorithm and model verification. This paper describes the criteria used to initiate the flash flood survey, the specific questions asked and information entered to the database, and then provides an analysis of results for flash flood data collected during the summer of 2008. It is envisioned that specific details provided by the SHAVE flash flood observation database will complement databases collected by operational agencies (i.e., US National Weather Service Storm Data reports) and thus lead to better tools to predict the likelihood of flash floods and ultimately reduce their impacts on society. [ABSTRACT FROM AUTHOR]

The MODerate resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) are onboard the same satellite platform NASA TERRA. Both MODIS and ASTER offer routine retrieval of land surface temperatures (LSTs), and the ASTER- and MODIS-retrieved LST products have been used worldwide. Because a large fraction of the earth surface consists of mountainous areas, variations in elevation, terrain slope and aspect angles can cause biases in the retrieved LSTs. However, terrain-induced effects are generally neglected in most satellite retrievals, which may generate discrepancy between ASTER and MODIS LSTs. In this paper, we reported the terrain effects on the LST discrepancy with a case examination over a relief area at the Loess Plateau of China. Results showed that the terrain-induced effects were not major, but nevertheless important for the total LST discrepancy. A large local slope did not necessarily lead to a large LST discrepancy. The angle of emitted radiance was more important than the angle of local slope in generating the LST discrepancy. Specifically, the conventional terrain correction may be unsuitable for densely vegetated areas. The distribution of ASTER-to-MODIS emissivity suggested that the terrain correction was included in the generalized split window (GSW) based approach used to rectify MODIS LSTs. Further study should include the classification-induced uncertainty in emissivity for reliable use of satellite-retrieved LSTs over relief areas. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) on EOS/Aura offers unprecedented spatial and spectral resolution, coupled with global coverage, for space-based UV measurements of sulfur dioxide (SO2). This paper describes an OMI SO2 algorithm (the band residual difference) that uses calibrated residuals at SO2 absorption band centers produced by the NASA operational ozone algorithm (OMTO3). By using optimum wavelengths for retrieval of SO2, the retrieval sensitivity is improved over NASA predecessor Total Ozone Mapping Spectrometer (TOMS) by factors of 10 to 20, depending on location. The ground footprint of OMI is eight times smaller than TOMS. These factors produce two orders of magnitude improvement in the minimum detectable mass of SO2. Thus, the diffuse boundaries of volcanic clouds can be imaged better and the clouds can be tracked longer. More significantly, the improved sensitivity now permits daily global measurement of passive volcanic degassing of SO2 and of heavy anthropogenic SO2 pollution to provide new information on the relative importance of these sources for climate studies. [ABSTRACT FROM AUTHOR]

The Earth Observing System (EOS) Microwave Limb Sounder (MLS) is an atmospheric remote sensing experiment led by the Jet Propulsion Laboratory of the California Institute of Technology. The objectives of the EOS MLS are to learn more about stratospheric chemistry and causes of ozone changes, processes affecting climate variability, and pollution in the upper troposphere. The EOS MLS is one of four instruments on the National Aeronautics and Space Administration (NASA) EOS Aura spacecraft launched on July 15, 2004, with an operational period extending at least 5 years after launch. This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major component—the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products. The Science Investigator-led Processing System is developed and operated by Raytheon Information Technology and Scientific Services of Pasadena under contract with Jet Propulsion Laboratory. [ABSTRACT FROM AUTHOR]