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Aisheng Wu

Bio: Aisheng Wu is an academic researcher. The author has contributed to research in topics: Moderate-resolution imaging spectroradiometer & Radiometric calibration. The author has an hindex of 24, co-authored 143 publications receiving 2179 citations.


Papers
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Journal ArticleDOI
TL;DR: The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership spacecraft has successfully operated since its launch in October 2011.
Abstract: The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership spacecraft has successfully operated since its launch in October 2011. The VIIRS collects data in 22 spectral bands that are calibrated by a set of onboard calibrators (OBC). In addition, lunar observations are made to independently track VIIRS long-term calibration stability for the reflective solar bands (RSB). This paper provides an overview of VIIRS OBC functions as well as its on-orbit operation and calibration activities. It also describes sensor calibration methodologies and demonstrates VIIRS on-orbit performance from launch to present. Results reported in this paper include on-orbit changes in sensor spectral band responses, detector noise characterization, and key calibration parameters. Issues identified and their potential impacts on sensor calibration are also discussed. Since launch, the VIIRS instrument nominal operation temperature has been stable to within ±1.0 K. The cold focal plane temperatures have been well controlled, with variations of less than 20 mK over a period of 1.5 years. In general, changes in thermal emissive bands (TEB) detector responses have been less than 0.5%. Despite large response degradation in several near-infrared and short-wave infrared bands and large SD degradation at short visible wavelengths, the VIIRS sensor and OBC overall performance has been excellent postlaunch. The degradation caused by the telescope mirror coating contamination has been modeled and its impact addressed through the use of modulated relative spectral response in the improved calibration and the current sensor data record data production. Based on current instrument characteristics and performance, it is expected that the VIIRS calibration will continue to meet its design requirements, including RSB detector signal to noise ratio and TEB detector noise equivalent temperature difference, throughout its 7 year design lifetime.

214 citations

Journal ArticleDOI
TL;DR: Two approaches have been used to derive the time-dependent RVS for MODIS RSB, and the derived time-independent RVS, implemented in C6, makes an important improvement to the quality of the MODIS L1B products.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments currently operate onboard the National Aeronautics and Space Administration (NASA's) Terra and Aqua spacecraft, launched on December 18, 1999 and May 4, 2002, respectively. MODIS has 36 spectral bands, among which 20 are reflective solar bands (RSBs) covering a spectral range from 0.412 to 2.13 μm. The RSBs are calibrated on orbit using a solar diffuser (SD) and an SD stability monitor and with additional measurements from lunar observations via a space view (SV) port. Selected pseudo-invariant desert sites are also used to track the RSB on-orbit gain change, particularly for short-wavelength bands. MODIS views the Earth surface, SV, and the onboard calibrators using a two-sided scan mirror. The response versus scan angle (RVS) of the scan mirror was characterized prior to launch, and its changes are tracked using observations made at different angles of incidence from onboard SD, lunar, and Earth view (EV) measurements. These observations show that the optical properties of the scan mirror have experienced large wavelength-dependent degradation in both the visible and near infrared spectral regions. Algorithms have been developed to track the on-orbit RVS change using the calibrators and the selected desert sites. These algorithms have been applied to both Terra and Aqua MODIS Level 1B (L1B) to improve the EV data accuracy since L1B Collection 4, refined in Collection 5, and further improved in the latest Collection 6 (C6). In C6, two approaches have been used to derive the time-dependent RVS for MODIS RSB. The first approach relies on data collected from sensor onboard calibrators and mirror side ratios from EV observations. The second approach uses onboard calibrators and EV response trending from selected desert sites. This approach is mainly used for the bands with much larger changes in their time-dependent RVS, such as the Terra MODIS bands 1-4, 8, and 9 and the Aqua MODIS bands 8 and 9. In this paper, the algorithms of these approaches are described, their performance is demonstrated, and their impact on L1B products is discussed. In general, the shorter wavelength bands have experienced a larger on-orbit RVS change, which, in general, are mirror side and detector dependent. The on-orbit RVS change due to the degradation of band 8 can be as large as 35% for Terra MODIS and 20% for Aqua MODIS. Vital to maintaining the accuracy of the MODIS L1B products is an accurate characterization of the on-orbit RVS change. The derived time-independent RVS, implemented in C6, makes an important improvement to the quality of the MODIS L1B products.

126 citations

Journal ArticleDOI
TL;DR: Results show that Terra MODIS BB operation has been extremely stable since launch, and most TEB detectors continue to meet or exceed their specified noise characterization requirements, thus enabling calibration accuracy and science data product quality to be maintained.
Abstract: Since its launch in December 1999, Terra MODIS has been making continuous Earth observations for more than seven years. It has produced a broad range of land, ocean, and atmospheric science data products for improvements in studies of global climate and environmental change. Among its 36 spectral bands, there are 20 reflective solar bands and 16 thermal emissive bands (TEBs). MODIS TEBs cover the mid-wave infrared and long-wave infrared spectral regions with wavelengths from 3.7 to 14.4 . They are calibrated on-orbit using an onboard blackbody (BB) with its temperature measured by a set of thermistors on a scan-by-scan basis. This paper will provide a brief overview of MODIS TEB calibration and characterization methodologies and illustrate onboard BB functions and TEB performance over more than seven years of on-orbit operation and calibration. Discussions will be focused on TEB detector short-term stability and noise characterization and changes in long-term response (or system gain). Results show that Terra MODIS BB operation has been extremely stable since launch. When operated at its nominal controlled temperature of 290 K, the BB temperature variation is typically less than 0.30 mK on a scan-by-scan basis, and there has been no time-dependent temperature drift. In addition to excellent short-term stability, most TEB detectors continue to meet or exceed their specified noise characterization requirements, thus enabling calibration accuracy and science data product quality to be maintained. Excluding the noisy detectors identified prelaunch and those that occurred postlaunch, the changes in TEB responses have been less than 0.7% on an annual basis. The optical leak corrections applied to bands 32-36 have been effective and stable over the entire mission.

102 citations

Journal ArticleDOI
TL;DR: In this article, the authors analyzed the consistency of calibrated reflectance from the operational L1B data between AVHRR on NOAA-16 and -17 and between the two satellite data sets, and showed that the difference between these two datasets was negligible.
Abstract: [1] Satellite detection of the global climate change signals as small as a few percent per decade in albedo critically depends on consistent and accurately calibrated Level 1B (L1B) data or Fundamental Climate Data Records (FCDRs). Detecting small changes in signal over decades is a major challenge not only to the retrieval of geophysical parameters from satellite observations, but more importantly to the current state-of-the-art calibration, since such small changes can easily be obscured by erroneous variations in the calibration, especially for instruments with no onboard calibration, such as the Advanced Very High Resolution Radiometer (AVHRR). Without dependable FCDRs, its derivative Thematic Climate Data Records (TCDRs) are bound to produce false trends with questionable scientific value. This has been increasingly recognized by more and more remote sensing scientists. In this study we analyzed the consistency of calibrated reflectance from the operational L1B data between AVHRR on NOAA-16 and -17 and between NOAA-16/AVHRR and Aqua/MODIS, based on Simultaneous Nadir Overpass (SNO) observation time series. Analyses suggest that the NOAA-16 and -17/AVHRR operationally calibrated reflectance became consistent two years after the launch of NOAA-17, although they still differ by 9% from the MODIS reflectance for the 0.63 μm band. This study also suggests that the SNO method has reached a high level of relative accuracy (∼1.5%) for estimating the consistency for both the 0.63 and 0.84 μm bands between AVHRRs, and a 0.9% relative accuracy between AVHRR and MODIS for the 0.63 μm band. It is believed that the methodology is applicable to all historical AVHRR data for improving the calibration consistency, and work is in progress generating FCDRs from the nearly 30 years of AVHRR data using the SNO and other complimentary methods. A more consistent historical AVHRR L1B data set will be produced for a variety of geophysical products including aerosol, vegetation, cloud, and surface albedo to support global climate change detection studies.

88 citations

Journal ArticleDOI
TL;DR: The MODIS Level 1B (L1B) algorithm as discussed by the authors converts uncalibrated geo-located observations and convert instrument response into calibrated reflectance and radiance, which are used to generate science data products.
Abstract: The moderate-resolution imaging spectroradiometer (MODIS) was launched on the Terra spacecraft on Dec.18, 1999 and on Aquaon May 4, 2002. The data acquired by these instruments have contributed to the long-term climate data record for more than a decade and represent a key component of NASA’s Earth observing system. Each MODIS instrument observes nearly the whole Earth each day, enabling the scientific characterization of the land, ocean, and atmosphere. The MODIS Level 1B (L1B) algorithms input uncalibrated geo-located observations and convert instrument response into calibrated reflectance and radiance, which are used to generate science data products. The instrument characterization needed to run the L1B code is currently implemented using time-dependent lookup tables. The MODIS characterization support team, working closely with the MODIS Science Team, has improved the product quality with each data reprocessing. We provide an overview of the new L1B algorithm release, designated collection 6. Recent improvements made as a consequence of on-orbit calibration, on-orbit analyses, and operational considerations are described. Instrument performance and the expected impact of L1B changes on the collection 6 L1B products are discussed.

79 citations


Cited by
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01 Jan 2016
TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for downloading remote sensing and image interpretation. As you may know, people have look hundreds times for their favorite novels like this remote sensing and image interpretation, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their computer. remote sensing and image interpretation is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the remote sensing and image interpretation is universally compatible with any devices to read.

1,802 citations

Journal ArticleDOI
TL;DR: The Collection 6 (C6) algorithm as mentioned in this paper was proposed to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance.
Abstract: . The twin Moderate resolution Imaging Spectroradiometer (MODIS) sensors have been flying on Terra since 2000 and Aqua since 2002, creating an extensive data set of global Earth observations. Here, we introduce the Collection 6 (C6) algorithm to retrieve aerosol optical depth (AOD) and aerosol size parameters from MODIS-observed spectral reflectance. While not a major overhaul from the previous Collection 5 (C5) version, there are enough changes that there are significant impacts to the products and their interpretation. The C6 aerosol data set will be created from three separate retrieval algorithms that operate over different surface types. These are the two "Dark Target" (DT) algorithms for retrieving (1) over ocean (dark in visible and longer wavelengths) and (2) over vegetated/dark-soiled land (dark in the visible), plus the "Deep Blue" (DB) algorithm developed originally for retrieving (3) over desert/arid land (bright in the visible). Here, we focus on DT-ocean and DT-land (#1 and #2). We have updated assumptions for central wavelengths, Rayleigh optical depths and gas (H2O, O3, CO2, etc.) absorption corrections, while relaxing the solar zenith angle limit (up to ≤ 84°) to increase poleward coverage. For DT-land, we have updated the cloud mask to allow heavy smoke retrievals, fine-tuned the assignments for aerosol type as function of season/location, corrected bugs in the Quality Assurance (QA) logic, and added diagnostic parameters such topographic altitude. For DT-ocean, improvements include a revised cloud mask for thin-cirrus detection, inclusion of wind speed dependence on the surface reflectance, updates to logic of QA Confidence flag (QAC) assignment, and additions of important diagnostic information. At the same time, we quantified how "upstream" changes to instrument calibration, land/sea masking and cloud masking will also impact the statistics of global AOD, and affect Terra and Aqua differently. For Aqua, all changes will result in reduced global AOD (by 0.02) over ocean and increased AOD (by 0.02) over land, along with changes in spatial coverage. We compared preliminary data to surface-based sun photometer data, and show that C6 should improve upon C5. C6 will include a merged DT/DB product over semi-arid land surfaces for reduced-gap coverage and better visualization, and new information about clouds in the aerosol field. Responding to the needs of the air quality community, in addition to the standard 10 km product, C6 will include a global (DT-land and DT-ocean) aerosol product at 3 km resolution.

1,628 citations

Journal ArticleDOI
TL;DR: In this article, the authors validate the MODIS along-orbit Level 2 products by comparing to quality assured Level 2 AERONET sunphotometer measurements at over 300 sites, and find that >66% (one standard deviation) of MODIS-retrieved aerosol optical depth (AOD) values compare to AERO-observed values within an expected error (EE) envelope of ±(0.05 + 15%), with high correlation (R = 0.9).
Abstract: . NASA's MODIS sensors have been observing the Earth from polar orbit, from Terra since early 2000 and from Aqua since mid 2002. We have applied a consistent retrieval and processing algorithm to both sensors to derive the Collection 5 (C005) dark-target aerosol products over land. Here, we validate the MODIS along-orbit Level 2 products by comparing to quality assured Level 2 AERONET sunphotometer measurements at over 300 sites. From 85 463 collocations, representing mutually cloud-free conditions, we find that >66% (one standard deviation) of MODIS-retrieved aerosol optical depth (AOD) values compare to AERONET-observed values within an expected error (EE) envelope of ±(0.05 + 15%), with high correlation (R = 0.9). Thus, the MODIS AOD product is validated and quantitative. However, even though we can define EEs for MODIS-reported Angstrom exponent and fine AOD over land, these products do not have similar physical validity. Although validated globally, MODIS-retrieved AOD does not fall within the EE envelope everywhere. We characterize some of the residual biases that are related to specific aerosol conditions, observation geometry, and/or surface properties, and relate them to situations where particular MODIS algorithm assumptions are violated. Both Terra's and Aqua's–retrieved AOD are similarly comparable to AERONET, however, Terra's global AOD bias changes with time, overestimating (by ~0.005) before 2004, and underestimating by similar magnitude after. This suggests how small calibration uncertainties of

1,069 citations

Journal ArticleDOI
TL;DR: The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present as discussed by the authors.
Abstract: The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of ± 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set.

855 citations

Journal ArticleDOI
13 Oct 2017-Science
TL;DR: 12 years of MODIS satellite data are used to quantify net annual changes in the aboveground carbon density of tropical woody live vegetation, providing direct, measurement-based evidence that the world’s tropical forests are a net carbon source.
Abstract: The carbon balance of tropical ecosystems remains uncertain, with top-down atmospheric studies suggesting an overall sink and bottom-up ecological approaches indicating a modest net source. Here we use 12 years (2003 to 2014) of MODIS pantropical satellite data to quantify net annual changes in the aboveground carbon density of tropical woody live vegetation, providing direct, measurement-based evidence that the world’s tropical forests are a net carbon source of 425.2 ± 92.0 teragrams of carbon per year (Tg C year –1 ). This net release of carbon consists of losses of 861.7 ± 80.2 Tg C year –1 and gains of 436.5 ± 31.0 Tg C year –1 . Gains result from forest growth; losses result from deforestation and from reductions in carbon density within standing forests (degradation or disturbance), with the latter accounting for 68.9% of overall losses.

537 citations