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Satellite Climate Data Records and Applications
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Satellite Climate Data Records and Applications

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (1 January 2016) | Viewed by 172134

Special Issue Editors

Dr. Xuepeng Zhao

E-Mail Website
Guest Editor
NOAA/NESDIS/NCEI, Asheville, NC 28801, USA
Interests: remote sensing and modeling of atmospheric aerosols and trace gases; radiative transfer calculations; study of climate forcing and air quality impact of aerosols and greenhouse gases
Dr. Wenze Yang

E-Mail Website
Guest Editor
UMD/ESSIC/CICS, College Park, MD 20740, USA
Interests: calibration and validation for microwave satellite observation; retrieval of hydrological products with passive microwave sensors; climate-ecosystem interactions and climate change; remote sensing of vegetation; satellite data evaluation, fusion and application
Dr. Viju John

E-Mail Website
Guest Editor
EUMETSAT, Darmstadt, Germany
Interests: creation of homogenized radiance data sets from microwave and infrared satellite measurements for climate monitoring and evaluation of climate models
Dr. Hui Lu

E-Mail Website
Guest Editor
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Interests: microwave remote sensing; soil moisture; land surface data assimilation; hydrological model; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Ken Knapp

E-Mail Website
Guest Editor
NOAA/NESDIS/NCEI, Asheville, NC 28801, USA
Interests: satellite remote sensing; satellite intercalibration; aerosols; tropical cyclones; geostationary imagers, polar orbiting imagers; satellite climate applications

Special Issue Information

Dear Colleagues,

Due to rapid advances in the past half-century in climate change observations, especially satellite observations from passive and active sensors, on-board both geostationary and polar-orbiting satellites, climate observation is transitioning from the practice of individual, regional, and short term observations to multiple, global, and long-term observations. Thus, long-term consistent Earth satellite observations and data records are becoming indispensable for providing information for improved detection, attribution, and prediction of global climate and environmental changes, as well as for helping decision makers and society to respond and adapt to the change and variability in a resilient fashion. The National Research Council of the U.S. National Academy of Science recommended the development of climate data records (CDRs) from environmental satellites in its 2004 and 2008 review reports. This Special Issue seeks papers addressing the development, production, and analysis of long-term satellite CDRs along with CDR applications in the study of climate and environment changes.

Specific Instructions to Authors can be found at: https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf

Potential topics include:

  • Development and production of CDRs from satellite observations.
  • Inter-satellite calibration approaches and retrieval methods for CDR development.
  • Uncertainty analysis and validation of CDR products.
  • Trend detection and climate variability analysis using CDRs.
  • Applications of CDRs in monitoring climate and environmental changes.
  • Usage of CDRs in numerical weather reanalysis and climate projection.
  • Studies of societal benefit of CDRs on serving the public sectors, including agriculture, forestry, energy, health, tourism, transportation, water, fisheries, etc.

Dr. Xuepeng Zhao
Dr. Wenze Yang
Dr. Viju John
Prof. Dr. Hui Lu
Dr. Ken Knapp
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate data record (cdr)
  • satellite observation
  • calibration and validation
  • retrieval algorithms and methods
  • data development and production
  • climate and environmental changes
  • trend detection and variability analysis

Published Papers (20 papers)

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1670 KiB  
Article
Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR
by Andrew Heidinger, Michael Foster, Denis Botambekov, Michael Hiley, Andi Walther and Yue Li
Remote Sens. 2016, 8(6), 511; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060511 - 18 Jun 2016
Cited by 18 | Viewed by 6469
Abstract
An important component of the AVHRR PATMOS-x climate date record (CDR)—or any satellite cloud climatology—is the performance of its cloud detection scheme and the subsequent quality of its cloud fraction CDR. PATMOS-x employs the NOAA Enterprise Cloud Mask for this, which is based [...] Read more.
An important component of the AVHRR PATMOS-x climate date record (CDR)—or any satellite cloud climatology—is the performance of its cloud detection scheme and the subsequent quality of its cloud fraction CDR. PATMOS-x employs the NOAA Enterprise Cloud Mask for this, which is based on a naïve Bayesian approach. The goal of this paper is to generate analysis of the PATMOS-x cloud fraction CDR to facilitate its use in climate studies. Performance of PATMOS-x cloud detection is compared to that of the well-established MYD35 and CALIPSO products from the EOS A-Train. Results show the AVHRR PATMOS-x CDR compares well against CALIPSO with most regions showing proportional correct values of 0.90 without any spatial filtering and 0.95 when a spatial filter is applied. Values are similar for the NASA MODIS MYD35 mask. A direct comparison of PATMOS-x and MYD35 from 2003 to 2014 also shows agreement over most regions in terms of mean cloud amount, inter-annual variability, and linear trends. Regional and seasonal differences are discussed. The analysis demonstrates that PATMOS-x cloud amount uncertainty could effectively screen regions where PATMOS-x differs from MYD35. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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3289 KiB  
Article
An Assessment of HIRS Surface Air Temperature with USCRN Data
by Steve T. Stegall and Lei Shi
Remote Sens. 2016, 8(6), 485; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060485 - 08 Jun 2016
Cited by 1 | Viewed by 3399
Abstract
The surface air temperature retrievals from the High Resolution Infrared Radiation Sounder (HIRS) are evaluated by using observations from the U.S. Climate Reference Network (USCRN) for the period of 2006 to 2013. One year of the USCRN data is also used as ground [...] Read more.
The surface air temperature retrievals from the High Resolution Infrared Radiation Sounder (HIRS) are evaluated by using observations from the U.S. Climate Reference Network (USCRN) for the period of 2006 to 2013. One year of the USCRN data is also used as ground truth in calibrating retrieval biases. The final retrieval results show that mean biases of HIRS retrievals from comparisons to all surface stations for each year are mostly in the range of ±0.2 °C, and the root mean square difference (RMSD) values are 3.2–3.5 °C. Results for biases of individual stations are mostly within ±2 °C. In average, RMSDs are smaller over the eastern U.S. than over the western U.S., smaller at nighttime than at daytime, and smaller at lower elevations. The comparison patterns are consistent from year to year and for different satellites, showing the potential of HIRS data for long-term studies. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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2439 KiB  
Article
PATMOS-x Cloud Climate Record Trend Sensitivity to Reanalysis Products
by Michael J. Foster, Andrew Heidinger, Michael Hiley, Steve Wanzong, Andi Walther and Denis Botambekov
Remote Sens. 2016, 8(5), 424; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8050424 - 18 May 2016
Cited by 5 | Viewed by 5548
Abstract
Continuous satellite-derived cloud records now extend over three decades, and are increasingly used for climate applications. Certain applications, such as trend detection, require a clear understanding of uncertainty as it relates to establishing statistical significance. The use of reanalysis products as sources of [...] Read more.
Continuous satellite-derived cloud records now extend over three decades, and are increasingly used for climate applications. Certain applications, such as trend detection, require a clear understanding of uncertainty as it relates to establishing statistical significance. The use of reanalysis products as sources of ancillary data could be construed as one such source of uncertainty, as there has been discussion regarding the suitability of reanalysis products for trend detection. Here we use three reanalysis products: Climate Forecast System Reanalysis (CFSR), Modern Era Retrospective Analysis for Research and Applications (MERRA) and European Center for Medium range Weather Forecasting (ECMWF) ERA-Interim (ERA-I) as sources of ancillary data for the Pathfinder Atmospheres Extended/Advanced Very High Resolution Radiometer (PATMOS-x/AVHRR) Satellite Cloud Climate Data Record (CDR), and perform inter-comparisons to determine how sensitive the climatology is to choice of ancillary data source. We find differences among reanalysis fields required for PATMOS-x processing, which translate to small but not insignificant differences in retrievals of cloud fraction, cloud top height and cloud optical depth. The retrieval variability due to choice of reanalysis product is on the order of one third the size of the retrieval uncertainty, making it a potentially significant factor in trend detection. Cloud fraction trends were impacted the most by choice of reanalysis while cloud optical depth trends were impacted the least. Metrics used to determine the skill of the reanalysis products for use as ancillary data found no clear best choice for use in PATMOS-x. We conclude use of reanalysis products as ancillary data in the PATMOS-x/AVHRR Cloud CDR do not preclude its use for trend detection, but for that application uncertainty in reanalysis fields should be better represented in the PATMOS-x retrieval uncertainty. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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8021 KiB  
Article
Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci)
by Thomas Popp, Gerrit De Leeuw, Christine Bingen, Christoph Brühl, Virginie Capelle, Alain Chedin, Lieven Clarisse, Oleg Dubovik, Roy Grainger, Jan Griesfeller, Andreas Heckel, Stefan Kinne, Lars Klüser, Miriam Kosmale, Pekka Kolmonen, Luca Lelli, Pavel Litvinov, Linlu Mei, Peter North, Simon Pinnock, Adam Povey, Charles Robert, Michael Schulz, Larisa Sogacheva, Kerstin Stebel, Deborah Stein Zweers, Gareth Thomas, Lieuwe Gijsbert Tilstra, Sophie Vandenbussche, Pepijn Veefkind, Marco Vountas and Yong Xueadd Show full author list remove Hide full author list
Remote Sens. 2016, 8(5), 421; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8050421 - 16 May 2016
Cited by 136 | Viewed by 13707
Abstract
Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing [...] Read more.
Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption). Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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8061 KiB  
Article
Evaluation of PERSIANN-CDR for Meteorological Drought Monitoring over China
by Hao Guo, Anming Bao, Tie Liu, Sheng Chen and Felix Ndayisaba
Remote Sens. 2016, 8(5), 379; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8050379 - 04 May 2016
Cited by 80 | Viewed by 8558
Abstract
In this paper, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is analyzed for the assessment of meteorological drought. The evaluation is conducted over China at 0.5° spatial resolution against a ground-based gridded China monthly Precipitation Analysis Product [...] Read more.
In this paper, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is analyzed for the assessment of meteorological drought. The evaluation is conducted over China at 0.5° spatial resolution against a ground-based gridded China monthly Precipitation Analysis Product (CPAP) from 1983 to 2014 (32 years). The Standardized Precipitation Index (SPI) at various time scales (1 month to 12 months) is calculated for detecting drought events. The results show that PERSIANN-CDR depicts similar drought behavior as the ground-based CPAP in terms of capturing the spatial and temporal patterns of drought events over eastern China, where the intensity of gauge networks and the frequency of droughts are high. 6-month SPI shows the best agreement with CPAP in identifying drought months. However, large differences between PERSIANN-CDR and CPAP in depicting drought patterns and identifying specific drought events are found over northwestern China, particularly in Xinjiang and Qinghai-Tibet Plateau region. Factors behind this may be due to the relatively sparse gauge networks, the complicated terrain and the performance of PERSIANN algorithm. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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4786 KiB  
Article
Sensor Stability for SST (3S): Toward Improved Long-Term Characterization of AVHRR Thermal Bands
by Kai He, Alexander Ignatov, Yury Kihai, Changyong Cao and John Stroup
Remote Sens. 2016, 8(4), 346; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8040346 - 20 Apr 2016
Cited by 9 | Viewed by 8136
Abstract
Recently, the National Oceanic and Atmospheric Administration (NOAA) performed sea surface temperature (SST) reanalysis (RAN1) from seven AVHRR/3s onboard NOAA-15 to -19 and Metop-A and -B, from 2002–present. Operational L1b data were used as input. The time series of clear-sky ocean brightness temperatures [...] Read more.
Recently, the National Oceanic and Atmospheric Administration (NOAA) performed sea surface temperature (SST) reanalysis (RAN1) from seven AVHRR/3s onboard NOAA-15 to -19 and Metop-A and -B, from 2002–present. Operational L1b data were used as input. The time series of clear-sky ocean brightness temperatures (BTs) and derived SSTs were found to be unstable. The SSTs were empirically stabilized against in situ SSTs using a 90-day moving filter, while the measured BTs were left intact. However, some users are interested in direct radiance assimilation and need stable BTs. Additionally, stabilized BTs will greatly benefit SST (by minimizing the need for their empirical stabilization), and other Level 2 products derived from AVHRR. To better understand the AVHRR calibration and stabilize its BTs, the Sensor Stability for SST (3S; www.star.nesdis.noaa.gov/sod/sst/3s/) system was established at NOAA, which monitors orbital statistics of the sensor measured blackbody temperatures (BBTs), blackbody counts (BCs), and the space counts (SCs), along with the derived calibration gains and offsets. Analyses are performed separately for the satellite night (when the satellite is in the Earth’s shadow) and day (on the sunlit part of its orbit). Factors affecting the BBT, BC and SC are also monitored, including the Sun and Moon position relative to the sensor, local equator crossing time, and duration of the satellite night. All AVHRRs show long-term and band-specific smooth changes in the calibration gains and offsets, which are occasionally perturbed by spurious non-monotonic anomalies. The most prominent irregularities occur shortly after the satellite crosses from the night into day, or when it is in a (near) full Sun orbit for extended periods of time. We argue that the operational quality control (QC) and calibration procedures are suboptimal and should be improved. Analyses in 3S suggest that a more stringent QC is needed, and scan lines where the calibration coefficients cannot be derived, due to poor quality SC, BC or BBT data, should be filled in by interpolation from the best parts of orbit or more broadly satellite lifetime. Work is underway to redesign the AVHRR QC and calibration algorithms and create a more stable long-term record of AVHRR calibration and BTs, and use them in the subsequent SST RANs. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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1763 KiB  
Article
Validation of ATMS Calibration Accuracy Using Suomi NPP Pitch Maneuver Observations
by Fuzhong Weng and Hu Yang
Remote Sens. 2016, 8(4), 332; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8040332 - 15 Apr 2016
Cited by 18 | Viewed by 7367
Abstract
The Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on 28 October, 2011, and carries the Advanced Technology Microwave Sounder (ATMS) onboard. Currently, ATMS performance in orbit is very stable and the calibration parameters (e.g., noise and accuracy) meet specifications. This study documents [...] Read more.
The Suomi National Polar-orbiting Partnership (SNPP) satellite was launched on 28 October, 2011, and carries the Advanced Technology Microwave Sounder (ATMS) onboard. Currently, ATMS performance in orbit is very stable and the calibration parameters (e.g., noise and accuracy) meet specifications. This study documents an ATMS calibration error budget model and its results for community reference. The calibration accuracy is also verified with the ATMS pitch maneuver observations of cold space. It is shown that the ATMS pitch maneuver cold space observations at center positions are inconsistent with the values predicted by the instrument calibration error budget model. The biases also depend on scan angle. This scan-angle dependence may be caused by the ATMS plane reflector emission. Thus, a physical model is developed to simulate the radiation emitted from the reflector and is recommended as part of ATMS radiance calibration to further improve the sensor data record (SDR) data quality. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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4411 KiB  
Article
AVHRR GAC SST Reanalysis Version 1 (RAN1)
by Alexander Ignatov, Xinjia Zhou, Boris Petrenko, Xingming Liang, Yury Kihai, Prasanjit Dash, John Stroup, John Sapper and Paul DiGiacomo
Remote Sens. 2016, 8(4), 315; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8040315 - 09 Apr 2016
Cited by 30 | Viewed by 8547
Abstract
In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans [...] Read more.
In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans (ACSPO) retrieval system. Initially, AVHRR/3 data from five NOAA and two Metop satellites from 2002 to 2015 have been reprocessed. The derived SSTs have been matched up with two reference SSTs—the quality controlled in situ SSTs from the NOAA in situ Quality Monitor (iQuam) and the Canadian Meteorological Centre (CMC) L4 SST analysis—and analyzed in the NOAA SST Quality Monitor (SQUAM) online system. The corresponding clear-sky ocean brightness temperatures (BT) in AVHRR bands 3b, 4 and 5 (centered at 3.7, 11, and 12 µm, respectively) have been compared with the Community Radiative Transfer Model simulations in another NOAA online system, Monitoring of Infrared Clear-sky Radiances over Ocean for SST (MICROS). For some AVHRRs, the time series of “AVHRR minus reference” SSTs and “observed minus model” BTs are unstable and inconsistent, with artifacts in the SSTs and BTs strongly correlated. In the official “Reanalysis version 1” (RAN1), data from only five platforms—two midmorning (NOAA-17 and Metop-A) and three afternoon (NOAA-16, -18 and -19)—were included during the most stable periods of their operations. The stability of the SST time series was further improved using variable regression SST coefficients, similarly to how it was done in the NOAA/NASA Pathfinder version 5.2 (PFV5.2) dataset. For data assimilation applications, especially those blending satellite and in situ SSTs, we recommend bias-correcting the RAN1 SSTs using the newly developed sensor-specific error statistics (SSES), which are reported in the product files. Relative performance of RAN1 and PFV5.2 SSTs is discussed. Work is underway to improve the calibration of AVHRR/3s and extend RAN time series, initially back to the mid-1990s and later to the early 1980s. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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10195 KiB  
Article
Climatology Analysis of Aerosol Effect on Marine Water Cloud from Long-Term Satellite Climate Data Records
by Xuepeng Zhao, Andrew K. Heidinger and Andi Walther
Remote Sens. 2016, 8(4), 300; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8040300 - 02 Apr 2016
Cited by 13 | Viewed by 6863
Abstract
Satellite aerosol and cloud climate data records (CDRs) have been used successfully to study the aerosol indirect effect (AIE). Data from the Advanced Very High Resolution Radiometer (AVHRR) now span more than 30 years and allow these studies to be conducted from a [...] Read more.
Satellite aerosol and cloud climate data records (CDRs) have been used successfully to study the aerosol indirect effect (AIE). Data from the Advanced Very High Resolution Radiometer (AVHRR) now span more than 30 years and allow these studies to be conducted from a climatology perspective. In this paper, AVHRR data are used to study the AIE on water clouds over the global oceans. Correlation analysis between aerosol optical thickness (AOT) and cloud parameters, including cloud droplet effective radius (CDER), cloud optical depth (COD), cloud water path (CWP), and cloud cover fraction (CCF), is performed. For the first time from satellite observations, the long-term trend in AIE over the global oceans is also examined. Three regimes have been identified: (1) AOT < 0.08, where CDER increases with AOT; (2) 0.08 < AOT < 0.3, where CDER generally decreases when AOT increases; and (3) AOT > 0.3, where CDER first increases with AOT and then levels off. AIE is easy to manifest in the CDER reduction in the second regime (named Regime 2), which is identified as the AIE sensitive/effective regime. The AIE manifested in the consistent changes of all four cloud variables (CDER, COD, CWP, and CCF) together is located only in limited areas and with evident seasonal variations. The long-term trend of CDER changes due to the AIE of AOT changes is detected and falls into three scenarios: Evident CDER decreasing (increasing) with significant AOT increasing (decreasing) and evident CDER decreasing with limited AOT increasing but AOT values fall in the AIE sensitive Regime 2. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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3648 KiB  
Article
Algorithm Development of Temperature and Humidity Profile Retrievals for Long-Term HIRS Observations
by Lei Shi, Jessica L. Matthews, Shu-peng Ho, Qiong Yang and John J. Bates
Remote Sens. 2016, 8(4), 280; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8040280 - 25 Mar 2016
Cited by 14 | Viewed by 7289
Abstract
A project for deriving temperature and humidity profiles from High-resolution Infrared Radiation Sounder (HIRS) observations is underway to build a long-term dataset for climate applications. The retrieval algorithm development of the project includes a neural network retrieval scheme, a two-tiered cloud screening method, [...] Read more.
A project for deriving temperature and humidity profiles from High-resolution Infrared Radiation Sounder (HIRS) observations is underway to build a long-term dataset for climate applications. The retrieval algorithm development of the project includes a neural network retrieval scheme, a two-tiered cloud screening method, and a calibration using radiosonde and Global Positioning System Radio Occultation (GPS RO) measurements. As atmospheric profiles over high surface elevations can differ significantly from those over low elevations, different neural networks are developed for three classifications of surface elevations. The significant impact from the increase of carbon dioxide in the last several decades on HIRS temperature sounding channel measurements is accounted for in the retrieval scheme. The cloud screening method added one more step from the HIRS-only approach by incorporating the Advanced Very High Resolution Radiometer (AVHRR) observations to assess the likelihood of cloudiness in HIRS pixels. Calibrating the retrievals with radiosonde and GPS RO reduces biases in retrieved temperature and humidity. Except for the lowest pressure level which exhibits larger variability, the mean biases are within ±0.3 °C for temperature and within ±0.2 g/kg for specific humidity at standard pressure levels, globally. Overall, the HIRS temperature and specific humidity retrievals closely align with radiosonde and GPS RO observations in providing measurements of the global atmosphere to support other relevant climate dataset development. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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2950 KiB  
Article
A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation
by Martin Claverie, Jessica L. Matthews, Eric F. Vermote and Christopher O. Justice
Remote Sens. 2016, 8(3), 263; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030263 - 22 Mar 2016
Cited by 107 | Viewed by 12771
Abstract
In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers [...] Read more.
In- land surface models, which are used to evaluate the role of vegetation in the context of global climate change and variability, LAI and FAPAR play a key role, specifically with respect to the carbon and water cycles. The AVHRR-based LAI/FAPAR dataset offers daily temporal resolution, an improvement over previous products. This climate data record is based on a carefully calibrated and corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitable for climate studies. It spans from mid-1981 to the present. Further, this operational dataset is available in near real-time allowing use for monitoring purposes. The algorithm relies on artificial neural networks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparison with MODIS products and in situ data show the dataset is consistent and reliable with overall uncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect is observed in the broadleaf forest biomes with high LAI (>4.5) and FAPAR (>0.8) values. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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6123 KiB  
Article
Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States
by Wenzhe Jiao, Lifu Zhang, Qing Chang, Dongjie Fu, Yi Cen and Qingxi Tong
Remote Sens. 2016, 8(3), 224; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030224 - 10 Mar 2016
Cited by 95 | Viewed by 13561
Abstract
Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index [...] Read more.
Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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3690 KiB  
Article
CERES Top-of-Atmosphere Earth Radiation Budget Climate Data Record: Accounting for in-Orbit Changes in Instrument Calibration
by Norman G. Loeb, Natividad Manalo-Smith, Wenying Su, Mohan Shankar and Susan Thomas
Remote Sens. 2016, 8(3), 182; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030182 - 25 Feb 2016
Cited by 65 | Viewed by 10285
Abstract
The Clouds and the Earth’s Radiant Energy System (CERES) project provides observations of Earth’s radiation budget using measurements from CERES instruments onboard the Terra, Aqua and Suomi National Polar-orbiting Partnership (S-NPP) satellites. As the objective is to create a long-term climate data record, [...] Read more.
The Clouds and the Earth’s Radiant Energy System (CERES) project provides observations of Earth’s radiation budget using measurements from CERES instruments onboard the Terra, Aqua and Suomi National Polar-orbiting Partnership (S-NPP) satellites. As the objective is to create a long-term climate data record, it is necessary to periodically reprocess the data in order to incorporate the latest calibration changes and algorithm improvements. Here, we focus on the improvements and validation of CERES Terra and Aqua radiances in Edition 4, which are used to generate higher-level climate data products. Onboard sources indicate that the total (TOT) channel response to longwave (LW) radiation has increased relative to the start of the missions by 0.4% to 1%. In the shortwave (SW), the sensor response change ranges from −0.4% to 0.6%. To account for in-orbit changes in SW spectral response function (SRF), direct nadir radiance comparisons between instrument pairs on the same satellite are made and an improved wavelength dependent degradation model is used to adjust the SRF of the instrument operating in a rotating azimuth plane scan mode. After applying SRF corrections independently to CERES Terra and Aqua, monthly variations amongst these instruments are highly correlated and the standard deviation in the difference of monthly anomalies is 0.2 Wm−2 for ocean and 0.3 Wm−2 for land/desert. Additionally, trends in CERES Terra and Aqua monthly anomalies are consistent to 0.21 Wm−2 per decade for ocean and 0.31 Wm−2 per decade for land/desert. In the LW, adjustments to the TOT channel SRF are made to ensure that removal of the contribution from the SW portion of the TOT channel with SW channel radiance measurements during daytime is consistent throughout the mission. Accordingly, anomalies in day–night LW difference in Edition 4 are more consistent compared to Edition 3, particularly for the Aqua land/desert case. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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6524 KiB  
Article
The AVHRR Polar Pathfinder Climate Data Records
by Jeffrey Key, Xuanji Wang, Yinghui Liu, Richard Dworak and Aaron Letterly
Remote Sens. 2016, 8(3), 167; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8030167 - 23 Feb 2016
Cited by 44 | Viewed by 8059
Abstract
With recent, dramatic changes in Arctic sea ice and the Antarctic ice sheets, the importance of monitoring the climate of the polar regions has never been greater. While many individual global satellite products exist, the AVHRR Polar Pathfinder products provide a comprehensive set [...] Read more.
With recent, dramatic changes in Arctic sea ice and the Antarctic ice sheets, the importance of monitoring the climate of the polar regions has never been greater. While many individual global satellite products exist, the AVHRR Polar Pathfinder products provide a comprehensive set of variables that can be used to study trends and interactions within the Arctic and Antarctic climate systems. This paper describes the AVHRR Polar Pathfinder (APP), which is a fundamental climate data record that provides channel reflectances and brightness temperatures, and the AVHRR Polar Pathfinder—Extended (APP-x), which is a thematic climate data record that builds on APP to provide information on surface and cloud properties and radiative fluxes. Both datasets cover the period from 1982 through the present, twice daily, over both polar regions. APP-x has been used in the study of trends in surface properties, cloud cover, and radiative fluxes, interactions between clouds and sea ice, and the role of land surface changes in summer warming. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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2872 KiB  
Article
Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method
by Xiaokang Kou, Lingmei Jiang, Yanchen Bo, Shuang Yan and Linna Chai
Remote Sens. 2016, 8(2), 105; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8020105 - 29 Jan 2016
Cited by 72 | Viewed by 7281
Abstract
Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared [...] Read more.
Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01° and 0.25° LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53 °C to 2.31 °C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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7266 KiB  
Article
Use of SSU/MSU Satellite Observations to Validate Upper Atmospheric Temperature Trends in CMIP5 Simulations
by Lilong Zhao, Jianjun Xu, Alfred M. Powell, Zhihong Jiang and Donghai Wang
Remote Sens. 2016, 8(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8010013 - 24 Dec 2015
Cited by 6 | Viewed by 8223
Abstract
The tropospheric and stratospheric temperature trends and uncertainties in the fifth Coupled Model Intercomparison Project (CMIP5) model simulations in the period of 1979–2005 have been compared with satellite observations. The satellite data include those from the Stratospheric Sounding Units (SSU), Microwave Sounding Units [...] Read more.
The tropospheric and stratospheric temperature trends and uncertainties in the fifth Coupled Model Intercomparison Project (CMIP5) model simulations in the period of 1979–2005 have been compared with satellite observations. The satellite data include those from the Stratospheric Sounding Units (SSU), Microwave Sounding Units (MSU), and the Advanced Microwave Sounding Unit-A (AMSU). The results show that the CMIP5 model simulations reproduced the common stratospheric cooling (−0.46–−0.95 K/decade) and tropospheric warming (0.05–0.19 K/decade) features although a significant discrepancy was found among the individual models being selected. The changes of global mean temperature in CMIP5 simulations are highly consistent with the SSU measurements in the stratosphere, and the temporal correlation coefficients between observation and model simulations vary from 0.6–0.99 at the 99% confidence level. At the same time, the spread of temperature mean in CMIP5 simulations increased from stratosphere to troposphere. Multiple linear regression analysis indicates that the temperature variability in the stratosphere is dominated by radioactive gases, volcanic events and solar forcing. Generally, the high-top models show better agreement with observations than the low-top model, especially in the lower stratosphere. The CMIP5 simulations underestimated the stratospheric cooling in the tropics and overestimated the cooling over the Antarctic compared to the satellite observations. The largest spread of temperature trends in CMIP5 simulations is seen in both the Arctic and Antarctic areas, especially in the stratospheric Antarctic. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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3740 KiB  
Article
Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil
by Justine Ringard, Melanie Becker, Frederique Seyler and Laurent Linguet
Remote Sens. 2015, 7(12), 16441-16459; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71215831 - 05 Dec 2015
Cited by 34 | Viewed by 6760
Abstract
Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main [...] Read more.
Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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2578 KiB  
Article
Quality Assessment of the CCI ECV Soil Moisture Product Using ENVISAT ASAR Wide Swath Data over Spain, Ireland and Finland
by Chiara Pratola, Brian Barrett, Alexander Gruber and Edward Dwyer
Remote Sens. 2015, 7(11), 15388-15423; https://0-doi-org.brum.beds.ac.uk/10.3390/rs71115388 - 18 Nov 2015
Cited by 22 | Viewed by 7585
Abstract
During the last decade, great progress has been made by the scientific community in generating satellite-derived global surface soil moisture products, as a valuable source of information to be used in a variety of applications, such as hydrology, meteorology and climatic modeling. Through [...] Read more.
During the last decade, great progress has been made by the scientific community in generating satellite-derived global surface soil moisture products, as a valuable source of information to be used in a variety of applications, such as hydrology, meteorology and climatic modeling. Through the European Space Agency Climate Change Initiative (ESA CCI), the most complete and consistent global soil moisture (SM) data record based on active and passive microwaves sensors is being developed. However, the coarse spatial resolution characterizing such data may be not sufficient to accurately represent the moisture conditions. The objective of this work is to assess the quality of the CCI Essential Climate Variable (ECV) SM product by using finer spatial resolution Advanced Synthetic Aperture Radar (ASAR) Wide Swath and in situ soil moisture data taken over three regions in Europe. Ireland, Spain, and Finland have been selected with the aim of assessing the spatial and temporal representativeness of the ECV SM product over areas that differ in climate, topography, land cover and soil type. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values. A good temporal and spatial agreement has been observed between the three soil moisture datasets for the Irish and Spanish sites, while poorer results have been found at the Finnish sites. Overall, the two different satellite derived products capture the soil moisture temporal variations well and are in good agreement with each other. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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1581 KiB  
Article
Use of the Standardized Precipitation Evapotranspiration Index (SPEI) to Characterize the Drying Trend in Southwest China from 1982–2012
by Xing Li, Binbin He, Xingwen Quan, Zhanmang Liao and Xiaojing Bai
Remote Sens. 2015, 7(8), 10917-10937; https://0-doi-org.brum.beds.ac.uk/10.3390/rs70810917 - 24 Aug 2015
Cited by 114 | Viewed by 12246
Abstract
In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize the drought conditions in Southwest China from 1982–2012. The SPEI was calculated by precipitation and temperature data for various accumulation periods. Based on the SPEI, the multi-scale patterns, the trend, [...] Read more.
In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize the drought conditions in Southwest China from 1982–2012. The SPEI was calculated by precipitation and temperature data for various accumulation periods. Based on the SPEI, the multi-scale patterns, the trend, and the spatio-temporal extent of drought were evaluated, respectively. The results explicitly showed a drying trend of Southwest China. The mean SPEI values at five time scales all decreased significantly. Some moderate and severe droughts were captured after 2005 and the droughts were even getting aggravated. By examining the spatio-temporal extent, the aggravating condition of drought was further revealed. To investigate the performance of SPEI, correlation analysis was conducted between SPEI and two remotely sensed drought indices: Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI). The comparison was also conducted with the Standardized Precipitation Index (SPI). The results showed that for both SMCI and VCI, the SPI and SPEI had approximate correlations with them. The SPEI could better monitor the soil moisture than the SPI in months with significant increase of temperature. The correlations between the VCI and SPI/SPEI were lower; nevertheless, the SPEI was slightly superior to the SPI. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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Review

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1312 KiB  
Review
Satellite Climate Data Records: Development, Applications, and Societal Benefits
by Wenze Yang, Viju O. John, Xuepeng Zhao, Hui Lu and Kenneth R. Knapp
Remote Sens. 2016, 8(4), 331; https://0-doi-org.brum.beds.ac.uk/10.3390/rs8040331 - 15 Apr 2016
Cited by 25 | Viewed by 7371
Abstract
This review paper discusses how to develop, produce, sustain, and serve satellite climate data records (CDRs) in the context of transitioning research to operation (R2O). Requirements and critical procedures of producing various CDRs, including Fundamental CDRs (FCDRs), Thematic CDRs (TCDRs), Interim CDRs (ICDRs), [...] Read more.
This review paper discusses how to develop, produce, sustain, and serve satellite climate data records (CDRs) in the context of transitioning research to operation (R2O). Requirements and critical procedures of producing various CDRs, including Fundamental CDRs (FCDRs), Thematic CDRs (TCDRs), Interim CDRs (ICDRs), and climate information records (CIRs) are discussed in detail, including radiance/reflectance and the essential climate variables (ECVs) of land, ocean, and atmosphere. Major international CDR initiatives, programs, and projects are summarized. Societal benefits of CDRs in various user sectors, including Agriculture, Forestry, Fisheries, Energy, Heath, Water, Transportation, and Tourism are also briefly discussed. The challenges and opportunities for CDR development, production and service are also addressed. It is essential to maintain credible CDR products by allowing free access to products and keeping the production process transparent by making source code and documentation available with the dataset. Full article
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
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