DOCSLIB.ORG

Evaluation of Quikscat Data for Monitoring Vegetation Phenology

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Evaluation of Quikscat Data for Monitoring Vegetation Phenology City University of New York (CUNY) CUNY Academic Works Dissertations and Theses City College of New York 2011 Evaluation of QuikSCAT data for Monitoring Vegetation Phenology Doralee Pellot CUNY City College How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/cc_etds_theses/27 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Evaluation of QuikSCAT data for Monitoring Vegetation Phenology Thesis Submitted in partial fulfillment of the requirements for the degree Master of Engineering (Civil) at The City College of New York of The City University of New York by Doralee Pellot May 2012 ____________________________________________ Professor Reza Khanbilvardi Dr. Tarendra Lakhankar Department of Civil Engineering Table of Contents List of Figures ................................................................................................................................. 1 Abstract ........................................................................................................................................... 3 1 Introduction ............................................................................................................................. 4 2 Data Sources ........................................................................................................................... 6 2.1 Radar Backscatter from QuikSCAT ................................................................................. 6 2.1.1 QuikSCAT ................................................................................................................ 6 2.1.2 Data Description ....................................................................................................... 7 2.1.3 Data Processing ......................................................................................................... 8 2.2 Vegetation Water Content (VWC) from AMSR-E .......................................................... 8 2.2.1 Data Description ....................................................................................................... 9 2.2.2 Data Processing ......................................................................................................... 9 2.3 Normalized Difference Vegetation Index from MODIS................................................ 10 2.3.1 Data Description ..................................................................................................... 10 2.3.2 Data Processing ....................................................................................................... 11 2.4 Land Cover Classification data from AVHRR .............................................................. 12 3 Methodology ......................................................................................................................... 13 4 Results and Discussions ........................................................................................................ 15 4.1 Site Analysis, Cropland: ................................................................................................. 15 4.2 Site Analysis , Deciduous broadleaf forest: ................................................................... 20 4.3 Site Analysis, Grassland:................................................................................................ 21 4.4 Site Analysis, Mixed Forest: .......................................................................................... 22 4.5 Site Analysis, Open Shrubland: ..................................................................................... 24 4.6 Site Analysis, Wooded Grassland: ................................................................................. 26 4.7 Site Analysis, Woodlands: ............................................................................................. 31 4.8 Site Analysis, Closed Shrubland: ................................................................................... 32 5 Comparative Analysis ........................................................................................................... 33 6 Conclusions and Future work ............................................................................................... 38 References ..................................................................................................................................... 39 Acknowledgments......................................................................................................................... 41 List of Figures Figure 1 : Diffuse reflection and specular reflection in red and blue respectively ......................... 6 Figure2 : SeaWinds Scatterometer .................................................................................................. 7 Figure 3 : QuikSCAT mean values for the eight year time period in study (2002-2009) .............. 8 Figure 4 : Passive microwave remote sensing ................................................................................ 9 Figure 5 : VWC mean values for the eight year time period in study (2002-2009) ..................... 10 Figure 6 :NDV Index mean values for the eight year time period in study (2002-2009) ............. 11 Figure 7 :Land Cover Classification data from AVHRR.............................................................. 12 Figure 8: Selected sites for analysis across the globe. .................................................................. 13 Figure 9: Cropland land cover scatter plot in USA region. .......................................................... 15 Figure 10: Time series analysis for Cropland land cover in USA region ..................................... 15 Figure 11: Cropland land cover scatter plot in South America region. ........................................ 16 Figure 12: Time series analysis for Cropland land cover in South America region ..................... 16 Figure 13: Cropland land cover scatter plot in Africa region. ..................................................... 17 Figure 14: Time series analysis for Cropland land cover in Africa region ................................... 17 Figure 15: Cropland land cover scatter plot in Europe region. .................................................... 18 Figure 16: Time series analysis for Cropland land cover in Europe region ................................. 18 Figure 17: Cropland land cover scatter plot in Australia region. .................................................. 19 Figure 18: Time series analysis for Cropland land cover in Australia region. ............................. 19 Figure 19: Correlation coefficient for deciduous broad leaf forest in USA region. ..................... 20 Figure 20: Time series analysis for deciduous broadleaf forest in USA region. .......................... 20 Figure 21: Correlation coefficient analysis for Grassland in USA region. ................................... 21 Figure 22: Time series analysis for Grassland in USA region ..................................................... 21 Figure 23 Correlation coefficient analysis for Grassland in South America region. ............ Error! Bookmark not defined. Figure 24: Time series analysis for Grassland in South America regionError! Bookmark not defined. Figure 27: Correlation Coefficient analysis for Mixed Forest in USA ........................................ 22 Figure 28: Time series analysis for Mixed Forest (USA) ............................................................. 22 Figure 29: Correlation Coefficient analysis for Mixed Forest in Africa .................................... 23 Figure 30: Time series analysis for Mixed Forest in Africa ......................................................... 23 1 Figure 31: Correlation Coefficient analysis for Open Shrubland in USA region ....................... 24 Figure 32: Time series analysis for Open Shrubland in USA region ........................................... 24 Figure 33: Correlation Coefficient analysis for Open Shrubland in Africa region. ..................... 25 Figure 34: Time series analysis for Open Shrubland in Africa region ......................................... 25 Figure 35: Correlation Coefficient analysis for wooded grassland in USA region ..................... 26 Figure 36: Time series analysis for Wooded Grassland (USA) .................................................... 26 Figure 37: Correlation Coefficient analysis for wooded grassland in South America region. .... 27 Figure 38: Time series analysis for Wooded Grassland South America. ..................................... 27 Figure 39: Coefficient analysis for wooded grassland in Africa region. .................................... 28 Figure 40: Time series analysis for Wooded Grassland, Africa ................................................... 28 Figure 41: Coefficient analysis for wooded grassland in Europe region. .................................... 29 Figure 42: Time series analysis for Wooded Grassland, Europe .................................................. 29 Figure 43: Coefficient analysis for wooded grassland in Australia region. ................................ 30 Figure 44: Time series analysis for Wooded Grassland Australia ................................................ 30 Figure 45: Correlation coefficient analysis for woodland in Africa region ................................ 31 Figure 46: Time series analysis for Woodland, Africa. ..............................................................
Load more

Recommended publications
  • Estimating Gale to Hurricane Force Winds Using the Satellite Altimeter
    VOLUME 28 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY APRIL 2011 Estimating Gale to Hurricane Force Winds Using the Satellite Altimeter YVES QUILFEN Space Oceanography Laboratory, IFREMER, Plouzane´, France DOUG VANDEMARK Ocean Process Analysis Laboratory, University of New Hampshire, Durham, New Hampshire BERTRAND CHAPRON Space Oceanography Laboratory, IFREMER, Plouzane´, France HUI FENG Ocean Process Analysis Laboratory, University of New Hampshire, Durham, New Hampshire JOE SIENKIEWICZ Ocean Prediction Center, NCEP/NOAA, Camp Springs, Maryland (Manuscript received 21 September 2010, in final form 29 November 2010) ABSTRACT A new model is provided for estimating maritime near-surface wind speeds (U10) from satellite altimeter backscatter data during high wind conditions. The model is built using coincident satellite scatterometer and altimeter observations obtained from QuikSCAT and Jason satellite orbit crossovers in 2008 and 2009. The new wind measurements are linear with inverse radar backscatter levels, a result close to the earlier altimeter high wind speed model of Young (1993). By design, the model only applies for wind speeds above 18 m s21. Above this level, standard altimeter wind speed algorithms are not reliable and typically underestimate the true value. Simple rules for applying the new model to the present-day suite of satellite altimeters (Jason-1, Jason-2, and Envisat RA-2) are provided, with a key objective being provision of enhanced data for near-real- time forecast and warning applications surrounding gale to hurricane force wind events. Model limitations and strengths are discussed and highlight the valuable 5-km spatial resolution sea state and wind speed al- timeter information that can complement other data sources included in forecast guidance and air–sea in- teraction studies.
    [Show full text]
  • GPM) Mission Applications Examples
    The Global Precipitation Measurement (GPM) Mission Applications Examples Dalia Kirschbaum GPM Deputy Project Scientist for Applications [email protected] www.nasa.gov/gpm Twitter: NASA_Rain Facebook: NASA.Rain 1 Applications Overview The new generation of GPM precipitation products advance the societal applications of the data to better address the needs of the end users and their applications areas. The demonstration of value of NASA Earth science data through applications activities has rapidly become an integral piece in translating satellite data into actionable information and knowledge used to inform policy and enhance decision-making at local to global scales. TRMM and GPM precipitation observations can be quickly and easily accessed via various data portals. This PowerPoint provides examples of how GPM is being applied routinely and operationally across a range of societal benefit areas. 2 Societal Benefit Areas Extreme Events and Disasters • Landslides • Floods • Tropical cyclones • Re-insurance Water Resources and Agriculture • Famine Early Warning System • Drought • Water Resource management • Agriculture Weather, Climate & Land Surface Modeling • Numerical Weather Prediction • Land System Modeling • Global Climate Modeling Public Health and Ecology • Disease tracking • Animal migration • Food Security 3 Numerical Weather PreDiction • Air Force Weather Agency (AFWA) (557th Weather Wing) incorporates GMI data into their Weather Research and Forecasting (WRF) Model, delivering operational worldwide weather products to Air Force and Army units, unified commands, National Programs, and the National Command Authorities. • Joint Center for Satellite Data Assimilation (JCSDA/NOAA): brings GMI data into their Global Data Assimilation System (GDAS), which is used by the Global Forecast System (GFS) model to initialize weather forecasts with observational data.
    [Show full text]
  • Comparison of AOD Between CALIPSO and MODIS: Significant
    Atmos. Meas. Tech. Discuss., 5, C3425–C3429, Atmospheric 2012 Measurement www.atmos-meas-tech-discuss.net/5/C3425/2012/ Techniques © Author(s) 2012. This work is distributed under Discussions the Creative Commons Attribute 3.0 License. Interactive comment on “Comparison of AOD between CALIPSO and MODIS: significant differences over major dust and biomass burning regions” by X. Ma et al. Anonymous Referee #1 Received and published: 24 December 2012 Comments to the Editor: In this paper, author compares the aerosol optical depth (AOD) retrieved by two sen- sors namely, CALIPSO/CALIOP and Terra-Aqua/MODIS, over major dust and biomass burning regions of the world. Author uses level-3 gridded dataset available from both sensors to perform the analysis. They find that though the spatial patterns in AOT appear similar CALIOP tends to retrieve much lower AOD over the Sahara and north- west China; both are source regions of dust outbreaks. On the other hand, CALIOP is found to be higher-than-MODIS in the retrieved AOD over southern African region C3425 where seasonal biomass burning takes place. However, during burning season over South America, CALIOP tends to be lower-than-MODIS which apparently linked to the aerosol load/AOD. Finally, author attributes the discrepancies observed between two sensors to the algorithmic issues such as lidar ratio in CALIOP inversion and aerosol model and surface reflectance in MODIS algorithm. Though author calls for further research to narrow down the exact source of bias, he/she doesn’t show in this paper which sensor is closer to the ground-truth. My main suggestion to author is that he/she should compare both satellite retrievals with AERONET-measured direct AOD values in order to establish the validity of the two products.
    [Show full text]
  • Hydrological Applications of Data from GRACE Satellites
    Weighing Earth, Tracking Water: Hydrological Applications of data from GRACE satellites Ariege Besson Adviser: Professor Ron Smith Second Reader: Professor Mark Brandon May 2 nd , 2018 A Senior Thesis presented to the faculty of the Department of Geology and Geophysics, Yale University, in partial fulfillment of the Bachelor’s Degree. In presenting this thesis in partial fulfillment of the Bachelor’s Degree from the Department of Geology and Geophysics, Yale University, I agree that the department may make copies or post it on the departmental website so that others may better understand the undergraduate research of the department. I further agree that extensive copying of this thesis is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this thesis for commercial purposes or financial gain is not allowed without my written consent. Ariege Besson, 02 May 2018 Abstract GRACE is a pair of satellites that fly 220 kilometers apart in a near-polar orbit, mapping Earth’s gravity field by accurately measuring changes in distance between the two satellites. With a 15 year continuous data record and improving data processing techniques, measurements from GRACE has contributed to many scientific findings in the fields of hydrology and geology. Several of the applications of GRACE data include mapping groundwater storage changes, ice mass changes, sea level rise, isostatic rebound from glaciers, and crustal deformation. Many applications of GRACE data have profound implications for society; some further our understanding of climate change and its effects on the water cycle, others are furthering our understanding of earthquake mechanisms.
    [Show full text]
  • FAME-C: Cloud Property Retrieval Using Synergistic AATSR and MERIS Observations
    Atmos. Meas. Tech., 7, 3873–3890, 2014 www.atmos-meas-tech.net/7/3873/2014/ doi:10.5194/amt-7-3873-2014 © Author(s) 2014. CC Attribution 3.0 License. FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations C. K. Carbajal Henken, R. Lindstrot, R. Preusker, and J. Fischer Institute for Space Sciences, Freie Universität Berlin (FUB), Berlin, Germany Correspondence to: C. K. Carbajal Henken ([email protected]) Received: 29 April 2014 – Published in Atmos. Meas. Tech. Discuss.: 19 May 2014 Revised: 17 September 2014 – Accepted: 11 October 2014 – Published: 25 November 2014 Abstract. A newly developed daytime cloud property re- trievals. Biases are generally smallest for marine stratocu- trieval algorithm, FAME-C (Freie Universität Berlin AATSR mulus clouds: −0.28, 0.41 µm and −0.18 g m−2 for cloud MERIS Cloud), is presented. Synergistic observations from optical thickness, effective radius and cloud water path, re- the Advanced Along-Track Scanning Radiometer (AATSR) spectively. This is also true for the root-mean-square devia- and the Medium Resolution Imaging Spectrometer (MERIS), tion. Furthermore, both cloud top height products are com- both mounted on the polar-orbiting Environmental Satellite pared to cloud top heights derived from ground-based cloud (Envisat), are used for cloud screening. For cloudy pixels radars located at several Atmospheric Radiation Measure- two main steps are carried out in a sequential form. First, ment (ARM) sites. FAME-C mostly shows an underestima- a cloud optical and microphysical property retrieval is per- tion of cloud top heights when compared to radar observa- formed using an AATSR near-infrared and visible channel.
    [Show full text]
  • Improved Quality Control for Quikscat Near Real-Time Data
    JP4.6 Improved Quality Control for QuikSCAT Near Real-time Data S. Mark Leidner, Ross N. Hoffman, and Mark C. Cerniglia Atmospheric and Environmental Research Inc., Lexington, Massachusetts Abstract errors. We will illustrate the types of errors that occur due to rain contamination and ambiguity removal. SeaWinds on QuikSCAT, launched in June 1999, We will also give examples of how the quality of the provides a new source of surface wind information retrieved winds varies across the satellite track, and over the world’s oceans. This new window on global varies with wind speed. surface vector winds has been a great aid to real- SeaWinds is an active, Ku-band microwave radar time operational users, especially in remote areas operating near ¢¤£¦¥¨§ © and is sensitive to centimeter- of the world. As with in situ observations, the qual- scale or capillary waves on the ocean surface. ity of remotely-sensed geophysical data is closely These waves are usually in equilibrium with the wind. tied to the characteristics of the instrument. But Each radar backscatter observation samples a patch remotely-sensed scatterometer winds also have a of ocean about . The vector wind is re- whole range of additional quality control concerns trieved by combining several backscatter observa- different from those of in situ observation systems. tions made from multiple viewing geometries as the The retrieval of geophysical information from the raw scatterometer passes overhead. The resolution of satellite measurements introduces uncertainties but the retrieved winds is . also produces diagnostics about the reliability of the Backscatter from capillary waves on the ocean retrieved quantities.
    [Show full text]
  • Watching the Winds Where Sea Meets Sky 14 August 2014, by Rosalie Murphy
    Watching the winds where sea meets sky 14 August 2014, by Rosalie Murphy the speed and direction of wind at the ocean's surface. "Before scatterometers, we could only measure ocean winds on ships, and sampling from ships is very limited," said Timothy Liu of NASA's Jet Propulsion Laboratory in Pasadena, California, who led the science team for NASA's QuikScat mission. Scatterometry began to emerge during World War II, when scientists realized wind disturbing the ocean's surface caused noise in their radar signals. NASA included an experimental scatterometer in its The SeaWinds scatterometer on NASA's QuikScat first space station in 1973 and again when it satellite stares into the eye of 1999's Hurricane Floyd as launched its SeaSat satellite in 1978. During its it hits the U.S. coast. The arrows indicate wind direction, three-month life, SeaSat's scatterometer provided while the colors represent wind speed, with orange and scientists with more individual wind observations yellow being the fastest. Credit: NASA/JPL-Caltech than ships had collected in the previous century. The ocean covers 71 percent of Earth's surface and affects weather over the entire globe. Hurricanes and storms that begin far out over the ocean affect people on land and interfere with shipping at sea. And the ocean stores carbon and heat, which are transported from the ocean to the air and back, allowing for photosynthesis and affecting Earth's climate. To understand all these processes, scientists need information about winds A JPL team then designed a mission called near the ocean's surface.
    [Show full text]
  • Aqua: an Earth-Observing Satellite Mission to Examine Water and Other Climate Variables Claire L
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 41, NO. 2, FEBRUARY 2003 173 Aqua: An Earth-Observing Satellite Mission to Examine Water and Other Climate Variables Claire L. Parkinson Abstract—Aqua is a major satellite mission of the Earth Observing System (EOS), an international program centered at the U.S. National Aeronautics and Space Administration (NASA). The Aqua satellite carries six distinct earth-observing instruments to measure numerous aspects of earth’s atmosphere, land, oceans, biosphere, and cryosphere, with a concentration on water in the earth system. Launched on May 4, 2002, the satellite is in a sun-synchronous orbit at an altitude of 705 km, with a track that takes it north across the equator at 1:30 P.M. and south across the equator at 1:30 A.M. All of its earth-observing instruments are operating, and all have the ability to obtain global measurements within two days. The Aqua data will be archived and available to the research community through four Distributed Active Archive Centers (DAACs). Index Terms—Aqua, Earth Observing System (EOS), remote sensing, satellites, water cycle. I. INTRODUCTION AUNCHED IN THE early morning hours of May 4, 2002, L Aqua is a major satellite mission of the Earth Observing System (EOS), an international program for satellite observa- tions of earth, centered at the National Aeronautics and Space Administration (NASA) [1], [2]. Aqua is the second of the large satellite observatories of the EOS program, essentially a sister satellite to Terra [3], the first of the large EOS observatories, launched in December 1999. Following the phraseology of Y.
    [Show full text]
  • NASA Earth Science Research Missions NASA Observing System INNOVATIONS
    NASA’s Earth Science Division Research Flight Applied Sciences Technology NASA Earth Science Division Overview AMS Washington Forum 2 Mayl 4, 2017 FY18 President’s Budget Blueprint 3/2017 (Pre)FormulationFormulation FY17 Program of Record (Pre)FormulationFormulation Implementation MAIA (~2021) Implementation MAIA (~2021) Landsat 9 Landsat 9 Primary Ops Primary Ops TROPICS (~2021) (2020) TROPICS (~2021) (2020) Extended Ops PACE (2022) Extended Ops XXPACE (2022) geoCARB (~2021) NISAR (2022) geoCARB (~2021) NISAR (2022) SWOT (2021) SWOT (2021) TEMPO (2018) TEMPO (2018) JPSS-2 (NOAA) JPSS-2 (NOAA) InVEST/Cubesats InVEST/Cubesats Sentinel-6A/B (2020, 2025) RBI, OMPS-Limb (2018) Sentinel-6A/B (2020, 2025) RBI, OMPS-Limb (2018) GRACE-FO (2) (2018) GRACE-FO (2) (2018) MiRaTA (2017) MiRaTA (2017) Earth Science Instruments on ISS: ICESat-2 (2018) Earth Science Instruments on ISS: ICESat-2 (2018) CATS, (2020) RAVAN (2016) CATS, (2020) RAVAN (2016) CYGNSS (>2018) CYGNSS (>2018) LIS, (2020) IceCube (2017) LIS, (2020) IceCube (2017) SAGE III, (2020) ISS HARP (2017) SAGE III, (2020) ISS HARP (2017) SORCE, (2017)NISTAR, EPIC (2019) TEMPEST-D (2018) SORCE, (2017)NISTAR, EPIC (2019) TEMPEST-D (2018) TSIS-1, (2018) TSIS-1, (2018) TCTE (NOAA) (NOAA’S DSCOVR) TCTE (NOAA) (NOAA’SXX DSCOVR) ECOSTRESS, (2017) ECOSTRESS, (2017) QuikSCAT (2017) RainCube (2018*) QuikSCAT (2017) RainCube (2018*) GEDI, (2018) CubeRRT (2018*) GEDI, (2018) CubeRRT (2018*) OCO-3, (2018) CIRiS (2018*) OCOXX-3, (2018) CIRiS (2018*) CLARREO-PF, (2020) EOXX-1 CLARREOXX XX-PF, (2020) EOXX-1
    [Show full text]
  • Latest Datasets and Services at the PO.DAAC
    Latest Datasets and Services at the PO.DAAC David Moroni [email protected] Jet Propulsion Laboratory, Pasadena, CA © 2016 California InsItute of Technology. Pasadena, CA. Government sponsorship acknowledged The Role of PO.DAAC Data Management & Data Access Stewardship Provide intuiIve services to discover, select, Preserve and distribute oceanographic data for the extract and uIlize oceanographic data. benefit of future generaons domesIcally and abroad. Map Generated By: www.travelIp.org Science Informaon Services and User Forum Team of science data specialists, mulIple user interacIon interfaces, and knowledgebase to help data users understand and beRer uIlize datasets and corresponding resource materials. 2 Overview of Data Sources Supported NASA Missions & Projects Other Missions & Projects Seasat, TOPEX/Poseidon, Jason-1, NSCAT, SeaWinds on ADEOS-II, AVHRR-Pathfinder, DMSP (SSMI, SSMIS), ERS-1/2, GEOS-3, GOES, QuikSCAT, ISS-RapidScat, Terra, MetOp-A/B, Oceansat-II, WindSat. Aqua, GRACE, GHRSST, MeASUREs, Aquarius, SPURS Physical Oceanographic Data • 565 Publicly Distributed Datasets • 108 in Near-Real-Time (NRT) Ø Calibrated Radiances Ø Gravity Ø Ocean Circulaon & Currents Ø Ocean Surface Salinity Ø Ocean Surface Topography Ø Ocean Vector Winds Ø Sea Surface Temperature Ø Sea Ice 3 14 New OVW Datasets 1. RapidScat Version 1.1: Released 1 December 2015 (h;p://bit.ly/rscatsigma0v1-1) – L1B, L2A 12.5-km, and L2A 25-km – First Ime RapidScat Sigma-0 data has been publicly released. 2. RapidScat Version 1.2 (same as above, plus L2B 12.5-km): Released 4 May 2016 – Time series: 2015-Aug-19 to present (7-day latency) – Connues from Version 1.1.
    [Show full text]
  • Cloudsat-CALIPSO Launch
    NATIONAL AERONAUTICS AND SPACE ADMINISTRATION CloudSat-CALIPSO Launch Press Kit April 2006 Media Contacts Erica Hupp Policy/Program (202) 358-1237 NASA Headquarters, Management [email protected] Washington Alan Buis CloudSat Mission (818) 354-0474 NASA Jet Propulsion Laboratory, [email protected] Pasadena, Calif. Emily Wilmsen Colorado Role - CloudSat (970) 491-2336 Colorado State University, [email protected] Fort Collins, Colo. Julie Simard Canada Role - CloudSat (450) 926-4370 Canadian Space Agency, [email protected] Saint-Hubert, Quebec, Canada Chris Rink CALIPSO Mission (757) 864-6786 NASA Langley Research Center, [email protected] Hampton, Va. Eliane Moreaux France Role - CALIPSO 011 33 5 61 27 33 44 Centre National d'Etudes [email protected] Spatiales, Toulouse, France George Diller Launch Operations (321) 867-2468 NASA Kennedy Space Center, [email protected] Fla. Contents General Release ......................................................................................................................... 3 Media Services Information ........................................................................................................ 5 Quick Facts ................................................................................................................................. 6 Mission Overview ....................................................................................................................... 7 CloudSat Satellite ....................................................................................................................
    [Show full text]
  • A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging
    remote sensing Article A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging Xin Yan 1 , Hua Chen 1,* , Bingru Tian 1, Sheng Sheng 1, Jinxing Wang 2 and Jong-Suk Kim 1 1 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China; [email protected] (X.Y.); [email protected] (B.T.); [email protected] (S.S.); [email protected] (J.-S.K.) 2 Information Center (Hydrology Monitor and Forecast Center), Ministry of Water Resources of the People’s Republic of China, Beijing 100053, China; [email protected] * Correspondence: [email protected] Abstract: High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precip- itation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01◦. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily Citation: Yan, X.; Chen, H.; Tian, B.; precipitation data, which retains the accuracy of the original GPM data and greatly improves their Sheng, S.; Wang, J.; Kim, J.-S.
    [Show full text]