Lecture 7 Earth observation missions
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1 Remote sensing for agricultural applications: principles and methods ( ) Instructor: Prof. Tao Cheng Nanjing Agricultural University Lecture 7 Earth observation missions May 7, 2014
2 Earth Observation To monitor changes in the Earth s environment To better understand the causes of those changes To protect our living planet Land (land cover and land use) Ocean (sea surface temperature) Ice (glacier, snow) Atmosphere (cloud, greenhouse gases) Biosphere (forests, crops) NDVI Sea surface temperature Figures from NASA Earth Observations Water vapor
3 Categories of earth observation data High temporal resolution / low spatial resolution: MODIS, VIIRS AVHRR High spatial resolution (commercial) Sub-meter: WorldView-2, GeoEye-1, QuickBird, IKONOS SPOT, RapidEye Medium resolution Landsat High spectral resolution Hyperion
4 Earth Science missions of the U.S. Added by NASA: March 21, 2014
5 MODIS Moderate Resolution Imaging Spectroradiometer Aboard the Terra (EOS AM, launched in 1999/12) and Aqua (EOS PM, launched in 2002/05) satellites Spatial resolution: 250 m (bands 1-2) 500 m (bands 3-7) 1000 m (bands 8-36) Quantization: 12 bits Field of view: ±55º Swath width: 2,330 km across-track
6 MODIS Special issue on MODIS in Remote Sensing of Environment Volume 83, Issues 1 2, Pages (November 2002) The Moderate Resolution Imaging Spectroradiometer (MODIS): a new generation of Land Surface Monitoring
7 MODIS spectral bands Primary Use Band Bandwidth 1 Required SNR 3 Land/Cloud/A erosols Boundaries Land/Cloud/A erosols Properties Ocean Color/ Phytoplankton / Biogeochemis try Atmospheric Water Vapor Source:
8 MODIS spectral bands Primary Use Band Bandwidth 1 Required NE[delta]T(K) 4 Surface/Cloud Temperature Atmospheric Temperature Cirrus Clouds Water Vapor Cloud Properties (SNR) Ozone Surface/Cloud Temperature Cloud Top Altitude Bands 1 to 19 are in nm; Bands 20 to 36 are in µm 3 SNR = Signal-to-noise ratio 4 NE(delta)T = Noise-equivalent temperature difference Note: Performance goal is 30-40% better than required Source:
9 MODIS data MODIS Level 1 data, geolocation, cloud mask, and Atmosphere products: MODIS land products: (we focus on LAND) Search MODIS data in Web of Science and limit the document type to Article. Let s look at the statistics. (accessed May 5, 2014) Articles published: 2002/1-2014/5 Articles found: 7978 Average citations per article: Published articles in each year Citations in each year
10 MODIS data products MOD 09 - Surface Reflectance MOD 11 - Land Surface Temperature & Emissivity MOD 12 - Land Cover/Land Cover Change MOD 13 - Gridded Vegetation Indices (Max NDVI & Integrated MVI) MOD 14 - Thermal Anomalies, Fires & Biomass Burning MOD 15 - Leaf Area Index & FPAR MOD 16 - Evapotranspiration MOD 17 - Net Photosynthesis and Primary Productivity MOD 43 - Surface Reflectance MOD 44 - Vegetation Cover Conversion These are standard products commonly used in earth science related disciplines. They may be available in different spatial and temporal resolutions. For more about MODIS data products, visit
11 Global vegetation distribution Greenness index derived from MODIS/Terra imagery. May 2013
12 Data access for global studies
13 Changes in canopy water content derived from MODIS imagery for the continental USA Year 2005 This is an example of using MODIS data to study the spatial patterns of ecosystem change. Trombetti et al. (2008)
14 MODIS subset data Use data for locations of interests (e.g., flux towers) Data available: 2000/2/18 present
15 Phenology studies An example of using MODIS data to study crop phenology. Yan et al. (2009), AEE, 129:
16 VIIRS Visible Infrared Imaging Radiometer Suite Onboard the Suomi National Polar-orbiting Partnership (NPP) satellite (launched in 2011/10) For better understanding of global climate change Extends and improves upon AVHRR and MODIS Data products are still in processing. Courtesy of Raytheon Space and Airborne Systems
17 Landsat Currently, Landsat 8 and Landsat 7 collect data. Other Landsat sensors were ceased. Landsat 8 collects >400 scenes/day and Landsat 7 collects ~300 scenes/day. Landsat 7 ETM+ has the Scan Line Corrector (SLC) failure and produces zig-zag gaps on imagery since 2003/5/31. Pixel size: OLI multispectral bands: 30 m OLI panchromatic band: 15 m TIRS thermal bands: 100 m but resampled to 30 m An L7-ETM+ image before and after the SLC failure. Image credit: USDA FAS Agricultural Applications Seminar L-7 L-8 Part of the Great Salt Lake, Utah as seen on 2013/3/29. Image credit: USGS.
18 Landsat download Search and Bulk-Download Data - EarthExplorer Browse and Download Data - GloVis If not found in the USGS archive, imagery may be available in the archive of USGS international ground stations. Current Landsat products (Climate Data Records): Surface reflectance SR-derived spectral indices Unavailable for Landsat 8 imagery yet. Future Landsat products (Essential Climate Variables, ECV): LAI Global Land Cover More information at
19 Crop field extraction from Landsat data a. Extracted crop fields (700x900 Landsat pixels) b. 2008Crop Data Layer (CDL) c CDL d CDL Yan & Roy (2014), RSE, 144:
20 Crop classification and LAI mapping Gonzalez-Sanpedro et al. (2008), RSE, 112:
21 IKONOS The first sub-meter satellite Launched in 1999/9 Pixel size: Pan: 1 m XS: 4 m Spectral bands Pan, B, G, R, NIR An IKONOS imagery of farms in Saudi Arabia 21
22 QuickBird Launched in 2001/10 Pixel size: Pan: 0.61 m XS: 2.44 m Spectral bands Pan, B, G, R, NIR Within-field variation as seen from the QuickBird multispectral imagery Source: Stephan J. Maas, Texas Technical University
23 WorldView-2 The only sub-meter satellite that can collect data in the red edge region (since 2009) Band Number Wavelength (um) Pixel size 1. Costal m 2. B m 3. G m 4. Y m 5. R m 6. RE m 7. NIR m 8. NIR m 9. Pan m Riverina, Australia Credit: Digital Globe
24 RapidEye The RapidEye constellation has 5 identical sensors Archive available since Feb 2009 Daily revisit capability (acquisition frequency may be different for your target area) Band Number Xinjiang, China Wavelength (um) 1. B m 2. G m 3. R m 4. RE m 5. NIR m Pixel size
25 Hyperspectral - Hyperion A hyperspectral sensor onboard the EO-1 satellite um, 220 bands, pixel size = 30 m Data acquisition: not systematically global, based on customers Data Acquisition Requests (DARs). Perhaps the most important thing Hyperion has done, is teach the community how to work with complex hyperspectral data. Elizabeth Middleton (NASA-GSFC) ( Demand: more spaceborne hyperspectral data, improved data quality
26 CHRIS/PROBA CHRIS: Compact High Resolution Imaging Spectrometer Onboard an European satellite PROBA (launched in 2001) nm 63 bands (36 m resolution) 19 bands (18 m resolution) Delegido et al. (2013), EJA, 46, LAI map derived from CHIRS data
27 EnMAP Environmental Mapping and Analysis Program: A German Earth Observation satellite Currently scheduled to be launched in From
28 Future hyperspectral spaceborne missions Under construction or ready for launch In planning (Staenz & Held, 2012, IGARSS) Plans are subject to change due to funding issues.
29 Paper discussion 2: hyperspectral remote sensing of vegetation 1. Garbulsky et al. (2011), RSE, 115, , PRI review. (Zhou/Li) 2. Jacquemoud et al. (2009), RSE, 113, , PROSAIL review. (Dai/Deng) 3. Kokaly et al. (2009), RSE, 113, , Canopy chemistry review. (Daniel/Li) 4. Ollinger et al. (2011), NP, 189, , canopy spectral variability review. (Wang/Zhou) 5. Schaepman et al. (2009), RSE, 113, S123-S137, EES review. (Sun/Zheng) 6. Sims & Gamon (2002), RSE, 81, , designing leaf pigment indexes. (Wu/Liu) 7. Ustin et al. (2009), RSE, 113, S67-S77, foliar pigment review. (He/Zhou)
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37 Evaluation of presentations Each presentation will be evaluated based on the criteria below on a 100-point scale. Each group will be evaluated by the other six groups and the instructor. The presentation weighs 20% of your final grade. 10% will be given to the average of student evaluations and 10% to the instructor s evaluation. Each speaker has 10 min to present and 2 min to answer questions. Were the main concepts presented in an understandable way? Were the slides well prepared such as text font size and propose use of text and pictures? Were the pace, fluency and duration of the presentation appropriate? Were questions handled well and answered appropriately? What is the overall impression of the presentation?
38 Lessons from last discussion Avoid reading the text in your slides Better prepare your slides for 10 minutes Try your best to answer questions
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