I was able to develop and test a code in R software using a simple pan sharpening formula (described here ) to create Pansharpened image of WorldView-2 (WV2) Multi-Spectral (MS) bands with high resolution Panchromatic (Pan) band. I have created a gif as shown in the figure above with Pan and MS ( a vegetation composite NIR2 in red, Yellow in green and Coastal in blue) images (data credit: esa).
Pansharpening is a process that merges/fuses high-resolution Pan data with medium-resolution MS data to create a high-resolution MS image (USGS).
WV2 is an imaging satellite of DigitalGlobe Inc., USA (a follow-on to WorldView-1 – WV1). WV2 sensor offers high resolution images in Pan 0.46 cm and unique MS 1.8 m at nadir. The MS bands are listed in the table below (credit: DigitalGlobe):
| Band Name | Wavelength | Detail |
| Coastal Blue | 400 – 450 nm | New band Absorbed by chlorophyll in healthy plants and aids in conducting vegetative analysis Least absorbed by water, and will be very useful in bathymetric studies Substantially influenced by atmospheric scattering and has the potential to improve atmospheric correction techniques |
| Blue | 450 – 510 nm | Identical to QuickBird Readily absorbed by chlorophyll in plants Provides good penetration of water Less affected by atmospheric scattering and absorption compared to the Coastal Blue band |
| Green | 510 – 580 nm | Narrower than the green band on QuickBird Able to focus more precisely on the peak reflectance of healthy vegetation Ideal for calculating plant vigor Very helpful in discriminating between types of plant material when used in conjunction with the Yellow band |
| Yellow | 585 – 625 nm | New band Very important for feature classification Detects the “yellowness” of particular vegetation, both on land and in the water |
| Red | 630 – 690 nm | Narrower than the red band on QuickBird and shifted to longer wavelengths Better focused on the absorption of red light by chlorophyll in healthy plant materials One of the most important bands for vegetation discrimination Very useful in classifying bare soils, roads, and geological features |
| Red-Edge | 705 – 745 nm | New band Centered strategically at the onset of the high reflectivity portion of vegetation response Very valuable in measuring plant health and aiding in the classification of vegetation |
| NIR1 | 770 – 895 nm | Narrower than the NIR1 band on QuickBird to provide more separation between it and the Red-Edge sensor Very effective for the estimation of moisture content and plant biomass Effectively separates water bodies from vegetation, identifies types of vegetation and also discriminates between soil types |
| NIR2 | 860 – 1040 nm | New band Overlaps the NIR1 band but is less affected by atmospheric influence Enables broader vegetation analysis and biomass studies |
I have also created a gif as shown in the figure above with Pan and MS ( a shadow composite NIR2 in red, Red Edge in green and Yellow in blue) images to compare results.
Satellite observed Rainfall and Land Surface Temperature data are used here to obtain a daily drought product called Keetch-Byram Drought Index (KBDI), which ranges from 0 (wet condition) to 800 (dry condition). Anomaly of drought index (KBDI) which is deviation from long term average if Drought Index is an Indicator of Drought Condition. Hourly global rainfall data at 0.1° spatial resolution is obtained from GSMaP NRT System by Japan Aerospace Exploration Agency (JAXA). It is derived from microwave radiometers (e.g., TMI, AMSR-E and SSM/I) and infrared radiometers (e.g., MTSAT, METEOSAT and GOES). This is an hourly rainfall product which is available to public after 4 hours after the observations. Land Surface Temperature (LST) data are obtained from MTSAT, a weather satellite of the Japan Meteorological Agency (JMA) with a spatial resolution of 4 km. LST is observed in every 30 minutes using 4 thermal-infrared channels.
MODIS (Terra) Fire and Thermal Anomalies Temporal Coverage: 8 May 2012 – present. The MODIS Fire and Thermal Anomalies layer shows active fire detections and thermal anomalies, such as volcanoes, and gas flares. Fires can be set naturally, such as by lightning, or by humans, whether intentionally or accidentally. Fire is often thought of as a menace and detriment to life, but in some ecosystems it is necessary to maintain the equilibrium, for example, some plants only release seeds under high temperatures that can only be achieved by fire, fires can also clear undergrowth and brush to help restore forests to good health, humans use fire in slash and burn agriculture, to clear away last year’s crop stubble and provide nutrients for the soil and to clear areas for pasture. The fire layer is useful for studying the spatial and temporal distribution of fire, to locate persistent hot spots such as volcanoes and gas flares, to locate the source of air pollution from smoke that may have adverse human health impacts.
