Apalachicola Bay is located on the Northwest coast of Florida and has been called the heart and soul of North Florida. The oysters produced at the bay are undoubtedly superior and are considered the finest in the nation, according to New York Times (New York Times, 2002). In addition, the bay is one of the most productive estuaries in the country. However, the total harvest dropped to around 1 million pounds in 2013 and the National Oceanic and Atmospheric Administration declared a fishery disaster on the bay (Alvarez, 2015). The effect on oystermen's life is even worse. They complained that they are not able to pull out enough oysters to pay for their boat gas.
During June and July aerosol loading was comparatively higher at around 2-6 km height compared to the rest of the months. This possibly explains the high values of COF (figure 15 (b)) at higher altitudes in these two months due to convection processes that might have contributed to the formation of CCN (Jiang et al., 2007; Jiang et al., 2011). Excess rainfall events during 2013 ISMR, had resulted in heavy wash out of aerosols in the entire season which can be observed from low AOF values. The month of June witnessed an early onset of monsoon because of which there were more washouts of aerosols and September being the offset phase of the monsoon, witnessed high aerosol loading. July and August witnessed low AOF values, except that August had higher value of nearly 30% than that of June which had nearly 10-25% at 0.5-2 km altitude range The aerosol loading during June, July and August above the lower troposphere up to 4-6 km altitude might have contributed to cloud formation at higher altitudes which can be seen in the COF values of nearly 10-20%, 15-20% and 10-30% respectively at 12-17 km height range.
Indirect measurements of atmospheric composition can host reliable sources of information around the climate of today in comparison with the historical climate. One way of gaining information about past
The San Francisco Bay estuarine system has experienced many threats from human activities over the last few centuries. Currently, the two most imminent threats to this Bay area estuarine system are being recognized as a result of global climate change. The first major water-related threat is associated with rising sea levels along the bay and outer coastal shorelines. This is problematic for many different reasons, including coastal and bay-side flooding and shoreline erosion. The second major water-related threat, which can be attributed to both rising sea levels and a reduction of fresh water runoff into the estuarine system is salt water intrusion into ground water reserves. With the recognition and acknowledgement of these serious
Source: Figure obtained from the National Oceanic and Atmospheric Administration. Graph retrieved 18 October 2009 from http://www.cpc. ncep.noaa.gov/products/stratosphere/uv_index/gif_ les/uvi_world_f1.gif. is U.S. Government material is not subject to copyright protection within the United States.
(2015) will be adapted to derive this transport from temperature and humid- ity retrievals from the Microwave Limb Sounder (MLS) on board the Aura satellite, for hurricane days and non hurricane days. MLS data have 1-3 km vertical resolution and ∼200 km horizontal resolution in the UTLS. Cross correlations between time series of the temperature/water content anomalies will be calculated for two consecutive latitudes at a given pressure level. The
Prior to atmospheric correction and radiometric calibration, both sentinel and Landsat 8 images were geo referenced to UTM 37N coordinate system (WGS 84 datum and Spheroid) and subset image was created. Landsat 8 image acquired in the form of digital number (DN) were converted to radiance and TOA reflectance values using ENVI 5.3 software. Atmospheric correction was also employed using dark object subtraction module of ENVI 5.3 (ExelisVisual Information Solutions, 2010). The DN of Sentinel-2A LIC productwere divided by 10000 to obtain TOA reflectance. The false color composite of Sentinel 2 and Landsat 8 image of the study site after preprocessing is shown in fig 3.
Ozone Monitoring Instrument, or OMI, is a mission formed in coalition with the Netherland’s Agency for Agency for Aerospace Programs, or NIVR, and Finnish Meteorological Institute, FMI to help with the EOS Aura mission. It will aid in recording total ozone measurements and other fields related to this topic. The instruments employed on OMI consist of hyperspectral imaging in a push-broom mode to observe the solar backscatter radiation. This hyperspectral component greatly strengthens the accuracy and precision of the ozone data collected. It also allows for pinpoint radiometric and wavelength auto-calibration over the mission timeline. The Earth, as a whole, will be viewed using 740 wavelength bands with a swath that is large enough to gain full coverage in 14 orbits, or one day (Dunbar, 2005).
RADARSAT-1 was also implemented to provide data products for commercial applications such as shipping, oil exploration, offshore oil drilling, and resource management. RADARSAT-1 has been used to collect substantial and significant science data sets of Antarctica and Arctic sea ice. For this study the ‘Standard Mode’ was used and the SAR images have a swath width of 100km and a spatial resolution of about 25–30 m. For this research, images were selected in beam modes 1, 2, 3, and 5 (where the S denotes ‘Standard Mode’), that provided lower incident angles between 20 – 42o in descending mode.
Cleugh et al. (2007) pointed out that the use of instantaneous measurements of the radiometric surface temperature to calculate time-averaged fluxes led to errors. They emphasized uncertainties in models which use the MODIS 8-day that is a composite of once-daily overpass at ~ 10:30 h local time. In this case, the radiometric temperature is determined under a view angle at the satellite overpass time, using emissivities based on vegetation classes at a 1-km grid that differs from that of the MODIS pixel location.
Conventional methods of estimating leaf water content in the field are time consuming and location specific. Remote sensing is an effective alternative to field sampling for the retrieval of leaf water content, being non-destructive and providing continuous spatial coverage of a large area (Sepulcre-Cantó et al., 2006; Ullah et al., 2012c). Plant water status can be assessed remotely by measuring canopy reflectance, since they change in response to crop water content (Pen˜uelas et al., 1997; Ustin et al., 1998; Stimson et al., 2005). As a technique, canopy spectral reflectance offers a number of advantages, such as easy and quick measurements, integration at the canopy level and the fact that additional parameters can be estimated simultaneously via a series of diverse spectral indices (i.e. photosynthetic capacity, leaf area index, intercepted radiation, and chlorophyll content) (Araus et al., 2001). Given its versatility, canopy reflectance is a valuable tool for high throughput phenotyping (Montes et al., 2007; Chapman, 2008). Leaf water status has been successfully estimated using the near infrared and shortwave infrared (Zygielbaum et al., 2009). In contrast, the mid and thermal infrared (2.5–14 μm) domain is mostly ignored because of a number of challenges, including unavailability of spectroradiometers (i.e. sensitive to the mid to thermal infrared), and the subtle variations in
Image resampling involves the conversion of satellite imagery at a relatively fine scale to a more coarse spatial resolution with imagery from similar or different satellite sensor with varying spatial resolution. 17 The choice of the resampling method depends, among others, on the ratio between input and output pixel size and the purpose of the resampled image (Bakker, et al., 2004). In this research, Landsat TM images were resampled using the nearest neighbor resampling technique to preserve the original image radiometric information (Serra, X, & Sauri, 2003)(Serra et al., 2003. In addition, Nearest neighbor assigns the digital number, DN (fig..x), value of the closest original pixel to the new pixel by retaining all spectral information for efficient image classification ((Parker, Kenyon, & Troxel, 1983). (Bakker, et al., 2004).
Preprocessed level 1 Satellite images were obtained from NASA Earth Explorer web site for this study. The images were downloaded from USGS Landsat archive web site and layer stacked after extracting the compiled file. Image selection and preference of seasons was based on, one for its free availability of the data and the other it was free from cloud coverage. After data acquisition, image to
For this Level 2 daily data of AOD and CF from MODIS (Aqua) are considered along with high resolution rainfall data from IMD. These data were incorporated in to the AFF model to observe the microphysical and radiative effects of the aerosol on cloud cover. The spatial and temporal variations of AOD, CF and RF in addition with other ancillary parameters like AE, AI, CER, LWP and OLR were studied. Also, the vertical distribution of aerosols and clouds from CALIPSO data were analysed in the present study.