Satellite and Radar Remote Sensing of Southern Plains Grass Fires: A Case Study

2010 ◽  
Vol 49 (10) ◽  
pp. 2133-2146 ◽  
Author(s):  
Thomas A. Jones ◽  
Sundar A. Christopher
Keyword(s):  
Remote Sensing ◽  
Brightness Temperature ◽  
Cross Sections ◽  
Radar Data ◽  
Southern Plains ◽  
Smoke Plumes ◽  
Fire Debris ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
The Individual

Abstract Many large grass fires occurred in north Texas and southern Oklahoma on 9 April 2009, destroying hundreds of homes and businesses and burning thousands of acres of grasslands, producing large smoke and debris plumes that were visible from various remote sensing platforms. At the same time, strong westerly winds were transporting large amounts of dust into the region, mixing with the smoke and debris already being generated. This research uses surface- and satellite-based remote sensing observations of this event to assess the locations of fires and the spatial distribution of smoke and dust aerosols. The authors present a unique perspective by analyzing radar observations of fire debris in conjunction with the satellite analysis of submicrometer smoke aerosol particles. Satellite data clearly show the location of the individual fires and the downwind smoke plumes as well as the large dust storm present over the region. In particular, Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness at 0.55 μm within the dust plume was around 0.5, and it increased to greater than 1.0 when combined with smoke. Using the difference in 11- versus 12-μm brightness temperature data combined with surface observations, the large extent of the dust plume was evident through much of north-central Texas, where visibilities were low and the 11–12-μm brightness temperature difference was negative. Conversely, smoke plumes were characterized by higher reflectance at 0.6 μm (visible wavelength). Cross sections of radar data through the several smoke and debris plumes indicated the burnt debris reached up to 5 km into the atmosphere. Plume height output from modified severe storm algorithms produced similar values. Since smoke aerosols are smaller and lighter when compared with the debris, they were likely being transported even higher into the atmosphere. These results show that the combination of satellite and radar data offers a unique perspective on observing the characteristics and evolution of smoke and debris plume emanating from grass fire events.

scholarly journals Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

2018 ◽  
Vol 10 (10) ◽  
pp. 1601 ◽  
Author(s):  
Carl Talsma ◽  
Stephen Good ◽  
Diego Miralles ◽  
Joshua Fisher ◽  
Brecht Martens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Laboratory Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Input Variables ◽  
Added Uncertainty ◽  
Model Components ◽  
The Individual

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

A Physically Based Algorithm for Non-Blackbody Correction of Cloud-Top Temperature and Application to Convection Study

2014 ◽  
Vol 53 (7) ◽  
pp. 1844-1857 ◽  
Author(s):  
Chunpeng Wang ◽  
Zhengzhao Johnny Luo ◽  
Xiuhong Chen ◽  
Xiping Zeng ◽  
Wei-Kuo Tao ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Weighting Function ◽  
Convective Cloud ◽  
Transfer Model ◽  
Height Range ◽  
Convective Clouds ◽  
Physically Based ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Cloud Top Temperature

AbstractCloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-μm brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat + Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-μm band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model. Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-μm channel is located at optical depth ~0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between −30 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-μm brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6–10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations.

Comparison of Ground-Based Radar and Geosynchronous Satellite Climatologies of Warm-Season Precipitation over the United States

2008 ◽  
Vol 47 (12) ◽  
pp. 3264-3270 ◽  
Author(s):  
John D. Tuttle ◽  
Richard E. Carbone ◽  
Phillip A. Arkin
Keyword(s):  
United States ◽  
Brightness Temperature ◽  
Satellite Data ◽  
Warm Season ◽  
Propagation Speed ◽  
Radar Data ◽  
The United States ◽  
Temperature Threshold ◽  
Brightness Temperatures ◽  
The Difference

Abstract Studies in the past several years have documented the climatology of warm-season precipitation-episode statistics (propagation speed, span, and duration) over the United States using a national composited radar dataset. These climatological studies have recently been extended to other continents, including Asia, Africa, and Australia. However, continental regions outside the United States have insufficient radar coverage, and the newer studies have had to rely on geostationary satellite data at infrared (IR) frequencies as a proxy for rainfall. It is well known that the use of IR brightness temperatures to infer rainfall is subject to large errors. In this study, the statistics of warm-season precipitation episodes derived from radar and satellite IR measurements over the United States are compared and biases introduced by the satellite data are evaluated. It is found that the satellite span and duration statistics are highly dependent upon the brightness temperature threshold used but with the appropriate choices of thresholds can be brought into good agreement with those based upon radar data. The propagation-speed statistics of satellite events are on average ∼4 m s−1 faster than radar events and are relatively insensitive to the brightness temperature threshold. A simple correction procedure based upon the difference between the steering winds for the precipitation core and the winds at the level of maximum anvil outflow is developed.

scholarly journals CORRECTION FOR THE CIRRUS CLOUD TOP HEIGHT OF MODIS BASED ON CALIPSO IN BEIJING-TIANJIN-HEBEI REGION

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 203-210
Author(s):  
B. Y. Yang ◽  
J. Liu ◽  
X. Jia
Keyword(s):  
Remote Sensing ◽  
Satellite Observation ◽  
Orthogonal Polarization ◽  
Cirrus Cloud ◽  
Weather System ◽  
Cloud Optical Depth ◽  
Lower Cloud ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Abstract. Cirrus plays an important role in atmospheric radiation. It affects weather system and climate change. Satellite remote sensing is an important kind of observation for cloud. As a passive remote sensing instrument, large bias was found for thin cirrus cloud top height retrieval from MODIS (Moderate Resolution Imaging Spectroradiometer). Comparatively, CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) aboard CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) which is an active remote sensing instrument can acquire more accurate characteristics of thin cirrus cloud. In this study, CALIPSO cirrus cloud top height data was used to correct MODIS cirrus cloud top height. The data analysis area was selected in Beijing-Tianjin-Hebei region and data came from 2013 to 2017. Linear fitting method was selected based on cross-validation method between MODIS and CALIPSO data. The results shows that the difference between MODIS and CALIPSO changes from −3~2 km to −2.0~2.5 km, and the maximum difference changes from about −0.8 km to about 0.2 km. In the context of different vertical levels and cloud optical depth, MODIS cirrus cloud top height is improved after correcting, which is more obvious at lower cloud top height and optical thinner cirrus.

scholarly journals Bi-Spectral Infrared Algorithm for Cloud Coverage over Oceans by the JEM-EUSO Mission Program

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6506
Author(s):  
David Santalices ◽  
Susana Briz ◽  
Antonio J. de Castro ◽  
Fernando López
Keyword(s):  
Brightness Temperature ◽  
Ad Hoc ◽  
Low Cost ◽  
Infrared Camera ◽  
Cosmic Ray ◽  
High Energy ◽  
Ir Camera ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Split Window Algorithm

The need to monitor specific areas for different applications requires high spatial and temporal resolution. This need has led to the proliferation of ad hoc systems on board nanosatellites, drones, etc. These systems require low cost, low power consumption, and low weight. The work we present follows this trend. Specifically, this article evaluates a method to determine the cloud map from the images provided by a simple bi-spectral infrared camera within the framework of JEM-EUSO (The Joint Experiment Missions-Extrem Universe Space Observatory). This program involves different experiments whose aim is determining properties of Ultra-High Energy Cosmic Ray (UHECR) via the detection of atmospheric fluorescence light. Since some of those projects use UV instruments on board space platforms, they require knowledge of the cloudiness state in the FoV of the instrument. For that reason, some systems will include an infrared (IR) camera. This study presents a test to generate a binary cloudiness mask (CM) over the ocean, employing bi-spectral IR data. The database is created from Moderate-Resolution Imaging Spectroradiometer (MODIS) data (bands 31 and 32). The CM is based on a split-window algorithm. It uses an estimation of the brightness temperature calculated from a statistical study of an IR images database along with an ancillary sea surface temperature. This statistical procedure to obtain the estimate of the brightness temperature is one of the novel contributions of this work. The difference between the measured and estimation of the brightness temperature determines whether a pixel is cover or clear. That classification requires defining several thresholds which depend on the scenarios. The procedure for determining those thresholds is also novel. Then, the results of the algorithm are compared with the MODIS CM. The agreement is above 90%. The performance of the proposed CM is similar to that of other studies. The validation also shows that cloud edges concentrate the vast majority of discrepancies with the MODIS CM. The relatively high accuracy of the algorithm is a relevant result for the JEM-EUSO program. Further work will combine the proposed algorithm with complementary studies in the framework of JEM-EUSO to reinforce the CM above the cloud edges.

Satellite remote sensing of primary productivity in the Bering and Chukchi Seas using an absorption-based approach

2012 ◽  
Vol 69 (7) ◽  
pp. 1194-1204 ◽  
Author(s):  
Toru Hirawake ◽  
Katsuhito Shinmyo ◽  
Amane Fujiwara ◽  
Sei-ichi Saitoh
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Satellite Remote Sensing ◽  
The Arctic ◽  
Estimation Accuracy ◽  
Chl A ◽  
Phytoplankton Absorption ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Ocean Colour Remote Sensing

Abstract Hirawake, T., Shinmyo, K., Fujiwara, A., and Saitoh, S. 2012. Satellite remote sensing of primary productivity in the Bering and Chukchi Seas using an absorption-based approach. – ICES Journal of Marine Science, 69: . Ocean colour remote sensing has been utilized for studying primary productivity in the Arctic Ocean. However, phytoplankton chlorophyll a (Chl a) is not predicted accurately because of the interference of coloured dissolved organic matter (CDOM) and non-algal particles (NAP). To enhance the estimation accuracy, a phytoplankton absorption-based primary productivity model (ABPM) was applied to the Bering and Chukchi Seas. The phytoplankton absorption coefficient was determined correctly from sea surface remote sensing reflectance (Rrs) and reduced the effect of CDOM and NAP in primary productivity (PPeu) estimates. PPeu retrieved from in situ Rrs using the ABPM satisfied a factor of 2 of measured values. PPeu estimated from the Moderate Resolution Imaging Spectroradiometer Rrs data were within the range of historical values. These estimated PPeu values were less than half of those of the model based on Chl a, and the difference between the two models reflected the influence of CDOM and NAP absorptions. Interannual variation in August and September over the period 2002–2010 showed an increase in primary productivity. The increase in 2007 was especially large, by a factor of 1.51–2.71, compared with 2006. The significant temporal increase in productivity detected here differs from earlier studies that detected little, if any, change in the region.

Empirical Study On Attributes Leading To Satisfaction Among Bank Customer

2019 ◽  
Vol 118 (7) ◽  
pp. 101-110
Author(s):  
Ms.U.Sakthi Veeralakshmi ◽  
Dr.G. Venkatesan
Keyword(s):  
Service Quality ◽  
Behavioral Intention ◽  
Banking Sector ◽  
Positive Impact ◽  
Quality Performance ◽  
Public And Private ◽  
The Difference ◽  
The Individual ◽  
Private Banking ◽  
Service Gap

This research aims at measuring the service quality in public and private banking sector and identifying its relationship to customer satisfaction and behavioral intention. The study was conducted among 500 bank customers by using revised SERVQUAL instrument with 26 items. Behavioral intention of the customers was measured by using the behavioral intention battery. The researcher has used a seven point likert scaling to measure the expected and perceived service quality (performance) and the behavioral intention of the customer. The instrument was selected as the most reliable device to measure the difference-score conceptualization. It is used to evaluate service gap between expectation and perception of service quality. Modifications are made on the SERVQUAL instrument to make it specific to the Banking sector. Questions were added to the instrument like Seating space for waiting (Tangibility), Parking space in the Bank (Tangibility), Variety of products / schemes available (Tangibility), Banks sincere steps to handling Grievances of the customers (Responsiveness). The findings of the study revealed that the customer’s perception (performance) is lower than expectation of the service quality rendered by banks. Responsiveness and Assurance SQ dimensions were the most important dimensions in service quality scored less SQ gap. The study concluded that the individual service quality dimensions have a positive impact on Overall Satisfaction.

Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

2017 ◽  
Vol 168 (3) ◽  
pp. 127-133
Author(s):  
Matthew Parkan
Keyword(s):  
Cross Sections ◽  
Forest Edge ◽  
Airborne Lidar ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Small Areas ◽  
Interactive 3D ◽  
The Individual ◽  
Airborne Lidar Data ◽  
Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

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