Satellite Remote Sensing of Cirrus

Cirrus ◽  
2002 ◽  
Author(s):  
Patrick Minnis
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Climate Models ◽  
Cirrus Clouds ◽  
Data Sets ◽  
Visible Radiation ◽  
Accurate Identification ◽  
Spatial And Temporal Scales ◽  
Sensing Systems ◽  
Particle Size And Shape

The determination of cirrus properties over large spatial and temporal scales will, in most instances, require the use of satellite data. Global coverage at resolutions as fine as several meters are attainable with instruments on Landsat, and temporal coverage at 1-min intervals is now available with the latest Geostationary Operational Environmental Satellite (GOES) imagers. Extracting information about cirrus clouds from these satellite data sets is often difficult because of variations in background, similarities to other cloud types, and the frequently semitransparent nature of cirrus clouds. From the surface, cirrus clouds are readily discerned by the human observer via the patterns of scattered visible radiation from the sun, moon, and stars. The relatively uniform background presented by the sky facilitates cloud detection and the familiar textures, structures, and apparent altitude of cirrus distinguish it from other cloud types. From satellites, cirrus can also be detected from scattered visible radiation, but the demands of accurate identification for different surface backgrounds over the entire diurnal cycle and quantification of the cirrus properties require the analysis of radiances scattered or emitted over a wide range of the electromagnetic spectrum. Many of these spectra and high-resolution satellite data can be used to understand certain aspects of cirrus clouds in particular situations. Intensive study of well-measured cases can yield a wealth of information about cirrus properties on fine scales (e.g., Minnis et al. 1990; Westphal et al. 1996). Production of a global climatology of cirrus clouds, however, requires compromises in spatial, temporal, and spectral coverage (e.g., Schiffer and Rossow 1983). This chapter summarizes both the state of the art and the potential for future passive remote sensing systems to aid the understanding of cirrus processes and to acquire sufficient statistics for constraining and refining weather and climate models. Theoretically, many different aspects of cirrus can be determined from passive sensing systems. A limited number of quantities are the focus of most efforts to describe cirrus clouds. These include the areal coverage, top and base altitude or pressure, thickness, top and base temperatures, optical depth, effective particle size and shape, vertical ice water path, and size, shape and spacing of the cloud cells.

scholarly journals INVENTÁRIO DE CARBONO EM FRAGMENTO DE FLORESTA OMBRÓFILA MISTA POR DETECÇÃO REMOTA

FLORESTA ◽  
2014 ◽  
Vol 44 (4) ◽  
pp. 697
Author(s):  
Henrique Luis Godinho Cassol ◽  
Dejanira Luderitz Saldanha ◽  
Tatiana Mora Kuplich
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Empirical Relationship ◽  
Data Sets ◽  
Above Ground Biomass ◽  
Continuous Forest ◽  
Using Data ◽  
Continuous Forest Inventory ◽  
Radiometric Data ◽  
Carbon Inventory

O trabalho teve como objetivo inventariar o carbono de um fragmento de Floresta Ombrófila Mista utilizando dados provenientes de sensores de média resolução espacial. Uma cena dos sensores ASTER, LISS e TM foi empregada na obtenção dos dados radiométricos (espectrais), e os dados de biomassa e carbono (biofísicos) foram oriundos de parcelas de inventário florestal contínuo em São João do Triunfo, PR. A metodologia consistiu em estabelecer a relação empírica entre esses conjuntos de dados por meio de equações lineares de regressão. À exceção do sensor TM, que apresentou resultado insatisfatório, o uso dos dados oriundos dos sensores LISS e ASTER foi adequado para se inventariar o carbono florestal por detecção remota, com erros inferiores aos estabelecidos nas campanhas de inventários tradicionais (α < 0,05).Palavras-chave: Estoque de carbono; sensoriamento remoto; ASTER; TM; LISS. AbstractCarbon inventory in a fragment of Mixed Ombrophylous Forest by remote sensing. The research aims to make inventory of carbon of a fragment of Araucaria Forest using data from medium spatial resolution sensors. Satellite data from ASTER, TM and LISS were used to obtain the radiometric data. The above ground biomass and carbon data (biophysical data) were derived from the continuous forest inventory located in São João do Triunfo, PR. The methodology consisted of establishing the empirical relationship between spectral and biophysical data sets using linear regression. Except for the TM data, which showed unsatisfactory results, the use of ASTER and LISS satellite data was suited to forest carbon inventory by remote sensing, with errors lower than those set in traditional inventory campaigns (α < 0,05).Keywords: Carbon stock; remote sensing; ASTER; TM; LISS.

Remote Sensing Prospecting Deposit Model Using Aster Data: A Case Study from Southern Part of Taxkorgan Tajik Autonomous County, China

2015 ◽  
Vol 733 ◽  
pp. 124-129
Author(s):  
Hui Zhi Wu ◽  
Qi Gang Jiang ◽  
Chao Jun Bai
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Mineral Exploration ◽  
Information Source ◽  
Remote Sensing Data ◽  
Screening Methods ◽  
Data Sets ◽  
Sensing Data ◽  
Work Area ◽  
Geological Conditions

This work uses multiple types of remote sensing data to develop a model-based mineral exploration method. Data used include Worldview-2 satellite data as the main information source supplemented by QuickBird satellite data to assist in geological interpretations and ASTER satellite data to extract remote sensing anomalies. We have enhanced the spectral and spatial resolution of the remote sensing data using ENVI software. Human-computer interaction methods have been used to confirm the geological conditions. We have interpreted 24 distinct lithologic units, including various types of metamorphic and sedimentary rocks. A total of 471 remote sensing anomalies were delineated, consisting of 173 hydroxyl anomalies and 298 iron-staining anomalies. Geological background screening methods were applied to identify 98 remote sensing anomalies, of which 29 were recommended for further study. Based on the interpretation of anomalies extracted from the ASTER and other geological remote sensing data sets, we have established a typical-deposit prospecting model. In the model, we delineated remote sensing prospecting targets by considering: remote sensing anomalies, geologic bodies and structures, geophysical anomalies and geochemical anomalies. Using this model, we divided the work area into two zones based on types of mineral generation. Seven prospecting targets (one A class, three B class and three C class) were identified. Trenching and block sorting methods were conducted for field verification, and resulted in the discovery of two copper and two iron occurrences with commercial potential.

Global conservation

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Biodiversity Loss ◽  
Global Scale ◽  
Data Sets ◽  
Global Level ◽  
Sensing Systems ◽  
Lunar Mission ◽  
Remote Sensing Systems ◽  
National Governments

On 24 December 1968, as they watched the half-illuminated earthrise over the surface of the moon, the crew of the Apollo 8 lunar mission captured an image that changed humankind’s view of our planet and our place on it. The earthrise image and other iconic global images like the “blue marble” photo taken by the crew of Apollo 17 in 1972 gave us, for the first time, a global view of our fragile home within the vastness of space. These early global images helped promote environmental awareness around the world and were instrumental in the development of the field of remote sensing (Lowman 1999). However, it would take some time for the research community to compile and use global-scale imagery from space in the ecological sciences. Improvements in passive and active remote sensing systems placed in orbit by national governments and the growing commercial satellite sector have given us an “end-to-end” remote sensing capability that allows us to make measurements of important environmental phenomena from very local to global spatial scales (of course, airborne remote sensing systems have long enhanced our ability to capture information at local scales). Data depicting the social and economic drivers of biodiversity loss are also available globally from a variety of sources. These different data sets can now be brought together with powerful, affordable, spatially referenced computing technologies, e.g., GIS and GPS, which were unimaginable when the Apollo missions sent back their images. The entire Apollo spacecraft’s computing power was less than that of today’s mobile phone. Taken together, these advances have made it possible to grapple with the complexities and scale of addressing conservation challenges at the global level. This chapter elaborates the role of remote sensing as one among several catalysts driving the development of new approaches to ecology and conservation biology at the global level. In the early 1980s, NASA initiated its Global Habitability program (NASA 1983; Waldrop 1986; Running et al. 2004). This program sought to answer the big question of how the biosphere partitions its energy and mass.

Synergy between ground-based remote sensing systems in microphysical analysis of cirrus clouds

2014 ◽  
Author(s):  
V. Wolf ◽  
J. Reichardt ◽  
U. Görsdorf ◽  
A. Reigert ◽  
R. Leinweber ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cirrus Clouds ◽  
Sensing Systems ◽  
Remote Sensing Systems

scholarly journals Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

2017 ◽  
Vol 11 (4) ◽  
pp. 1625-1645 ◽  
Author(s):  
Silvia Terzago ◽  
Jost von Hardenberg ◽  
Elisa Palazzi ◽  
Antonello Provenzale
Keyword(s):  
Remote Sensing ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Gridded Data ◽  
Mountain Areas ◽  
Ensemble Mean ◽  
Snow Water

Abstract. The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow–climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes – near-surface air temperature and precipitation – against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

Remote sensing of global wetland dynamics with multiple satellite data sets

2001 ◽  
Vol 28 (24) ◽  
pp. 4631-4634 ◽  
Author(s):  
Catherine Prigent ◽  
Elaine Matthews ◽  
Filipe Aires ◽  
William B. Rossow
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Data Sets ◽  
Wetland Dynamics

scholarly journals Assessing modelled spatial distributions of ice water path using satellite data

2010 ◽  
Vol 10 (5) ◽  
pp. 12185-12224 ◽  
Author(s):  
S. Eliasson ◽  
S. A. Buehler ◽  
M. Milz ◽  
P. Eriksson ◽  
V. O. John
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
Climate Models ◽  
Data Sets ◽  
Data Set ◽  
Ice Water Path ◽  
Cloud Ice ◽  
The Tropics ◽  
Ipcc Ar4 ◽  
Ice Water

Abstract. The climate models used in the IPCC AR4 show large differences in monthly mean cloud ice. The most valuable source of information that can be used to potentially constrain the models is global satellite data. For this, the data sets must be long enough to capture the inter-annual variability of Ice Water Path (IWP). PATMOS-x was used together with ISCCP for the annual cycle evaluation in Fig. 7 while ECHAM-5 was used for the correlation with other models in Table 3. A clear distinction between ice categories in satellite retrievals, as desired from a model point of view, is currently impossible. However, long-term satellite data sets may still be used to indicate the climatology of IWP spatial distribution. We evaluated satellite data sets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, to determine which data sets can be used to evaluate the climate models. IWP data from CloudSat cloud profiling radar provides the most advanced data set on clouds. As CloudSat data are too short to evaluate the model data directly, it was mainly used here to evaluate IWP from the other satellite data sets. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the best of the two. As PATMOS-x extends over more than 25 years and is in fairly close agreement with CloudSat, it was chosen as the reference data set for the model evaluation. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is the GCM from IPCC AR4 closest to satellite observations.

scholarly journals The Iris Effect: A Review

2021 ◽  
Author(s):  
Richard S. Lindzen ◽  
Yong-Sang Choi
Keyword(s):  
Infrared Radiation ◽  
Climate Models ◽  
Cirrus Clouds ◽  
Climate Feedback ◽  
Visible Radiation ◽  
Precipitation Efficiency ◽  
Aggregation Effect ◽  
Earth’S Climate ◽  
Anvil Cirrus

AbstractThis study reviews the research of the past 20-years on the role of anvil cirrus in the Earth’s climate – research initiated by Lindzen et al. (Bull. Am. Meteor. Soc. 82:417-432, 2001). The original study suggested that the anvil cirrus would shrink with warming, which was estimated to induce longwave cooling for the Earth. This is referred to as the iris effect since the areal change hypothetically resembles the light control by the human eye’s iris. If the effect is strong enough, it exerts a significant negative climate feedback which stabilizes tropical temperatures and limits climate sensitivity. Initial responses to Lindzen et al. (Bull. Am. Meteor. Soc. 82:417-432, 2001) denied the existence and effectiveness of the iris effect. Assessment of the debatable issues in these responses will be presented later in this review paper. At this point, the strong areal reduction of cirrus with warming appears very clearly in both climate models and satellite observations. Current studies found that the iris effect may not only come from the decreased cirrus outflow due to increased precipitation efficiency, but also from concentration of cumulus cores over warmer areas (the so-called aggregation effect). Yet, different opinions remain as to the radiative effect of cirrus clouds participating in the iris effect. For the iris effect to be most important, it must involve cirrus clouds that are not as opaque for visible radiation as they are for infrared radiation. However, current climate models often simulate cirrus clouds that are opaque in both visible and infrared radiation. This issue requires thorough examination as it seems to be opposed to conventional wisdom based on explicit observations. This paper was written in the hope of stimulating more effort to carefully evaluate these important issues.

scholarly journals Suitability of satellite remote sensing data for yield estimation in northeast Germany

2021 ◽  
Author(s):  
Claudia Vallentin ◽  
Katharina Harfenmeister ◽  
Sibylle Itzerott ◽  
Birgit Kleinschmit ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Satellite Images ◽  
Spectral Band ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Yield Estimation ◽  
Yield Data ◽  
Sensing Data

AbstractInformation provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm management and optimize precision agriculture applications. A vast amount of data is made available both as map material and from space. However, it is up to the user to select the appropriate data for a particular problem. Without the appropriate knowledge, this may even entail an economic risk. This study therefore investigates the direct relationship between satellite data from six different optical sensors as well as different soil and relief parameters and yield data from cereal and canola recorded by the thresher in the field. A time series of 13 years is considered, with 947 yield data sets consisting of dense point data sets and 755 satellite images. To answer the question of how well the relationship between remote sensing data and yield is, the correlation coefficient r per field is calculated and interpreted in terms of crop type, phenology, and sensor characteristics. The correlation value r is particularly high when a field and its crop are spatially heterogeneous and when the correct phenological time of the crop is reached at the time of satellite imaging. Satellite images with higher resolution, such as RapidEye and Sentinel-2 performed better in comparison with lower resolution sensors of the Landsat series. The additional Red Edge spectral band also has advantage, especially for cereal yield estimation. The study concludes that there are high correlation values between yield data and satellite data, but several conditions must be met which are presented and discussed here.

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