Operational snow mapping using multitemporal Meteosat SEVIRI imagery

Wildt, Martijn de Ruyter de; Seiz, Gabriela; Gruen, Armin

doi:10.1016/j.rse.2006.12.008

Cited by 61 publications

(19 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…lakes, lagoons and ponds) (Derrien and Le Gléau, ). Even though snow‐ice surface temperature varies significantly in relation to air temperature fluctuations (Armstrong and Brun, ), we found that only pure snow‐ice pixels have brightness temperature ≤273 K (De Ruyter de Wildt et al , ). In contrast, due to the spatial resolution of the AVHRR sensor (1.1 km) and the heterogeneous topography, snow‐ice surfaces within a pixel can contain dark patches made of bare soil, rock, or trees (Oesch et al , ).…”

Section: Daytime Over Land Multispectral Cloud Masking Algorithmmentioning

confidence: 83%

“…The dark grey lines represent the threshold values (between 265 and 285 K) found for computing snow-ice masks. (Armstrong and Brun, 2008), we found that only pure snow-ice pixels have brightness temperature ≤273 K (De Ruyter de Wildt et al, 2006). In contrast, due to the spatial resolution of the AVHRR sensor (1.1 km) and the heterogeneous topography, snow-ice surfaces within a pixel can contain dark patches made of bare soil, rock, or trees (Oesch et al, 2002).…”

Section: Daytime Over Land Multispectral Cloud Masking Algorithmmentioning

confidence: 96%

See 1 more Smart Citation

A daytime over land algorithm for computing AVHRR convective cloud climatologies for the Iberian Peninsula and the Balearic Islands

Azorín-Molina

Baena-Calatrava

Echave-Calvo³

et al. 2012

Intl Journal of Climatology

View full text Add to dashboard Cite

ABSTRACT:A daytime over land multispectral cloud detection algorithm is presented to derive accurate convective cloud climatologies with high spatial resolution (1.1 km) over the Iberian Peninsula (IP) and the Balearic Islands (BI). The cloud detection scheme was designed to process Advanced Very High Resolution Radiometer (AVHRR) HRPT data and is tested here on NOAA-17 morning (0900-1200 UTC) and NOAA-16 afternoon (1200-1500 UTC) overpasses for the warm 6-month study period May-October. The algorithm consists of four spectral threshold tests applied to each pixel. Test 1 corresponds to the snow-ice removal, test 2 is the thermal infrared test, test 3 is the albedo or visible test and test 4 is the ratio between near-infrared and visible channels. The algorithm discretizes all AVHRR data into four groups called cloud-filled, cloud-free, snow-ice and snow-free radiances. The high-resolution convective cloud masks are obtained by subtracting snow-ice pixels from cloudy ones. In this article, a detailed description of the convective cloud detection scheme and the sources of error detected for each test are given, and the first seasonal and monthly regional convective cloud frequency composites are presented. Future applications of the newly proposed threshold algorithm in climate and meteorology are also discussed in this article, particularly the production of convective cloud composites for climate monitoring of storms over the IP and BI.

show abstract

Section: Daytime Over Land Multispectral Cloud Masking Algorithmmentioning

confidence: 83%

Section: Daytime Over Land Multispectral Cloud Masking Algorithmmentioning

confidence: 96%

A daytime over land algorithm for computing AVHRR convective cloud climatologies for the Iberian Peninsula and the Balearic Islands

Azorín-Molina

Baena-Calatrava

Echave-Calvo³

et al. 2012

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…The scientific literature is populated by many different approaches for tackling this problem; the most simple ones consist of a sequence of threshold tests [47] that have to be properly set according to the particular sensor and the utilized channels. More complex techniques exploit also the spatial and temporal correlation of cloudy pixels between sequences of images [48], [49] for improving the classification performances, but they often entail a significant computation effort.…”

Section: Cloud Detectionmentioning

confidence: 99%

Batch Methods for Resolution Enhancement of TIR Image Sequences

Addesso

Longo

Maltese

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Thermal infrared (TIR) time series are exploited by many methods based on Earth observation (EO), for such applications as agriculture, forest management, and meteorology. However, due to physical limitations, data acquired by a single sensor are often unsatisfactory in terms of spatial or temporal resolution. This issue can be tackled by using remotely sensed data acquired by multiple sensors with complementary features. When nonreal-time functioning or at least near real-time functioning is admitted, the measurements can be profitably fed to a sequential Bayesian algorithm, which allows to account for the correlation embedded in the successive acquisitions. In this work, we focus on applications that allow the batch processing of the whole data sequences acquired in a fixed time interval. In this case, multiple options for improving the final product are offered by the Bayesian framework, based on both sequential and smoothing techniques. We consider several such Bayesian strategies and comparatively assess their performances in practical applications and through real thermal data acquired by the SEVIRI and MODIS sensors, encompassing the presence of multiple disturbance source, e.g., the cloud coverage of the illuminated scene

show abstract

“…The algorithm of spectral classification used to detect snow mainly using a "snow index" (SI, defined as the ratio VIS/IR1) and several other thresholds. The brightness temperature difference of IR1 and IR3 is used to discriminate the ice cloud and snow [6]. Temporal stability test, surface temperature climatology test are carried out for further distinguish snow and cloud [4].…”

Section: Vissr Snow Cover Mappingmentioning

confidence: 99%

Monitoring snow cover over China with FY-2E VISSR and FY-3B MWRI

Yang

Jiang

Shi

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

Snow cover is an important variable for global climate change research and hydrological application. Recent years, the snowfall events in southern China indicate the limitation of using optical or passive microwave remote sensing respectively. In this paper, China's first generation of geostationary meteorological satellite Fengyun-2E and the second generation of polar-orbit meteorological satellite Fengyun-3B are used to monitor snow cover in China from Jan 1 to 31, 2011. In order to monitor snow cover in real time and make use of China's meteorological satellites, FY-2E VISSR and FY-3B MWRI are mainly used. AMSR-E is also used, since MWRI can't completely cover China daily. The main purpose of this study is to propose an effective method of monitoring snow cover daily mainly using China's meteorological satellites.Index Terms-snow cover, geostationary satellite, FY-2E, FY-3B

show abstract

Operational snow mapping using multitemporal Meteosat SEVIRI imagery

Cited by 61 publications

References 33 publications

A daytime over land algorithm for computing AVHRR convective cloud climatologies for the Iberian Peninsula and the Balearic Islands

A daytime over land algorithm for computing AVHRR convective cloud climatologies for the Iberian Peninsula and the Balearic Islands

Batch Methods for Resolution Enhancement of TIR Image Sequences

Monitoring snow cover over China with FY-2E VISSR and FY-3B MWRI

Contact Info

Product

Resources

About