The accuracy of snow melt-off day derived from optical and microwave radiometer data — A study for Europe

Metsämäki, Sari; Böttcher, Kristin; Pulliainen, Jouni; Luojus, Kari; Cohen, Juval; Takala, Matias; Mattila, Olli-Pekka; Schwaizer, Gabriele; Derksen, Chris; Koponen, Sampsa

doi:10.1016/j.rse.2018.03.029

Cited by 25 publications

(30 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At-pixel fractional snow cover (% of ground area covered by snow) is extracted from the pre-Copernicus CryoLand snow mapping service (Nagler et al, 2015), which provides Pan-European FSC maps starting from 2001 at 500 m (0.05 • ) spatial resolution. The method applied in the FSC retrieval is SCAmod (Metsämäki et al, 2005(Metsämäki et al, , 2012, complemented by some additional NDSI (normalized difference snow index) rules for detecting the snow-free areas (Metsämäki et al, 2018). SCAmod detects FSC not only for non-forested areas, but is also able to capture the under-canopy snow.…”

Section: Fractional Snow Cover Datamentioning

confidence: 99%

“…The detection of the snow-melt-off date from the FSC map time series provides for each pixel the first day with snowfree terrain, but ignores short (a few days) intervening snowfree periods within a long snow season (Metsämäki et al, 2018). The melt-off date from the FSC time series is identified as the beginning of a snow-free period (FSC = 0 %) after a period of snow observations (FSC > 0 %).…”

Section: Snow-melt-off Date From Fractional Snow Cover Datamentioning

confidence: 99%

“…The surface albedo data record, CLARA-A2 SAL (Anttila et al, 2016a, b;Karlsson et al, 2017), was used to study trends in the snowmelt start and end dates, the melting season length and the albedo value preceding the melt onset. We also use the snow-melt-off date derived from the EU FP7 CryoLand project Pan-European Fractional Snow Cover (FSC) time series available for years (Metsämäki et al, 2018. The snowmelt end dates operationally produced at the Finnish Meteorological Institute (FMI) from weather station observations are used as reference data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring changes in forestry and seasonal snow using surface albedo during 1982–2016 as an indicator

et al. 2019

Self Cite

View full text Add to dashboard Cite

The surface albedo time series, CLARA-A2 SAL, was used to study trends in the snowmelt start and end dates, the melting season length and the albedo value preceding the melt onset in Finland during 1982-2016. In addition, the melt onset from the JSBACH land surface model was compared with the timing of green-up estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Moreover, the melt onset was compared with the timing of the greening up based on MODIS data. Similarly, the end of snowmelt timing predicted by JSBACH was compared with the meltoff dates based on the Finnish Meteorological Institute (FMI) operational in situ measurements and the Fractional Snow Cover (FSC) time-series product provided by the EU FP7 CryoLand project. It was found that the snowmelt date estimated using the 20 % threshold of the albedo range during the melting period corresponded well to the melt estimate of the permanent snow layer. The longest period, during which the ground is continuously half or more covered by snow, defines the permanent snow layer (Solantie et al., 1996). The greening up followed within 5-13 days the date when the albedo reached the 1 % threshold of the albedo dynamic range during the melting period. The time difference between greening up and complete snowmelt was smaller in mountainous areas than in coastal areas. In two northern vegetation map areas (Northern Karelia-Kainuu and Southwestern Lapland), a clear trend towards earlier snowmelt onset (5-6 days per decade) and increasing melting season length (6-7 days per decade) was observed. In the forested part of northern Finland, a clear decreasing trend in albedo (2 %-3 % per decade in absolute albedo percentage) before the start of the melt onset was observed. The decreasing albedo trend was found to be due to the increased stem volume.

show abstract

Section: Fractional Snow Cover Datamentioning

confidence: 99%

Section: Snow-melt-off Date From Fractional Snow Cover Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring changes in forestry and seasonal snow using surface albedo during 1982–2016 as an indicator

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Snow cover in spring is especially sensitive to warming (Brown & Mote, 2009;Derksen & Brown, 2012;Déry & Brown, 2007), and therefore, monitoring timing and length of the melt season is important for climate change monitoring. Recent studies show clear trends toward earlier melt season (Huang et al, 2018;Mao et al, 2015;Metsämäki et al, 2018;Takala et al, 2009;Tedesco et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Timing of the melt season has been studied in a number of studies, where it is estimated using either optical satellite data (Anttila et al, 2018;Malnes et al, 2016;Manninen et al, 2019;Rinne et al, 2009) or passive microwave satellite data (Markus et al, 2009;Takala et al, 2009;Tedesco et al, 2009;Wang et al, 2013). However, only one prior study exists on intercomparison of the optical and microwave satellite methods; Metsämäki et al (2018) compared snow melt-off dates derived from optical and microwave radiometer data, but their study only covered Europe for the period of 16 years and did not consider the onset of melt. Thus, our study is to the authors' knowledge the first, where snow melt onset date (MOD) estimates based on optical and microwave data are intercompared for a time period of almost 40 years over the NH.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of Snow Melt Onset Date Estimates From Optical and Microwave Satellite Instruments Over the Northern Hemisphere for the Period 1982–2015

et al. 2019

View full text Add to dashboard Cite

Robust melt season timing and length estimates are important for hydrological and climatological applications; due to the large area and sparse in situ measurements, snow melt monitoring at the continental scale is only possible from satellites. We intercompared melt onset date (MOD) estimates obtained from optical and microwave satellite sensors over the Northern Hemisphere between 1982 and 2015 and subsequently analyzed the causes of the similarities and dissimilarities found. The optical satellite data are based on the mean surface albedo from the Satellite Application Facility for Climate Monitoring (CM SAF) CLouds, Albedo and RAdiation second release Surface ALbedo (CLARA‐A2 SAL) data set. The microwave satellite data are based on temporal variations in the differences of the brightness temperature from satellite passive microwave radiometers. The analysis shows that the microwave‐based method detects melt onset on average 10 days later than the albedo‐based method, which results from the different melt detection methods; the albedo‐based method observes the point when the spring snow metamorphism begins to have a detectable effect on snow albedo, whereas the microwave‐based method detects the appearance of meltwater in snowpack. The difference in MOD decreases in forests, because canopy protects snow from sunlight delaying snow metamorphism. Additionally, we analyzed the MOD estimates for trends across the Northern Hemisphere and separately for Eurasia and North America. A statistically significant negative trend toward earlier melt onset exists in all cases, which is consistent with previous studies.

show abstract

Predictive power of remote sensing versus temperature‐derived variables in modelling phenology of herbivorous insects

Pöyry

Böttcher

Fronzek

et al. 2017

Remote Sens Ecol Conserv

Self Cite

View full text Add to dashboard Cite

Application of remote sensing datasets in modelling phenology of heterotrophic animals has received little attention. In this work, we compare the predictive power of remote sensing versus temperature-derived variables in modelling peak flight periods of herbivorous insects, as exemplified by nocturnal moths. Moth phenology observations consisted of weekly observations of five focal moth species (Orthosia gothica, Ectropis crepuscularia, Cabera exanthemata, Dysstroma citrata and Operophtera brumata) gathered in a national moth monitoring scheme in Finland. These species were common and widespread and had peak flight periods in different seasons. Temperature-derived data were represented by weekly accumulating growing degree days (GDD) calculated from gridded temperature observations. Remote sensing data were obtained from three sources: (1) snow melt-off date from the MODIS daily snow maps, (2) greening date using the NDWI from MODIS data and (3) dates of start, maximum and end of growing season based on the JRC FAPAR products. Peak phenology observations of moths were related to different explanatory variables by using linear mixed effect models (LMM), with 70% of the data randomly selected for model calibration. Predictive power of models was tested using the remaining 30% of the data. Remote sensing data (snow melt-off and vegetation greening date) showed the highest predictive power in two moth species flying in the early and late spring, whereas in the three other species none of the variables showed reasonable predictive power. Flight period of the spring species coincides with natural events such as snow melt or vegetation greening that can easily be observed using remote sensing techniques. We demonstrate the applicability of our methodology by predictive spatial maps of peak flight phenology covering the entire Finland for two of the focal species. The methods are applicable in situations that require spatial predictions of animal activity, such as the management of populations of insect pest species.

show abstract

The accuracy of snow melt-off day derived from optical and microwave radiometer data — A study for Europe

Cited by 25 publications

References 24 publications

Monitoring changes in forestry and seasonal snow using surface albedo during 1982–2016 as an indicator

Monitoring changes in forestry and seasonal snow using surface albedo during 1982–2016 as an indicator

Intercomparison of Snow Melt Onset Date Estimates From Optical and Microwave Satellite Instruments Over the Northern Hemisphere for the Period 1982–2015

Predictive power of remote sensing versus temperature‐derived variables in modelling phenology of herbivorous insects

Contact Info

Product

Resources

About