Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps

O’Leary, Donal S.; Hall, Dorothy K.; Medler, Michael J.; Flower, Aquila

doi:10.1007/s11707-018-0719-7

Cited by 12 publications

(8 citation statements)

References 49 publications

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“…Similar investigations were conducted for Pi, Pi+3, and Pi+4 to determine whether they are included into or excluded from the snowmelt period. We also noted that some methods have been developed to detect snow melt-off day based on optical or microwave radiometer data [33][34][35] and several snowmelt timing datasets have been released [36]. We did not adopt these methods and used the existing datasets because of two reasons.…”

Section: Quantifying Gud Uncertainty Caused By Spring Snowmeltmentioning

confidence: 99%

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

et al. 2020

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Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

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Section: Quantifying Gud Uncertainty Caused By Spring Snowmeltmentioning

confidence: 99%

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

et al. 2020

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“…The actual date of snow melt was identified on a pixelwise, per‐annum basis as the first three consecutive days with NDSI below 0.35, corresponding to 35% snow cover averaged across the pixel. This threshold was determined by a sensitivity analysis; including thresholds of 10%, 15%, 35%, and 50%, based on the thresholds of 50% and 10% as used by O'Leary et al (2018) and Jing et al (2022), respectively. We concluded that 35% gave snow melt dates that aligned with those of O'Leary et al (2018) and of ERA‐5 Land snow cover data, whereas lower thresholds were unduly late, likely influenced by surface water, and higher thresholds were characterized by a high degree of noise and erroneous mid‐winter snow melt.…”

Section: Methodsmentioning

confidence: 99%

“…This threshold was determined by a sensitivity analysis; including thresholds of 10%, 15%, 35%, and 50%, based on the thresholds of 50% and 10% as used by O'Leary et al (2018) and Jing et al (2022), respectively. We concluded that 35% gave snow melt dates that aligned with those of O'Leary et al (2018) and of ERA‐5 Land snow cover data, whereas lower thresholds were unduly late, likely influenced by surface water, and higher thresholds were characterized by a high degree of noise and erroneous mid‐winter snow melt. To avoid the influence of late snow melt at high elevations and at high latitudes, the full snow melt dataset was masked to forested land cover excluding subpolar taiga forests using the 2010 Canadian Centre for Remote Sensing Land Cover of North America dataset (Latifovic et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Broadleaf tree phenology and springtime wildfire occurrence in boreal Canada

Parisien

Barber

Flannigan

et al. 2023

Global Change Biology

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Although broadleaf tree species of the boreal biome have a lower flammability compared to conifers, there is a period following snow melt and prior to leaf flush (i.e., greenup), termed the “spring window” by fire managers, when these forests are relatively conducive to wildfire ignition and spread. The goal of this study was to characterize the duration, timing, and fire proneness of the spring window across boreal Canada and assess the link between these phenological variables and the incidence of springtime wildfires. We used remotely sensed snow cover and greenup data to identify the annual spring window for five boreal ecozones from 2001 to 2021 and then compared seasonality of wildfire starts (by cause) and fire‐conducive weather in relation to this window, averaged over the 21‐year period. We conducted a path analysis to concomitantly evaluate the influence of the spring window's duration, the timing of greenup, and fire‐conducive weather on the annual number and the seasonality of spring wildfires. Results show that the characteristics of spring windows vary substantially from year to year and among geographic zones, with the interior west of Canada having the longest and most fire‐conducive spread window and, accordingly, the greatest springtime wildfire activity. We also provide support for the belief that springtime weather generally promotes wind‐driven, rather than drought‐driven wildfires. The path analyses show idiosyncratic behavior among ecozones, but, in general, the seasonality of the wildfire season is mainly driven by the timing of the greenup, whereas the number of spring wildfires mostly responds to the duration of the spring window and the frequency of fire‐conducive weather. The results of this study allows us to better understand and anticipate the biome‐wide changes projected for the northern forests of North America.

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“…However these maps are available only once every 8 d, the maps frequently retain some cloud cover, and it is difficult to determine on which days during the 8 d period snow was or was not observed; furthermore, only maximum observed snow cover is provided for any given 8 d period. In spite of the limitations, the 8 d maximum snow maps have been useful in many studies (e.g., O'Leary et al, 2018;Hammond et al, 2018). The cloud-gap-filling cloud-clearing method that uses current day and/or previous day(s) of MODIS daily snowcover products to fill gaps created by cloud cover is far superior to the 8 d maximum method of cloud clearing.…”

Section: Methods To Reduce or Eliminate Cloud Cover In Modis-derived mentioning

confidence: 99%

“…Using the Rutgers CDR, researchers have shown that SCE has been declining and melt has been occurring earlier in the Northern Hemisphere (e.g., Déry and Brown, 2007). This shortening of the snow season has many implications; for example, in the western US (Mote et al, 2005;Stewart, 2009;Hall et al, 2015), earlier snowmelt contributes to a longer fire season (Westerling et al, 2006;O'Leary et al, 2018) and other environmental and societal problems. However, the coarse resolution of the Rutgers CDR is not suitable for regional and basin-scale studies.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record

Hall

Riggs

DiGirolamo

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 – following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS – and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and ∼5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 km2, which is ∼0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s.

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Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps

Cited by 12 publications

References 49 publications

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Broadleaf tree phenology and springtime wildfire occurrence in boreal Canada

Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record

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