Evaluation of MODIS and VIIRS Cloud-Gap-Filled Snow-Cover Products for Production of an Earth Science Data Record

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 sqkm, 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

The recent developments in cloud removal approaches of MODIS snow cover product

The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.</p

Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data

Lake ice is a robust indicator of climate change. The availability of information contained in Moderate Resolution Imaging Spectroradiometer daily snow products from 2000 to 2017 could be greatly improved after cloud removal by gap filling. Thresholds based on open water pixel numbers are used to extract the freezeup start and breakup end dates for 58 lakes on the Tibetan Plateau (TP); 18 lakes are also selected to extract the freezeup end and breakup start dates. The lake ice durations are further calculated based on freezeup and breakup dates. Lakes on the TP begin to freezeup in late October and all the lakes start the ice cover period in mid‐January of the following year. In late March, some lakes begin to break up, and all the lakes end the ice cover period in early July. Generally, the lakes in the northern Inner‐TP have earlier freezeup dates and later breakup dates (i.e., longer ice cover durations) than those in the southern Inner‐TP. Over 17 years, the mean ice cover duration of 58 lakes is 157.78 days, 18 (31%) lakes have a mean extending rate of 1.11 day/year, and 40 (69%) lakes have a mean shortening rate of 0.80 day/year. Geographical location and climate conditions determine the spatial heterogeneity of the lake ice phenology, especially the ones of breakup dates, while the physico‐chemical characteristics mainly affect the freezeup dates of the lake ice in this study. Ice cover duration is affected by both climatic and lake specific physico‐chemical factors, which can reflect the climatic and environmental change for lakes on the TP

Evaluation of Machine Learning Algorithms for Lake Ice Classification from Optical Remote Sensing Data

The topic of lake ice cover mapping from satellite remote sensing data has gained interest in recent years since it allows the extent of lake ice and the dynamics of ice phenology over large areas to be monitored. Mapping lake ice extent can record the loss of the perennial ice cover for lakes located in the High Arctic. Moreover, ice phenology dates, retrieved from lake ice maps, are useful for assessing long-term trends and variability in climate, particularly due to their sensitivity to changes in near-surface air temperature. However, existing knowledge-driven (threshold-based) retrieval algorithms for lake ice-water classification that use top-of-the-atmosphere (TOA) reflectance products do not perform well under the condition of large solar zenith angles, resulting in low TOA reflectance. Machine learning (ML) techniques have received considerable attention in the remote sensing field for the past several decades, but they have not yet been applied in lake ice classification from optical remote sensing imagery. Therefore, this research has evaluated the capability of ML classifiers to enhance lake ice mapping using multispectral optical remote sensing data (MODIS L1B (TOA) product). Chapter 3, the main manuscript of this thesis, presents an investigation of four ML classifiers (i.e. multinomial logistic regression, MLR; support vector machine, SVM; random forest, RF; gradient boosting trees, GBT) in lake ice classification. Results are reported using 17 lakes located in the Northern Hemisphere, which represent different characteristics regarding area, altitude, freezing frequency, and ice cover duration. According to the overall accuracy assessment using a random k-fold cross-validation (k = 100), all ML classifiers were able to produce classification accuracies above 94%, and RF and GBT provided above 98% classification accuracies. Moreover, the RF and GBT algorithms provided a more visually accurate depiction of lake ice cover under challenging conditions (i.e., high solar zenith angles, black ice, and thin cloud cover). The two tree-based classifiers were found to provide the most robust spatial transferability over the 17 lakes and performed consistently well across three ice seasons, better than the other classifiers. Moreover, RF was insensitive to the choice of the hyperparameters compared to the other three classifiers. The results demonstrate that RF and GBT provide a great potential to map accurately lake ice cover globally over a long time-series. Additionally, a case study applying a convolution neural network (CNN) model for ice classification in Great Slave Lake, Canada is presented in Appendix A. Eighteen images acquired during the the ice season of 2009-2010 were used in this study. The proposed CNN produced a 98.03% accuracy with the testing dataset; however, the accuracy dropped to 90.13% using an independent (out-of-sample) validation dataset. Results show the powerful learning performance of the proposed CNN with the testing data accuracy obtained. At the same time, the accuracy reduction of the validation dataset indicates the overfitting behavior of the proposed model. A follow-up investigation would be needed to improve its performance. This thesis investigated the capability of ML algorithms (both pixel-based and spatial-based) in lake ice classification from the MODIS L1B product. Overall, ML techniques showed promising performances for lake ice cover mapping from the optical remote sensing data. The tree-based classifiers (pixel-based) exhibited the potential to produce accurate lake ice classification at a large-scale over long time-series. In addition, more work would be of benefit for improving the application of CNN in lake ice cover mapping from optical remote sensing imagery

How are Interannual Variations of Land Surface Phenology in the Highland Pastures of Kyrgyzstan Modulated by Terrain, Snow Cover Seasonality, and Climate Oscillations? An Investigation Using Multi-Source Remote Sensing Data

In the semiarid, continental climates of montane Central Asia, with its constant moisture deficit and low relative humidity, agropastoralism constitutes the foundation of the rural economy. In Kyrgyzstan, an impoverished, landlocked republic in Central Asia, herders of the highlands practice vertical transhumance—the annual movement of livestock to higher elevation pastures to take advantage of seasonally available forage resources. Dependency on pasture resource availability during the short mountain growing season makes herds and herders susceptible to changing weather and climate patterns. This dissertation focuses on using remote sensing observations over the highland pastures in Kyrgyzstan to address five interrelated topics: (i) changes in snow cover and its seasonality from 2002 through 2016; (ii) pasture phenology from the perspective of land surface phenology using multi-scale data from 2001 through 2017; (iii) relationships between snow cover seasonality and subsequent land surface phenology; (iv) terrain effects on the snow-phenology interrelations; and (v) the influence of atmospheric teleconnections on modulating the relationships between snow cover seasonality, growing season duration, and pasture phenology. Results indicate that more territory has been experiencing earlier snow onset than earlier snowmelt, and around equivalent areas with longer and shorter duration of snow seasons. Significant shifts toward earlier snow onset (snowmelt) occurred in western and central (eastern) Kyrgyzstan, and significant duration of the snow season shortening (extension) across western and eastern (northern and southwestern) Kyrgyzstan. Below 3400 m, there was a general trend of significantly earlier snowmelt, and this area of earlier snowmelt was 15 times greater in eastern than western rayons. In terms of land surface phenology, there was a predominant and significant trend of increasing peak greenness, and a significant positive relationship between snow covered dates and modeled peak greenness. While there were more negative correlations between snow cover onset and peak greenness, there were more positive correlations between snowmelt timing and peak greenness, meaning that a longer snow cover season increased the amplitude of peak greenness. The amount of thermal time (measured in accumulated growing degree-days) to reach peak greenness was significantly negatively correlated both with the number of snow covered dates and the snowmelt date. Thus, more snow covered dates translated into fewer growing degree-days accumulated to reach peak greenness in the subsequent growing season. Terrain features influenced the timing of snowmelt more strongly than the number of snow covered dates. Slope was more important than aspect, but the strongest effect appeared from the interaction of aspect and the steepest slopes. The influence of atmospheric teleconnection arising from climate oscillation modes was marginal at the spatial resolutions of this study. Thermal time accumulation could be largely explained with Partial Least Squares (PLS) regression models by elevation and snow cover metrics. However, explanation of peak greenness was less susceptible to terrain and snow cover variables. This research study provides a comprehensive evaluation of the spatial variation of interannual phenology in the highland pastures that serve as the foundation of rural Kyrgyz economy. Finally, it contributes to a better understanding of recent environmental changes in remote highland Central Asia

Empirical approach to satellite snow detection

Lumipeitteellä on huomattava vaikutus säähän, ilmastoon, luontoon ja yhteiskuntaan. Pelkästään sääasemilla tehtävät lumihavainnot (lumen syvyys ja maanpinnan laatu) eivät anna kattavaa kuvaa lumen peittävyydestä tai muista lumipeitteen ominaisuuksista. Sääasemien tuottamia havaintoja voidaan täydentää satelliiteista tehtävillä havainnoilla. Geostationaariset sääsatelliitit tuottavat havaintoja tihein välein, mutta havaintoresoluutio on heikko monilla alueilla, joilla esiintyy kausittaista lunta. Polaariradoilla sääsatelliittien havaintoresoluutio on napa-alueiden läheisyydessä huomattavasti parempi, mutta silloinkaan satelliitit eivät tuota jatkuvaa havaintopeittoa. Tiheimmän havaintoresoluution tuottavat sääsatelliittiradiometrit, jotka toimivat optisilla aallonpituuksilla (näkyvä valo ja infrapuna). Lumipeitteen kaukokartoitusta satelliiteista vaikeuttavat lumipeitteen oman vaihtelun lisäksi pinnan ominaisuuksien vaihtelu (kasvillisuus, vesistöt, topografia) ja valaistusolojen vaihtelu. Epävarma ja osittain puutteellinen tieto pinnan ja kasvipeitteen ominaisuuksista vaikeuttaa luotettavan automaattisen analyyttisen lumentunnistusmenetelmän kehittämistä ja siksi empiirinen lähestymistapa saattaa olla toimivin vaihtoehto automaattista lumentunnistusmenetelmää kehitettäessä. Tässä työssä esitellään kaksi EUMETSATin osittain rahoittamassa H SAFissa kehitettyä lumituotetta ja niissä käytetyt empiiristä lähestymistapaa soveltaen kehitetyt algoritmit. Geostationaarinen MSG/SEVIRI H31 lumituote on saatavilla vuodesta 2008 alkaen ja polaarituote Metop/AVHRR H32 vuodesta 2015 alkaen. Lisäksi esitellään pintahavaintoihin perustuvat validointitulokset, jotka osoittavat tuotteiden saavuttavan määritellyt tavoitteet.Snow cover plays a significant role in the weather and climate system, ecosystems and many human activities, such as traffic. Weather station snow observations (snow depth and state of the ground) do not provide highresolution continental or global snow coverage data. The satellite observations complement in situ observations from weather stations. Geostationary weather satellites provide observations at high temporal resolution, but the spatial resolution is low, especially in polar regions. Polarorbiting weather satellites provide better spatial resolution in polar regions with limited temporal resolution. The best detection resolution is provided by optical and infra-red radiometers onboard weather satellites. Snow cover in itself is highly variable. Also, the variability of the surface properties (such as vegetation, water bodies, topography) and changing light conditions make satellite snow detection challenging. Much of this variability is in subpixel scales, and this uncertainty creates additional challenges for the development of snow detection methods. Thus, an empirical approach may be the most practical option when developing algorithms for automatic snow detection. In this work, which is a part of the EUMETSAT-funded H SAF project, two new empirically developed snow extent products for the EUMETSAT weather satellites are presented. The geostationary MSG/SEVIRI H32 snow product has been in operational production since 2008. The polar product Metop/AVHRR H32 is available since 2015. In addition, validation results based on weather station snow observations between 2015 and 2019 are presented. The results show that both products achieve the requirements set by the H SAF

Satellite remote sensing observations of snow cover extent during the melt-out season in the Thompson-Okanagan Region, British Columbia from 2003-2019

Snow is a critical component of the earth’s overall energy budget and it contributes significantly to water resources especially in mountainous regions, coining the term the “water towers” for downstream communities (Viviroli et al., 2006). Studies have shown an increase in snow cover variability due in part by climate change. Most evident throughout the research is an earlier freshet period throughout the northern hemisphere, elevation-dependent warming in mountainous regions and regional climate models indicating transitions from snow to rain dominated basins (Pepin et al., 2015; Rangwala & Miller, 2012). Studies throughout British Columbia have shown evidence of earlier peak runoff from river gauges, a decrease in snow duration and increases in temperature by 1.4ᵒ (Shrestha et al., 2012; Kang et al., 2014; Islam et al., 2017). The Thompson Okanagan region is a semi-arid snow dominated region located in the southern portion of British Columbia (Kang et al., 2014). The spring freshet in Thompson Okanagan is affected by large atmospheric systems as well, including the Pacific North American Pattern (PNA), the Pacific Decadal Oscillation (PDO) and the Oceanic Nino Index (ONI). This research focuses on identifying variations in snow cover during the spring freshet (April 1st-June 30th) in Thompson Okanagan with remote sensing observations from 2003-2019. Snow cover mapping is achieved using visible-infrared observations of snow. High albedo is easily distinguishable in the visible spectrum; however, cloud contamination impedes analysis using visible infrared observations. Steps to mitigate the impact of cloud cover adopted a multi-step methodology. This improved the ability to characterize snow cover extent variability during the spring freshet. The methodology includes: i) a daily combination of Terra/Aqua (from 2003-2012) and VIIRS (from 2012-2019) observations; ii) an adjacent temporal deduction (ATD) technique which replaces cloud pixels with non-cloudy pixels from +/-2 adjacent days; iii) a spatial filter to interpolate snow in cloudy pixels; iv) and the identification of a regional snowline elevation above which cloud-labelled pixels are classified as snow, and cloud pixels below the elevation for no-snow are classified as no-snow. This methodology significantly reduced cloud cover from an average of 71.5% to 1.6% annually. Using stratified random sampling approach, reference points were gathered for a range of elevation bands for four watersheds within the region to test the snow mapping accuracy. The last day of snow (LDS) was extracted for each point from 2003-2019. Large scale atmospheric patterns (Pacific Decadal Oscillation (PDO), Pacific-North American (PNA) teleconnection pattern and Oceanic Nino Index (ONI)) were analyzed using simple and multiple linear regression to assess the variability within the LDS dataset that could be explained by these patterns. This analysis showed that the PNA did not significantly account the variability, but the PDO did with an R2 value reaching 64%, with a significance level of >95%. The simple linear regression models showed that the ONI explained 78% of the LDS variation during the March-April-May (MAM) months, with p>95%; this was more than any other 3-month interval studied. Also, the ONI R2 value decreased as elevation increased. Overall, El Nino years showed snow disappearance of ~23 days earlier than La Nina years at low elevation, ~18 days sooner at mid elevation and ~13 days sooner at high elevations. Earlier snow melt-out during El Nino phases have implications for water resources in the region, for residential and crop use as well as economic impacts for tourism (Westering, 2016; Winkler et al., 2017). This also contributes to area burned in forest fires and rapid melting snow can cause flooding in surrounding urban areas within Thompson Okanagan. Extending the study period into the future could allow further insights on potential effects of climate change within the region

Evolución espaciotemporal de la cobertura de nieve de las cuencas de alta montaña entre los 29°S - 37°S mediante el uso de imágenes satelitales y su influencia en el caudal

Tesis (Geóloga)La cobertura de nieve y hielo en zonas de cordillera representa una información importante en los procesos hidrológicos. Los datos obtenidos desde estaciones terrestres son escasos y pobremente distribuidos, con ello la estimación a partir de observaciones de sensores remotos se ha consolidado como una alternativa atractiva. Determinar la cobertura de nieve es particularmente importante durante primavera y verano en terreno montañoso, en el cual la nieve puede derretirse rápidamente provocando grandes variaciones espaciales en la cubierta de nieve. Este trabajo presenta el estudio y cálculo de la variabilidad espaciotemporal de la cubierta de nieve, en las cuencas de alta montaña entre los 29°S – 37° S durante los años 2003 al 2020 y su asociación con los valores de caudales obtenidos de las estaciones seleccionadas ubicada en el área de estudio. Para determinar la cobertura de nieve se emplearon imágenes satelitales del sensor Moderate Resolution Imaging Spectroradiometer (MODIS) a partir de productos diarios de cobertura de nieve (Snow Cover MOD10A1), todo el procesamiento se llevó a cabo a través de la plataforma online Google Earth Engine (GEE). Para la obtención de datos de caudal se utilizaron los datos de las estaciones fluviométricas de la DGA. Para determinar la relación entre estas dos variables, se utilizó la relación estadística coeficiente de determinación (R2), el cual inicialmente al evaluar estas variables día a día generó valores bajos de R2 (0,0 - 0,4) en la mayoría de las cuencas (desfase 0). Posteriormente, se evaluó la relación a partir de un desfase consecutivo de la variable caudal, este valor comenzó a mejorar y generó un patrón de variación temporal de la relación R2 (valores sobre 0,5). Finalmente, producto del patrón de variación del R2, las cuencas se agruparon en tres grupos, según el día de desfase en el cual presentaran su mayor R2, siendo las cuencas de los extremos su mayor R2 a los 60 días de desfase, las cuencas centrales a los 120 días y una única cuenca RV6 a los 160 días.Snow and ice cover in mountain range areas represents important information in hydrological processes. The data obtained from ground stations are scarce and poorly distributed, thus the estimation from remote sensing observations has established itself as an attractive alternative. Determining snow cover is particularly important during spring and summer in mountainous terrain, where snow can melt rapidly causing large spatial variations in snow cover. This work presents the study and calculation of the spatiotemporal variability of the snow cover, in the high mountain basins between 29 ° S - 37 ° S during the years 2003 to 2020 and its association with the flow values obtained from the stations located in the study area. To determine the snow cover, satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor were used from daily snow cover products (Snow Cover MOD10A1), all the processing was carried out through the online platform Google Earth Engine. (CARAMBA). To obtain flow data, it will be used in the data from the DGA fluviometric stations. To determine the relationship between these two variables, the statistical relationship coefficient of determination (R2) was used, which first when evaluating these variables day by day generated low values of R2 (0.0 - 0.4) in most of the watersheds (lag 0). Subsequently, the relationship was evaluated from a consecutive lag of the flow variable, this value began to improve and generated a pattern of temporal variation of the R2 relationship (values above 0.5). Finally, as a result of the R2 variation pattern, the basins were grouped into three groups, according to the day of lag on which they presented their greatest R2, the extreme basins being their highest R2 on day 60 of lag, the central basins at 120 days and a single RV6 basin at 160 days

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing