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

2019 ◽  
Vol 23 (5) ◽  
pp. 2401-2416 ◽  
Author(s):  
Xinghua Li ◽  
Yinghong Jing ◽  
Huanfeng Shen ◽  
Liangpei Zhang
Keyword(s):  
Snow Cover ◽  
Cloud Cover ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Optical Observations ◽  
Optical Remote Sensing ◽  
Spatial Methods ◽  
Fusion Methods ◽  
Spatio Temporal ◽  
Cloud Removal

Abstract. 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.

scholarly journals The recent developments in spatio-temporally continuous snow cover product generation

2019 ◽  
Author(s):  
Xinghua Li ◽  
Yinghong Jing ◽  
Huanfeng Shen ◽  
Liangpei Zhang
Keyword(s):  
Snow Cover ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Optical Remote Sensing ◽  
Temporal Discontinuity ◽  
Fusion Methods ◽  
Recent Developments ◽  
Remote Sensing Systems ◽  
Spatio Temporal ◽  
Cloud Removal

Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, the energy balance, and so on. However, 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 snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating spatio-temporally continuous snow cover products, and to expose the development trends. The methods of generating spatio-temporally continuous snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. Experiments were conducted to validate the reconstruction effect of the representative methods.

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

2008 ◽  
Vol 22 (16) ◽  
pp. 2930-2942 ◽  
Author(s):  
Baolin Li ◽  
A‐Xing Zhu ◽  
Chenghu Zhou ◽  
Yichi Zhang ◽  
Tao Pei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Cloud Cover ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Automatic Mapping

scholarly journals Daily Terra–Aqua MODIS cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019

2021 ◽  
Vol 13 (2) ◽  
pp. 767-776
Author(s):  
Sher Muhammad ◽  
Amrit Thapa
Keyword(s):  
Snow Cover ◽  
Cloud Cover ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Training Data ◽  
High Mountain ◽  
Optical Remote Sensing ◽  
Glacier Inventory ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual

Abstract. Snow is a dominant water resource in high-mountain Asia (HMA) and crucial for mountain communities and downstream populations. Snow cover monitoring is significant to understand regional climate change, managing meltwater, and associated hazards/disasters. The uncertainties in passive optical remote-sensing snow products, mainly underestimation caused by cloud cover and overestimation associated with sensors' limitations, hamper the understanding of snow dynamics. We reduced the biases in Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily snow data and generated a combined daily snow product for high-mountain Asia between 2002 and 2019. An improved MODIS 8 d composite MOYDGL06* product was used as a training data for reducing the underestimation and overestimation of snow in daily products. The daily MODIS Terra and Aqua images were improved by implementing cloud removal algorithms followed by gap filling and reduction in overestimated snow beyond the respective 8 d composite snow extent of the MOYDGL06* product. The daily Terra and Aqua snow products were combined and merged with the Randolph Glacier Inventory version 6.0 (RGI 6.0) described as M*D10A1GL06 to make a more complete cryosphere product with 500 m spatial resolution. The pixel values in the daily combined product are preserved and reversible to the individual Terra and Aqua improved products. We suggest a weight of 0.5 and 1 to snow pixels in either or both Terra and Aqua products, respectively, for deriving snow cover statistics from our final snow product. The values 200, 242, and 252 indicate snow pixels in both Terra and Aqua and have a weight of 1, whereas pixels with snow in one of the Terra or Aqua products have a weight of 0.5. On average, the M*D10A1GL06 product reduces 39.1 % of uncertainty compared to the MOYDGL06* product. The uncertainties due to cloud cover (underestimation) and sensor limitations, mainly larger solar zenith angle (SZA) (overestimation) reduced in this product, are approximately 32.9 % and 6.2 %, respectively. The data in this paper are mainly useful for observation and simulation of climate, hydro-glaciological forcings, calibration, validation, and other water-related studies. The data are available at https://doi.org/10.1594/PANGAEA.918198 (Muhammad, 2020) and the algorithm source code at https://doi.org/10.5281/zenodo.3862058 (Thapa, 2020).

scholarly journals An improved Terra–Aqua MODIS daily cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019

2020 ◽  
Author(s):  
Sher Muhammad ◽  
Amrit Thapa
Keyword(s):  
Snow Cover ◽  
Regional Climate ◽  
Probabilistic Approach ◽  
Regional Climate Change ◽  
High Mountain ◽  
Optical Remote Sensing ◽  
Glacier Inventory ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Removal ◽  
The Individual

Abstract. Snow is a dominant water resource in High Mountain Asia (HMA) and crucial for the mountain communities and downstream population. Snow cover monitoring is significant to understand regional climate change, managing meltwater, and associated hazards/disasters. The uncertainties in passive optical remote sensing snow products mainly underestimation caused by cloud-cover and overestimation associated with sensorsˈ limitations hamper the understand snow dynamics. We reduced the biases in Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily snow data and generated a combined daily snow product for High Mountain Asia between 2002 and 2019. An improved MODIS 8-day composite MOYDGL06* product was used as a base for reducing the underestimation and overestimation of snow in daily products. The daily MODIS Terra and Aqua images were improved by the corresponding 8-day composite image of the MOYDGL06* product by implementing cloud removal algorithms followed by gap filling and reduction in overestimated snow beyond the respective 8-day composite snow extent. The daily Terra and Aqua snow products were combined and merged with the Randolph Glacier Inventory (RGI) Version 6.0 to make a more complete cryosphere product. The pixel values in the daily combined product are preserved and reversible to the individual Terra and Aqua improved products. We suggest a probabilistic approach for deriving snow cover statistics from our final snow product. The pixels with values 200, 242, and 252 indicate snow in both Terra and Aqua and has a 100 % probability, whereas pixels with snow in one of the Terra or Aqua products have a 50 % probability. The data associated with this paper are available for the end-users mainly useful for observation and simulation of climate, hydro-glaciological forcings, calibration, validation, and other water-related studies. The data are available at https://doi.pangaea.de/10.1594/PANGAEA.918198 (Muhammad, 2020) and the algorithm source code at https://doi.org/10.5281/zenodo.3862058 (Thapa, 2020).

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

scholarly journals Regions of intensification of extreme snowfall under future warming

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lennart Quante ◽  
Sven N. Willner ◽  
Robin Middelanis ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Observational Data ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Average Intensity ◽  
High Latitudes ◽  
Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

scholarly journals New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hirofumi Hashimoto ◽  
Weile Wang ◽  
Jennifer L. Dungan ◽  
Shuang Li ◽  
Andrew R. Michaelis ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Seasonal Patterns ◽  
Optical Remote Sensing ◽  
Amazon Forest ◽  
Evergreen Forests ◽  
Geostationary Satellites ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
New Generation

AbstractAssessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

scholarly journals Spatio-Temporal Variations in COVID-19 in Relation to the Global Climate Distribution and Fluctuations

2021 ◽  
pp. 100417
Author(s):  
Olaniran Jonathan Matthew ◽  
Adebayo Oluwole Eludoyin ◽  
Kehinde Sunday Oluwadiya
Keyword(s):  
Global Climate ◽  
Temporal Variations ◽  
Spatio Temporal

scholarly journals Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

2021 ◽  
Vol 13 (15) ◽  
pp. 2961
Author(s):  
Rui Jiang ◽  
Arturo Sanchez-Azofeifa ◽  
Kati Laakso ◽  
Yan Xu ◽  
Zhiyan Zhou ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Southern China ◽  
Vegetation Indices ◽  
Paddy Rice ◽  
Rice Fields ◽  
Optical Observations ◽  
Landsat 8 ◽  
Monthly Basis ◽  
Satellite Sensors ◽  
Sentinel 2

Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover.

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

2016 ◽  
Vol 29 (17) ◽  
pp. 6065-6083 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Correlation Coefficients ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Skill Score ◽  
Global Climate Models ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

Sign in / Sign up

Export Citation Format

Share Document