Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

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Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

The Cryosphere Discussions ◽

10.5194/tcd-9-2745-2015 ◽

2015 ◽

Vol 9 (3) ◽

pp. 2745-2782

Author(s):

K. Atlaskina ◽

F. Berninger ◽

G. de Leeuw

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Air Temperature ◽

Land Surface ◽

Surface Albedo ◽

Vegetation Index ◽

Precipitation Amount ◽

Snow Cover Fraction ◽

Air Temperatures ◽

Land Surfaces

Abstract. Thirteen years of MODIS surface albedo data for the Northern Hemisphere during the spring months (March–May) were analysed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analysed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56% of variation of albedo in March, 76% in April and 92% in May. Therefore the effects of other parameters were investigated only for areas with 100% SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds −15 °C. At monthly mean air temperatures below this value no albedo changes are observed. Enhanced vegetation index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100% SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in Eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

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Investigation of Spatial and Temporal Changes in the Land Surface Albedo for the Entire Chinese Territory

Geosciences ◽

10.3390/geosciences10090362 ◽

2020 ◽

Vol 10 (9) ◽

pp. 362

Author(s):

Jihui Yuan

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Global Climate ◽

Temporal Changes ◽

Spatial And Temporal Changes ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Satellite

Currently, global climate change (GCC) and the urban heat island (UHI) phenomena are becoming serious problems, partly due to the artificial construction of the land surface. When sunlight reaches the land surface, some of it is absorbed and some is reflected. The state of the land surface directly affects the surface albedo, which determines the magnitude of solar radiation reflected by the land surface in the daytime. In order to better understand the spatial and temporal changes in surface albedo, this study investigated and analyzed the surface albedo from 2000 to 2016 (2000, 2008, and 2016) in the entire Chinese territory, based on the measurement database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, aboard NASA’s Terra satellite. It was shown that the Northeast China exhibited the largest decline in surface albedo and North China showed the largest rising trend of surface albedo from 2000 to 2016. The correlation between changes in surface albedo and the Normalized Difference Vegetation Index (NDVI) indicated that the change trend of surface albedo was opposite to that of NDVI. In addition, in order to better understand the distribution of surface albedo in the entire Chinese territory, the classifications of surface albedo in three years (2000, 2008, and 2016) were implemented using five classification methods in this study.

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Representing Variability in Subgrid Snow Cover and Snow Depth in a Global Land Model: Offline Validation

Journal of Climate ◽

10.1175/jcli-d-13-00310.1 ◽

2014 ◽

Vol 27 (9) ◽

pp. 3318-3330 ◽

Cited By ~ 33

Author(s):

T. Nitta ◽

K. Yoshimura ◽

K. Takata ◽

R. O’ishi ◽

T. Sueyoshi ◽

...

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Land Surface ◽

Snow Water Equivalent ◽

Land Surface Model ◽

Surface Model ◽

Snow Cover Fraction ◽

Snow Covered Area ◽

Covered Area ◽

Global Land

Abstract Subgrid snow cover is one of the key parameters in global land models since snow cover has large impacts on the surface energy and moisture budgets, and hence the surface temperature. In this study, the Subgrid Snow Distribution (SSNOWD) snow cover parameterization was incorporated into the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) land surface model. SSNOWD assumes that the subgrid snow water equivalent (SWE) distribution follows a lognormal distribution function, and its parameters are physically derived from geoclimatic information. Two 29-yr global offline simulations, with and without SSNOWD, were performed while forced with the Japanese 25-yr Reanalysis (JRA-25) dataset combined with an observed precipitation dataset. The simulated spatial patterns of mean monthly snow cover fraction were compared with satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The snow cover fraction was improved by the inclusion of SSNOWD, particularly for the accumulation season and/or regions with relatively small amounts of snowfall; snow cover fraction was typically underestimated in the simulation without SSNOWD. In the Northern Hemisphere, the daily snow-covered area was validated using Interactive Multisensor Snow and Ice Mapping System (IMS) snow analysis datasets. In the simulation with SSNOWD, snow-covered area largely agreed with the IMS snow analysis and the seasonal cycle in the Northern Hemisphere was improved. This was because SSNOWD formulates the snow cover fraction differently for the accumulation season and ablation season, and represents the hysteresis of the snow cover fraction between different seasons. The effects of including SSNOWD on hydrological properties and snow mass were also examined.

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Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

10.5194/acp-2016-747 ◽

2016 ◽

Author(s):

Hongbo Zhang ◽

Fan Zhang ◽

Guoqing Zhang ◽

Xiaobo He ◽

Lide Tian

Keyword(s):

Tibetan Plateau ◽

Air Temperature ◽

Land Surface ◽

Estimation Accuracy ◽

Daily Maximum ◽

The Tibetan Plateau ◽

Cloud Detection ◽

Air Temperatures ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data have played a significant role in estimating the air temperature (Tair) due to the sparseness of ground measurements, especially for remote mountainous areas. Generally, two types of air temperatures are studied including daily maximum (Tmax) and minimum (Tmin) air temperatures. MODIS daytime and nighttime LST are often used as proxies for estimating Tmax and Tmin, respectively. The Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %). The presence of clouds can affect the relationship between Tair and LST and can further affect the estimation accuracies. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from over the TP. Comparisons made between in-situ cloudiness observations and MODIS claimed clear-sky records show that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Our validation of the MODIS LST values under different cloudiness constraining conditions shows that the accuracy of MODIS nighttime LST is severely affected by undetected clouds. Large errors introduced by undetected clouds are found to significantly affect the Tmin estimations based on nighttime LST through cloud effect tests. However, clouds are mainly found to affect Tmax estimation by affecting the essential relationship between Tmax and daytime LST. The obviously larger errors of Tmax estimation than those of Tmin could be attributed to larger MODIS daytime LST errors resulting from higher degrees of daytime LST heterogeneity within MODIS pixel than those of nighttime LST. Constraining all four MODIS observations per day to non-cloudy observations can efficiently screen samples to build a strong fit of Tmin estimation using MODIS nighttime LST. The present study reveals the effects of clouds on Tair estimation through MODIS LST and will thus help improve the estimation accuracy levels while alleviating the problems associated with severe data sparseness over the TP.

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Monthly Air Temperatures over Northern China Estimated by Integrating MODIS Data with GIS Techniques

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-12-0264.1 ◽

2013 ◽

Vol 52 (9) ◽

pp. 1987-2000 ◽

Cited By ~ 15

Author(s):

Xiao Zheng ◽

Jiaojun Zhu ◽

Qiaoling Yan

Keyword(s):

Remote Sensing ◽

Air Temperature ◽

Land Surface ◽

Spatial Interpolation ◽

Large Scale ◽

Stepwise Regression ◽

Vegetation Index ◽

Regression Modeling ◽

Air Temperatures ◽

Interpolation Techniques

AbstractThe Three-North Shelter/Protective Forest Programme (TNSFP), the largest ecological afforestation program in the world, was launched in 1978 and will last until 2050 to improve ecological conditions in the Three-North regions of China. To manage the shelter forests sustainably, it is necessary to accurately estimate air temperature on a large scale, but the spatial distribution of ground meteorological stations is limited. A hybrid method was established by combining stepwise regression modeling and spatial interpolation techniques (SRMSIT) to construct the monthly mean, minimum, and maximum air temperatures (Tmean, Tmin, and Tmax, respectively) at a 1 km × 1 km grid size in the Three-North regions. Stepwise regression modeling was applied to construct the relationship between air temperatures (Tmean, Tmin, and Tmax—the dependent variables) and geographical and Moderate Resolution Imaging Spectroradiometer (MODIS) variables (the independent variables). Spatial interpolation techniques were used to correct the residual values. According to the factor analysis, three geographic (altitude, latitude, and continentality) and two MODIS variables [nighttime land surface temperature (LST) and normalized difference vegetation index] were selected in stepwise regression modeling, and nighttime LST was the most powerful remote sensing variable. The SRMSIT method, in which the spatial interpolation of the residuals was done with inverse distance weighting, achieved average root-mean-square error values at 0.86°, 1.10°, and 1.13°C for Tmean, Tmin, and Tmax, respectively. Therefore, the simple regression algorithms derived from the combination of remote sensing and geographical variables, together with residual interpolation techniques, have the potential to accurately estimate monthly air temperature in large regions.

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Evaluating Cloud Contamination in Clear-Sky MODIS Terra Daytime Land Surface Temperatures Using Ground-Based Meteorology Station Observations

Journal of Climate ◽

10.1175/jcli-d-12-00250.1 ◽

2013 ◽

Vol 26 (5) ◽

pp. 1551-1560 ◽

Cited By ~ 39

Author(s):

Scott N. Williamson ◽

David S. Hik ◽

John A. Gamon ◽

Jeffrey L. Kavanaugh ◽

Saewan Koh

Keyword(s):

Air Temperature ◽

Land Surface ◽

Maximum Temperature ◽

Average Error ◽

Cold Bias ◽

Clear Sky ◽

Air Temperatures ◽

Moderate Resolution Imaging Spectroradiometer ◽

Highly Correlated ◽

Sky Conditions

Abstract Environment Canada meteorological station hourly sampled air temperatures Tair at four stations in the southwest Yukon were used to identify cloud contamination in the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra clear-sky daytime land surface temperature (LST) and emissivity daily level-3 global 1-km grid product (MOD11A1, Collection 5) that is not flagged by the MODIS quality algorithm as contaminated. The additional cloud masking used qualitative ground-based sky condition observations, collected at two of the four stations, and coincident MODIS quality flag information. The results indicate that air temperature observed at a variety of discrete spatial locations having different land cover is highly correlated with MODIS LST collected at 1-km grid spacing. Quadratic relationships between LST and air temperature, constrained by ground observations of “clear” sky conditions, show less variability than relationships found under “mainly clear” and “mostly cloudy” sky conditions, and the more clouds observed in the sky coincides with a decreasing y intercept. Analysis of MODIS LST and its associated quality flags show a cold bias (<0°C) in the assignment of the ≤3-K-average LST error, indicating MODIS LST has a maximum average error of ≤2 K over a warm surface (>0°C). Analysis of two observation stations shows that unidentified clouds in MODIS LST are between 13% and 17%, a result that agrees well with previous studies. Analysis of daytime values is important because many processes are dependent on daylight and maximum temperature. The daytime clear-sky LST–Tair relationship observed for the good-quality confirmed cloud-free-sky MODIS LST quality flag can be used to discriminate cloud-contaminated grid cells beyond the standard MODIS cloud mask.

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Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-13681-2016 ◽

2016 ◽

Vol 16 (21) ◽

pp. 13681-13696 ◽

Cited By ~ 22

Author(s):

Hongbo Zhang ◽

Fan Zhang ◽

Guoqing Zhang ◽

Xiaobo He ◽

Lide Tian

Keyword(s):

Tibetan Plateau ◽

Air Temperature ◽

Land Surface ◽

Estimation Accuracy ◽

Daily Maximum ◽

The Tibetan Plateau ◽

Cloud Detection ◽

Air Temperatures ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (Tmax) and minimum (Tmin) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %), which may affect the air temperature (Tair) estimation accuracy. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the Tmin estimation accuracy. However, for Tmax estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between Tmax and daytime LST. The errors of Tmax estimation are obviously larger than those of Tmin and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate Tmin estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on Tmax and Tmin estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the Tair estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.

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Evaluation of Arctic Land Snow Cover Characteristics, Surface Albedo, and Temperature during the Transition Seasons from Regional Climate Model Simulations and Satellite Data

Advances in Meteorology ◽

10.1155/2014/604157 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 12

Author(s):

X. Zhou ◽

H. Matthes ◽

A. Rinke ◽

K. Klehmet ◽

B. Heim ◽

...

Keyword(s):

Surface Temperature ◽

Regional Climate Model ◽

Snow Cover ◽

Land Surface Temperature ◽

Land Surface ◽

Surface Albedo ◽

Snow Water Equivalent ◽

Climate Model ◽

Regional Climate ◽

Snow Cover Fraction

This paper evaluates the simulated Arctic land snow cover duration, snow water equivalent, snow cover fraction, surface albedo, and land surface temperature in the regional climate model HIRHAM5 during 2008–2010, compared with various satellite and reanalysis data and one further regional climate model (COSMO-CLM). HIRHAM5 shows a general agreement in the spatial patterns and annual course of these variables, although distinct biases for specific regions and months are obvious. The most prominent biases occur for east Siberian deciduous forest albedo, which is overestimated in the simulation for snow covered conditions in spring. This may be caused by the simplified albedo parameterization (e.g., nonconsideration of different forest types and neglecting the effect of fallen leaves and branches on snow for deciduous tree forest). The land surface temperature biases mirror the albedo biases in their spatial and temporal structures. The snow cover fraction and albedo biases can explain the simulated land surface temperature bias of ca. −3°C over the Siberian forest area in spring.

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A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽

10.3390/technologies9020040 ◽

2021 ◽

Vol 9 (2) ◽

pp. 40

Author(s):

Guang Yang ◽

Yuntao Ma ◽

Jiaqi Hu

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Extraction Methods ◽

Average Error ◽

Geographic Coordinate System ◽

Optimal Thresholds ◽

Moderate Resolution Imaging Spectroradiometer ◽

Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

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Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

Remote Sensing ◽

10.3390/rs13101992 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1992

Author(s):

Alessio Lattanzio ◽

Michael Grant ◽

Marie Doutriaux-Boucher ◽

Rob Roebeling ◽

Jörg Schulz

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Time Range ◽

Essential Variable ◽

Spatial Coverage ◽

Data Record ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Mask ◽

Cloud Screening

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record.

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