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Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (11): 1315-1339    DOI: 10.1007/s40333-023-0069-5     CSTR: 32276.14.s40333-023-0069-5
Research article     
Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China
YUAN Shuai1,2, LIU Yongqiang1,2,*(), QIN Yan1,2, ZHANG Kun1,2
1College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
2Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China
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Abstract  

Surface albedo is a quantitative indicator for land surface processes and climate modeling, and plays an important role in surface radiation balance and climate change. In this study, by means of the MCD43A3 surface albedo product developed on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS), we analyzed the spatiotemporal variation, persistence status, land cover type differences, and annual and seasonal differences of surface albedo, as well as the relationship between surface albedo and various influencing factors (including Normalized Difference Snow Index (NDSI), precipitation, Normalized Difference Vegetation Index (NDVI), land surface temperature, soil moisture, air temperature, and digital elevation model (DEM)) in the north of Xinjiang Uygur Autonomous Region (northern Xinjiang) of Northwest China from 2010 to 2020 based on the unary linear regression, Hurst index, and Pearson's correlation coefficient analyses. Combined with the random forest (RF) model and geographical detector (Geodetector), the importance of the above-mentioned influencing factors as well as their interactions on surface albedo were quantitatively evaluated. The results showed that the seasonal average surface albedo in northern Xinjiang was the highest in winter and the lowest in summer. The annual average surface albedo from 2010 to 2020 was high in the west and north and low in the east and south, showing a weak decreasing trend and a small and stable overall variation. Land cover types had a significant impact on the variation of surface albedo. The annual average surface albedo in most regions of northern Xinjiang was positively correlated with NDSI and precipitation, and negatively correlated with NDVI, land surface temperature, soil moisture, and air temperature. In addition, the correlations between surface albedo and various influencing factors showed significant differences for different land cover types and in different seasons. To be specific, NDSI had the largest influence on surface albedo, followed by precipitation, land surface temperature, and soil moisture; whereas NDVI, air temperature, and DEM showed relatively weak influences. However, the interactions of any two influencing factors on surface albedo were enhanced, especially the interaction of air temperature and DEM. NDVI showed a nonlinear enhancement of influence on surface albedo when interacted with land surface temperature or precipitation, with an explanatory power greater than 92.00%. This study has a guiding significance in correctly understanding the land-atmosphere interactions in northern Xinjiang and improving the regional land-surface process simulation and climate prediction.

Key words： surface albedo      MCD43A3      Hurst index      random forest (RF) model      geographical detector (Geodetector)      Normalized Difference Snow Index (NDSI)      northern Xinjiang     
Received: 21 February 2023      Published: 30 November 2023
Corresponding Authors: * LIU Yongqiang (E-mail: liuyq@xju.edu.cn)
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YUAN Shuai
LIU Yongqiang
QIN Yan
ZHANG Kun
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YUAN Shuai, LIU Yongqiang, QIN Yan, ZHANG Kun. Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China. Journal of Arid Land, 2023, 15(11): 1315-1339.

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http://jal.xjegi.com/10.1007/s40333-023-0069-5     OR     http://jal.xjegi.com/Y2023/V15/I11/1315

Fig. 1 Overview of northern Xinjiang based on digital elevation model (DEM) (a) and spatial distribution of land cover types in northern Xinjiang in 2020 (b). Note that the figures are based on the standard map (GS (2019) 1822) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html), and the standard map has not been modified.
Fig. 2 Flow chart of analysis in this study. (a), spatial distribution analysis; (b), trend analysis; (c), influencing factor analysis. NDSI, Normalized Difference Snow Index; NDVI, Normalized Difference Vegetation Index; Geodetector, geographical detector; h, the stratification of surface albedo (or its influencing factors) in the Geodetector.
Spatial variation trend Slope P Spatial distribution function value
Extremely increasing >0 <0.01 1.00
Intermediately increasing >0 0.01-0.05 0.50
Extremely decreasing <0 P<0.01 -1.00
Intermediately decreasing <0 0.01-0.05 -0.50
Table 1 Classification criteria and spatial distribution function values for the variation trends of surface albedo and its influencing factors
H Persistence status of time series
0.50-1.00 Persistence series, i.e., future change is highly likely to be consistent with the past trend.
0.50 Random discrete series, i.e., the trend of future change is independent of the past trend.
0.00-0.50 Anti-persistence series, i.e., future change is most likely to show the opposite trend from the past trend.
Table 2 Three main forms of the Hurst index indicating the persistence status of time series
H Persistence status Spatial distribution function value
0.00-0.20 Extremely strong anti-persistence Significant anti-persistence -1.00
0.20-0.25 Strong anti-persistence -0.50
0.25-0.35 Stronger anti-persistence -0.25
0.35-0.65 Weak anti-persistence or weak persistence -
0.65-0.75 Stronger persistence Significant
persistence		 0.25
0.75-0.80 Strong persistence 0.50
0.80-1.00 Extremely strong persistence 1.00
Table 3 Seven levels of the Hurst index indicating the persistence status of time series and their corresponding spatial distribution function values
Slope H Integrated trend (past trend and future change) Spatial distribution function value
>0.0000 >0.50 Trending up in the past, and continuing to rise in the future 1.00
>0.0000 <0.50 Trending up in the past, but trending the opposite way in the future -1.00
<0.0000 >0.50 Trending down in the past, and continuing to decline in the future 1.00
<0.0000 <0.50 Trending down in the past, but trending the opposite way in the future -1.00
- 0.50 Unchanged and discrete sequence -
Table 4 Integrated analysis on the past trend and future change of surface albedo based on the slope of the unary linear regression and the Hurst index
Correlation Correlation
coefficient		 P Spatial distribution
function value
Significant positive correlation Extreme positive correlation >0.00 <0.01 1.00
Intermediate positive correlation >0.00 0.01-0.05 0.50
Significant negative correlation Extreme negative correlation <0.00 <0.01 -1.00
Intermediate negative correlation <0.00 0.01-0.05 -0.50
Table 5 Classification criteria and spatial distribution function values for the correlation between surface albedo and each influencing factor
Interval Interaction explanation
q(X1∩X2)<Min[q(X1), q(X2)] Nonlinear weakening
Min[q(X1), q(X2)]<q(X1∩X2)<Max[q(X1), q(X2)] Single-factor nonlinear weakening
q(X1∩X2)>Max[q(X1), q(X2)] Dual factor enhancement
q(X1∩X2)=q(X1)+q(X2) Independence
q(X1∩X2)>q(X1)+q(X2) Nonlinear enhancement
Table 6 Influence criterion intervals and interaction types caused by the interaction between influencing factors
Fig. 3 Spatial distribution characteristics of the seasonal average surface albedo (a-d) and annual average surface albedo (e) from 2010 to 2020. The gray area maps show the average surface albedo values in the longitudinal and latitudinal directions. Note that the figures are based on the standard map (GS (2019) 1822) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html), and the standard map has not been modified. ASAV, average surface albedo value.
Fig. 4 Temporal variations of the annual and seasonal average surface albedo and its influencing factors from 2010 to 2020. (a1-a5), surface albedo; (b1-b5), NDSI; (c1-c5), precipitation; (d1-d5), NDVI; (e1-e5), land surface temperature; (f1-f5), soil moisture; (g1-g5), air temperature. Blue circles are data values, black lines are trend lines, and gray areas are 95% confidence intervals. Slope indicates the linear trend per decade. **, P<0.01 level; *, P<0.05 level.
Fig. 5 Spatial distribution characteristics of the variation trends of the annual and seasonal average surface albedo (a-e) and their area proportions in different land cover types (except for the area proportion of regions with unchanged trend of surface albedo; f1-f5) from 2010 to 2020. The gray area maps show the spatial distribution function values (the closer the spatial distribution function value is to 1.00, the more significantly the surface albedo increases; and the closer the value to -1.00, the more significantly the surface albedo decreases; see Table 1 for details) of the variation trends of surface albedo in the latitudinal and longitudinal directions. The semitransparent color band between histograms represents the change of area proportion in adjacent land cover types. Note that the figures are based on the standard map (GS (2019) 1822) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html), and the standard map has not been modified. SDFV, spatial distribution function value.
Fig. 6 Spatial distribution characteristics of the persistence status of the annual and seasonal average surface albedo (a-e) and their area proportions in different land cover types (except for the area proportion of regions with weak anti-persistence or weak persistence of surface albedo; f1-f5) from 2010 to 2020. The gray area maps show the spatial distribution function values (the closer the spatial distribution function value to 1.00, the stronger the persistence, and the closer the value to -1.00, the stronger the anti-persistence; see Table 3 for details) of the persistence status of surface albedo in the latitudinal and longitudinal directions. The semitransparent color band between histograms represents the change of area proportion in adjacent land types. Note that the figures are based on the standard map (GS (2019) 1822) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html), and the standard map has not been modified. SDFV, spatial distribution function value.
Fig. 7 Spatial distribution characteristics of the integrated trends of the annual and seasonal average surface albedo (a-e) and their area proportions in different land cover types (except for the area proportion of regions with unchanged and discrete sequence of surface albedo; f1-f5) from 2010 to 2020. The gray area maps show the spatial distribution function values (the closer the spatial distribution function value to 1.00, the more similar in the change trend of surface albedo between the past and future, and the closer the spatial distribution function value to -1.00, the more opposite in the change trend of surface albedo between the past and future; see Table 4 for details) of integrated trends in the latitudinal and longitudinal directions. The semitransparent color band between histograms represents the change of area proportion in adjacent land types. Note that the figures are based on the standard map (GS (2019) 1822) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/ index.html), and the standard map has not been modified. SDFV, spatial distribution function value.
Fig. 8 Spatial distribution characteristics of correlations between the annual average surface albedo and influencing factors (a-f) and their area proportions in different land cover types (except for the area proportion of regions with no significant correlation; g1-g6) from 2010 to 2020. The gray area maps show the spatial distribution function values (the closer the spatial distribution function value to 1.00, the more significant positive correlation between the annual average surface albedo and influencing factor, and the closer the spatial distribution function value to -1.00, the more significant negative correlation between the annual average surface albedo and influencing factor; see Table 5 for details) of correlations in the latitudinal and longitudinal directions. The semitransparent color band between histograms represents the change of area proportion in adjacent land types. Note that the figures are based on the standard map (GS (2019) 1822) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html), and the standard map has not been modified. SDFV, spatial distribution function value.
Fig. 9 Spatial distribution characteristics of correlations between the seasonal average surface albedo and influencing factors from 2010 to 2020. (a1-a4), NDSI; (b1-b4), precipitation; (c1-c4), NDVI; (d1-d4), land surface temperature; (e1-e4), soil moisture; (f1-f4), air temperature. Note that the figures are based on the standard map (GS (2019) 1822) from the Standard Map Service System (http://bzdt.ch.mnr.gov.cn/index.html), and the standard map has not been modified.
Influencing factor %IncMSE (%) IncNodePurity
NDSI 65.30 1.75
Precipitation 38.77 0.89
Land surface temperature 34.61 0.81
Soil moisture 46.31 0.73
DEM 52.58 0.61
NDVI 36.31 0.46
Air temperature 34.85 0.42
Table 7 Importance analysis of each influencing factor on the average surface albedo from 2010 to 2018 based on the random forest model
Fig. 10 Box-plot (a) and line chart (b) of the IncNodePurity values for each influencing factor of surface albedo from 2010 to 2018. IncNodePurity, the average increase in node purity.
Fig. 11 Results of factor detector (a) and interaction detector (b) in Geodetector analysis showing the impact of each influencing factor on surface albedo. *, 99% of confidence interval (P<0.01). Dark blue and light blue represent the two-factor interaction with nonlinear enhancement and dual factor enhancement, respectively. Values in the circles indicate the q-values.
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