黔东南稳定林地地表反照率时空变化与影响因子分析

图1 黔东南位置及2003—2018年稳定林地分布情况

Fig.1 Location of Qiandongnan and distribution of stable forest land from 2003 to 2018

1.2 数据源及处理

地表反照率数据使用美国国家航空航天局MODIS反照率产品(MCD43A3),空间分辨率为500 m,时间分辨率为1 d,MODIS地表反照率在全球范围内精度验证显示其绝对误差在±0.02之间,相对误差在10%左右^{[9,11 -12]},满足气候模式对地表反照率精度要求^[11]。地表真实反照率采用黑空和白空反照率进行线性加权平均,公式为:

(1)

α = (1 - S) α_{b s a} + S α_{w s a}

，

(2)

S = 0.122 + 0.85 e x p (- 4.8 μ_{0})

，

式中: $α_{b s a}$ 为黑空反照率; $α_{w s a}$ 为白空反照率; $α$ 为地表真实反照率; S为天空散射比因子; $μ_{0}$ 为MCD43A2产品中提供的正午太阳天顶角参数。

由于黔东南多云雨天气导致MODIS MCD43A3地表反照率数据存在空缺值,不能完整展现每个月份影像。因此本文使用ArcGIS叠合每月MODIS反照率影像的并集,再求得每个栅格均值。EVI使用MODIS MOD13Q1,时间分辨率16 d,空间分辨率250 m,该产品最大限度地减少了冠层背景变化,对高植被覆盖度具有很强的敏感性^[13]。

土壤水分数据采用国家青藏高原科学数据中心,中国土壤水分数据集(2003—2018年),时间分辨率为月,空间分辨率0.05°。气温、降水数据使用中国地面气候资料日值数据集(V3.0),时间分辨率为日值。土地利用数据采用2003和2018年MODIS MCD12Q1数据集,使用其IGBP分类规则产品(图1)。在ArcGIS中使用相交工具提取相同植被类型。本文在分析时均对时间分辨率重采样到月,空间分辨率重采样到500 m。以上数据质量均符合本文需求。

2 研究方法

2.1 趋势分析

采用Mann-Kendall (M-K) 和Theil-Sen(T-S)检验地表反照率的时空变化。M-K检验对季节变敏感性强^[6],对异常值不敏感,不需要长时间序列的数据呈正态和独立性分布。T-S具有简单的计算性,对异常值具有稳定性等,适用于有噪声的时间序列数据^[14]。M-K检验统计量S和Z分别如式(4)和式(6)所示,通过耦合双尾Z检验来确定显著性变化。具体计算公式为:

(3)

s g n (X_{j} - X_{k}) = \{\begin{array}{l} 1 & X_{j} - X_{k} > 0 \\ 0 & X_{j} - X_{k} = 0 \\ - 1 & X_{j} - X_{k} < 0 \end{array}

，

(4)

S = \overset{n - 1}{\sum_{k = 1}} \overset{n}{\sum_{j = k + 1}} s g n (X_{j} - X_{k}), j > k

，

(5)

σ^{2} = \frac{n (n - 1) (2 n + 5) - \overset{q}{\sum_{p = 1}} t_{p} (t_{p} - 1) (2 t_{p} + 5)}{18}

，

(6)

Z = \{\begin{array}{l} \frac{S - 1}{σ} & S > 0 \\ 0 & S = 0 \\ \frac{S + 1}{σ} & S > 0 \end{array}

，

(7)

T S = M e d i a n (\frac{X_{j} - X_{k}}{t_{j} + t_{k}})

，

式中: $X_{j}$ , $X_{k}$ 分别为第 $j$ 年和第 $k$ 年各因子均值; n为时间长度; sgn为符号函数; $q$ 为数据组的数目; $t_{p}$ 为第 $p$ 组的数据个数; σ²为S的方差。本结论中 $|Z| \geq$ 1.65,1.96和2.58时,表示趋势分别通过信度为90%,95%和99%的显著性检验,指导意义分别为微显著、显著和极显著变化。式(7)中,TS>0为增加趋势; TS<0为递减趋势。

2.2 相关分析

相关分析常用于研究地表反照率与各变量间的密切程度,相关系数取值范围为[-1,1],大于0呈正相关,越接近1相关性越大,反之亦然,计算公式为:

(8)

R = \frac{\overset{n}{\sum_{i = 1}} (X_{i} - \bar{X}) (Y_{i} - \bar{Y})}{\sqrt{\overset{n}{\sum_{i = 1}} (X_{i} {- \bar{X})}^{2}} \sqrt{\overset{n}{\sum_{i = 1}} (Y_{i} {- \bar{Y})}^{2}}}

，

式中: R为X和Y的相关系数; $X_{i}$ 和 $Y_{i}$ 为2个因子第 $i$ 年的值; $\bar{X}$ 和 $\bar{Y}$ 为2个因子的平均值; $n$ 为年份; $i$ 为不同年份。

2.3 相对重要性分析

多元回归分析旨在建造自变量与因变量之间的线性关系,展现多种自变量与一种因变量影响强度,而在长时间序列中地表反照率被多种环境因子共同影响^[15],使用多元回归分析具有实用意义。本文基于栅格尺度量化每个栅格的相对重要性,并对回归系数进行标准化,取标准化系数绝对值最大的变量作为该栅格影响因变量的最重要变量。

3 研究结果

3.1 地表反照率时空变化趋势

黔东南稳定林地地表反照率时间变化趋势如图2所示,地表反照率在年际、生长季和休眠季R²值较小,表明地表反照率变化平稳,均呈缓慢上升趋势。生长季、年际和休眠季地表反照率均值分别为0.110,0.104和0.100。生长季的地表反照率最大,究其原因主要是该区域多为多树草原和针阔混交林,生长季植被覆盖度迅速变大,增加了植被冠层近红外反射率,从而增加地表反照率^[16]。而休眠季植被覆盖度降低,地表粗糙度增大,减小地表反照率^[17]。

图2

图2 2003—2018年稳定林地地表反照率时间变化趋势

Fig.2 Temporal trends in surface albedo of stable forest land during 2003—2018

从图3可得,年际、生长季和休眠季地表反照率均呈中间低、四周高的分布格局,在3个时段中,研究区边缘平均约12.338%的零星区域反照率呈微显著、显著和极显著增加,中部约10.081%的零星区域呈微显著、显著和极显著减少。图4中,2003—2010年约40.415%的区域地表反照率呈增加趋势,主要分布于北部、东部、南部等边缘部分; 2011—2018年地表反照率增加区域转移到西北部,约占总面积的34.804%,可见该阶段东部区域植被状态比2003—2010年良好。总体上看,稳定林地地表反照率中部低、四周高,并由东向西增加。这主要由于中部区域分布混交林、常绿阔叶林,EVI值常年较高,叶面积较大,而冠层可大量吸收可见光波段,导致地表反照率下降^[18]。四周边缘靠近城镇,以稀树草原为主,EVI值较低,人类活动频繁,地表粗糙度较大,导致地表反照率较高。

图3

图3 2003—2018年稳定林地地表反照率年际、生长季和休眠季空间变化趋势

Fig.3 Interannual, growing season and dormant season spatial trends in surface albedo of stable forest land during 2003—2018

图4

图4 2003—2010年、2011—2018年稳定林地地表反照率空间变化趋势

Fig.4 Spatial trends in surface albedo of stable forest land during 2003—2010 and 2011—2018

3.2 地表反照率与各因子的相关性

在年际尺度上,地表反照率与各因子的相关性如图5所示,生长季地表反射率与各因子的相关性如图6所示。由图5可知,土壤水分与地表反照率正负相关性最高,分别为0.919和-0.951。其中约7.551%呈正相关的区域通过0.005的显著性检验,主要分布于中部和西南部植被覆盖度高的区域,该区域植被常年繁茂需水量大,土壤水分较低,可能导致该区域地表反照率与土壤水分的正相关性; 约5.486%呈负相关的面积通过0.005的显著性检验,主要分布稳定林地边缘,符合地表反照率随土壤水分减少而增加^[19]。地表反照率与气温和降水仅在西部和东部边缘零星区域呈正相关其余大部分为负相关,分别约5.227%和5.077%的面积通过0.005的显著性检验,主要分布于东北-西南一线,这主要由于该区域气温高降水多在促进植被生长的同时,也降低了地表反照率^[19]。EVI仅在东部和南部边缘通过0.005显著性检验与反照率呈正相关。在年际变化中,各因子与反照率的相关系数绝对值大小排序为: 土壤水分>气温>降水>EVI。

图5

图5 2003—2018年稳定林地年际地表反照率与各因子的相关性

Fig.5 Correlation between interannual surface albedo and various factors on stable forest land from 2003 to 2018

图6

图6 2003—2018年稳定林地生长季地表反照率与各因子的相关性

Fig.6 Correlation of surface albedo with each factor in the growing season of stable forest land from 2003 to 2018

如图6所示,生长季地表反照率与各因子的相关系数绝对值大小排序为: 土壤水分>降水>气温>EVI。土壤水分与降水和反照率相关系数分别为-0.934和-0.903,其空间分布与年际分布相似。EVI在稳定林地边缘零星区域通过0.005显著性检验,与反照率呈正相关。气温与地表反照率的相关性空间分布中,约5.368%的面积通过0.005显著性检验,与地表反照率主要呈正相关^[20],分布在稳定林地东南部,这是由于东南部纬度较低,气温常年偏高,同时东南部EVI值较低且靠近城镇,受人类活动影响较大,可能导致反照率偏高。

休眠季地表反照率与各因子相关系数绝对值大小排序为: EVI>土壤水分>气温>降水。图7为2003—2018年稳定林地休眠季地表反照率与各因子的相关性,如图所示,在EVI和地表反照率的相关性空间分布中,约1 316.386 km²通过0.005显著性检验,其中二者呈正相关的区域占91.508%,主要分布在除中部、中北部以外的大部分区域。土壤水分与地表反照率的相关性分布与年际和生长季类似。气温与地表反照率约11.491%的面积通过0.005显著性检验,主要呈负相关,分布于东南部、中部和东北部大部分区域。降水与反照率主要呈负相关,相关系数较低。

图7

图7 2003—2018年稳定林地休眠季地表反照率与各因子的相关性

Fig.7 Correlation of surface albedo with each factor in the dormant season of stable forest land from 2003 to 2018

综上所述,不同时段气象因子、EVI和土壤水分与地表反照率正负相关性的空间分布大致相同,但由于区域植被类型和覆盖度等差异,导致反照率与各因子的相关性在分布上仍存在一定差异。

3.3 地表反照率驱动因素

稳定林地地表反照率驱动因子采用多元回归分析。如表1所示,提取标准化系数绝对值最大值,分别得到年际、生长季和休眠季地表反照率的驱动因子排序: EVI>土壤水分>气温>降水,气温>EVI>土壤水分>降水,土壤水分>气温>EVI>降水。

表1 2003—2018年黔东南地表反照率驱动因子标准化系数绝对值

Tab.1 Absolute values of standardized coefficients of surface albedo drivers in Qiandongnan during 2003—2018

驱动因子	休眠季	生长季	年均值
降水	0.889	1.892	1.371
气温	1.199	11.332	1.564
EVI	1.197	6.287	9.168
土壤水分	1.319	1.508	2.246

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图8为2003—2018年反照率与各因子在年度均值、生长季和休眠季的相对重要性的空间分布,如图所示,在年际中,稳定林地地表反照率主要受EVI驱动,符合以往研究结论^[20-21],受EVI驱动的面积约为总面积的12.295%。西南部、中部和东北部零星区域地表反照率受EVI负向驱动,其余区域为正向驱动; 受土壤水分驱动的面积最广,约占总面积的47.576%,南部、东部等边缘区域地表反照率受土壤水分负向驱动,在中部区域受正向驱动; 地表反照率受气温和降水驱动较小,且正负驱动分布较为统一,在稳定林地中部为负向驱动,边缘为正向驱动。

图8

图8 2003—2018年反照率与各因子在年度均值、生长季和休眠季的相对重要性的空间分布

Fig.8 Spatial distribution of albedo with relative importance of each factor in annual mean, growing and dormant seasons from 2003 to 2018

在生长季,研究区约总面积的21.489%的区域地表反照率受气温驱动。在中东部和西部部分区域地表反照率受气温驱动力最大,且呈负向驱动,这可能与生长季气温上升,在促进植被生长的同时,植被通过光合作用对可见光波段大量吸收导致地表反照率下降^[22]; 在西南部向北一线呈正相关,但气温驱动力小分布范围广。在西南部、西北部和中部零星区域地表反照率受EVI负向驱动较大,边缘区域EVI正向驱动力较小。土壤水分和降水对地表反照率驱动力均较小。

休眠季各因子对地表反照率的驱动力与年际和生长季相比整体较小,主要受土壤水分驱动,在中部区域正向驱动力最大,在北部和东部边缘区域呈负向驱动。研究区在休眠季受气温驱动的面积最广,约占总面积的41.512%,其中除西北小部分区域呈正向驱动外其他区域均呈负向驱动。地表反照率受EVI驱动的区域与年际和生长季类似,但休眠季地表反照率受EVI的正向驱动,仅在中部零星区域受EVI负向驱动。休眠季地表反照率受降水驱动最小,主要呈负向驱动。

4 讨论

研究区地表反照率在年际、生长季和休眠季均呈缓慢上升,整体变化趋势平稳且空间分布均呈中部减少、四周增加。2003—2010年研究区东部地表反照率呈上升趋势,而在2011—2018年则向西部转移,究其原因主要受西部区域进行大规模城市化建设、人口增多和经济活动频繁等的影响,导致该区域地-气系统能量收支平衡受到干扰,而在中部区域林地较集中,较少受到干扰,反照率变化较小。可见人类活动对稳定林地的影响是有限的并存在一定的过渡区域。

不同影响因子与地表反照率的相关性存在空间上的差异性,该结论与现有研究结果一致^[20,22],但在逐像元分布上仍有些许差别。究其原因主要与本文着重研究稳定林地的年际、生长季和休眠季时段与其他文献所研究的时段和土地利用类型不同有关,从而造成各因子与地表反照率存在逐像元空间分布的差异性。

从相对重要性分析结果来看,在年际尺度上研究区地表反照率受EVI负向驱动^[13],说明研究区森林构成的物种虽然复杂但地处亚热带湿润气候区,仍以常绿植被为主,EVI值较高且稳定,导致反照率较低,因此在年际长时间序列对比中削弱了土壤湿度、气温和降水驱动力。在生长季地表反照率受气温负向驱动,表明在生长季期,气温快速上升,植被加速生长,植被蒸散较大,土壤含水量降低,使得反照率逐渐下降,此时研究区由于植物物种空间分布(图1)的差异,EVI的异质性依然很大^[20],从而导致植被在生长季的反照率在空间上存在较大差异,EVI影响力度下降,使得地表反照率在气温驱动下EVI、土壤水分和降水影响力降低。研究区休眠季地表反照率主要受土壤水分正向驱动,且受EVI正向和气温负向驱动与土壤水分的驱动力相近,表明休眠季相对于年际和生长季植被覆盖度降低使得反照率降低,气温降低促使反照率上升,使二者在一定程度上相互抵消。同时该时段研究区气温较低降水减少,植被覆盖降低地表裸露较大使得地表粗糙度增大,从而使土壤湿度偏低而减少反照率。

稳定林地地表反照率受多种因素共同影响,如植被类型、疏密程度、太阳高度角等因子,在接下来的研究中将进一步明确其他因子与林地地表反照率的内在联系。

5 结论

本研究使用2003—2018年的MCD43A3地表反照率数据分析了黔东南稳定林地地表反照率在年际、生长季和休眠季的时空变化、与各因子的相关性及主要驱动因子,研究结果归纳如下:

1)研究区地表反照率在年际、生长季和休眠季整体呈缓慢上升趋势,均呈中部低、四周高的空间分布格局,地表反照率大小排序分别为: 生长季>年际>休眠季。其中生长季上升幅度最大。2003—2018年间由东部高地表反照率转移到西部。

2)研究区地表反照率与各因子在年际、生长季中与土壤水分高度负相关,均分布于稳定林地边缘,休眠季与EVI高度正相关性,分布于东部、南部、西南部和中部区域。

3)在长时间序列中,年际、生长季和休眠季的地表反照率分别受EVI、气温负向驱动和土壤水分正向驱动,标准化系数绝对值分别为9.168,11.332和1.319。

参考文献

原文顺序

文献年度倒序

文中引用次数倒序

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Contribution of semi-arid forests to the climate system

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Forests both take up CO2 and enhance absorption of solar radiation, with contrasting effects on global temperature. Based on a 9-year study in the forests' dry timberline, we show that substantial carbon sequestration (cooling effect) is maintained in the large dry transition zone (precipitation from 200 to 600 millimeters) by shifts in peak photosynthetic activities from summer to early spring, and this is counteracted by longwave radiation (L) suppression (warming effect), doubling the forestation shortwave (S) albedo effect. Several decades of carbon accumulation are required to balance the twofold S + L effect. Desertification over the past several decades, however, contributed negative forcing at Earth's surface equivalent to approximately 20% of the global anthropogenic CO2 effect over the same period, moderating warming trends.

[5]

翟俊, 刘荣高, 刘纪远, 等.

1990—2010年中国土地覆被变化引起反照率改变的辐射强迫

[J]. 地理学报, 2013, 68(7):875-885.

土地覆被变化通过改变地表反照率而影响地表辐射收支与能量平衡,从而对区域和全球气候产生影响。本文利用高时空分辨率遥感数据分析1990-2010 年中国土地覆被变化改变地表反照率的时空驱动机制,并计算全国50 个生态区地表反照率变化导致的年际尺度辐射强迫,揭示土地覆被变化在生态区尺度上影响气候变化的生物地球物理机制。结果表明:1990-2010 年全国土地覆被变化以耕地开垦、草地沙化、城市化等人类土地利用活动导致的土地覆被变化最为明显,全国草地与林地面积分别减少了0.60%和0.11%;建设用地和耕地面积分别增加了0.60%和0.19%。全国土地覆被变化通过改变地表反照率引起的平均辐射强迫为0.062 W/m2,表现为增温的气候效应,但在生态区尺度辐射强迫空间差异很大。京津唐城镇与农城郊农业生态区主要土地覆被变化为耕地转为建设用地,引起地表反照率降低了0.00456,产生0.863 W/m2的辐射强迫,表现为增温的气候效应;而三江平原温带湿润农业与湿地生态区主要的土地覆被变化为林草地转为耕地,引起地表反照率升高了0.00152,产生-0.184 W/m2的辐射强迫,表现为降温的气候效应。

Zhai

, Liu

R G

, Liu

J Y

, et al.

Radiative forcing over China due to albedo change caused by land cover change during 1990—2010

[J]. Acta Geographica Sinica, 2013, 68(7):875-885.

[6]

Planque

, Carrer

, Roujean

J L

Analysis of MODIS albedo changes over steady woody covers in France during the period of 2001—2013

[J]. Remote Sensing of Environment, 2017, 191:13-29.

DOI:10.1016/j.rse.2016.12.019 URL [本文引用: 2]

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Kuusinen

, Stenberg

, Korhonen

, et al.

Structural factors driving boreal forest albedo in Finland

[J]. Remote Sensing of Environment, 2016, 175:43-51.

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[8]

赵久佳, 张晓丽.

中国北方地区森林覆盖及反照率年际变化

[J]. 浙江农林大学学报, 2015, 32(5): 683-690.

Zhao

J J

, Zhang

X L

Interannual variation of forest cover and albedo in Northern China

[J]. Journal of Zhejiang University of Agriculture and Forestry, 2015, 32(5):683-690.

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Bonan

G B

Forests and climate change:Forcings,feedbacks,and the climate benefits of forests

[J]. Science, 2008, 320(5882):1444-1449.

DOI:10.1126/science.1155121 URL [本文引用: 2]

The world's forests influence climate through physical, chemical, and biological processes that affect planetary energetics, the hydrologic cycle, and atmospheric composition. These complex and nonlinear forest-atmosphere interactions can dampen or amplify anthropogenic climate change. Tropical, temperate, and boreal reforestation and afforestation attenuate global warming through carbon sequestration. Biogeophysical feedbacks can enhance or diminish this negative climate forcing. Tropical forests mitigate warming through evaporative cooling, but the low albedo of boreal forests is a positive climate forcing. The evaporative effect of temperate forests is unclear. The net climate forcing from these and other processes is not known. Forests are under tremendous pressure from global change. Interdisciplinary science that integrates knowledge of the many interacting climate services of forests with the impacts of global change is necessary to identify and understand as yet unexplored feedbacks in the Earth system and the potential of forests to mitigate climate change.

[10]

邓小进, 井长青, 郭文章, 等.

准噶尔盆地地表反照率时空变化特征及其影响因素分析

[J]. 干旱区研究, 2021, 38(2):314-326.

Deng

X J

, Jing

C Q

, Guo

W Z

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Spatio-temporal variation characteristics of surface albedo and analysis of influential factors in the Junggar Basin

[J]. Arid Zone Research, 2021, 38(2):314-326.

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, Schaaf

C B

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Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals:2.Validation

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A A

, Ghahfarokhi

S M M

, Khorasani

H T

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Fernandes

, Leblanc

S G

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[J]. Remote Sensing of Environment, 2005, 95(3):303-316.

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阮颖, 王传宽, 刘帆, 等.

落叶阔叶林反照率的时间变化与影响因素

[J]. 应用生态学报, 2022, 33(8):2068-2076.

反照率原位测量对生态系统能量收支及其遥感应用至关重要,但目前坡面地形反照率的测量方式有局限且可见光与近红外波段反照率时间变化的差异尚不清楚。本研究以东北地区帽儿山森林生态站的落叶阔叶林为例,探究入射和反射太阳辐射(SR,300～2800 nm)、光合有效辐射(PAR,400～700 nm)、近红外辐射(NIR,700～2800 nm)的反照率时间变化特征及其影响因子,同时分析了两种辐射表安装方式反照率的差异。结果表明: 晴天SR和NIR反照率日变化呈上下午不对称的U型曲线,但PAR从早到晚递增;阴天反照率均先急剧下降后趋于稳定。平行于坡面测量增大了反照率的日均值,但缓和了SR、NIR反照率日不对称的现象。从整个生长季来看,SR、NIR与PAR反照率水平测量时最大值分别为0.16、0.27和0.11,最小值分别为0.07、0.11和0.03。SR和NIR反照率季节变化均为先增大后减小(7月为峰值),PAR则相反,SR反照率主要受NIR而不是PAR控制。各波段反照率季节变化的影响因子按照贡献率排序为宽带归一化植被指数(61.7%～78.5%,可表征叶面积指数)>太阳高度角(15.4%～36.9%)>晴空指数(0.4%～36.9%)。

Ruan

, Wang

C K

, Liu

, et al.

Temporal variation and influencing factors of albedo in a deciduous broad-leaved forest

[J]. Chinese Journal of Applied Ecology, 2022, 33(8):2068-2076.

DOI:10.13287/j.1001-9332.202208.011 [本文引用: 1]

In situ measurement of albedo is important for estimating ecosystem energy budget and its remote sensing application. However, the measurement method of albedo on sloping land is limited and the difference in temporal variation in albedo between visible and near-infrared bands remains unclear. Taking a deciduous broad-leaved forest at the Maoershan Forest Ecological Station in Northeast China as an example, we explored the temporal variation and influencing factors of albedo for three bands: incident and reflected solar radiation (SR, 300-2800 nm), photosynthetically active radiation (PAR, 400-700 nm), and near infrared radiation (NIR, 700-2800 nm). The temporal difference in albedo measurements between the two installation methods of radiometers was analyzed. The results showed that, in sunny days, the diurnal variation in SR and NIR albedo had an asymmetric U-shaped curve around the local noon, while PAR increased from sunrise to sunset. In cloudy days, the albedo decreased sharply and then tended to be stable. The measurement with parallel sensors to the slope increased the daily mean value of albedo, but reduced the daily asymmetry of SR and NIR. For the whole growing season, the maximum albedos of SR, NIR and PAR in horizontal measurement were 0.16, 0.27 and 0.11, respectively, and the minimums were 0.07, 0.11 and 0.03, respectively. Albedo in the SR and NIR wavebands increased first and then decreased (the peak value was in July), while PAR showed a contrasting pattern. SR albedo was mainly controlled by NIR rather than PAR. The contribution of the influencing factors was ranked in the order of normalized difference vegetation index (61.7%-78.5%, representing leaf area index) > solar altitude angle (15.4%-36.9%) > clearness index (0.4%-36.9%).

[16]

余予, 李扬云, 童应祥, 等.

寿县地区小麦和水稻田地表反照率观测分析

[J]. 气候与环境研究, 2009, 14(6):639-645.

, Li

Y Y

, Tong

Y X

, et al.

Observation and analysis of surface albedo of wheat and rice fields in Shouxian region

[J]. Climatic and Environmental Research, 2009, 14(6):639-645.

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Betts

A K

, Desjardins

R L

, Worth

Impact of agriculture,forest and cloud feedback on the surface energy budget in BOREAS

[J]. Agricultural and Forest Meteorology, 2007, 142(2-4):156-169.

DOI:10.1016/j.agrformet.2006.08.020 URL [本文引用: 1]

[18]

朱躲萍, 叶辉, 王军邦, 等.

青海三江源区高寒植被地表反照率变化及其辐射温度效应

[J]. 生态学报, 2022, 42(14):5630-5641.

Zhu

D P

, Ye

, Wang

J B

, et al.

Albedo changes of alpine vegetation and its radiative temperature effect in the Three-River Headwaters Region of Qinghai

[J]. Acta Ecologica Sinica, 2022, 42(14):5630-5641.

[19]

张学珍.

锡林郭勒草原地表反照率对气候变化的响应

[J]. 地理研究, 2012, 31(2):299-310.

Zhang

X Z

The responses of surface albedo to climatic changes in Xilin Gol grassland

[J]. Geographical Research, 2012, 31(2):299-310.

DOI:10.11821/yj2012020010 [本文引用: 2]

This study assessed the reliability of Moderate Resolution Imaging Spectroradiometer(MODIS)-derived land surface albedo products for Xilin Gol grassland,illustrated the seasonal cycles and inter-annual variations of land surface albedo in Xilin Gol grassland,and analyzed correlations between surface albedo variations and climatic changes.The results show MODIS-derived dataset is able to capture seasonal cycle and inter-annual variations of surface albedo,though there is a difference between the MODIS-derived albedo and ground instrumental measurements.The MODIS-derived dataset illustrates that the seasonal cycle patterns of surface albedo vary with spectrum.For the visible band surface albedo,the seasonal cycle presents a "V"-shaped variation with the bottom in the first ten days of August.For the near-infrared band surface albedo,the seasonal cycle is "U"-shaped with the bottom in the period from June to September.However,both visible band surface albedo and near-infrared band surface albedo had consistent inter-annual variations.Moreover,the inter-annual variations of surface albedo were partly attributed to climatic variations.The effects of temperature were significant in the early(April to May) and late(September to November) growth season.The correlation coefficients between temperature and surface albedo were-0.67 and 0.63 in the early and late growth season,respectively.The effects of precipitation were significant through out the growth season.The correlation coefficients between precipitation and surface albedo ranged from-0.54 to-0.76.It is worthy noting that the effects of precipitation were usually lagged by 2-3 months.

[20]

陆云波, 王伦澈, 牛自耕, 等.

2000—2017年中国区域地表反照率变化及其影响因子

[J]. 地理研究, 2022, 41(2):562-579.

DOI:10.11821/dlyj020210005 [本文引用: 4]

在全球气候变暖的背景下,地表反照率已成为地表辐射平衡和气候研究的重要参数之一。利用中国陆地生态系统研究网络（Chinese Ecosystem Research Network,CERN）提供的34个站点辐射数据、GLASS地表反照率产品、ERA-Interim再分析资料、MODIS EVI（MOD13A3）和中国气象数据共享网提供的气象数据,基于Sen's Slope趋势分析方法,分析不同生态系统地表反照率的变化特征;利用全子集回归和分层分解方法计算地表反照率与各要素之间的相关性和相对重要性;探讨各气候因子对地表反照率的影响。结果表明,2000—2017年裸土地和裸岩砾石地变化率最大,冬季斜率达-0.083% yr-1。生长季地表反照率与降水、增强型植被指数（Enhanced Vegetation Index,EVI）、土壤水分和气温显著相关的像元分别占总像元的73%、79%、56%和86%。EVI是干旱和半干旱地区地表反照率变化的主导因素,其对地表反照率变化的独立贡献率分别为41%和56.18%。7月东北地区降水量和气温对地表反照率的影响大约滞后2个月;内蒙古沙漠地区和长江中下游平原土壤水分对地表反照率的影响大约滞后1~2个月。

Y B

, Wang

L C

, Niu

Z G

, et al.

Variations of land surface albedo and its influencing factors in China from 2000 to 2017

[J]. Geographical Research, 2022, 41(2):562-579.

DOI:10.11821/dlyj020210005 [本文引用: 4]

In the context of global warming, land surface albedo has become one of the important input parameters for climate simulation. The radiation data of 34 sites provided by Chinese Ecosystem Research Network (CERN), GLASS land surface albedo products, ERA-Interim reanalysis data, MODIS EVI (MOD13A3) and meteorological data provided by China Meteorological Administration were used to analyze the variation characteristics of the land surface albedo in different ecosystems based on Sen's Slope trend analysis; correlations and relative importance between land surface albedo and climate variables were calculated using all-subsets regression and hierarchical partitioning methods. The results showed that the bare land and rock or gravel land had the largest slope value from 2000 to 2017, being -0.083% yr-1 in winter. Pixels with significant correlations between land surface albedo and precipitation, EVI, soil moisture, and temperature during the growing season accounted for 73%, 79%, 56%, and 86% of the total pixels, respectively. EVI was the dominant factor in the change of land surface albedo in arid and semi-arid regions, and the independent contribution rates of these two types of regions were 41% and 56.18%, respectively. The effects of precipitation and temperature on land surface albedo in Northeast China in July lagged about 2 months. The influence of soil moisture on land surface albedo lagged about 1-2 months in the desert areas of Inner Mongolia and Middle-Lower Yangtze River Plain.

[21]

高婷, 沈润平, 李磊, 等.

基于MODIS数据地表反照率时空变化特征及影响因子研究

[J]. 气候与环境研究, 2021, 26(6):648-662.

Gao

, Shen

R P

, Li

, et al.

Spatial and temporal variations of land surface albedo and its influencing factors based on MODIS data

[J]. Climatic and Environmental Research, 2021, 26(6):648-662.

[22]

赵之重, 赵凯, 徐剑波, 等.

三江源地表反照率时空变化及其与气候因子的关系

[J]. 干旱区研究, 2014, 31(6):1031-1038.

Zhao

Z Z

, Zhao

, Xu

J B

, et al.

Spatial-temporal changes of surface albedo and its relationship with climate factors in the source of three rivers region

[J]. Arid Zone Research, 2014, 31(6):1031-1038.