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GEOGRAPHICAL RESEARCH >

2017 , Vol. 36 >Issue 11: 2251 - 2260

DOI: https://doi.org/10.11821/dlyj201711017

Articles

Estimation of land surface temperature from MODIS in Northeast China

  • LIU Shibo ,
  • ZANG Shuying ,
  • ZHANG Lijuan ,
  • NA Xiaodong
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  • Key Laboratory of Remote Sensing Monitoring of Geographic Environment, College of Heilongjiang Province, Harbin Normal University, Harbin 150025, China

Received date: 2017-05-08

  Request revised date: 2017-09-16

  Online published: 2017-11-20

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《地理研究》编辑部
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Abstract

Land surface temperature (LST) is an important parameter driving dynamics of biogeophysical processes on Earth surface. It has significant impacts on the distribution of permafrost and the change of the active layer depth. Conventional acquisition of LST data usually comes from weather station monitoring in a small and discrete scope. NASA's MOD11 A1 surface temperature product can provide a wide range of surface temperature data. In winter, however, the confusion of clouds and snow often leads to a large amount of data missing in the MOD11 A1 products in the permafrost region. In this paper, an improved split-window algorithm was selected to re-build the LST products in Northeast China, one of the major permafrost regions in China. Within the common land covers extracted from remote sensing classification results, such as vegetation, bare soil, water and snow. We extracted LST in each cover type from four cloud-free MODIS 1B satellite images in 2006. Both our results and the original MOD11 A1 products were statistically compared with ground measurements at weather stations. The average difference between our results and measurements at meteorological stations was small, reaching a room-mean-square error (RMSE) of 1.24 ℃. In comparison with the original MOD11 A1 products, our results took advantage of land covers and revealed better distributions of land surface temperature in snow area, and had a high consistency with the surface temperature products. This study provides a good approach to filling in the gaps of current land surface temperature products due to confusion caused by the cloud and snow.

Key words： Northeast China; permafrost; MODIS; split window algorithm; land surface temperature; gap filling of data products

Cite this article

LIU Shibo , ZANG Shuying , ZHANG Lijuan , NA Xiaodong . Estimation of land surface temperature from MODIS in Northeast China[J]. GEOGRAPHICAL RESEARCH, 2017 , 36(11) : 2251 -2260 . DOI: 10.11821/dlyj201711017

1 引言

冻土在岩石圈与大气圈能量交换中扮演着重要的角色,是冰冻圈的重要组成部分[1]。由于冰的存在,冻土对环境和气候的变化感应十分灵敏。冻土的变化也可以反映气候环境的变化,起到指示作用。自20世纪以来,由于气候环境的变化和人类活动的加剧,全球地表持续升温,2013年IPCC第五次评估报告指出,1980-2012年间全球地表平均温度上升了0.85 ℃[2]。冻土在冻融过程,对地气交换产生重大的影响,释放了大量的CH4和CO2[3-5],进一步加剧了气候变暖的进程,对环境及人类的活动产生重大影响。地表温度是影响冻土冻融的关键要素,对冻土区冻融过程的研究有重大的意义。
近年来,国内外专家学者对如何获取连续且准确的地表温度数据进行了深入研究[7,8],并取得了大量的成果。但常规的监测手段获取的数据范围有限,在自然条件恶劣的环境下获取资料较为困难。而遥感资料以监测范围大、时间持续长的特点,可以快速同步地获取大范围的、连续的二维地表温度空间分布信息[9],逐渐成为获取地表温度的重要数据来源[9]。目前很多专家对地表温度反演理论和应用做了很多的探索,提出了许多的反演算法,应用最为广泛的有单通道算法[10]和劈窗算法[11]。单通道算法用于只有一个热红外波段的遥感数据,如Landsat系列卫星,具有较高的空间分辨率,适用于小范围的地表温度反演[12]。而劈窗算法多针对于MODIS数据,由于其相近的热红外波段存在吸收上的差异,可以消除大气在反演过程的影响,提高了反演精度,适合获取较大区域范围内地表温度数据。Wan等[13]建立了针对MODIS数据的通用劈窗算法,并完成了全球地表温度产品的制作。后来许多学者作了进一步研究,不断改善该算法,Becker等[14]在计算公式中加入水汽含量、Sobrino等[15]加入了观测角度、Coll等[16]加入地面比辐射率等因子,使得结果更加准确。毛克彪等[17]较为全面地研究了针对MODIS和Landsat影像的地表温度反演算法,并对其进行改进,得到了适合中国地区的劈窗算法。Ri Changin等对地表和有效大气辐射按照不同的亮度温度区间分别进行简化[18],提高了反演的精度。以上研究在所选区域都取得了较好的反演结果,但在气候条件较为复杂的东北冻土区,目前进行地表温度反演的研究较少,利用劈窗算法能否在气候寒冷、充满冰雪的冻土区得到准确的地表温度数据,尚未有较为准确的定论。
在自然条件复杂的东北冻土区,人们往往通过MODIS地表温度产品获得大范围的地表温度数据,该数据具有波谱分辨率高、时间周期快、获取容易的特点[19]。但温度产品是针对全球尺度制作,对于东北冻土区适用性不够高。且在东北冻土区,MODIS地表温度产品对于冬季积雪与云的混淆使数据存在大量缺失的情况,影响了地表温度数据的使用。本文在处理MODIS温度产品的时候发现冬季数据往往比夏季数据缺失更为严重。本文利用MODIS1B数据基于劈窗算法反演地表温度,并根据东北冻土区实际情况,在分类和地表比辐射率计算时加入冰雪面,选取适合东北冻土区地表温度反演的大气水含量函数和比辐射率参数。最终与MODIS地表温度产品进行对比并与实测的地表温度数据进行验证分析,讨论该方法反演得到的东北冻土区地表温度结果的精度,以及在冻土研究中的应用前景。

2 研究方法与数据来源

2.1 研究区概况

东北冻土区是中国境内主要的高纬度冻土区,是中国第二大冻土区,主要发育“兴安—贝加尔型”冻土。位于38°40′N~53°30′N、115°05′E~135°02′E。全区年均气温在0 ℃以下,年降水量 460 mm,属寒温带大陆性气候,冬季漫长而寒冷干燥,夏季短暂而温暖[21]。本文研究的东北冻土区包括黑龙江、吉林、辽宁三省。根据中国科学院寒旱所金会军等的调查,东北冻土区以嫩江—伊春一线为界[20],以北为多年冻土区,以南为季节性冻土区,冻土区内又有着不同类型的冻土,如图1所示。冻土受温度影响较大,存在极为不稳定。
Fig. 1 Distributions of permafrost types in the Northeast China

图1 东北冻土区冻土类型分布图

2.2 站点数据

本文所用的常规气象站验证资料来自于中国科学气象数据共享服务网,数据基于地面基础气象资料建设项目归档的“1951-2010年中国国家级地面站数据更正后的月报数据文件基础资料集"研制,共得到三个省共计23个气象站的日平均地表温度,站点位置如图1所示。由于MODIS数据对地物穿透力较低,在冬季有积雪的情况下得到的反演结果实际为雪面温度。本文选取2006年气象站点地表温度数据（在有积雪的情况下为人工观测的雪面温度数据）,在冬季得到的是积雪表层的温度。本文利用站点监测的地表温度数据去验证反演结果的精度。

2.3 MODIS L1B数据与MODIS地表温度产品

（1）MODIS L1B数据。本文下载的MODIS L1B数据是2006年2月23日、3月19日、10月5日及11月24日HDF格式的白天数据和夜间数据（所选4日影像整体的天空云覆盖面积较小,便于地表站点观测数据的验证）,空间分辨率为1 km。4幅影像中10月的影像没有积雪覆盖,其他3幅均有积雪。利用有积雪和无积雪的反演情况说明积雪对地表温度反演的影响。由于积雪的存在,本文得到的MODIS地表温度数据是积雪表层的温度。近几年的气象站点地表温度数据是自动监测,在冬季所得到的地表温度数据为积雪下的地表温度数据,在2006年有人工测量的雪面数据,所以选择2006年的MODIS数据。该数据需通过辐射定标公式将数据原始DN值转换为辐射亮度,其步骤在ENVI软件中实现。
（2）MODIS地表温度数据。本文利用NASA官网提供的全球每日1 km分辨率SIN投影坐标网格产品,等级为3的HDF格式存储的MODIS地表温度数据,包含12个波段。本文利用白天和晚上的温度波段进行反演结果的初步验证。该套产品采用了基于物理的白天/黑夜地表温度反演方法[22],在验证前需通过MODIS重采样工具MRT进行投影坐标转换和影像镶嵌,然后利用研究区矢量区域进行裁切并进行温度转换。将反演得到地表温度结果与地表温度产品数据做比较。

2.4 MODIS地表温度估算方法

2.4.1 Qin劈窗算法 Qin[23]提出的地表温度计算方法为：
Us = B 0 + B 1 K 31 - B 2 K 32 （1）
式中：Us表示地表温度,K31、K32分别为MODIS第31、第32波段的亮温,算法系数B0、B1、B2的计算公式为式（2）~式（4）：
B 0 = e 31 P 32 ( 1 - N 31 - P 31 ) ( P 32 N 31 - P 31 N 32 - e 32 P 31 1 - N 32 - P 32 ) ( P 32 N 31 - P 31 N 32 （2）
B 1 = 1 + P 31 ( P 32 N 31 - P 31 N 32 ) + f 31 P 32 ( 1 - N 31 - P 31 ) ( P 32 N 31 - P 31 N 32 （3）
B 2 = P 31 ( P 32 N 31 - P 31 N 32 ) + f 31 P 32 ( 1 - N 31 - P 31 ) ( P 32 N 31 - P 31 N 32 （4）
式中：ei、fi(i=31,32)根据传感器的波段特征确定,针对MODIS数据,e31=-64.60363、f31=0.440817、e32=-68.72575、f32=0.473453。Ni、Pi(i=31,32)的定义分别为：
Ni = γiδi ( θ ) （5）
Pi = 1 - δi θ 1 1 - γi δi θ ] （6）
其中：γi为i波段地表比辐射率;δi(θ)为i波段传感器视角θ处的大气透过率。
2.4.2 大气水含量 作为计算地表温度的基本参数,大气透过率 δi θ 往往多用大气水汽含量来计算。经多位专家学者研究,可以用MODIS第2和第19波段来反演大气水分的含量,然后利用大气水分含量与大气透过率之间的函数关系来计算大气透过率。估计大气水分含量公式如下[24]：
Z = [ ( σ - ln TZ ) / τ ] 2 （7）
式中： TZ 是大气水汽吸收波段地面反射率与大气窗口波段地面反射率的比值;σ、τ为参数,分别取σ=0.02,τ=0.651。根据式（7）所得,东北冻土区大气水含量为0.4~1.4 g·cm2。毛克彪等针对MODIS的波段特征,通过LOWTRAN模拟大气水汽含量与MODIS31和32热红外波段透过率之间的线性方程[25]。本文选取符合东北冻土区大气水含量的线性方程,计算公式为：
γ 31 = 1.101089 - 0 . 09656 z （8）
γ 32 = 0 . 97022 - 0 . 08057 z （9）
式中：γ31,γ32为MODIS数据第31,32波段的大气透过率;z为大气水汽含量。
2.4.3 亮度温度 热辐射强度通过MODIS影像上的DN值定标得到之后,可以用普朗克函数得到它的亮度温度,计算公式为[12]：
J i = Q i 2 / ln ( 1 + Q i 1 / I i ) （10）
式中,Qi1和Qi2是常量,对于第i=31波段,分别为Q31,1=729.541636 W·m-2·sr-1·um-1,Q31,2=1304.413871 K;对于第i=32波段,为Q32,1=474.684780 W·m-2·sr-1·um-1,Q31,2=1196.978785 K。
2.4.4 地表比辐射率计算 地表的比辐射率是反演地表温度的重要参数,受地表物质组成与传感器的波段区间影响[26]。基于IGBP（17种分类）和USGS（24种分类）的土地覆盖分类系统比辐射率数据集,以及东北冻土区下垫面的实际情况,选取适合的地表比辐射率参数。从MODIS 1 km空间分辨率来看,东北冻土区地表大致可以看作由4种地表类型构成：植被、裸土、水体、以及冰雪面。由于MODIS产品制作过程中存在冬季无法完全识别雪面与云的混淆,影响分类结果,本文利用MODIS第1、第4、第3这3个波段假彩色合成后采用最小距离法进行监督分类。表1为MODIS第31、第32波段不同地表类型的比辐射率。
Tab. 1 Emissivity of different land surfaces

表1 不同地物地表比辐射率

地表类型 第31波段地表比辐射率 第32波段地表比辐射率
植被 0.986 0.989
冰雪 0.988 0.971
裸土 0.967 0.977
水体 0.996 0.992
2.4.5 反演精度验证 用东北三省共计23个气象站点的日平均地表温度观测资料与该经纬度像元反演的地表温度结果进行对比,结果如表2所示,本文计算了4个时期地表温度反演值与站点地表温度实测值的平均值、平均绝对误差、均方根误差、R2。这些指标用于验证反演值与实测值的误差,即反演的精度。验证两种数据误差的大小用平均绝对误差,计算公式为：
MAE = 1 n ∑ i = 0 n | L MODIS i - L MS ( i ) | （11）
对其离散程度指标用均方根误差,计算公式为：
RMSE = 1 n ∑ k = 1 n ( L MODIS i - L MS i ) 2 （12）
式中： L MODIS i 和 L MS ( i ) 分别表示与站点对应的MODIS地表温度反演值和站点测量的日均地表温度值。
Tab. 2 Errors of the MODIS LST products against observation data at meteorological stations

表2 地表温度反演值与站点值误差分析

日期 反演平均
值（℃）		 站点实测
平均值（℃）		 平均绝对
误差		 均方根
偏差		 R2
2月23日 -8.05 -7.69 1.28 1.52 0.853
3月19日 0.77 1.42 1.41 1.67 0.737
10月5日 17.10 17.16 1.06 1.19 0.872
11月24日 -5.26 -5.19 1.21 1.37 0.828

3 结果分析

3.1 反演结果与站点实测值的验证

图2显示了4个时期地表温度反演值与站点实测值验证的散点图,反演结果和实测值分布在斜线两侧,且表现出较好的线性关系,相关性较高。说明反演结果与实测值较为一致,反演精度较好。表2显示了其统计结果。通过这4个时期地表温度反演值和实测值的平均值可以看出,反演值略低于站点地表温度的实测值。10月5日反演的平均值与站点实测的平均值最为接近,平均绝对误差仅为1.06 ℃。11月24日地表温度反演得到的平均值与实测平均值误差波动最大,平均绝对误差为1.41 ℃。10月5日地表温度反演值与站点地表温度实测值的平均绝对误差、均方根和R2差最小,反演精度较高。3月19日地表温度反演值与站点地表温度实测值的平均绝对误差和均方根差最大,R2值最低,说明这一天反演精度较低。其他日期R2值都超过了0.8,离散程度较为理想,反演精度较高。通过各项数值的比较本文发现,10月5日的MODIS数据反演结果最好,精度最高,此时地表温度较高,积雪较少,其他3幅影像地表温度较低,积雪大量存在。由此可见,积雪对地表温度反演的影响较大,会造成一定的误差。
Fig. 2 Scatter plots of LST

图2 地表温度散点图

3.2 反演结果与MODIS产品的比较

图3为东北冻土区地表温度反演结果和产品的彩色空间分布图,通过对白天数据和夜间数据反演后求平均值得出的地表温度日均值。通过算法反演得到的地表温度结果与MODIS产品地表温度色彩空间图的分布较一致。从4幅影像的反演结果可以看出,东北冻土区地表温度从南向北温度随纬度增大而逐渐降低,在大兴安岭北部地区为地表温度最低的区域。在辽宁南部为地表温度最高值地区。在黑龙江省东部,由于降水和季风环流的缘故,黑龙江省东部地表温度略低于西部。整个区域内地表温度受纬度和地形影响明显,高值区集中在平原和沿海区域,低值区集中在山地高原区域。从11月24日的地表温度反演图（图3d）,可以发现在黑龙江省西南地区出现大量异常值,这是由于云层对反演结果造成了影响,扣除了云层造成的异常区域后在云层边缘还有残留的异常值,导致图像颜色异常,这一天也是所选影像中云量最大的影像。本文通过温度色彩可以推断,云层对地表温度反演会造成很大的影响,很可能会大幅度提高地表温度的数值。
Fig. 3 Spatial distributions of the MODIS products and our LST results

图3 MODIS产品和地表温度反演结果图

本文对MODIS反演数据与产品在不同地表类型下进行比较。由于2月23日和11月24日产品数据缺失过多,所以本文比较3月19日和10月5日这两天的反演数据与产品。根据分类结果裁切反演结果和产品,发现10月5日东北地区地表温度较高,还没有积雪的存在。10月5日植被和裸土反演平均值分别为14.01 ℃和19.81 ℃,产品平均值分别为14.65 ℃和19.01 ℃。3月19日植被和裸土反演平均值分别为-1.83 ℃和3.46 ℃,产品平均值分别为0.43 ℃和2.77 ℃。而在积雪区域差别较大,反演的平均值和产品平均值分别是-10.51 ℃和-3.53 ℃。积雪区域平均值差距较大是因为产品在冰雪区域数据缺失较多,且多集中在纬度较高的地区,所以地表温度产品积雪区域数值偏高。可以看出,在没有积雪的情况下,各地表类型地表温度的反演值和产品值的数值比较接近。而在气温较低,有积雪的情况下,产品数据的缺失导致积雪区域两种数据的平均值差异较大,地表温度反演值低于产品值。裸土在反演数据和产品数据都高于植被,这也是受植被自身温度和植被多分布在高纬地区有关。
表3是对数据受云影响导致像元缺失的统计,数据所占区域百分比越多,显示的温度信息也就越多。由于本文所选的影像云量较少,反演结果像元所占研究区百分比较高,都在90%以上。说明本文所反演的数据质量较好,受云层影响较小。而温度产品则有明显的像元缺失情况,尤其是2月23日和11月24日这两幅影像像元缺失较为严重,像元约占研究区30%,且缺失像元多集中在高纬度的多年冻土区。这是由于MODIS产品对于云和积雪的分类很可能容易混淆,MODIS产品将大兴安岭区域和东北东部地区识别为云的结果很可能是错误的。通过目视解译发现,这两幅影像中大兴安岭地区及东北北部地区处于冰雪覆盖的状态,这也符合2月和11月的天气状况。如表3所示,通过统计研究区像元个数,反演结果可以很好地弥补MODIS地表温度产品像元缺失,尤其是在气温较低,积雪存在的情况下,可以较好地展现出研究区地表温度的空间分布。
Tab. 3 Statistical summaries of the MODIS products and our LST results

表3 MODIS产品和地表温度反演结果的像元统计

日期 像元个数（个） 所占百分比（%）
2月23日反演结果
2月23日产品结果
3月19日反演结果
3月19日产品结果
10月5日反演结果
10月5日产品结果
11月24日反演结果
11月24日产品结果		 786487
226634
752665
593210
785362
746927
742378
239984		 99.96
28.80
95.66
75.39
99.81
94.93
94.35
30.50
表4为地表温度反演结果与MODIS产品最大值、最小值及平均值的对比（其中的最大值、最小值以统计结果中超过10个像元的温度作为边界最值）。图4是地表温度反演结果和产品的直方图。由于地表温度产品像元值有缺失,本文选取数据较为完整的10月5日的地表温度产品和反演结果为例。MODIS地表温度反演结果与产品的最大值和最小值结果较为接近,反演值高于产品值,分别为0.31 ℃和0.41 ℃。平均值反演值低于产品值,差距为0.91 ℃。图4所示地表温度结果与MODIS产品地表温度直方图的分布具有较一致的特征都是在16 ℃和17 ℃达到像元数最大值,并从最大值两侧像元数目逐渐降低,数值分布趋势较为一致。在产品数据没有受到云雪混淆的情况下,两种数据的一致性较高。
Fig. 4 Histograms of the MODIS products and our LST results

图4 MODIS产品温度和地表温度反演结果分布直方图

Tab. 4 Comparison of our LST results and the MODIS products (℃)

表4 遥感反演地表温度结果与MODIS产品的对比（℃）

最大值 最小值 平均值
地表温度反演值 24.26 6.11 15.34
MODIS产品值 23.95 5.7 16.25

4 结论与讨论

本文基于改进后的劈窗算法对2006年MODIS L1B卫星数据进行反演得到了东北冻土区2月23日、3月19日、10月5日和11月24日共4日的地表温度数据,最后分别与MODIS每日的地表温度产品及气象观测站的地表温度数据进行了对比验证分析,结论 如下：
算法反演得到了4个日期的地表温度结果,通过用站点实测值检验表明反演结果精度较高,且与MODIS数据产品显示出了较好的一致性。水体、冰雪表面及其他地面类型都显示了较好的温度空间分布特点。4幅影像也能够简单反映4个月份季节性的温度变化特征及由南向北地表温度降低的空间变化特征。在大兴安岭北部地区为地表温度最低的区域。在辽宁南部为地表温度最高值地区。在黑龙江省东部,由于降水和季风环流的缘故,地表温度略低于西部。因此,这种地表温度的反演方法是可行的。可以在东北冻土区进行大范围的应用,弥补气象站点监测的不足。
地表温度数据作为冻土研究的基础数据,关系着冻土冻融过程及冻土南界的变化,对整个寒区冻土的研究具有重要的意义。利用劈窗算法反演地表温度不需要其他地表参数及气象参数的输入,而仅仅依赖原始遥感数据,实用性强,且精度较高。监督分类结果与东北冻土区地表状况也较为吻合,反演结果也较为可信,大大弥补了地表温度产品像元的缺失。它能够帮助本文加深对东北冻土区的了解,对冻土分布及冻土活动层厚度变化等研究有很大的帮助。
在东北冻土区,积雪和冰由于自身特性,都会对地表温度产生影响。冰的存在主要以河流湖泊冻结为主,较其他的地表类型分布较少或者冻结厚度较小。而降雪作为一种天气现象,分布范围较大,且东北冻土区降雪量多,积雪深度较厚,对地表温度的影响更为主要。由于MODIS数据自身的特性,在地表有积雪的情况下,本文获取的只是积雪表面的温度,而积雪覆盖下真实的地表温度,本文暂时无法利用MODIS数据获取。利用目前的遥感手段或气象站点数据本文可以获取该地区的积雪深度数据,可以研究积雪厚度对地表温度的影响。在之后的研究中,可以考虑通过积雪深度等要素与积雪面温度的函数关系,对积雪下的地表温度进行反演,得到真实的地表温度,更好地研究冻土区地表温度的变化及其对冻土的影响。

The authors have declared that no competing interests exist.

References

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黄培培, 南卓铜. 基于Wavelet-ANFIS和MODIS地表温度产品的青藏高原0 cm土壤温度估算方法. 冰川冻土, 2013, 35(1): 74-83.lt;p>0 cm土壤温度是冻土模型的上边界条件, 连续的、 高质量的青藏高原0 cm土壤温度数据是进行准确冻土模拟的必要条件. 然而受复杂下垫面的影响, 遥感手段无法获取可靠的0 cm土壤温度. 利用自适应网络模糊推理系统(ANFIS)结合青藏高原实测资料建立遥感地表温度产品(LST)与0 cm土壤温度的关系, 以实现通过LST估算青藏高原逐日0 cm土壤温度. 研究了ANFIS的各种参数组合, 发现筛选合适的小波函数、 小波窗口、 小波层数建立起来的Wavelet-ANFIS模型能较准确实现估算0 cm土壤温度的目的. 验证表明, 估算结果与气象站点实测0 cm土壤温度绝对误差在2 K以下, 相关系数0.98以上. 考虑到原始MODIS LST误差在0~2 K之间, 该方法可以获取较为理想的0 cm土壤温度, 为冻土模型提供准确的上边界输入.</p>

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[Huang Peipei, Nan Zhuotong.Estimation of 0 cm soil temperature over the Tibetan Plateau based on the wavelet analysis and adaptive network-fuzzy inference system. Journal of Glaciology & Geocryology, 2013, 35(1): 74-83.]

[10]
Jiménez-Muñoz, Juan C, Sobrino, José A.A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research Atmospheres, 2003, 108(D22): 2015-2023.1] Many papers have developed algorithms to retrieve land surface temperature from at-sensor and land surface emissivity data. These algorithms have been specified for different thermal sensors on board satellites, i.e., the algorithm used for one thermal sensor (or a combination of thermal sensors) cannot be used for other thermal sensor. The main goal of this paper is to propose a generalized single-channel algorithm that only uses the total atmospheric water vapour content and the channel effective wavelength (assuming that emissivity is known), and can be applied to thermal sensors characterized with a FWHM (Full-Width Half-Maximum) of around 1 0204m actually operative on board satellites. The main advantage of this algorithm compared with the other single-channel methods is that in-situ radiosoundings or effective mean atmospheric temperature values are not needed, whereas the main advantage of this algorithm compared with split-window and dual-angle methods is that it can be applied to different thermal sensors using the same equation and coefficients. The validation for different test sites shows root mean square deviations lower than 2 K for AVHRR channel 4 (0203 090908 10.8 0204m) and ATSR-2 channel 2 (0203 090908 11 0204m), and lower than 1.5 K for Landsat Thematic Mapper (TM) band 6 (0203 090908 11.5 0204m).

DOI

[11]
Qin Z, Dall'Olmo G, Karnieli A, et al. Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data. Journal of Geophysical Research Atmospheres, 2001, 106(D19): 22655-22670.Retrieval of land surface temperature (LST) from advanced very high resolution radiometer (AVHRR) data is an important methodology in remote sensing. Several split window algorithms have been proposed in last two decades. In this paper we intend to present a better algorithm with less parameters and high accuacry. The algorithm involves only two essential parameters (transmittance and emissivity). The principle and method for the linearization of Planck's radiance equation, the mathematical derivation process of the algorithm, and the method for determining the atmospheric transmittance are discussed with details. Sensitivity analysis of the algorithm has been performed for evaluation of probable LST estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of LST from AVHRR data. Assuming an error of 0.05 in atmospheric transmittance estimate and 0.01 in ground emissivity for the two AVHRR thermal channels, the average LST error with the algorithm is 1.100°C. Two methods have been used to validate the proposed algortihm. Comparison has also been done with the existing 11 algorithms in literature. Results from validation and comparison using the standard atmospheric simulation for various situations and the ground truth data sets demonstrate the applicability of the algorithm. According to the root mean square (RMS) errors of the retrieved LSTs from the measured or assumed LSTs, the proposed algorithm is among the best three. Considering the insignificant RMS error difference among the three, the proposed algorithm is better than the other two because they require more parameters for LST retrieval. Validation with standard atmospheric simulation indicates that this algorithm can achieve the accuacry of 0.2500°C in LST retrieval for the case without error in both transmittance and emissivity estimates. The accuary of this algorithm is 1.7500°C for the ground truth data set without precise in situ atmospheric water vapor contents. The accuracy increases to 0.2400°C for another ground truth data set with precise in situ atmospheric water vapor contents. The much higher accuracy for this data set confirms the appplicability of the proposed algorithm as an alternative for the accurate LST retrieval from AVHRR data.

DOI

[12]
覃志豪, 高懋芳, 秦晓敏, 等. 农业旱灾监测中的地表温度遥感反演方法: 以MODIS数据为例. 自然灾害学报, 2005, 14(4): 64-71.以目前农业旱灾监测中应用较广泛的多波段MOD IS卫星遥感数据为例,探讨农业旱灾遥感监测中所需要的地表温度反演问题,尤其是反演算法的选择、基本参数的估计和具体反演中的工作流程,为快速地进行农业旱灾监测中的水热遥感参数估计提供方法选择。虽然MOD IS有8个热红外波段用来监测地表热量变化,但波段31和32特别适用于农业旱灾监测中所需要的地表温度遥感反演。因此,在算法上,我们将选择计算过程相对简便但反演精度又很高的两因素分裂窗算法。详细地讨论了如何快速地估计分裂窗算法中大气透过率和地表比辐射率这两个基本参数。从实际应用来看,本文所提出的方法能快速地用来反演我国农业旱灾监测中所需要的农田地表温度参数,并获得很好的反演结果。

DOI

[Qin Zhihao, Gao Maofang, Qin Xiaomin, et al.Methodology to retrieve land surface temperature from MODIS data for agricultural drought monitoring in China. Journal of Natural Disasters, 2005, 14(4): 64-71.]

[13]
Wan Z, Dozier J.A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on Geosciences and Remote Sensing, 1996, 34(4): 892-905.Proposes a generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must vary with the viewing an...

DOI

[14]
Becker F, Li Z L. Becker F.Towards a local split window method over land surfaces. International Journal of Remote Sensing, 1990, 11(3): 369-393.

DOI

[15]
Sobrino J, Coll C, Caselles V.Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment, 1991, 38(1):19-34.In this work, a theoretical model that permits relating the land surface temperature with the temperatures measured by thermal infrared sensors has been developed. The model has been derived by linearization of Planck's function and atmospheric trasmittance. In this way a split-window equation is obtained, which depends on atmospheric water vapor, viewing angle, and channel surface emissivities. Simulations of satellite measurements of land surface temperatures are made using the atmospheric transmittance-radiance model LOWTRAN-7 for NOAA-11 AVHRR Channels 4 and 5. From these simulations the accuracies of linearizations have been checked. The dependence of the split-window coefficients on the aforementioned magnitudes has been analyzed. Thus, it has been demonstrated that the angular dependence can be avoided while the total water vapor dependence must be retained. Therefore, we have obtained a split-window equation for each atmosphere in which the emissivity dependence is linearized in terms of Channel 4 emissivity 03 4 and channel emissivity difference, 03 4 –03 5 . The analysis of the emissivity influence shows that the emissivity measurement should be made with an accuracy of 0.005 in order to get an error below to 0.4 K on land surface temperature. Finally, two applications of the split-window method for obtaining sea surface temperature and crop temperatures have been included.

DOI

[16]
Coll C, Caselles V.A split-window algorithm for land surface temperature from advanced very high resolution radiometer data: Validation and algorithm comparison. Journal of Geophysical Research: Atmospheres, 1997, 1021(D14): 16697-16714.A split-window algorithm for deriving land surface temperatures (LSTs) from advanced very high resolution radiometer (AVHRR) channels 4 and 5 is proposed and validated with in situ measured temperatures. On the basis of the radiative transfer theory the algorithm defines a set of surface-independent coefficients which are equivalent to the classical split-window coefficients for sea surface temperature (SST). These coefficients are calculated using SST matchups (coincident AVHRR and buoy measurements) provided by the National Oceanic and Atmospheric Administration (NOAA)-NASA Pathfinder Database of worldwide measurements. Thus calibration of the split-window coefficients is done using real data. The variability of atmospheric attenuation is represented in the proposed algorithm by a quadratic dependence on the brightness temperature difference. For LST determination the emissivity effect is modeled through an additive coefficient which depends on surface emissivity in the AVHRR channels 4 and 5. The algorithm is validated for both SST and LST by using independent ground-based and AVHRR data. The database used in the validation of LST was obtained for a wide range of surface types in a semiarid environment. The same databases are used to compare the accuracies of other published split-window algorithms. The proposed algorithm yields standard errors of temperature estimate between 卤1.0 and 卤1.5 K, and no significant biases are observed. Although results are encouraging, more validation is required principally for moist atmospheric conditions.

DOI

[17]
毛克彪, 覃志豪, 施建成. 用MODIS影像和劈窗算法反演山东半岛的地表温度. 中国矿业大学学报, 2005, 34(1): 46-50.简要介绍了劈窗算法,并对Planck方程进行了线性简化,从而简化了劈窗算法的方程计算.针对MODIS的波段设置特点,先用近红外波段反演大气水汽含量,然后根据LOWTRAN模拟大气水汽含量与透过率的关系,这样可以根据反演得到的水汽计算31和32波段的透过率.最后运用劈窗算法反演山东半岛地区的地表温度(LST),结果比较合理.

DOI

[Mao Kebiao, Qin Zhihao, Shi Jiancheng.Retrieval of land surface temperature of shandong peninsula using MODIS image and split window algorithm. Journal of China University of Mining & Technology, 2005, 34(1): 46-50.]

[18]
Changin R I, 柳钦火, 历华, 等. 针对Terra/MODIS数据的改进分裂窗地表温度反演算法. 遥感学报, 2013, 17(4): 830-840.针对Terra/MODIS数据提出改进的分裂窗地表温度反演算法。充分考虑了传感器观测角度(VZA)的影响,并对地表和有效大气辐射按照不同的亮度温度区间分别进行Planck函数简化。利用TIGR3大气廓线库中的875条晴空大气廓线,ASTER波谱库中的106条地物发射率波谱,结合MODTRAN4大气辐射传输模型模拟得到分裂窗算法系数。利用MODTRAN4模拟数据对算法精度进行验证,结果表明本文的改进算法和原算法的均方根误差RMSE分别为0.34 K 和0.65 K。敏感性分析表明,在中等湿润的大气条件下,算法对大气水汽含量并不敏感。该算法降低了传感器观测角度带来的地表温度反演误差。利用2009年6月美国SURFRAD辐射观测网6个站点的实测数据对改进算法、原算法以及MOD11_L2地表温度产品进行了对比验证,RMSE分别是0.93 K、1.49 K和1.0 K,表明本文算法可以提高反演精度。

DOI

[Changin R I, Liu Qinhuo, Li Hua, et al.Improved split window algorithm to retrieve LST from Terra/MODIS data. Journal of Remote Sensing, 2013, 17(4): 830-840.]

[19]
饶胜, 张惠远, 金陶陶, 等. 基于MODIS的珠江三角洲地区区域热岛的分布特征. 地理研究, 2010, 29(1): 127-136.通过MODIS地温数据,研究了珠三角地区由于快速的城市化过程造成的区域性大范围温度升高现象,即"区域"热岛现象.分析结果表明,MODIS数据能够较好地反映出区域城市化进程中区域地表热环境的变化.不同下垫面的温度差异是形成区域热岛的基础.在大规模连片的城市化过程中,城镇用地的周边区域受到温度升高的影响,地表温度也相应升高,从而造成了区域大面积的温度升高.形成了区域热岛.从空间形态看,区域热岛的空间格局与城镇用地的空间布局具有较高的相关性,大城市或城市连绵区往往是区域热岛的中心.城市连绵区及其周边区域的热岛现象十分明显,而位于研究区的西南和东北方位的城镇分布比较分散,对应的区域热岛现象并不显著.

DOI

[Rao Sheng, Zhang Huiyuan Jin Taotao, et al. The spatial character of regional heat island in Pearl River Delta using MODIS remote sensing data. Geographical Research, 2010, 29(1): 127-136.]

[20]
魏智, 金会军, 张建明, 等. 气候变化条件下东北地区多年冻土变化预测. 中国科学: 地球科学, 2011, 41(1): 74-84.

[Wei Zhi, Jin HuiJun, Zhang Jianming, et al. Prediction of permafrost changes in northeastern China under a changing climate. Science China: Earth Sciences, 2011, 41(1): 74-84.]

[21]
郑红星, 刘静. 东北地区近40年干燥指数变化趋势及其气候敏感性. 地理研究, 2011, 30(10): 1765-1774.

[Zheng Hongxing, Liu Jing.Long-term trends of aridity index and its sensitivity to climate factors in Northeast China: 1971-2008. Geographical Research, 2011, 30(10): 1765-1774.]

[22]
Wan Z.New refinements and validation of the MODIS land-surface temperature/emissivity products. Remote Sensing of Environment, 2014, 112(1): 59-74.This paper discusses the lessons learned from analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Land-Surface Temperature/Emissivity (LST) products in the current (V4) and previous versions, and presents eight new refinements for V5 product generation executive code (PGE16) and the test results with real Terra and Aqua MODIS data. The major refinements include considering surface elevation when using the MODIS cloudmask product, removal of temporal averaging in the 1km daily level-3 LST product, removal of cloud-contaminated LSTs in level-3 LST products, and the refinements for the day/night LST algorithm. These refinements significantly improved the spatial coverage of LSTs, especially in highland regions, and the accuracy and stability of the MODIS LST products. Comparisons between V5 LSTs and in-situ values in 47 clear-sky cases (in the LST range from 10 C to 58 C and atmospheric column water vapor range from 0.4 to 3.5cm) indicate that the accuracy of the MODIS LST product is better than 1K in most cases (39 out of 47) and the root of mean squares of differences is less than 0.7K for all 47 cases or 0.5K for all but the 8 cases apparently with heavy aerosol loadings. Emissivities retrieved by the day/night algorithm are well compared to the surface emissivity spectra measured by a sun-shadow method in two field campaigns. The time series of V5 MODIS LST product over two sites (Lake Tahoe in California and Namco lake in Tibet) in 2003 are evaluated, showing that the quantity and quality of MODIS LST products depend on clear-sky conditions because of the inherent limitation of the thermal infrared remote sensing.

DOI

[23]
Qin Z, Dall'Olmo G, Karnieli A, et al. Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data. Journal of Geophysical Research: Atmospheres, 2001, 106(D19): 22655-22670.Retrieval of land surface temperature (LST) from advanced very high resolution radiometer (AVHRR) data is an important methodology in remote sensing. Several split window algorithms have been proposed in last two decades. In this paper we intend to present a better algorithm with less parameters and high accuacry. The algorithm involves only two essential parameters (transmittance and emissivity). The principle and method for the linearization of Planck's radiance equation, the mathematical derivation process of the algorithm, and the method for determining the atmospheric transmittance are discussed with details. Sensitivity analysis of the algorithm has been performed for evaluation of probable LST estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of LST from AVHRR data. Assuming an error of 0.05 in atmospheric transmittance estimate and 0.01 in ground emissivity for the two AVHRR thermal channels, the average LST error with the algorithm is 1.100°C. Two methods have been used to validate the proposed algortihm. Comparison has also been done with the existing 11 algorithms in literature. Results from validation and comparison using the standard atmospheric simulation for various situations and the ground truth data sets demonstrate the applicability of the algorithm. According to the root mean square (RMS) errors of the retrieved LSTs from the measured or assumed LSTs, the proposed algorithm is among the best three. Considering the insignificant RMS error difference among the three, the proposed algorithm is better than the other two because they require more parameters for LST retrieval. Validation with standard atmospheric simulation indicates that this algorithm can achieve the accuacry of 0.2500°C in LST retrieval for the case without error in both transmittance and emissivity estimates. The accuary of this algorithm is 1.7500°C for the ground truth data set without precise in situ atmospheric water vapor contents. The accuracy increases to 0.2400°C for another ground truth data set with precise in situ atmospheric water vapor contents. The much higher accuracy for this data set confirms the appplicability of the proposed algorithm as an alternative for the accurate LST retrieval from AVHRR data.

DOI

[24]
Kaufman Y J, Gao B C.Remote sensing of water vapour in the near IR from EOS/MODIS. IEEE Transactions on Geoscience & Remote Sensing, 1992, 30(5): 871-884.The LOWTRAN-7 code was used to simulate remote sensing of water vapor over 20 different surface covers. The simulation was used to optimize the water vapor channel selection and to test the accuracy of the remote sensing method. The channel selection minimizes the uncertainty in the derived water vapor due to variations in the spectral dependence of the surface reflectance. The selection also minimizes the sensitivity of the selected channels to possible drift in the channel position. The use of additional MODIS channels reduces the errors due to the effect of haze, subpixel clouds and uncertainties in the temperature profile. Remote sensing of the variation of water vapor from day to day will be more accurate, because the surface reflectances vary slowly with time. The method was applied to Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data.

DOI

[25]
丁莉东, 覃志豪, 毛克彪. 基于MODIS影像数据的劈窗算法研究及其参数确定. 遥感技术与应用, 2005, 20(2): 284-289.lt;p>劈窗算法是目前由热红外遥感数据获取陆面温度的主要方法。在介绍劈窗算法的一般表现形式的基础上, 我们推导出适合于MOD IS 影像数据的劈窗算法。大气透过率和地表比辐射率是求解地表温度的两个关键参数。由于MOD IS 图像分辨率较低,MOD IS 像元主要由水面、植被和裸土3种地物类型构成, 故可依据这3 种地物的构成比例确定地表比辐射率。从遥感影像上反演大气的水汽含量, 再根据大气水汽含量与大气透过率的关系计算出大气透过率。最后将文中推导的劈窗算法用于江苏省地表温度的反演。反演出来的地表温度图显示出明显的地表温度空间差异、城市热岛效应和不同的地物类型。</p>

DOI

[Ding Lidong, Qin Zhihao, Mao Kebiao.A research of split window algorithm based on MODIS image data and parameter determination. Remote Sensing Technology & Application, 2005, 20(2): 284-289.]

[26]
高懋芳, 覃志豪, 徐斌. 用MODIS数据反演地表温度的基本参数估计方法. 干旱区研究, 2007, 24(1): 113-119.利用MODIS数据反演地表温度所需的2个基本参数(大气透过率和地表比辐射率)的估计方法,由MODIS的可见光近红外波段计算得到大气水汽含量,根据大气透过率与大气水汽含量的关系,用辐射传输模型MODTRAN模拟得到大气透过率.由于MODIS的扫描带宽为2 330 km,遥感视角和大气温度会对大气透过率有比较大的影响,因此,提出了大气透过率的遥感器视角校正函数和温度校正函数.地表比辐射率主要取决于地表物质构成,尤其是植被覆盖程度,为此,提出了根据地表植被覆盖差异来估计地表比辐射率的方法,其中还引入了辐射比率参数及其确定方法.

[Gao Maofang, Qin Zhihao, Xu Bin.Estimation of the basic parameters for deriving surface temperature from MODIS Data. Arid Zone Research, 2007, 24(1): 113-119.]

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