三种高分辨率地表蒸散发产品在华北地区的验证与对比

doi:10.18402/resci.2020.10.19

摘要/Abstract

摘要：

华北平原是中国重要的农业主产区,同时也是世界上水资源短缺最为严峻的地区之一。地表蒸散发（ET）是水资源消耗的最大项,因此获取准确的ET数据是华北平原水资源管理的重要基础。本文对全球3种高分辨率ET产品在华北地区进行精度验证和时空对比,以期为选择更适用于华北平原的高分辨率ET数据提供参考信息,更好地为水资源的研究和管理服务。通过与涡动相关测量数据对比,研究显示PML_V2产品在华北地区精度最高,其次是SSEBop_V4,最后是MOD16A2,相关系数分别为0.81、0.74和0.52;均方根误差分别为0.87、1.52和1.44 mm/d,PML_V2与站点观测值的波动趋势一致性最高。3种产品在小麦生长季的估算值与观测值的相关性均高于玉米生长季,SSEBop_V4和PML_V2估算值分别在小麦季和玉米季与观测值具有最高的相关性。通过3种产品相互之间的对比,结果发现PML_V2和SSEBop_V4在空间分布上较一致,相关系数最高,为0.76;MOD16A2的空间分布与其他2个产品差异较大;三者的最大差异出现在耕地区。从2003—2018年的变化趋势上,MOD16A2在3种土地利用类型下明显呈增加趋势,而SSEBop_V4和PML_V2无明显变化。该研究结果有助于评估每种产品的质量和不确定性,以改进ET算法和产品质量。

关键词: 蒸散发, 高分辨率, 精度验证, 时空变化, 涡动相关, Google Earth Engine (GEE), 生长季, 华北平原

Abstract:

The North China Plain is a critical agricultural region in China and one of the most severe water deficient areas in the world. Surface evapotranspiration (ET) is the largest component of water resource consumption, therefore obtaining accurate ET data is an important basis for water resource management on the North China Plain. In this study, accuracy verification and spatiotemporal comparison of three global high-resolution ET products were conducted in North China in order to provide a reference for the selection of a high-resolution ET data product that is more suitable for the North China Plain and can better serve the purpose of water resource research and management. Through the comparison with the eddy correlation measurement, the research showed that the PML_V2 product had the highest accuracy in North China, followed by SSEBop_V4 and MOD16A2, with correlation coefficients of 0.81, 0.74, and 0.52, respectively. The root mean square errors were 0.87, 1.52, and 1.44 (mm/d), respectively. PML_V2 showed the highest consistency with the fluctuation trend observed at the site. The correlation between the estimated and observed ET of the three products in the growing season of wheat was higher than that of maize. SSEBop_V4 ET and PML_V2 ET estimates had the highest correlation with the observed ET in wheat season and maize season, respectively. Comparatively, PML_V2 and SSEBop_V4 are more similar in spatial distribution, with the highest correlation coefficient of 0.76. The spatial distribution of MOD16A2 is very different from that of the other two products. The biggest difference of the three products appears in the cultivated land area. In 2003-2018, MOD16A2 showed a clear trend of increase for three land use types, while SSEBop_V4 and PML_V2 showed no obvious change.

Key words: evapotranspiration, high resolution, accuracy verification, spatiotemporal change, eddy covariance, Google Earth Engine (GEE), growing season, North China Plain

何韶阳, 田静, 张永强. 三种高分辨率地表蒸散发产品在华北地区的验证与对比[J]. 资源科学, 2020, 42(10): 2035-2046.

HE Shaoyang, TIAN Jing, ZHANG Yongqiang. Verification and comparison of three high-resolution surface evapotranspiration products in North China[J]. Resources Science, 2020, 42(10): 2035-2046.

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参考文献 34

[1]	易珍言. 农田区域蒸散发和土壤含水量协同获取方法研究与应用[D]. 北京: 中国水利水电科学研究院, 2019.
	[ Yi Z Y. Research and Application of Collaborating Acquisition of Evapotranspiration and Surface Soil Moisture over Irrigated Area[D]. Beijing: China Institute of Water Resources and Hydropower Research, 2019.]
[2]	郭梦瑶, 佘敦先, 张利平, 等. 渭河流域潜在蒸散量变化的气候归因[J]. 资源科学, 2020,42(5):907-919.
	[ Guo M Y, She D X, Zhang L P, et al. Climate explanation of the potential evapotranspiration changes in Weihe River Basin[J]. Resources Science, 2020,42(5):907-919.]
[3]	Wang J F, 刘元波, 张珂. 最大熵增地表蒸散模型: 原理及应用综述[J]. 地球科学进展, 2019,34(6):596-605.
	[ Wang J F, Liu Y B, Zhang K. The maximum entropy production approach for estimating evapotranspiration: Principle and applications[J]. Advances in Earth Science, 2019,34(6):596-605.]
[4]	Li X Y, He Y, Zeng Z Z, et al. Spatiotemporal pattern of terrestrial evapotranspiration in China during the past thirty years[J]. Agricultural and Forest Meteorology, 2018,259(15):131-140. doi: 10.1016/j.agrformet.2018.04.020
[5]	陈鹤, 杨大文, 刘钰, 等. 基于遥感模型的华北平原农田区蒸散发量估算[J]. 灌溉排水学报, 2014,33(Z1):21-25.
	[ Chen H, Yang D W, Liu Y, et al. Cropland evapotranspiration estimation based on remote sensing model in the North China Plain[J]. Journal of Irrigation and Drainage, 2014,33(Z1):21-25.]
[6]	Jia Z Z, Liu S M, Xu Z W, et al. Validation of remotely sensed evapotranspiration over the Hai River Basin, China[J]. Journal of Geophysical Research Atmospheres, 2012, DOI: 10.1029/2011JD017037. doi: 10.1029/1998je900034 pmid: 11542931
[7]	莫兴国, 刘苏峡, 林忠辉, 等. 华北平原蒸散和GPP格局及其对气候波动的响应[J]. 地理学报, 2011,66(5):589-598.
	[ Mo X G, Liu S X, Lin Z H, et al. Patterns of evapotranspiration and GPP and their responses to climate variations over the North China[J]. Acta Geographica Sinica, 2011,66(5):589-598.]
[8]	田静, 苏红波, 孙晓敏, 等. 遥感反演土壤蒸发/植被蒸腾二层模型在华北地区的应用[J]. 地理研究, 2009,28(5):1297-1306.
	[ Tian J, Su H B, Sun X M, et al. Application of an operational two-layer model for soil evaporation and vegetation transpiration retrievals in North China[J]. Geographical Research, 2009,28(5):1297-1306.]
[9]	Zhang Y Q, Kong D D, Gan R, et al. Coupled estimation of 500  m and 8-day resolution global evapotranspiration and gross primary production in 2002-2017[J]. Remote Sensing of Environment, 2019,222:165-182. doi: 10.1016/j.rse.2018.12.031
[10]	张东, 徐全芝, 张万昌. 汉江上游流域蒸散量计算方法的比较及改进[J]. 资源科学, 2005,27(1):97-103.
	[ Zhang D, Xu Q Z, Zhang W C. Comparison and modification of evapotranspiration estimation method in upper Hanjiang Basin[J]. Resources Science, 2005,27(1):97-103.]
[11]	Chen Y, Xia J Z, Liang S L, et al. Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China[J]. Remote Sensing of Environment, 2014,140:279-293. doi: 10.1016/j.rse.2013.08.045
[12]	苏涛. 基于多套再分析资料的全球蒸发量时空变化特征及其成因研究[D]. 兰州: 兰州大学, 2016.
	[ Su T. Research on Spatial-temporal Variation Characteristics and Its Causes of Global Evaporation Based on Multi-reanalyses Datasets[D]. Lanzhou: Lanzhou University, 2016.]
[13]	Xue B L, Wang L, Li X P, et al. Evaluation of evapotranspiration estimates for two river basins on the Tibetan Plateau by a water balance method[J]. Journal of Hydrology, 2013,492(7):290-297. doi: 10.1016/j.jhydrol.2013.04.005
[14]	Zhang K, Kimball J S, Nemani R R, et al. A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006[J]. Water Resources Research, 2010, DOI: 10.1029/2009WR008800. doi: 10.1029/2017WR022485 pmid: 30906078
[15]	中国国家统计局. 分省年度数据[EB/OL]. (2019-12) [2020-02-01]. https://data.stats.gov.cn/easyquery.htm?cn=E0103.
	[ National Bureau of Statistics of the People’s Republic of China. Provincial Annual Data[EB/OL]. (2019-12) [2020-02-01]. https://data.stats.gov.cn/easyquery.htm?cn=E0103.]
[16]	武夏宁, 胡铁松, 王修贵, 等. 区域蒸散发估算测定方法综述[J]. 农业工程学报, 2006,22(10):257-262.
	[ Wu X N, Hu T S, Wang X G, et al. Review of estimating and measuring regional evapotranspiration[J]. Transactions of the Chinese Society of Agricultural Engineering, 2006,22(10):257-262.]
[17]	胡振通, 王亚华. 华北地下水超采综合治理效果评估: 以冬小麦春灌节水政策为例[J]. 干旱区资源与环境, 2019,33(5):101-106.
	[ Hu Z T, Wang Y H. Effectiveness assessment on comprehensive governance of groundwater over-exploition in North China Plain: The policy of winter wheat’s water-saving by reducing spring irrigation[J]. Journal of Arid Land Resources and Environment, 2019,33(5):101-106.]
[18]	肖李俏. 华北平原地下水节水压采条件下作物种植制度优化[D]. 长沙: 长沙理工大学, 2018.
	[ Xiao L Q. Optimization of Crop Planting System under the Condition of Reducing the Exploitation of Groundwater in the North of China[D]. Changsha: Changsha University of Science and Technology, 2018.]
[19]	曹永强, 李维佳, 赵博雅. 气候变化下辽西北春玉米生育期需水量研究[J]. 资源科学, 2018,40(1):150-160.
	[ Cao Y Q, Li W J, Zhao B Y. Water requirements of spring maize in Northwest Liaoning Province under climate change[J]. Resources Science, 2018,40(1):150-160.]
[20]	Zhang Y Q, Peña-Arancibia J L, McVicar T R, et al. Multi-decadal trends in global terrestrial evapotranspiration and its components[J]. Scientific Reports, 2016,6:19124-19124. pmid: 26750505
[21]	Mu Q Z, Zhao M S, Running S W. Improvements to a MODIS global terrestrial evapotranspiration algorithm[J]. Remote Sensing of Environment, 2011,115(8):1781-1800. doi: 10.1016/j.rse.2011.02.019
[22]	Song L S, Liu S M, Kustas W P, et al. Monitoring and validating spatially and temporally continuous daily evaporation and transpiration at river basin scale[J]. Remote Sensing of Environment, 2018,219(15):72-88. doi: 10.1016/j.rse.2018.10.002
[23]	Chen M S, Senay G B, Singh R K, et al. Uncertainty analysis of the operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites[J]. Journal of Hydrology, 2016,536:384-399. doi: 10.1016/j.jhydrol.2016.02.026
[24]	Senay G B, Bohms S, Singh R K, et al. Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach[J]. Jawra Journal of the American Water Resources Association, 2013,49(3):577-591. doi: 10.1111/jawr.2013.49.issue-3
[25]	Senay G B, Budde M E, Verdin J P. Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model[J]. Agricultural Water Management, 2011,98:606-618. doi: 10.1016/j.agwat.2010.10.014
[26]	Liu S M, Xu Z W, Zhu Z L, et al. Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China[J]. Journal of Hydrology, 2013,536:384-399. doi: 10.1016/j.jhydrol.2016.02.026
[27]	杨光超, 朱忠礼, 谭磊, 等. 怀来地区蒸渗仪测定玉米田蒸散发分析[J]. 高原气象, 2015, (4):1095-1106.
	[ Yang G C, Zhu Z L, Tan L, et al. Analysis on evapotranspiration of maize field measured by lysimeters in Huailai[J]. Plateau Meteorology, 2015, (4):1095-1106.]
[28]	Friedl M A, McIver D K, et al. Global land cover mapping from MODIS: Algorithms and preliminary results[J]. Remote Sensing of Environment, 2002,83(1-2):287-302. doi: 10.1016/S0034-4257(02)00078-0
[29]	Friedl M A, Sulla-Menashe D, Tan B, et al. MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets[J]. Remote Sensing of Environment, 2010,114(1):168-182. doi: 10.1016/j.rse.2009.08.016
[30]	Wang Q F, Tang J, Zeng J Y, et al. Spatial-temporal evolution of vegetation evapotranspiration in Hebei Province, China[J]. Journal of Integrative Agriculture, 2018,17(9):2107-2117. doi: 10.1016/S2095-3119(17)61900-2
[31]	Li X, Kiu S M, Li H X, et al. Intercomparison of six upscaling evapotranspiration methods: From site to the satellite pixel[J]. Journal of Geophysical Research: Atmospheres, 2018,123(13):6777-6803. doi: 10.1029/2018JD028422
[32]	Liu S M, Xu Z W, Song L S, et al. Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces[J]. Agricultural and Forest Meteorology, 2016, 230-231:97-113. doi: 10.1016/j.agrformet.2016.04.008
[33]	Xu T R, Guo Z X, Liu X M, et al. Evaluating different machine learning methods for upscaling evapotranspiration from flux towers to the regional scale[J]. Journal of Geophysical Research: Atmospheres, 2018,123(16):8674-8690. doi: 10.1029/2018JD028447
[34]	张圆, 贾贞贞, 刘绍民, 等. 遥感估算地表蒸散发真实性检验研究进展[J]. 遥感学报, 2020,24(8):975-999.
	[ Zhang Y, Jia Z Z, Liu S M, et al. Advances in validation of remotely sensed land surface evapotranspiration[J]. Journal of Remote Sensing, 2020,24(8):975-999.]

产品名称	时间跨度	时间分辨率/天	空间分辨率/m	方法原理
PML_V2	2002.7—2019.9	8	500	基于Penman-Monteith公式和光合速率-气孔导度模型计算而得
MOD16A2	2001.1—现在	8	500	基于Penman-Monteith公式和Jarvis气孔导度模型计算而得
SSEBop_V4	2003.1—现在	10	1000	基于简化的地表能量平衡（SSEB）方法对每个像素使用了预定义的“热”和“冷”边界条件估算蒸散发

站点名称	站点简称	所在县(区、市)	经度/°E	纬度/°N	海拔/m	观测时间
大兴站	DX	北京市大兴区	116.43	39.62	20	2008年1月1日—2010年12月27日
密云站	MY	北京市密云区	117.32	40.63	350	2008年1月1日—2010年12月29日
怀来站	HL	河北省怀来县	115.79	40.35	480	2013年1月1日—2017年12月31日
馆陶站	GT	河北省馆陶县	115.13	36.52	30	2008年1月1日—2010年12月31日
禹城站	YC	山东省禹城市	116.57	36.82	28	2008年1月1日—2010年12月31日

重归类的土地覆盖类型	包含的IGBP土地覆盖类型	对应的IGBP编码
林地	常绿阔叶林、落叶针叶林、落叶阔叶林、针阔混交林、郁闭灌丛、稀疏灌丛	1、2、3、4、5、6、7
草地	木本稀树草原、稀树草原、草地	8、9、10
耕地	耕地、耕地和天然植被混合体	12、14
建设用地	城市和建成区	13
水域	水域	17
其他用地	永久湿地、永久冰雪、裸地和低植被覆盖区	11、15、16

站点名称	PML_V2			MOD16A2			SSEBop_V4
站点名称	R	RMSE/(mm/d)	Bias/%	R	RMSE/(mm/d)	Bias/%	R	RMSE/(mm/d)	Bias/%
大兴站	0.80	1.08	-24.22	0.51	1.92	-57.71	0.73	1.19	-8.15
馆陶站	0.91	0.61	12.08	0.56	1.24	-33.04	0.90	1.14	26.21
怀来站	0.80	0.91	0.68	0.55	1.29	-14.45	0.75	2.27	114.76
密云站	0.95	0.49	-12.32	0.90	0.81	-24.81	0.88	0.77	-0.01
禹城站	0.77	0.98	-5.50	0.20	3.40	-65.87	0.83	1.29	29.55

蒸散发产品	小麦生长季（当年10月初—次年6月中旬）			玉米生长季（当年6月中旬—9月末）
蒸散发产品	R	RMSE/(mm/d)	Bias/%	R	RMSE/(mm/d)	Bias/%
PML_V2	0.85	0.89	-26.16	0.46	1.03	10.41
MOD16A2	0.61	1.63	-51.32	0.29	1.49	-21.50
SSEBop_V4	0.86	0.88	-3.81	0.20	1.86	41.24