灌区蒸散发遥感反演及其时空变化特征分析——以漳河灌区为例

doi:10.13474/j.cnki.11-2246.2022.0357

测绘通报 ›› 2022, Vol. 0 ›› Issue (12): 57-63.doi: 10.13474/j.cnki.11-2246.2022.0357

灌区蒸散发遥感反演及其时空变化特征分析——以漳河灌区为例

张娣¹, 魏征¹, 冯天权², 陈祖梅², 王凤静¹, 黄葵¹

1. 中国水利水电科学研究院, 北京 100048;
2. 湖北省漳河工程管理局, 湖北荆门 448156

收稿日期:2022-07-11 发布日期:2023-01-05
通讯作者: 魏征。E-mail:weizheng@iwhr.com
作者简介:张娣(1988-),女,工程师,主要研究方向为水文水资源。E-mail:809022760@qq.com
基金资助:
国家自然科学基金重点项目（52130906）；中国水利水电科学研究院流域水循环模拟与调控国家重点实验室自主研究（SKL2022TS13）；中国水科院基本科研业务费专项（ID0145B022021；ID0145B052021）

Analysis of evapotranspiration estimation and its spatial-temporal characteristics: taking Zhanghe irrigation district as an example

ZHANG Di¹, WEI Zheng¹, FENG Tianquan², CHEN Zumei², WANG Fengjing¹, HUANG Kui¹

1. China Institute of Water Resources and Hydropower Research, Beijing 100048, China;
2. Hubei Zhanghe Engineering Administration Bureau, Jingmen 448156, China

Received:2022-07-11 Published:2023-01-05

摘要/Abstract

摘要： 准确反演区域尺度的蒸散发对于水资源循环、气候变化、科学灌溉及干旱与洪涝监测等方面的研究具有重要的科学意义，本文以漳河灌区为典型案例，构建了遥感蒸散发的SEBS模型，验证了其精度，分析了其时空规律。结果表明：①2009—2018年钟祥、荆门、团林站点的（蒸散发） ET观测值与模拟值的平均相关系数分别为0.86、0.84和0.83，RMSE平均值分别为0.61、0.67和0.62 mm/d。年际间的ET观测值与模拟值变化幅度均小于10%，相关系数范围为0.7～0.9，RMSE范围为0.4～0.8 mm/d，标准差比率范围为0.8～1.5。②研究区2009—2018年ET的平均值为427.34 mm，整体呈上升趋势。③年尺度、季尺度和月尺度ET与气温和降水在时间序列上均有较好的一致性。多时间尺度ET均呈东北和西南部高、西部低的空间分布特征。研究结果能够较好地呈现ET的高时空异质特征和年际间空间差异，本文将为漳河灌区水资源开发管理和科学灌溉提供科学依据。

关键词: 蒸散发, MODIS, 时空特征, SEBS, 漳河灌区

Abstract: Regional scale accurate inversion of evaporation for water cycle, climate change, scientific irriga-tion and drought and flood monitoring research has important scientific significance, this article taka Zhanghe irrigation district as typical irrigation area, builds the SEBS model of remote sensing evaporation, verifies the accuracy of re-mote sensing evaporation, the temporal and spatial law of evaporation is analyzed. The results show that:① the average correlation coefficients between ET observation values and simulated values at zhongxiang, jingmen and tuanlin stations from 2009 to 2018 are 0.86, 0.84 and 0.83 respectively, and RMSE valu-es are 0.61, 0.67 and 0.62 mm/d respectively. The interannual variation of ET observed and simulated values is less than 10%, the correlation coefficient ranges from 0.7 to 0.9, RMSE ranges from 0.4 to 0.8 mm/d, and standard deviation ratio ranges from 0.8 to 1.5. ② The average value of ET in the study area from 2009 to 2018 is 427.34 mm, and ET shows an overall upward trend.③ The time series of ET and precipitation at annual, seasonal and monthly scales are consistent. ET is higher in northeast and southwest and lower in west at multiple time scales. The research results can better present the high spatio-temporal heterogeneity of ET and the spatial differences of ET between years. This study will provide scientific basis for the development and management of water resources and scientific irrigation in Zhanghe irrigation district.

Key words: evaporation, MODIS, the space-time characteristics, SEBS, Zhanghe irrigation district

中图分类号:

P237

张娣, 魏征, 冯天权, 陈祖梅, 王凤静, 黄葵. 灌区蒸散发遥感反演及其时空变化特征分析——以漳河灌区为例[J]. 测绘通报, 2022, 0(12): 57-63.

ZHANG Di, WEI Zheng, FENG Tianquan, CHEN Zumei, WANG Fengjing, HUANG Kui. Analysis of evapotranspiration estimation and its spatial-temporal characteristics: taking Zhanghe irrigation district as an example[J]. Bulletin of Surveying and Mapping, 2022, 0(12): 57-63.

参考文献

[1] BISQUERT M, SÁNCHEZ J M, LÓPEZ-URREA R, et al.Estimating high resolution evapotranspiration from disaggregated thermal images[J].Remote Sensing of Environment, 2016, 187:423-433.
[2] ROERINK G J, SU Z, MENENTI M.S-SEBI:a simple remote sensing algorithm to estimate the surface energy balance[J].Physics and Chemistry of the Earth, Part B:Hydrology, Oceans and Atmosphere, 2000, 25(2):147-157.
[3] BASTIAANSSEN W G M, MENENTI M, FE-DDES R A, et al.A remote sensing surface energy balance algorithm for land (SEBAL).1.Formulation[J].Journal of Hydrology, 1998, 212/213:198-212.
[4] SU Z.The surface energy balance system (SEBS) for estimation of turbulent heat fluxes[J].Hydrology and Earth System Sciences, 2002, 6(1):85-100.
[5] ALLEN R G, TASUMI M, TREZZA R.Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model[J].Journal of Irrigation and Drainage Engineering, 2007, 133(4):380-394.
[6] YANG Yuting, SHANG Songhao, JIANG Lei.Remote sensing temporal and spatial patterns of evapotranspiration and the responses to water management in a large irrigation district of North China[J].Agricultural and Forest Meteorology, 2012, 164:112-122.
[7] 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(S1):21-25.
[8] SEMMENS K A, ANDERSON M C, KUSTAS W P, et al.Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach[J].Remote Sensing of Environment, 2016, 185:155-170.
[9] YANG Yuting, SHANG Songhao.A hybrid dual-source scheme and trapezoid framework-based evapotranspiration model (HTEM) using satellite images:algorithm and model test[J].Journal of Geophysical Research Atmospheres, 2013, 118(5):2284-2300.
[10] SHUTTLEWORTH W J, WALLACE J S.Evaporation from sparse crops-an energy combination theory[J].Quarterly Journal of the Royal Meteorological Society, 1985, 111(469):839-855.
[11] YANG Yuting, LONG Di, GUAN Huade, et al.Comparison of three dual-source remote sensing evapotranspiration models during the MUSOEXE-12 campaign:revisit of model physics[J].Water Resources Research, 2015, 51(5):3145-3165.
[12] 杨雨亭, 尚松浩.双源蒸散发模型估算潜在蒸散发量的对比[J].农业工程学报, 2012, 28(24):85-91.
[13] 刘梦, 罗玉峰, 汪文超, 等.基于天气预报的漳河灌区参考作物腾发量预报方法比较[J].农业工程学报, 2017, 33(19):156-162.
[14] 岳琼, 郭萍, 王友芝, 等.基于区间两阶段模糊可信性约束模型的灌区水资源配置[J].农业机械学报, 2019, 50(4):228-235.
[15] KALMA J D, MCVICAR T R, MCCABE M F.Estimating land surface evaporation:a review of methods using remotely sensed surface temperature data[J].Surveys in Geophysics, 2008, 29(4/5):421-469.

灌区蒸散发遥感反演及其时空变化特征分析——以漳河灌区为例

Analysis of evapotranspiration estimation and its spatial-temporal characteristics: taking Zhanghe irrigation district as an example

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	况旭, 刘传立, 李明海. 粤港澳大湾区建成区扩张时空特征分析[J]. 测绘通报, 2022, 0(3): 90-95,110.
[2]	张云菲, 张泽旭, 朱芳琪. 利用时空密度聚类的高速公路交通事故黑点路段鉴别[J]. 测绘通报, 2022, 0(10): 73-79.
[3]	齐丹宁, 胡政军, 赵尚民. 山西省西山煤田长时间序列NDVI时空变化趋势及对气候响应[J]. 测绘通报, 2021, 0(9): 98-102,107.
[4]	李紫微, 马庆勋, 吕杰. BP神经网络的近地面臭氧估算及时空特征分析[J]. 测绘通报, 2021, 0(6): 28-32,126.
[5]	杨瑞芳, 尹思阳. 全球热带森林分布区NPP变化及其气候响应分析[J]. 测绘通报, 2021, 0(5): 49-53,110.
[6]	赵金龙, 赵慧. 基于DMSP-OLS和MODIS数据的宁夏城市化与生态环境耦合协调分析[J]. 测绘通报, 2021, 0(10): 9-14,27.
[7]	孙玉梅, 史保林, 徐玉静, 贾庆堂. 西双版纳土地利用时空分形特征分析[J]. 测绘通报, 2020, 0(11): 108-111.
[8]	沈谦, 朱长明, 张新. GF-2支持下的干旱区稀疏植被区植被覆盖度估算[J]. 测绘通报, 2019, 0(7): 33-38.
[9]	梁明, 陈文静, 段平, 李佳. 轨迹压缩的典型方法评价[J]. 测绘通报, 2019, 0(4): 60-64,70.
[10]	汪桂生, 仇凯健. 利用MODIS NDVI进行淮南矿区植被覆盖度动态监测[J]. 测绘通报, 2018, 0(6): 34-40.
[11]	马静宇, 秦怡, 任宇鹏, 王鑫, 谷娟, 郭仲皓, 邹乐君, 沈晓华. 基于MODIS植被丰度的沙漠化界线提取方法探究——以陕甘宁地区为例[J]. 测绘通报, 2018, 0(5): 102-107.
[12]	卢秀, 李佳, 段平, 程峰. 利用MODIS研究徐州市气溶胶光学厚度及其时空分析[J]. 测绘通报, 2018, 0(12): 41-45.
[13]	李广超, 李如仁, 赵阳阳, 杨震, 卢月明. 基于二次多项式的AOD数据融合方法[J]. 测绘通报, 2018, 0(1): 67-71.
[14]	裔传祥, 胡继超, 李小军. 土地覆盖类型对城市热岛效应的响应分析[J]. 测绘通报, 2018, 0(1): 72-76,116.
[15]	李祥, 彭玲, 池天河, 李浩川, 徐逸之. 北京市空气质量时空特征分析[J]. 测绘通报, 2016, 0(9): 47-51.