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地理学报    2018, Vol. 73 Issue (9): 1778-1791     DOI: 10.11821/dlxb201809013
  气候变化与生态环境 本期目录 | 过刊浏览 | 高级检索 |
基于卫星遥感和再分析数据的青藏高原土壤湿度数据评估
范科科1,2,3(),张强1,2,3(),史培军1,2,3,孙鹏4,余慧倩1,2,3
1. 北京师范大学环境演变与自然灾害教育部重点实验室,北京 100875
2. 北京师范大学减灾与应急管理研究院,北京 100875
3. 北京师范大学地理科学学部,北京 100875
4. 安徽师范大学国土资源与旅游学院,芜湖 241002
Evaluation of remote sensing and reanalysis soil moisture products on the Tibetan Plateau
FAN Keke1,2,3(),ZHANG Qiang1,2,3(),SHI Peijun1,2,3,SUN Peng4,YU Huiqian1,2,3
1. Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
2. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
3. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
4. College of Territorial Resources and Tourism, Anhui Normal University, Wuhu 241002, Anhui, China
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摘要 

土壤水是地表与大气在水热交换方面的关键纽带,是关键水循环要素,更是地表产汇流过程的关键控制因子。青藏高原是地球第三极,也是亚洲水塔,探讨青藏高原土壤水变化对于探讨青藏高原热力学特征变化及其对东亚乃至全球气候变化的影响具有重要意义,而获取高精度长序列大尺度土壤水数据集则是其关键。本文利用青藏高原100个土壤水站点观测数据,从多空间尺度(0.25°×0.25°,0.5°×0.5°,1°×1°)、多时间段(冻结和融化期)等角度,采用多评价指标(R、RMSE、Bias),对多套遥感反演和同化数据(ECV、ERA-Interim、MERRA、Noah)进行全面评估。结果表明:① 除ERA外,其他数据均能反映青藏高原土壤水变化,且与降水量变化一致。而在那曲地区,遥感反演和同化数据均明显低估实测土壤水含量。从空间分布来看,MERRA和Noah与植被指数最为一致,可很好地反映土壤水空间变化特征;② 青藏高原大部分地区土壤水变化主要受降水影响,其中青藏高原西部边缘与喜马拉雅地区土壤水变化则受冰雪融水和降水的共同影响;③ 除阿里地区外,大部分遥感反演和同化数据在融化期与实测土壤水相关性高于冻结期,其中在那曲地区,遥感反演和同化数据均高估冻结期土壤含水量,却低估融化期土壤含水量。另外,遥感反演和同化数据对中大空间尺度土壤水的估计要好于对小空间尺度土壤水的估计。本研究为青藏高原土壤水研究的数据集选择提供重要理论依据。

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范科科
张强
史培军
孙鹏
余慧倩
关键词 卫星遥感数据再分析数据土壤湿度青藏高原 
Abstract

Soil water is the key link between land surface and atmosphere in water-heat exchange and it is the key element of water cycle. It is also the key control factor affecting the process of surface runoff. The Himalayan-Tibetan Plateau (HTP), also known as the "Asian Water Tower", is the source region of many Asian rivers. Meanwhile, HTP has direct impacts on its surrounding climate via hydro-meteorological processes, and on establishment and maintenance of Asian monsoon. This study collected observed soil moisture data from 100 in-situ soil moisture observatory stations and evaluated applicability of the available remote sensing and reanalysis soil moisture datasets such as ECV, ERA-Interim, MERRA, and Noah at different spatial resolutions (0.25°×0.25°, 0.5°×0.5°, 1°×1°) during different time intervals such as non-freezing and freezing periods. Statistical indicators such as R, RMSE and Bias were used to evaluate the performances of these remote sensing and reanalysis soil moisture datasets. The results indicated that: (1) All remote sensing and reanalysis soil moisture datasets except ERA can well estimate soil moisture changes of the Tibetan Plateau and the soil moisture changes are in generally good line with precipitation changes. In the Naqu region, however, the remote sensing and reanalysis soil moisture datasets substantially underestimate observed soil moisture. In space, MERRA and Noah are mostly consistent with the change of vegetation index, and can well estimate spatial distribution of soil moisture changes. (2) Soil moisture changes across most parts of the Tibetan Plateau are greatly influenced by precipitation changes. In addition, soil moisture changes in the western flank of the Tibetan Plateau and Himalayas are the combined results of melting snow/glaciers and precipitation. (3) Except in the Ngari region, soil moisture during non-freezing period is usually higher than that during freezing period. In the Naqu region, all remote sensing and reanalysis soil moisture datasets overestimate soil moisture amount during freezing periods, while they underestimate it during non-freezing periods. Besides, from a spatial scale viewpoint, at medium and large scales, remote sensing and reanalysis soil moisture datasets can better evaluate soil moisture availability compared with at small scale. This study provides a theoretical basis for selection of the right remote sensing and reanalysis soil moisture datasets for evaluation and analysis of soil moisture of the Tibetan Plateau.

Key wordsremote sensing dataset    reanalysis dataset    soil moisture    Tibetan Plateau
收稿日期: 2017-08-31      出版日期: 2018-09-19
基金资助:国家自然科学基金委创新群体项目(41621061);国家杰出青年科学基金项目(51425903)
引用本文:   
范科科, 张强, 史培军等 . 基于卫星遥感和再分析数据的青藏高原土壤湿度数据评估[J]. 地理学报, 2018, 73(9): 1778-1791.
FAN Keke, ZHANG Qiang, SHI Peijun et al . Evaluation of remote sensing and reanalysis soil moisture products on the Tibetan Plateau[J]. Acta Geographica Sinica, 2018, 73(9): 1778-1791.
链接本文:  
http://www.geog.com.cn/CN/10.11821/dlxb201809013      或      http://www.geog.com.cn/CN/Y2018/V73/I9/1778
Fig. 1  青藏高原和实测土壤水观测站网位置示意图
Fig. 2  阿里地区月均土壤水和降水时间变化
Fig. 3  那曲地区月均土壤水和降水时间变化
Fig. 4  玛曲地区月均土壤水和降水时间变化
Fig. 5  2009-2014年平均土壤水(ECV、ERA、MERRA、Noah)、降水和植被指数空间图
ECV ERA MERRA Noah
R RMSE Bias P R RMSE Bias P R RMSE Bias P R RMSE Bias P
1 0.222 0.183 -0.17 0.186 0.468 0.096 -0.01 0.000 0.237 0.123 -0.068 0.064 0.675 0.151 -0.131 0.000
2 0.259 0.352 -0.326 0.107 0.521 0.206 -0.122 0.000 0.225 0.248 -0.179 0.079 0.638 0.280 -0.241 0.000
3 0.440 0.241 -0.227 0.006 0.572 0.139 -0.033 0.000 0.237 0.165 -0.085 0.081 0.737 0.173 -0.137 0.000
4 0.610 0.163 -0.138 0.000 0.654 0.116 0.019 0.000 0.423 0.122 -0.036 0.005 0.727 0.122 -0.08 0.000
5 0.568 0.232 -0.226 0.000 0.481 0.108 -0.044 0.000 0.343 0.145 -0.105 0.011 0.728 0.176 -0.16 0.000
6 0.613 0.107 -0.09 0.000 0.512 0.120 0.083 0.000 0.214 0.098 0.025 0.120 0.769 0.065 -0.021 0.000
7 0.425 0.140 -0.125 0.010 0.367 0.107 0.035 0.006 0.196 0.108 -0.023 0.156 0.736 0.107 -0.078 0.000
8 0.473 0.097 -0.067 0.002 0.388 0.121 0.083 0.004 0.126 0.099 0.025 0.366 0.681 0.074 -0.03 0.000
9 0.414 0.183 -0.157 0.006 0.449 0.106 0.006 0.001 0.223 0.122 -0.051 0.104 0.585 0.135 -0.098 0.000
10 0.439 0.165 -0.148 0.005 0.460 0.099 0.023 0.000 0.185 0.107 -0.025 0.181 0.680 0.111 -0.081 0.000
11 0.278 0.124 0.112 0.168 0.436 0.230 0.229 0.006 0.225 0.179 0.176 0.169 0.692 0.128 0.118 0.000
12 0.153 0.080 0.067 0.586 0.098 0.116 0.108 0.553 0.339 0.048 0.017 0.035 0.247 0.087 0.06 0.130
13 0.581 0.107 0.101 0.023 0.548 0.132 0.13 0.000 0.748 0.039 0.027 0.000 0.841 0.096 0.086 0.000
14 0.835 0.146 0.144 0.000 0.762 0.168 0.167 0.000 0.726 0.075 0.069 0.000 0.683 0.127 0.117 0.000
15 0.803 0.059 0.056 0.000 0.538 0.117 0.114 0.000 0.558 0.040 0.017 0.000 0.680 0.084 0.069 0.000
16 0.790 0.039 -0.002 0.000 0.673 0.148 0.138 0.000 0.564 0.103 0.088 0.000 0.773 0.049 0.019 0.000
17 -0.457 0.236 -0.222 0.117 -0.165 0.156 -0.064 0.238 0.310 0.206 -0.159 0.024 0.724 0.162 -0.12 0.000
18 0.243 0.076 0.007 0.130 0.672 0.162 0.143 0.000 0.525 0.122 0.096 0.000 0.778 0.074 0.049 0.000
19 0.310 0.090 -0.036 0.051 0.670 0.148 0.12 0.000 0.592 0.109 0.07 0.000 0.765 0.072 0.031 0.000
20 0.547 0.173 -0.139 0.000 0.687 0.125 0.034 0.000 0.439 0.135 -0.048 0.001 0.780 0.111 -0.056 0.000
21 0.333 0.066 0.009 0.047 0.708 0.145 0.125 0.000 0.486 0.119 0.092 0.000 0.837 0.058 0.034 0.000
22 0.475 0.077 -0.021 0.002 0.687 0.140 0.112 0.000 0.510 0.116 0.08 0.000 0.817 0.062 0.021 0.000
23 0.554 0.112 -0.088 0.000 0.626 0.104 0.063 0.000 0.462 0.087 0.027 0.001 0.716 0.070 -0.024 0.000
24 0.669 0.071 -0.037 0.000 0.722 0.138 0.115 0.000 0.482 0.097 0.057 0.000 0.782 0.063 0.029 0.000
25 0.479 0.062 0.008 0.000 0.598 0.162 0.151 0.000 0.305 0.113 0.091 0.026 0.675 0.086 0.069 0.000
26 0.362 0.092 0.057 0.045 0.354 0.188 0.172 0.021 0.507 0.155 0.138 0.001 0.665 0.098 0.077 0.000
27 0.589 0.084 0.072 0.000 0.695 0.201 0.194 0.000 0.333 0.150 0.138 0.015 0.687 0.108 0.098 0.000
Tab. 1  27个小尺度栅格实测土壤水和估计土壤水统计值
ECV ERA MERRA Noah
R RMSE Bias P R RMSE Bias P R RMSE Bias P R RMSE Bias P
a 0.690 0.096 0.092 0.004 0.556 0.132 0.13 0.000 0.699 0.045 0.032 0.000 0.748 0.095 0.083 0.000
b 0.701 0.067 -0.026 0.000 0.693 0.132 0.11 0.000 0.470 0.098 0.064 0.000 0.784 0.058 0.024 0.000
c 0.620 0.100 -0.065 0.000 0.702 0.124 0.079 0.000 0.473 0.101 0.022 0.000 0.824 0.069 -0.011 0.000
d 0.830 0.056 0.045 0.000 0.741 0.176 0.168 0.000 0.546 0.134 0.123 0.000 0.820 0.075 0.066 0.000
e 0.356 0.097 -0.051 0.024 0.680 0.116 0.066 0.000 0.633 0.089 0.002 0.000 0.866 0.066 -0.013 0.000
f 0.455 0.250 -0.238 0.002 0.523 0.130 -0.053 0.000 0.269 0.167 -0.112 0.034 0.715 0.194 -0.17 0.000
g 0.626 0.127 -0.113 0.000 0.444 0.097 0.041 0.001 0.204 0.095 -0.016 0.139 0.744 0.093 -0.069 0.000
A 0.780 0.058 -0.013 0.000 0.734 0.131 0.108 0.000 0.567 0.092 0.056 0.000 0.837 0.053 0.02 0.000
B 0.525 0.190 -0.173 0.000 0.453 0.107 -0.003 0.000 0.191 0.130 -0.063 0.138 0.664 0.146 -0.118 0.000
Tab. 2  中/大尺度栅格实测土壤水和估计土壤水统计值
Fig. 6  不同地区(阿里地区:a,d;那曲地区:b,e;玛曲地区:c、f)各土壤水估计数据集(ECV、ERA、MERRA、Noah)的相关泰勒图
Fig. 7  3个地区实测土壤水与估计土壤水(ECV、ERA、MERRA、Noah)散点图
Fig. 8  各地区的估计土壤水统计特征值(R、RMSE、Bias)分布
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