Please wait a minute...
img

官方微信

遥感技术与应用  2020, Vol. 35 Issue (1): 33-47    DOI: 10.11873/j.issn.1004-0323.2020.1.0033
土壤水分专栏     
基于AMSR2多频亮温的黑河流域中上游土壤水分估算研究
陆峥1(),韩孟磊2,卢麾3,彭雪婷4,蒙莎莎5,刘进1,杨晓帆1()
1. 北京师范大学地表过程与资源生态国家重点实验室,地理科学学部自然资源学院,北京 100875
2. 北京市气象灾害防御中心,北京 100089
3. 清华大学地球系统科学系,北京 100084
4. 中国21世纪议程管理中心,北京 100038
5. 北京师范大学地理科学学部地理学院,北京 100875
Estimating Soil Moisture in the Middle and Upper Reaches of the Heihe River Basin based on AMSR2 Multi-brightness Temperature
Zheng Lu1(),Menglei Han2,Hui Lu3,Xueting Peng4,Shasha Meng5,Jin Liu1,Xiaofan Yang1()
1. State Key Laboratory of Earth Surface Processes and Resource Ecology and School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2. Department of Earth System Science, Tsinghua University, Beijing 10084, China
3. Beijing Meteorological Disaster Prevention Center, Beijing 100089, China
4. The Administrative Center of China’s Agenda 21, Beijing 100038, China
5. School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100084, China
 全文: PDF(9869 KB)   HTML
摘要:

以黑河流域中上游为研究区,初步探究了利用AMSR2卫星的多频亮度温度数据估算土壤水分的方法。基于土壤水分和土壤发射率的统计关系,通过黑河流域上游的4个像元2013年7月至2014年6月内的实测土壤水分和土壤温度数据,采用了“四像元交叉拟合法”获得了统计系数,并用此方法估算出了黑河流域中上游的土壤水分。采用2014年7月至2014年10月内估算的土壤水分,连同与AMSR2的4个常用的土壤水分产品和GLDAS土壤水分产品在时间序列上,与八宝河流域WSN土壤水分地面观测展开了对比验证,结果表明估算土壤水分精度明显高于上述5种产品。同时借助高程和土地覆被辅助数据,与GLDAS土壤水分在空间格局上进行了比较,发现估算土壤水分时空分布特征更加合理。该方法可为流域尺度的土壤水分反演与监测提供了一种简而易行的思想方法和可行之路。

关键词: 土壤水分AMSR2土壤发射率土壤温度黑河流域中上游    
Abstract:

An approach to estimate soil moisture from AMSR2 multi-brightness temperature observations is preliminarily investigated in the middle and upper reaches of the Heihe River Basin (HRB). Based on the statistical relation between soil moisture and microwave emissivity from soil, a method called “4-grid cross-fitness” is used to obtain the statistical relation between in-situ soil moisture and soil temperature measurements of 4 WSN pixels in the upper reaches of the HRB during the period July, 2013~June, 2014. Then soil moisture in the middle and upper reaches of the HRB is retrieved. The retrieved soil moisture is compared with WSN soil moisture measurements, along with AMSR2 soil moisture products and GLDAS soil moisture product during the period July, 2014~October, 2014. The results show that the retrieved soil moisture perform remarkably better than the other 5 soil moisture products.Moreover, spatial patterns of the retrieved soil moisture and GLDAS soil moisture products are compared and analyzed as well via the auxiliary DEM and Landcover datasets. The results indicate that the spatial distribution of retrieved soil moisture perform with higher reasonability than GLDAS. This method may probably provide a feasible way for soil moisture retrieval over basin scales.

Key words: Soil moisture    AMSR2    Soil emissivity    Soil temperature    The middle and upper reaches of the Heihe River Basin
收稿日期: 2019-12-15 出版日期: 2020-04-01
ZTFLH:  TP79  
通讯作者: 杨晓帆     E-mail: legend.lz@mail.bnu.edu.cn;xfyang@bnu.edu.cn
作者简介: 陆 峥(1990-),男,江苏扬中人,博士研究生,主要从事生态水文模拟等方面的研究。E?mail:legend.lz@mail.bnu.edu.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
陆峥
韩孟磊
卢麾
彭雪婷
蒙莎莎
刘进
杨晓帆

引用本文:

陆峥,韩孟磊,卢麾,彭雪婷,蒙莎莎,刘进,杨晓帆. 基于AMSR2多频亮温的黑河流域中上游土壤水分估算研究[J]. 遥感技术与应用, 2020, 35(1): 33-47.

Zheng Lu,Menglei Han,Hui Lu,Xueting Peng,Shasha Meng,Jin Liu,Xiaofan Yang. Estimating Soil Moisture in the Middle and Upper Reaches of the Heihe River Basin based on AMSR2 Multi-brightness Temperature. Remote Sensing Technology and Application, 2020, 35(1): 33-47.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0033        http://www.rsta.ac.cn/CN/Y2020/V35/I1/33

图1  研究区概况及土壤水分观测网络高程、位置和下垫面地类图
升轨 降轨
RMSE/K R RMSE/K R
3.95 0.73 1.32 0.93
表1  AMSR2 5个波段(6.9、10.7、18.7、23.8和36.5 GHz)V极化亮度温度与实测土壤温度的拟合结果
图2  2013.7.1-2014.6.30内升轨3种拟合方法得到的估算土壤水分与实测土壤水分散点图
图3  2013年7月1日~2014年6月30日内降轨3种拟合方法得到的估算土壤水分与实测土壤水分散点图
升轨 降轨
轮次 验证像元 每轮验证 最终验证 每轮验证 最终验证
RMSE R BIAS RMSE R BIAS RMSE R BIAS RMSE R BIAS
1 WATER-NET 4 0.062 0.267 0.017 0.084 0.264 -0.054 0.045 0.592 0.022 0.050 0.497 -0.053
2 WATER-NET 3 0.051 0.379 -0.024 0.085 0.375 0.074 0.044 0.435 -0.021 0.052 0.540 0.080
3 WATER-NET 2 0.067 0.350 -0.053 0.069 0.245 0.030 0.051 0.486 -0.033 0.058 0.488 0.056
4 WATER-NET 1 0.060 0.323 -0.006 0.127 0.178 -0.108 0.47 0.598 0.009 0.093 0.468 -0.090
表2  2013年7月1日~2014年6月30日内3种拟合方法的表现统计表
产品名称 升轨 降轨
RMSE R BIAS RMSE R BIAS
WATER-NET 1 JAXA 0.271 0.432 -0.266 0.322 0.377 -0.317
LPRM_C1 0.529 -0.002 0.510 0.360 0.173 0.313
LPRM_C2 0.543 0.038 0.526 0.377 0.192 0.332
LPRM_X 0.182 0.283 0.136 0.232 0.405 0.191
GLDAS 0.094 -0.402 -0.074 0.130 -0.386 -0.119
估算土壤水分 0.040 0.302 0.014 0.029 0.651 -0.014
WATER-NET 2 JAXA 0.229 0.418 -0.222 0.244 0.021 -0.237
LPRM_C1 0.622 -0.058 0.602 0.486 -0.111 0.425
LPRM_C2 0.635 -0.036 0.618 0.511 -0.090 0.451
LPRM_X 0.202 0.595 0.163 0.325 0.167 0.277
GLDAS 0.074 0.352 -0.058 0.073 0.369 -0.057
估算土壤水分 0.064 0.623 0.051 0.071 0.510 0.055
WATER-NET 3 JAXA 0.177 0.440 -0.155 0.236 0.319 -0.223
LPRM_C1 0.544 0.467 0.524 0.381 0.444 0.339
LPRM_C2 0.561 0.478 0.543 0.410 0.449 0.366
LPRM_X 0.145 0.300 0.095 0.231 0.572 0.198
GLDAS 0.141 0.415 -0.133 0.140 0.379 -0.130
估算土壤水分 0.043 0.279 0.013 0.059 0.622 0.040
WATER-NET 4 JAXA 0.299 0.535 -0.288 0.333 0.439 -0.321
LPRM_C1 0.491 0.321 0.468 0.349 0.524 0.296
LPRM_C2 0.507 0.295 0.485 0.380 0.517 0.325
LPRM_X 0.153 -0.077 0.024 0.213 0.416 0.156
GLDAS 0.220 0.267 0.044 0.219 0.222 -0.201
估算土壤水分 0.107 0.212 -0.060 0.081 0.396 -0.0357
WATER-NET 1~4 4个像元上的平均 JAXA 0.242 0.472 -0.235 0.283 0.357 -0.277
LPRM_C1 0.539 0.276 -0.519 0.389 0.351 -0.337
LPRM_C2 0.554 0.288 -0.536 0.413 0.358 -0.361
LPRM_X 0.155 0.300 -0.104 0.242 0.447 -0.199
GLDAS 0.132 0.227 0.121 0.141 0.201 0.131
估算土壤水分 0.047 0.378 0.004 0.037 0.666 0.009
表3  2014年7月1日~2014年10月31日时间段内各土壤水分产品验证表现统计表
图4  2013年7月1日-2014年12月31日内升轨土壤水分的验证时间序列图(估算的土壤水分,JAXA产品,LPRM C1、C2和X波段产品,GLDAS土壤水分产品与实测土壤水分的比较)
图5  2013年7月1日-2014年12月31日内降轨土壤水分的验证时间序列图(估算的土壤水分,JAXA产品,LPRM C1、C2和X波段产品,GLDAS土壤水分产品与实测土壤水分的比较)
图6  估算土壤水分范围及下垫面地类信息图
图7  2014年7~10月的每月第一日升、降轨估算土壤水分和日平均GLDAS土壤水分在黑河流域中上游的空间分布图(7月1日降轨无数据)
1 Jackson T J .III. Measuring Surface Soil Moisture Using Passive Microwave Remote Sensing[J].Hydrological processes,1993,7(2):139-152.
2 Liu Qiang .Analysis of AMSR-E Soil Moisture and Downscaling of TRMM Precipitation[D].Beijing:Institute of Remote Sensing Applications,Chinese Acdemy of Sciences,2014[刘强. AMSR-E土壤水分分析和TRMM降雨降尺度研究[D].北京:中国科学院遥感应用研究所,2014.]
3 Wigneron J P , Calvet J C , Pellarin T ,et al .Retrieving Near-Surface Soil Moisture from Microwave Radiometric Observations: Current Status and Future Plans[J].Remote Sensing of Environment,2003,85(4):489-506.
4 Chen Shulin , Liu Yuanbo , Wen Zuomin .Satellite Retrieval of Soil Moisture:An Overview[J].Advances in Earth Sciences,2012,27(11):1192-1203.陈书林,刘元波,温作民.卫星遥感反演土壤水分研究综述[J].地球科学进展,2012,27(11):1192-1203.
5 Lan Xinyu , Guo Ziqi , Tian Ye ,et al .Review in Soil Moisture Remote Sensing Estimation based on Data Assimilation[J].Advances in Earth Sciences,2015,06:668-679.兰鑫宇,郭子祺,田野, 等 .土壤湿度遥感估算同化研究综述[J].地球科学进展,2015,06:668-679.
6 Shi Jiancheng , Du Yang , Du Jinyang ,et al .Progresses on Microwave Remote Sensing of Land Surface Parameters[J].Science China: Earth Science,2012, (06):814-842.施建成,杜阳,杜今阳, 等 .微波遥感地表参数反演进展[J].中国科学:地球科学,2012,06:814-842.
7 Zheng Zhiyuan , Wei Zhigang , Li Zhenzhao ,et al .A Study of Variation Characteristics of Surface Broadband Emissivity over Three Typical Bare Soil Underlying Surfaces in Northwestern China[J].Chinese Journal of Atmospheric Sciences (in Chinese),2016,40(6):1227-1241.郑志远,韦志刚,李振朝, 等 .中国西北三类典型裸土下垫面地表宽波段发射率变化特征研究[J].大气科学,2016,40(6):1227-1241.
8 He Wenying , Chen Hongbin , Xuan Yuejian ,et al .Field Measurements of the Surface Microwave Emissivity For Different Surface Types[J].Progress in Geophysics,2010,25(6):1983-1993.何文英,陈洪滨,宣越健, 等 .几种地表微波比辐射率变化特征的地面观测[J].地球物理学进展,2010,25(6):1983-1993.
9 Wang Yongqian , Shi Jiancheng , Liu Zhihong ,et al .Retrieval Algorithm for Microwave Surface Emissivities based on Multi-source, Remote Sensing Data: An Assessment on the Qinghai-Tibet Plateau[J].Science China: Earth Sciences,2013,43(2):271-279.王永前,施建成,刘志红, 等 .基于AMSR-E的微波波段地表发射率反演—以青藏高原为例[J].中国科学:地球科学,2013,43(2):271-279.
10 Imaoka K , Kachi M , Fujii H ,et al .Global Change Observation Mission (GCOM) for Monitoring Carbon, Water Cycles, and Climate Change[J].Proceedings of the IEEE,2010,98(5):717-734.
11 Dai Shuying , Wang Cun'en .GCOM of JAXA[J].Space International.2012(3):8-17.戴舒颖,王存恩.日本“地球环境变化观测任务”卫星[J].国际太空,2012(3):8-17.
12 Zou Xiaolei , Wen Fuzhong , Tian Xiaoxu .An Effective Mitigation of Radio Frequency Interference over Land by Adding a New C-band on AMSR2[J].Advances in Meteorological Science and Technology,2015(2):35-41.邹晓蕾,翁富忠,田小旭.AMSR2仪器上新增设的C波段通道对陆地无线电频率干扰的有效缓解[J].气象科技进展,2015(2):35-41.
13 Lu Zheng .Evaluation of AMSR2 Soil Moisture and Time-series Soil Moisture Reconstruction over the Heihe River Basin[D].Beijing:Beijing Normal University,2016.
13 陆峥 .AMSR2土壤水分产品在黑河流域的验证与时序重建[D].北京:北京师范大学,2016.
14 Wu Q , Liu H , Wang L ,et al .Evaluation of AMSR2 Soil Moisture Products over the Contiguous United States Using in Situ Data from the International Soil Moisture Network[J].International Journal of Applied Earth Observation and Geoinformation,2016,45:187-199.
15 Cho E , Moon H , Choi M .First Assessment of the Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Contents in Northeast Asia[J].Journal of the Meteorological Society of Japan. Ser. II,2015,93(1):117-129.
16 Zeng J , Li Z , Chen Q ,et al .Evaluation of Remotely Sensed and Reanalysis Soil Moisture Products over The Tibetan Plateau Using In-Situ Observations[J].Remote Sensing of Environment,2015,163:91-110.
17 Yang K , Qin J , Zhao L ,et al .A Multiscale Soil Moisture And Freeze-thaw Monitoring Network on The Third Pole[J].Bulletin of the American Meteorological Society,2013,94(12):1907-1916.
18 Lu Zheng , Chai Linna , Zhang Tao ,et al .Evaluation of AMSR2 Retrievals Using Observation of Soil Moisture Network on The Upper And Middle Reaches of Heihe River Basin[J].Remote Sensing Technology and Application,2017,32(2):324-337.陆峥,柴琳娜,张涛, 等 .AMSR2土壤水分产品在黑河流域中上游的验证[J].遥感技术与应用,2017,32(2):324-337.
19 Li Xin , Ma Mingguo , Wang Jian ,et al .Simultaneous Remote Sensing and Ground-based Experiment in the Heihe River Basin: Scientific Objectives and Experiment Design [J].Advances in Earth Sciences,2008(9):897-914.李新,马明国,王建, 等 .黑河流域遥感—地面观测同步试验:科学目标与试验方案[J].地球科学进展,2008(9):897-914.
20 Li Xin , Liu Shaomin , Ma Mingguo ,et al .HiWATER: An Integrated Remote Sensing Experiment on Hydrological and Ecological Processes in the Heihe River Basin[J].Advances in Earth Sciences,2012,27(5):481-498.李新,刘绍民,马明国, 等 .黑河流域生态—水文过程综合遥感观测联合试验总体设计[J].地球科学进展,2012,27(5):481-498.
21 Li Xin , Li Xiaowen , Li Zengyuan ,et al .Progresses on the Watershed Allied Telemetry Experimental Research (WATER) [J].Remote Sensing Technology and Application,2012,27(5):637-649.李新,李小文,李增元, 等 .黑河综合遥感联合试验研究进展:概述[J].遥感技术与应用,2012,27(5):637-649.
22 Li X , Cheng G , Liu S ,et al .Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design[J].Bulletin of the American Meteorological Society,2013,94(8):1145-1160.
23 Jin R , Li X , Yan B ,et al .A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China[J].IEEE Geoscience and Remote Sensing Letters,2014,11(11):2015-2019.
24 Owe M , De Jeu R , Walker J .A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(8):1643-1654.
25 Owe M , de Jeu R , Holmes T .Multisensor Historical Climatology Of Satellite - Derived Global Land Surface Moisture[J].Journal of Geophysical Research: Earth Surface ( 2003–2012, 2008,113(F 1:196-199.
26 de Nijs A H A , Parinussa R M , de Jeu R A M ,et al .A Methodology to Determine Radio-Frequency Interference in AMSR2 Observations[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(9):5148-5159.
27 Lu Z , Chai L , Liu S ,et al .Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China[J].Remote Sensing,2017,9(6):574;doi:10.3390/rs9060574
doi: 10.3390/rs9060574
28 Cui H , Jiang L , Du J ,et al .Evaluation and Analysis of AMSR-2, SMOS, and SMAP Soil Moisture Products in the Genhe Area of China[J].Journal of Geophysical Research: Atmospheres,2017,122(16):8650-8666.
29 Wang W , Wang X , Wang P .Assessing the Applicability of GLDAS Monthly Precipitation Data in China[J].Advances in Water Science,2014,25(6):769-778.王文,汪小菊,王鹏.GLDAS月降水数据在中国区的适用性评估[J].水科学进展,2014,25(6):769-778.
30 Danielson J J , Jeffrey J .Delineation of Drainage basins from 1 km African Digital Elevation Data[C]∥Pecora Thirteen,Human Interactions with the Environment-perspectives from Space, Sioux Falls, South Dakota,1996.
31 Zhong Bo , Ma Peng , Nie Aihua ,et al .Land Cover Mapping Using Time Series HJ-1/CCD Data[J].Science China: Earth Sciences,2014,44(5):967-977.仲波,马鹏,聂爱华, 等 .基于时间序列HJ-1/CCD数据的土地覆盖分类方法[J].中国科学:地球科学,2014,44(5):967-977.
32 Li X , Liu S , Xiao Q ,et al .A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system [J].Scientific Data,2017,4:170083;doi:10.1038/sdata.2017.83
doi: 10.1038/sdata.2017.83
33 Zhang Tao .Improvement Of Soil Moisture Retrieval Algorithm And Validation Method Using Passive Microwave Remote Sensing Data[D].Beijing:Beijing Normal University,2014.
33 张涛 .被动微波遥感土壤水分反演算法和验证方改进[D].北京:北京师范大学,2014.
34 Zhao Tianjie .Passive Microwave Remote Sensing Of Soil Moisture [D].Beijing:Beijing Normal University,2012[赵天杰. 被动微波遥感土壤水分[D].北京:北京师范大学,2012.]
35 Zheng Xingming .Research on Soil Moisture Passive Microwave Remote Sensing Inversion Method in Northeast of China[D].Changchun:Northest Institute of Geography and Agroecology,Chinese Academy of Sciences,2012[郑兴明. 东北地区土壤湿度被动微波遥感高精度反演方法研究[D].长春:中国科学院东北地理与农业生态研究所,2012.]
36 Lin Libin , Bao Yansong , Zuo Quan ,et al .Soil Moisture Retrieval over Vegeteted Areas based on Sentinel-1 and FY-3C Data[J].Remote Sensing Technology and Application,2018,33(4):750-758.
36 林利斌,鲍艳松,左泉,等 .基于Sentinel-1与FY-3C数据反演植被覆盖地表土壤水分[J].遥感技术与应用,2018,33(4):750-758.
37 Mao Kebiao , Hu Deyong , Huang Jianxi ,et al .An Algorithm for Retrieving Soil Moisture from AMSR-E Passive Microwave Data[J].High Technology Letters,2010,20(6):651-659.毛克彪,胡德勇,黄健熙, 等 .针对被动微波AMSR-E数据的土壤水分反演算法[J].高技术通讯,2010,20(6):651-659.
38 Ulaby,F T,Moore,R K,Fung,A K,et al .Microwave Remote Sensing: Active and Passive. Volume 2-Radar Remote Sensing and Surface Scattering and Emission theory[M].Beijing:Science Press,1988.
38 F .T.乌拉比,R.K.穆尔,冯建超, 等 .微波遥感—第二卷:雷达遥感和面目标的散射、辐射理论[M].北京:科学出版社,1988.
39 Liu Jun , Zhao Shaojie , Jiang Lingmei ,et al .Research Progress on Dielectric Constant Model of Soil at Microwave Frequency[J].Remote Sensing Information,2015,30(1):5-13.刘军,赵少杰,蒋玲梅, 等 .微波波段土壤的介电常数模型研究进展[J].遥感信息,2015,30(1):5-13.
40 Liu J , Chai L , Lu Z ,et al .Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas[J].Remote Sensing,2019,11(7):792;doi:10.3390/rs11070792
doi: 10.3390/rs11070792
41 Liu Jing , Ma Hongzhang , Yang Le ,et al .A Survey of Surface Temperature Retrieval by Passive Microwave Remote Sensing[J].Remote Sensing Technology and Application,2012,6:812-821.刘晶,马红章,杨乐, 等 .基于被动微波的地表温度反演研究综述[J].遥感技术与应用,2012,6:812-821.
42 Jia Yuanyuan , Li Zhaoliang .Progress in Land Surface Temperature Retrieval from Passive Microwave Remotely Sensed Data[J].Progress in Geography,2006,25(3):96-105.贾媛媛,李召良.被动微波遥感数据反演地表温度研究进展[J].地理科学进展,2006,25(3):96-105.
43 Han M , Lu H , Yang K .Development of Passive Microwave Retrieval Algorithm for Estimation of Surface Soil Temperature from AMSR-E Data[C]∥IEEE International Geoscience and Remote Sensing Symposium,2016:1671-1674.
44 Xiang Hui .Research on Ensemble Model for Credit Scoring and Its Application[D].Changsha:Hunan University,2011.
44 向晖 .个人信用评分组合模型研究与应用[D].长沙:湖南大学,2011.
45 Fan Yongdong .A Summary of Cross-Validation in Model Selection[D].Taiyuan:Shanxi University,2013.
45 范永东 .模型选择中的交叉验证方法综述[D].太原:山西大学,2013.
46 Li Yang .Multiple Kernel Learning SVM and Lung Nodule Recognition[D].Jilin:Jilin University,2014.
46 李阳 .多核学习SVM算法研究及肺结节识别[D].吉林:吉林大学,2014.
47 Mao Taiying .Error Theory and Pecision Aalysis[M].Beijing:National Defence Industry Press,1982[毛英泰. 误差理论与精度分析[M].北京:国防工业出版社,1982.]
48 Fei Yetai .Error Theory and Data Process[M].Beijing:China Machine Press,2010.
48 费业泰 .误差理论与数据处理[M].北京:机械工业出版社,2010.
49 Tao Benzao , Qiu Weining , Zhang Shubi ,et al .Error Theory and Surveying error-adjustment[M].Wuhan:Wuhan University Press,2012.陶本藻,邱卫宁,张书毕, 等 .误差理论与测量平差[M].武汉:武汉大学出版社,2012.
50 Cheng Shanjun , Guan Xiaodan , Huang Jianping ,et al .Analysis of Response of Soil Moisture to Climate Change in Semi-arid Loess Plateau in China Based on GLDAS Data[J].Journal of Arid Meteorology,2013,31(4):641-649.程善俊,管晓丹,黄建平, 等 .利用GLDAS资料分析黄土高原半干旱区土壤湿度对气候变化的响应[J].干旱气象,2013,31(4):641-649.
51 Song Haiqing , Li Yunpeng , Zhang Jingru ,et al .Evaluation of Soil Moisture over Inner Mongolia Using Milti-Data[J].Journal of Arid Land Resources and Environment,2016,30(8):139-144.宋海清,李云鹏,张静茹, 等 .内蒙古地区多种土壤湿度资料的初步评估[J].干旱区资源与环境,2016,30(8):139-144.
52 Zhu Zhi , Shi Chunxiang .Simulation and Evaluation of CLDAS and GLDAS Soil Moisture Data in China[J].Science Technology and Engineering,2014,14(32):138-144.朱智,师春香.中国气象局陆面同化系统和全球陆面同化系统对中国区域土壤湿度的模拟与评估[J].科学技术与工程,2014,14(32):138-144.
53 Liu Chuan , Yu Ye , Xie Jing ,et al .Applicability of Soil Temperature and Moisture in Several Datasets over Qinghai-Xizang Plateau[J].Plateau Meteorology,2015,34(3):653-665.刘川,余晔,解晋, 等 .多套土壤温湿度资料在青藏高原的适用性[J].高原气象,2015,34(3):653-665.
54 Chen Y , Yang K , Qin J ,et al .Evaluation of AMSR-E Retrievals and Gldas Simulations Against Observations of A Soil Moisture Network on The Central Tibetan Plateau[J].Journal of Geophysical Research: Atmospheres,2013,118(10):4466-4475.
55 Romaguera M , Krol M S , Salama M ,et al .Determining Irrigated Areas and Quantifying Blue Water Use in Europe Using Remote Sensing Meteosat Second Generation (MSG) Products and Global Land Data Assimilation System (GLDAS) Data[J].Photogrammetric Engineering and Remote Sensing,2012,78(8):861-873.
56 Wang S , Mo X , Liu S ,et al .Validation and Trend Analysis of ECV Soil Moisture Data on Cropland in North China Plain During 1981-2010[J].International Journal of Applied Earth Observation and Geoinformation,2016,48:110-121.
57 Ansari H , Marofi S , Mohamadi M .Topography and Land Cover Effects on Snow Water Equivalent Estimation Using AMSR-E and GLDAS Data[J].Water Resources Management,2019,33(5):1699-1715.
58 Lary D J , Alavi A H , Gandomi A H ,et al .Machine Learning in Geosciences and Remote Sensing[J].Geoscience Frontiers,2016,7(1):3-10.
59 Reichstein M , Camps-Valls G , Stevens B ,et al .Deep Learning and Process Understanding for Data-driven Earth System Science[J].Nature,2019,566(7743):195-204.
[1] 王树果, 马春锋, 赵泽斌, 魏龙. 基于Sentinel-1及Landsat 8数据的黑河中游农田土壤水分估算[J]. 遥感技术与应用, 2020, 35(1): 13-22.
[2] 李雷,郑兴明,赵凯,李晓峰,王广蕊. 基于CCI土壤水分产品的干旱指数精度评价及其对东北地区粮食产量的影响[J]. 遥感技术与应用, 2020, 35(1): 111-119.
[3] 罗家顺,邱建秀,赵天杰,王大刚. 基于Sentinel-1数据的黑河中游土壤水分反演[J]. 遥感技术与应用, 2020, 35(1): 23-32.
[4] 胡路,赵天杰,施建成,李尚楠,樊东,王平凯,耿德源,肖青,崔倩,陈德清. 基于地基微波辐射观测的土壤水分反演算法评估[J]. 遥感技术与应用, 2020, 35(1): 74-84.
[5] 陈勇强,杨娜,胡新,佟明远. SMOS与SMAP过境时段表层土壤水分的稳定性研究[J]. 遥感技术与应用, 2020, 35(1): 58-64.
[6] 劳从坤,杨娜,徐少博,汤燕杰,张恒杰. 反演策略对SMOS土壤水分反演算法的影响研究[J]. 遥感技术与应用, 2020, 35(1): 65-73.
[7] 范悦,邱建秀,董建志,张小虎,王大刚. 基于Triple Collocation方法的微波土壤水分产品不确定性分析与时空变化规律研究[J]. 遥感技术与应用, 2020, 35(1): 85-96.
[8] 王树果,刘伟,梁亮. 基于Triple-Collocation方法的微波遥感土壤水分产品不确定性分析及数据融合[J]. 遥感技术与应用, 2019, 34(6): 1227-1234.
[9] 刘克俭,闫敏,冯琦. 多层土壤观测数据同化的森林碳、水通量模拟[J]. 遥感技术与应用, 2019, 34(5): 950-958.
[10] 李瑞娟, 李兆富, 郝睿, 张舒昱, 潘剑君. 亚洲区域AMSR2与SMOS土壤水分产品对比研究[J]. 遥感技术与应用, 2019, 34(1): 125-134.
[11] 白瑜,孟治国,赵凯. 像元尺度土壤水分监测网络及其对L波段土壤水分产品的初步验证结果[J]. 遥感技术与应用, 2018, 33(1): 78-87.
[12] 向怡衡,张明敏,张兰慧,贺缠生,王一博,白晓. 祁连山区不同植被类型上的SMOS遥感土壤水分产品质量评估[J]. 遥感技术与应用, 2017, 32(5): 835-843.
[13] 曹永攀,黄春林,陈玮婧,张莹. 联合同化MODIS地表温度与机载L波段微波亮度温度估计土壤水分[J]. 遥感技术与应用, 2017, 32(4): 606-614.
[14] 胡文星,柴琳娜,赵少杰,赵天杰. 寒区复杂地表冻融状态判别式算法改进[J]. 遥感技术与应用, 2017, 32(3): 395-405.
[15] 王增艳,王建,车涛. 机载L波段微波辐射计数据反演表层土壤水分研究[J]. 遥感技术与应用, 2017, 32(2): 185-194.