一种改进的土壤水分微波遥感反演模型
Improved soil moisture retrieval model from remotely sensed microwave data
查看参考文献19篇
|
文摘
|
利用微波遥感数据反演地表土壤水分有着较好的物理基础,可实现大范围土壤水分状况的遥感监测。本文基于被动微波传感器AMSR-E的X波段数据,将土壤水分值分解成基准值和日变化量两个部分,并分别建立反演模型,同时引入降雨修正因子来进一步提高土壤水分的估算精度;利用IDL语言实现了我们所研发的模型,并集成为新疆土壤水分遥感反演系统模块之一;利用WatchDog2400与传统铝盒采样获取的新疆地面土壤水分数据,提取适合的模型经验参数,并对模型结果进行精度评价。结果表明,经改进的模型反演得到的新疆土壤水分结果比美国冰雪数据中心的土壤水分产品在精度上有显著提高:均方根误差由8.4%降低为4.25%;所研发的软件模块可为相关应用部门提供快速的大范围土壤水分监测产品。 |
|
其他语种文摘
|
Retrieving land surface soil water content from remotely sensed passive microwave data has a good physical basis. Thus, it can provide dynamic monitoring of large-range soil moisture condition. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data were used to derive soil moisture base value and daily variation for each image pixel, respectively, to build an inversion model for retrieving soil moisture information. A precipitation impact factor was proposed and incorporated into the modeling process to improve the accuracy of soil moisture retrieval. The IDL language was used to implement the proposed model as software modules of the System of Xinjiang Soil Moisture Inversion from Remotely Sensed Data. The in-situ measured soil moisture data by the WatchDog 2400 instrument and Loss-on-Drying method were used to derive empirical parameters for the regressive model that are suited to the conditions in Xinjiang, and to verify the proposed model output. The results show that, with reference to the data of in-situ measurements, our improved model can achieve better estimation of Xinxiang’s soil moisture than the soil moisture products of US National Snow & Ice Data Center (NSIDC). The RMSE is improved from 8.4% to 4.25%. The software modules developed in this study can provide a tool for quick soil moisture monitoring in a large area such as Xinjiang. |
|
来源
|
地理科学进展
,2013,32(1):78-86 【核心库】
|
|
关键词
|
微波
;
土壤水分
;
AMSR-E
;
ENVI/IDL
;
干旱区
|
|
地址
|
北京大学遥感与地理信息系统研究所, 北京, 100871
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1007-6301 |
|
学科
|
测绘学;农业基础科学 |
|
基金
|
国家科技支撑计划项目
;
国家自然科学基金
|
|
文献收藏号
|
CSCD:4756496
|
参考文献 共
19
共1页
|
|
1.
Bindlish R. Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States.
Remote Sensing of Environment,2003,85(4):507-515
|
CSCD被引
8
次
|
|
|
|
|
2.
陈亮. 基于物理模型的被动微波遥感反演土壤水分.
水科学进展,2009,25(2):663-667
|
CSCD被引
1
次
|
|
|
|
|
3.
Chen S. A simple retrieval method of land surface temperature from AMSR-E passive microwave data--A case study over Southern China during the strong snow disaster of 2008.
International Journal of Applied Earth Observation and Geoinformation,2011,13(1):140-151
|
CSCD被引
14
次
|
|
|
|
|
4.
Draper C S. An evaluation of AMSR-E derived soil moisture over Australia.
Remote Sensing of Environment,2009,113(4):703-710
|
CSCD被引
22
次
|
|
|
|
|
5.
盖迎春. IDL与.Net环境通信机制研究.
遥感技术与应用,2005,20(3):350-354
|
CSCD被引
11
次
|
|
|
|
|
6.
Jackson T J. Soil moisture mapping at regional scales using microwave radiometry: The Southern Great Plains Hydrology Experiment.
IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2136-2151
|
CSCD被引
43
次
|
|
|
|
|
7.
Jackson T J. Validation of Advanced Microwave Scanning Radiometer soil moisture products.
IEEE Transactions on Geoscience and Remote Sensing,2010,48(12):4256-4272
|
CSCD被引
30
次
|
|
|
|
|
8.
Njoku E G. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz.
IEEE Transactions on Geoscience and Remote Sensing,1999,37(1):79-93
|
CSCD被引
85
次
|
|
|
|
|
9.
Njoku E G. Soil moisture retrieval from AMSR-E.
IEEE Transactions on Geoscience and Remote Sensing,2003,41(2):215-229
|
CSCD被引
121
次
|
|
|
|
|
10.
Njoku E G. Global survey and statistics of radio-frequency interference in AMSR-E land observations.
IEEE Transactions on Geoscience and Remote Sensing,2005,43(5):938-947
|
CSCD被引
48
次
|
|
|
|
|
11.
Njoku E G. Vegetation and surface roughness effects on AMSR-E land observations.
Remote Sensing of Environment,2006,100(2):190-199
|
CSCD被引
43
次
|
|
|
|
|
12.
Owe M. A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index.
IEEE Transactions on Geoscience and Remote Sensing,2001,39(8):1643-1654
|
CSCD被引
38
次
|
|
|
|
|
13.
Owe M. Multisensor historical climatology of satellite-derived global land surface moisture.
Journal of Geophysics Research,2008,113(F1):F1002
|
CSCD被引
28
次
|
|
|
|
|
14.
Shi J. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E.
Remote Sensing of Environment,2008,112(12):4285-4300
|
CSCD被引
32
次
|
|
|
|
|
15.
孙权. 区域生态环境空间信息服务系统的设计与实现.
计算机应用与软件,2012,29(1):191-195
|
CSCD被引
1
次
|
|
|
|
|
16.
Temimi M. A combination of remote sensing data and topographic attributes for the spatial and temporal monitoring of soil wetness.
Journal of Hydrology,2010,388(1/2):28-40
|
CSCD被引
3
次
|
|
|
|
|
17.
Zhang X. Soil moisture retrieval from AMSR-E data in Xinjiang (China): Models and validation.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2010,4(1):1-11
|
CSCD被引
9
次
|
|
|
|
|
18.
赵杰鹏. 基于可见光红外与被动微波遥感的土壤水分协同反演.
红外与毫米波,2012,31(2):137-142
|
CSCD被引
1
次
|
|
|
|
|
19.
Zhu L. Optimization of ecosystem model parameters using spatio-temporal soil moisture information.
Ecological Modelling,2009,220(18):2121-2136
|
CSCD被引
7
次
|
|
|
|
|
了解气象卫星更多信息，请访问