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遥感技术与应用  2020, Vol. 35 Issue (4): 832-844    DOI: 10.11873/j.issn.1004-0323.2020.4.0832
甘肃遥感学会专栏     
温度植被干旱指数时空融合模型对比
李超1,2,3(),李雪梅1,2,3(),田亚林1,2,3,任瑞1,2,3
1.兰州交通大学测绘与地理信息学院,甘肃 兰州 730070
2.甘肃省地理国情监测工程实验室,甘肃 兰州 730070
3.地理国情监测技术应用国家地方联合工程研究中心,甘肃 兰州 730070
Time and Space Fusion Model Comparison of Temperature Vegetation Drought Index
Chao Li1,2,3(),Xuemei Li1,2,3(),Yalin Tian1,2,3,Rui Ren1,2,3
1.Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
2.Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
3.National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
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摘要:

为实时准确地对新疆农业干旱程度进行反演监测,以新疆焉耆盆地为例,通过运用时空自适应反射率融合模型(Spatio Temporal Adaptive Reflectivity Fusion Model,STARFM)、增强型STARFM(Enhanced STARFM,ESTARFM)模型及灵活的时空数据融合模型(Flexible Spatio Temporal Data Fusion,FSDAF)这3种常见的模型对Landsat 8和MODIS数据进行融合,构建了温度植被干旱指数(Temperature Vegetation Dryness Index,TVDI),并采用土壤相对湿度(Relative Soil Moisture,RSM)数据对TVDI反演结果进行了验证。结果表明:①3种数据融合模型所模拟预测的干旱因子(归一化植被指数和地表温度)与真实Landsat 8数据所反演的干旱因子相比,ESTARFM模型模拟预测的干旱因子判定系数(R2)和均方根误差(RMSE)均优于其他两种模型,归一化植被指数(NDVI)的R2和RMSE分别达到了0.924和0.076,地表温度(LST)的R2和RMSE分别达到了0.877和2.799;②3种数据融合模型模拟预测的TVDI通过与真实Landsat 8数据反演的TVDI及RSM数据进行对比验证,发现ESTARFM模型模拟预测的TVDI与上述两种数据之间的R2也均优于其他两种模型,分别达到了0.873和0.248。ESTARFM模型在一定程度上更能准确地模拟预测同时期Landsat 8影像的TVDI分布状况。

关键词: Landsat 8?MODISSTARFMESTARFMFSDAFTVDI    
Abstract:

Drought is the first disaster affecting agricultural production. The annual precipitation in Xinjiang of China is scarce and the climate is dry. This is one of the major obstacles to the agricultural transformation and rural revitalization in Xinjiang. Therefore, timely and accurate monitoring of agricultural drought in Xinjiang is of great significance for safeguarding agricultural production. Yanqi Basin in Xinjiang was took as an example. Landsat8 and MODIS data were used. The Spatio Temporal Adaptive Reflectivity Fusion Model (STARFM), the Enhanced STARFM (Enhanced STARFM, ESTARFM) Model and Flexible Spatio Temporal Data Fusion (FSDAF) model were used to construct the Temperature Vegetation Dryness Index (TVDI). At the same time, the Relative Soil Moisture (RSM) was used to verify the TVDI inversion results. The results show that coefficient of determination (R2) and root mean square error (RMSE) of the drought factors(NDVI and surface temperature) simulated by the ESTARFM model were better than that by the other two models. And the R2 and RMSE of NDVI simulated by the ESTARFM model reached 0.924 and 0.076. In addition, the R2 and RMSE of surface temperature simulated by the ESTARFM model reached 0.877 and 2.799. Comparing with TVDI of the real Landsat8 data inversion and RSM data, it was found that the TVDI simulated by the ESTARFM model is better than the other two models, with 0.873 of R2 and 0.248 of RMSE. The ESTARFM model can more accurately simulate the TVDI distribution of the Landsat8 images in the same period, so as to monitor the drought degree of the farmland in Xinjiang.

Key words: Landsat 8-MODIS    STARFM    ESTARFM    FSDAF    TVDI
收稿日期: 2019-07-09 出版日期: 2020-09-15
ZTFLH:  TP75  
基金资助: 国家自然科学基金项目(41761014);兰州交通大学“百名青年优秀人才培养计划”,兰州交通大学(201806┫优秀平台资助)
通讯作者: 李雪梅     E-mail: 1373910491@qq.com;lixuemei@mail.lzjtu.cn
作者简介: 李超(1994-),男,甘肃平凉人,硕士研究生,主要从事生态水文遥感研究。E?mail: 1373910491@qq.com
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引用本文:

李超,李雪梅,田亚林,任瑞. 温度植被干旱指数时空融合模型对比[J]. 遥感技术与应用, 2020, 35(4): 832-844.

Chao Li,Xuemei Li,Yalin Tian,Rui Ren. Time and Space Fusion Model Comparison of Temperature Vegetation Drought Index. Remote Sensing Technology and Application, 2020, 35(4): 832-844.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0832        http://www.rsta.ac.cn/CN/Y2020/V35/I4/832

图1  研究区Landsat 8假彩色影像(2018/8)
图2  真实Landsat 8-NDVI与对应预测的Landsat 8-NDVI
图3  真实Landsat 8-NDVI与对应预测的Landsat 8-NDVI散点图
图4  真实Landsat 8-LST与对应的预测Landsat 8-LST
图5  真实Landsat 8-LST与对应的预测Landsat 8-LST散点图
图6  真实NDVI-LST与对应预测的NDVI-LST线性拟合
TVDI类型干边方程R2湿边方程R2
真实Landsat 8y = - 15.673 x+46.8840.884 2y = 3.552 x+22.1080.371 4
STARFMy = - 7.898 x+53.8380.709 8y = 1.542 x+22.6730.218 5
ESTARFMy = - 14.938 x+53.0410.802 4y = 3.151 x+20.6110.299 3
FSDAFy = - 10.838 x+53.2380.775 1y = 1.850 x+20.7730.265 6
表1  真实NDVI-LST与对应预测的NDVI-LST干湿边统计
图7  真实Landsat 8-TVDI与对应预测的Landsat 8-TVDI
模型平均值标准差最小值最大值
STARFM-0.0440.085-0.3200.280
ESTARFM-0.0130.037-0.2000.260
FSDAF-0.0390.079-0.2800.290
表2  预测TVDI与Landsat 8-TVDI差值统计
图8  各模型预测TVDI与Landsat8-TVDI差值影像图
图9  真实Landsat 8-TVDI与对应预测的Landsat 8-TVDI散点图
图10  10 cm RSM与TVDI的拟合
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