MODIS和Landsat时空融合影像在土壤盐渍化监测中的适用性研究——以渭干河—库车河三角洲绿洲为例

doi:10.12118/j.issn.1000-6060.2021.551

Abstract

Abstract:

Remote sensing monitoring of soil salinization relies on high spatiotemporal resolution images, which are difficult to obtain due to high cost, revisit period of the satellite, and weather effects. The spatiotemporal fusion technology of remote sensing images has the advantages of low cost and good reliability and has been widely used in several studies, such as vegetation growth, flood monitoring, and temperature monitoring. This study investigates the effectiveness of spatiotemporal fusion images in soil salinization monitoring at Ugan River-Kuqa River Delta Oasis, south Xinjiang, China. An enhanced spatial and temporal adaptive reflectance fusion model and flexible spatiotemporal data fusion model were employed to fuse MODIS and Landsat images and generate high spatiotemporal resolution images. A random forest prediction model for soil conductivity (EC) was created using the fusion images. Furthermore, the precision of both models was evaluated. Results show that the fusion images made by the ESTARFM and FSDAF models have high spatial resolution and good uniformity compared with the verification image. Verification revealed the reliability of the fusion results from the ESTARFM algorithm, R²(Red)=0.8066 and R²(SWIR2)=0.8444, and the FSDAF algorithm, R²(Red)=0.6999 and R²(SWIR2)=0.7493. The EC prediction model created using the ESTARFM fusion images had the best accuracy with R² of 0.9268. Meanwhile, the R² of the EC prediction model constructed from FSDAF was 0.8987 and that constructed based on Landsat verification images was 0.9103. This finding showed that spatiotemporal fusion technology has good application in the remote sensing monitoring of soil salinization. Comparison of the salinity prediction results from the two spatiotemporal image fusion models revealed that the ESTARFM model is suitable for salinity prediction.

Key words: temporal and spatial fusion, random forest, electric conductivity, soil salinization

ZHAO Qiaozhen,DING Jianli,HAN Lijing,JIN Xiaoye,HAO Jianping. Exploring the application of MODIS and Landsat spatiotemporal fusion images in soil salinization: A case of Ugan River-Kuqa River Delta Oasis[J].Arid Land Geography, 2022, 45(4): 1155-1164.

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波段名称	Landsat波段	分辨率/m	波谱范围/nm	波段名称	MODIS波段	分辨率/m	波谱范围/nm
Blue	2	30	450~515	Blue	3	500	459~479
Green	3	30	525~600	Green	4	500	545~565
Red	4	30	630~680	Red	1	500	620~670
NIR	5	30	845~885	NIR	2	500	841~876
SWIR1	6	30	1560~1651	SWIR1	5	500	1628~1652
SWIR2	7	30	2100~2300	SWIR2	6	500	2105~2155

指数	公式
归一化植被指数(NDVI)^[16]	(NIR-Red)/(NIR+Red)
增强型植被指数(EVI)^[16]	2.5×[(NIR-Red)/(NIR+6Red-7.5Blue+1)]
扩展差值植被指数(EDVI)^[17]	NIR+SWIR1-Red
比值植被指数(RVI)^[18]	NIR/Green
扩展的归一化植被指数(ENDVI)^[19]	(NIR+SWIR2-Red)/(NIR+SWIR2+Red)
扩展的增强型植被指数(EEVI)^[20]	2.5×[(NIR+SWIR1-Red)/(NIR+SWIR1+6Red-7.5Blue+1)]
盐分指数(SIT)^[21]	(Red/NIR)×100
盐分指数(SI)^[22]	(Blue×Red)^0.5
盐分指数(SI1)^[22]	(Green×Red)^0.5
盐分指数(SI2)^[22]	(Green²+Red²+NIR²)^0.5
盐分指数(SI3)^[23]	(Red²+Green²)^0.5
归一化盐分指数(NDSI)^[24]	(Red-NIR)/(Red+NIR)
盐分指数(S1)^[25]	Blue/Red
盐分指数(S2)^[26]	(Blue-Red)/(Blue+Red)
盐分指数(S3)^[26]	(Green×Red)/Blue

模型	Blue	Green	Red	NIR	SWIR1	SWIR2
ESTARFM 模型	y=-128.8+1.0848x R²=0.6443	y=-113.7+1.0552x R²=0.6996	y=-272.8+1.1213x R²=0.8066	y=-91.10+1.0880x R²=0.7496	y=407.07+0.8961x R²=0.6358	y=-59.69+1.0485x R²=0.8444
FSDAF 模型	y=-263.2+1.2043x R²=0.4959	y=-411.1+1.2268x R²=0.5525	y=-521.9+1.2656x R²=0.6999	y=-11.97+1.1058x R²=0.6202	y=445.28+0.9519x R²=0.4909	y=-267.6+1.1818x R²=0.7493

Exploring the application of MODIS and Landsat spatiotemporal fusion images in soil salinization: A case of Ugan River-Kuqa River Delta Oasis

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