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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (2) : 60-69     DOI: 10.6046/zrzyyg.2023032
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Comparing the applicability of five typical spatio-temporal information fusion algorithms based on remote sensing data in vegetation index reconstruction of wetland areas
LUO Jiahuan1(), YAN Yi1(), XIAO Fei2, LIU Huan1, HU Zhengzheng2,3, WANG Zhou2,3
1. Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environment, South-Central Minzu University, Wuhan 430074, China
2. Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

This study aims to explore the applicability of various spatio-temporal information fusion algorithms based on remote sensing data to wetland areas characterized by frequent land-water conversion and diverse surface features. With the Poyang Lake sample area as the study area, this study examined five typical spatio-temporal information fusion algorithms (STARFM, ESTARFM, FSDAF, Fit-FC, and STNLFFM). Considering the differences in surface features among different periods, Landsat and MODIS remote sensing data were selected to conduct image fusion experiments for normalized difference vegetation indices (NDVIs) during low- and normal-water periods. Moreover, the accuracy of these algorithms was evaluated in spatial and spectral dimensions. The results of this study are as follows: ① In the case of only one pair of coarse- and fine-resolution images as input, the FSDAF exhibited the optimal fusion prediction effect for the low-water period, with an overall error of 0.433 5, whereas the STNLFFM manifested the optimal fusion prediction effect for the normal-water period, with an overall error of 0.514 7; ② In the case of two pairs of coarse- and fine-resolution images of low- and normal-water periods as input, the ESTARFM demonstrated the optimal fusion prediction effect, with an overall error of 0.467 0; ③ The applicability of different algorithms to a wetland area is associated with the proportion of water bodies in the study area. The STNLFFM displayed the optimal fusion prediction effect for water bodies.
Keywords spatio-temporal information fusion      Poyang Lake wetland      FSDAF      STNLFFM      ESTARFM     
ZTFLH:  TP79  
Issue Date: 14 June 2024
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Jiahuan LUO
Yi YAN
Fei XIAO
Huan LIU
Zhengzheng HU
Zhou WANG
Cite this article:   
Jiahuan LUO,Yi YAN,Fei XIAO, et al. Comparing the applicability of five typical spatio-temporal information fusion algorithms based on remote sensing data in vegetation index reconstruction of wetland areas[J]. Remote Sensing for Natural Resources, 2024, 36(2): 60-69.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023032     OR     https://www.gtzyyg.com/EN/Y2024/V36/I2/60
Fig.1  Location of the study area
Landsat8/9 OLI MODIS
2021年12月06日 2021年12月06日
2021年01月12日 2021年01月11日
2020年04月15日 2020年04月16日
2020年03月14日 2020年03月15日
Tab.1  Landsat8/9 OLI and MODIS images acquisition time
Fig.2  Experiment scheme of image fusion
算法 最优窗口 AD RMSE Edge LBP 总体误差
STARFM 50 0.011 4 0.136 6 -0.289 6 -0.077 1 0.514 7
FSDAF 15 0.022 1 0.149 7 -0.241 8 0.019 9 0.433 5
Fit-FC 50 0.017 7 0.149 5 -0.279 6 0.055 1 0.501 9
STNLFFM 51 0.017 1 0.141 0 -0.275 3 -0.050 3 0.483 7
Tab.2  Accuracy of NDVI image fusion results in experiment 01to12
Fig.3  NDVI image fusion results of experiment 01to12
算法 最优窗口 AD RMSE Edge LBP 总体误差
STARFM 15 0.034 8 0.173 9 -0.359 5 -0.048 1 0.616 3
FSDAF 20 0.043 8 0.191 0 -0.233 6 0.037 7 0.506 1
Fit-FC 50 0.038 7 0.199 5 -0.302 6 0.058 8 0.599 6
STNLFFM 51 0.035 2 0.185 6 -0.294 5 -0.042 4 0.557 7
ESTARFM 50 -0.014 9 0.175 2 -0.214 4 0.062 5 0.467 0
STNLFFM2 51 -0.005 0 0.163 7 -0.291 9 0.009 2 0.469 8
Tab.3  Accuracy of NDVI image fusion results in experiment 01to03
Fig.4  NDVI image fusion results of experiment 01to03
算法 最优窗口 AD RMSE Edge LBP 总体误差
STARFM 15 -0.041 2 0.196 3 -0.326 7 -0.037 2 0.601 4
FSDAF 30 -0.045 9 0.211 3 -0.253 1 0.046 2 0.556 5
Fit-FC 50 0.002 0 0.234 4 -0.311 0 0.037 7 0.585 1
STNLFFM 15 -0.040 4 0.212 5 -0.251 2 -0.010 6 0.514 7
ESTARFM 50 -0.014 9 0.175 2 -0.214 4 0.062 5 0.467 0
STNLFFM2 51 -0.005 0 0.163 7 -0.291 9 0.009 2 0.469 8
Tab.4  Accuracy of NDVI image fusion results in experiment 04to03
Fig.5  NDVI image fusion results of experiment 04to03
算法_窗口 区域 AD RMSE Edge LBP 总体误差
STARFM_15 水体 0.031 0 0.136 9 -0.110 1 -0.003 3 0.281 3
非水体 0.003 8 0.107 3 -0.061 6 -0.043 0 0.215 7
FSDAF_20 水体 0.033 3 0.137 4 -0.072 0 0.041 6 0.284 3
非水体 0.010 5 0.132 6 -0.031 2 -0.001 5 0.175 9
Fit-FC_50 水体 0.039 1 0.160 0 -0.056 5 0.055 9 0.311 5
非水体 -0.000 4 0.119 2 -0.060 9 0.009 9 0.190 5
STNLFFM_51 水体 0.034 9 0.146 9 -0.082 3 -0.016 0 0.280 2
非水体 0.000 3 0.113 4 -0.050 8 -0.024 8 0.189 4
ESTARFM_50 水体 -0.012 2 0.138 7 -0.042 9 0.048 8 0.242 5
非水体 -0.002 7 0.107 0 -0.045 7 0.015 0 0.170 4
STNLFFM2_51 水体 0.000 5 0.131 0 -0.067 3 0.018 9 0.217 7
非水体 -0.005 4 0.098 1 -0.060 3 -0.008 7 0.172 5
Tab.5  Accuracy of NDVI image fusion in water and non-water areas
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