Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

Mizuochi, Hiroki; Sasagawa, Taiga; Ito, Akihiko; Iijima, Yoshihiro; Park, Hotaek; Nagano, Hirohiko; Ichii, Kazuhito; Hiyama, Tetsuya

doi:10.1186/s40645-024-00614-1

Progress in Earth and Planetary Science

Table 7 Accuracy of RF prediction averaged for each tile, for each season with standard deviation

From: Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

	Slope	Offset	Mean error	RMSE	Correlation coefficient
h20v02	0.72 ± 0.43	0.24 ± 0.28	− 0.01 ± 0.03	0.05 ± 0.02	0.58 ± 0.30
h20v02	1.32 ± 0.33	− 0.02 ± 0.03	0.01 ± 0.02	0.09 ± 0.17	0.80 ± 0.12
h20v03	0.86 ± 0.12	0.01 ± 0.20	0.00 ± 0.11	0.12 ± 0.07	0.48 ± 0.22
h20v03	0.87 ± 0.10	0.02 ± 0.05	0.03 ± 0.05	0.03 ± 0.02	0.60 ± 0.16
h21v02	1.19 ± 0.28	− 0.07 ± 0.24	− 0.01 ± 0.02	0.41 ± 0.74	0.34 ± 0.35
h21v02	1.10 ± 0.15	− 0.02 ± 0.05	0.01 ± 0.05	0.12 ± 0.22	0.54 ± 0.28
h21v03	0.81 ± 0.35	0.12 ± 0.27	0.01 ± 0.09	0.09 ± 0.09	0.40 ± 0.30
h21v03	0.98 ± 0.16	− 0.01 ± 0.04	− 0.01 ± 0.03	0.02 ± 0.01	0.67 ± 0.23
h22v02	0.79 ± 0.27	0.18 ± 0.25	− 0.05 ± 0.07	0.03 ± 0.02	0.43 ± 0.18
h22v02	0.80 ± 0.30	0.00 ± 0.09	0.03 ± 0.06	0.01 ± 0.00	0.72 ± 0.12
h22v03	0.67 ± 0.28	0.22 ± 0.34	− 0.09 ± 0.19	0.15 ± 0.15	0.54 ± 0.29
h22v03	0.74 ± 0.41	0.01 ± 0.03	0.04 ± 0.09	0.03 ± 0.02	0.58 ± 0.22
h23v02	1.03 ± 0.21	− 0.01 ± 0.22	− 0.01 ± 0.08	0.03 ± 0.03	0.56 ± 0.24
h23v02	0.61 ± 0.29	− 0.01 ± 0.05	0.04 ± 0.06	0.02 ± 0.02	0.50 ± 0.19
h23v03	0.75 ± 0.34	0.08 ± 0.28	0.01 ± 0.04	0.24 ± 0.11	0.34 ± 0.29
h23v03	0.92 ± 0.12	0.05 ± 0.07	− 0.01 ± 0.05	0.10 ± 0.16	0.61 ± 0.23
h24v02	1.00 ± 0.27	0.05 ± 0.13	0.02 ± 0.05	0.02 ± 0.00	0.54 ± 0.38
h24v02	0.53 ± 0.35	− 0.00 ± 0.05	0.03 ± 0.01	0.01 ± 0.02	0.51 ± 0.35
h24v03	1.03 ± 0.09	− 0.05 ± 0.11	0.03 ± 0.07	0.15 ± 0.16	0.67 ± 0.19
h24v03	1.05 ± 0.25	0.01 ± 0.05	0.00 ± 0.08	0.17 ± 0.13	0.49 ± 0.09
h25v02	0.82 ± 0.26	0.22 ± 0.19	− 0.05 ± 0.07	0.05 ± 0.06	0.59 ± 0.26
h25v02	0.75 ± 0.37	− 0.05 ± 0.04	0.07 ± 0.07	0.16 ± 0.19	0.65 ± 0.11
h25v03	1.10 ± 0.07	− 0.06 ± 0.11	0.05 ± 0.14	0.25 ± 0.13	0.66 ± 0.04
h25v03	1.01 ± 0.19	− 0.01 ± 0.12	0.03 ± 0.14	0.34 ± 0.37	0.61 ± 0.17
Spring	0.87 ± 0.23	0.02 ± 0.25	0.02 ± 0.11	0.08 ± 0.07	0.75 ± 0.11
Spring	0.83 ± 0.22	− 0.01 ± 0.05	0.04 ± 0.06	0.05 ± 0.07	0.74 ± 0.14
Summer	1.05 ± 0.14	0.03 ± 0.05	− 0.03 ± 0.05	0.20 ± 0.43	0.62 ± 0.26
Summer	1.03 ± 0.14	0.02 ± 0.10	0.00 ± 0.09	0.24 ± 0.25	0.64 ± 0.20
Fall	0.83 ± 0.43	0.11 ± 0.35	− 0.01 ± 0.13	0.16 ± 0.15	0.36 ± 0.19
Fall	0.82 ± 0.39	− 0.01 ± 0.05	0.03 ± 0.07	0.07 ± 0.13	0.45 ± 0.13
Winter	0.84 ± 0.24	0.15 ± 0.17	− 0.01 ± 0.03	0.09 ± 0.09	0.31 ± 0.16
Winter	0.89 ± 0.42	− 0.02 ± 0.02	0.01 ± 0.02	0.01 ± 0.02	0.59 ± 0.21
All tile average	0.90 ± 0.17	0.08 ± 0.12	− 0.01 ± 0.04	0.13 ± 0.12	0.51 ± 0.11
All tile average	0.89 ± 0.22	− 0.00 ± 0.02	0.02 ± 0.02	0.09 ± 0.10	0.61 ± 0.09

Regression was performed using the created values as the X-axis and the original values as the Y-axis, with 1% samples randomly extracted from entire pixels. For each cell, the upper row is the validation of the NDWI and the lower row is that of the NDVI
RF: random forest, NDWI: normalized difference water index, NDVI: normalized difference vegetation index, RMSE: root mean squared error

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