Correction: Wu, Y.; Zhao, W. Fine Identification of Lake Water Bodies and Near-Water Land Using Multi-Source Remote Sensing Fusion: A Case Study of Weishan Lake, China. Sustainability 2026, 18, 344

The authors would like to make the following corrections to the published paper [1]. The changes are as follows:

• Since reference 43 has been retracted for some reason, the authors wish to replace it with a new reference.

Replacing a reference in the citation list:

• 43.

  Li, P.; Wang, Q.; Umair, M.; Shamshieva, N.; Zheng, Y. Three decades of wetland transformation in the middle and lower Yangtze River Basin: Classification, inundation dynamics, and ecological impacts. Ecol. Indic. 2025, 177, 113615. https://doi.org/10.1016/j.ecolind.2025.113615.

with

• 43.

  Zhao, Z.; Li, H.; Song, X.; Sun, W. Dynamic Monitoring of Surface Water Bodies and Their Influencing Factors in the Yellow River Basin. Remote Sens. 2023, 15, 5157. https://doi.org/10.3390/rs15215157.

2.

The authors would like to change the confidence interval of the table content, so we need to replace the original Table 3:

Table 3. Machine learning monthly accuracy and reliability.

Month (2024)	RF OA (95% CI)	RF Kappa (95% CI)	CART OA (95% CI)	CART Kappa (95% CI)
January	0.9704 (0.9704–0.9704)	0.9367 (0.9367–0.9367)	0.9752 (0.9752–0.9752)	0.9483 (0.9483–0.9483)
February	0.9980 (0.9980–0.9980)	0.9956 (0.9956–0.9956)	0.9938 (0.9938–0.9938)	0.9868 (0.9868–0.9868)
March	0.9898 (0.9898–0.9898)	0.9608 (0.9608–0.9608)	0.9766 (0.9766–0.9766)	0.9498 (0.9498–0.9498)
April	0.9845 (0.9845–0.9845)	0.9672 (0.9672–0.9672)	0.9796 (0.9796–0.9796)	0.9566 (0.9566–0.9566)
May	0.9438 (0.9438–0.9438)	0.8776 (0.8776–0.8776)	0.9392 (0.9392–0.9392)	0.8683 (0.8683–0.8683)
June	0.9308 (0.9308–0.9308)	0.8502 (0.8502–0.8502)	0.9011 (0.9011–0.9011)	0.7934 (0.7934–0.7934)
July	–	–	–	–
August	0.9516 (0.9516–0.9516)	0.8959 (0.8959–0.8959)	0.9477 (0.9477–0.9477)	0.8885 (0.8885–0.8885)
September	0.9568 (0.9568–0.9568)	0.9094 (0.9094–0.9094)	0.9779 (0.9779–0.9779)	0.9534 (0.9534–0.9534)
October	0.9628 (0.9628–0.9628)	0.9177 (0.9177–0.9177)	0.9826 (0.9826–0.9826)	0.9622 (0.9622–0.9622)
November	0.9936 (0.9936–0.9936)	0.9868 (0.9868–0.9868)	0.9916 (0.9916–0.9916)	0.9824 (0.9824–0.9824)
December	–	–	–	–

Table 3. Machine learning monthly accuracy and reliability.

Month (2024)	RF OA (95% CI)	RF Kappa (95% CI)	CART OA (95% CI)	CART Kappa (95% CI)
January	0.9704 (0.9704–0.9704)	0.9367 (0.9367–0.9367)	0.9752 (0.9752–0.9752)	0.9483 (0.9483–0.9483)
February	0.9980 (0.9980–0.9980)	0.9956 (0.9956–0.9956)	0.9938 (0.9938–0.9938)	0.9868 (0.9868–0.9868)
March	0.9898 (0.9898–0.9898)	0.9608 (0.9608–0.9608)	0.9766 (0.9766–0.9766)	0.9498 (0.9498–0.9498)
April	0.9845 (0.9845–0.9845)	0.9672 (0.9672–0.9672)	0.9796 (0.9796–0.9796)	0.9566 (0.9566–0.9566)
May	0.9438 (0.9438–0.9438)	0.8776 (0.8776–0.8776)	0.9392 (0.9392–0.9392)	0.8683 (0.8683–0.8683)
June	0.9308 (0.9308–0.9308)	0.8502 (0.8502–0.8502)	0.9011 (0.9011–0.9011)	0.7934 (0.7934–0.7934)
July	–	–	–	–
August	0.9516 (0.9516–0.9516)	0.8959 (0.8959–0.8959)	0.9477 (0.9477–0.9477)	0.8885 (0.8885–0.8885)
September	0.9568 (0.9568–0.9568)	0.9094 (0.9094–0.9094)	0.9779 (0.9779–0.9779)	0.9534 (0.9534–0.9534)
October	0.9628 (0.9628–0.9628)	0.9177 (0.9177–0.9177)	0.9826 (0.9826–0.9826)	0.9622 (0.9622–0.9622)
November	0.9936 (0.9936–0.9936)	0.9868 (0.9868–0.9868)	0.9916 (0.9916–0.9916)	0.9824 (0.9824–0.9824)
December	–	–	–	–

with

Table 3. Machine learning monthly accuracy and reliability.

Month (2024)	RF OA (95% CI)	RF Kappa (95% CI)	CART OA (95% CI)	CART Kappa (95% CI)
January	0.9704 (0.9607–0.9814)	0.9367 (0.9158–0.9601)	0.9752 (0.9675–0.9855)	0.9483 (0.9324–0.9695)
February	0.9980 (0.9853–0.9996)	0.9956 (0.9756–0.9930)	0.9938 (0.9925–0.9979)	0.9868 (0.9946–0.9956)
March	0.9898 (0.9743–0.9887)	0.9608 (0.9443–0.9762)	0.9766 (0.9760–0.9910)	0.9498 (0.9480–0.9807)
April	0.9845 (0.9602–0.9803)	0.9672 (0.9113–0.9569)	0.9796 (0.9644–0.9851)	0.9566 (0.9243–0.9684)
May	0.9438 (0.9297–0.9585)	0.8776 (0.8470–0.9088)	0.9392 (0.9247–0.9508)	0.8683 (0.8367–0.8946)
June	0.9308 (0.9157–0.9458)	0.8502 (0.8155–0.8817)	0.9011 (0.8826–0.9182)	0.7934 (0.7529–0.8316)
July	–	–	–	–
August	0.9516 (0.9408–0.9659)	0.8959 (0.8713–0.9279)	0.9477 (0.9295–0.9575)	0.8885 (0.8505–0.9102)
September	0.9568 (0.9455–0.9672)	0.9094 (0.8855–0.9313)	0.9779 (0.9613–0.9801)	0.9534 (0.9177–0.9580)
October	0.9628 (0.9517–0.9747)	0.9177 (0.8923–0.9442)	0.9826 (0.9724–0.9906)	0.9622 (0.9403–0.9792)
November	0.9936 (0.9906–0.9972)	0.9868 (0.9803–0.9942)	0.9916 (0.9878–0.9970)	0.9824 (0.9744–0.9938)
December	–	–	–	–

Table 3. Machine learning monthly accuracy and reliability.

Month (2024)	RF OA (95% CI)	RF Kappa (95% CI)	CART OA (95% CI)	CART Kappa (95% CI)
January	0.9704 (0.9607–0.9814)	0.9367 (0.9158–0.9601)	0.9752 (0.9675–0.9855)	0.9483 (0.9324–0.9695)
February	0.9980 (0.9853–0.9996)	0.9956 (0.9756–0.9930)	0.9938 (0.9925–0.9979)	0.9868 (0.9946–0.9956)
March	0.9898 (0.9743–0.9887)	0.9608 (0.9443–0.9762)	0.9766 (0.9760–0.9910)	0.9498 (0.9480–0.9807)
April	0.9845 (0.9602–0.9803)	0.9672 (0.9113–0.9569)	0.9796 (0.9644–0.9851)	0.9566 (0.9243–0.9684)
May	0.9438 (0.9297–0.9585)	0.8776 (0.8470–0.9088)	0.9392 (0.9247–0.9508)	0.8683 (0.8367–0.8946)
June	0.9308 (0.9157–0.9458)	0.8502 (0.8155–0.8817)	0.9011 (0.8826–0.9182)	0.7934 (0.7529–0.8316)
July	–	–	–	–
August	0.9516 (0.9408–0.9659)	0.8959 (0.8713–0.9279)	0.9477 (0.9295–0.9575)	0.8885 (0.8505–0.9102)
September	0.9568 (0.9455–0.9672)	0.9094 (0.8855–0.9313)	0.9779 (0.9613–0.9801)	0.9534 (0.9177–0.9580)
October	0.9628 (0.9517–0.9747)	0.9177 (0.8923–0.9442)	0.9826 (0.9724–0.9906)	0.9622 (0.9403–0.9792)
November	0.9936 (0.9906–0.9972)	0.9868 (0.9803–0.9942)	0.9916 (0.9878–0.9970)	0.9824 (0.9744–0.9938)
December	–	–	–	–

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.