Su, Q.; Lv, J.; Fan, J.; Zeng, W.; Pan, R.; Liao, Y.; Song, Y.; Zhao, C.; Qin, Z.; Defourny, P. Remote Sensing-Based Classification of Winter Irrigation Fields Using the Random Forest Algorithm and GF-1 Data: A Case Study of Jinzhong Basin, North China. Remote Sens.2023, 15, 4599.
Su, Q.; Lv, J.; Fan, J.; Zeng, W.; Pan, R.; Liao, Y.; Song, Y.; Zhao, C.; Qin, Z.; Defourny, P. Remote Sensing-Based Classification of Winter Irrigation Fields Using the Random Forest Algorithm and GF-1 Data: A Case Study of Jinzhong Basin, North China. Remote Sens. 2023, 15, 4599.
Su, Q.; Lv, J.; Fan, J.; Zeng, W.; Pan, R.; Liao, Y.; Song, Y.; Zhao, C.; Qin, Z.; Defourny, P. Remote Sensing-Based Classification of Winter Irrigation Fields Using the Random Forest Algorithm and GF-1 Data: A Case Study of Jinzhong Basin, North China. Remote Sens.2023, 15, 4599.
Su, Q.; Lv, J.; Fan, J.; Zeng, W.; Pan, R.; Liao, Y.; Song, Y.; Zhao, C.; Qin, Z.; Defourny, P. Remote Sensing-Based Classification of Winter Irrigation Fields Using the Random Forest Algorithm and GF-1 Data: A Case Study of Jinzhong Basin, North China. Remote Sens. 2023, 15, 4599.
Abstract
Irrigation is one of key agricultural management practices of crop cultivation in the world. Irrigation practice is traceable on satellite image. Most irrigated area mapping methods were developed based on time series of NDVI or back scatter coefficient within the growing season. However, it found the winter irrigation out of growing season is also dominating in north China. This kind of irrigation aims to increase the soil moisture for coping with spring drought, and also reduce the wind erosion with the wet soil in the field surface as the strong wind happens in spring. This study developed a remote sensing-based classification approach to identify irrigated fields with Radom Forest algorithm out of growing season. The results showed that the mean of the highest accuracies of 7 RF models was 94.9% and the mean of the averaged accuracies of 7 RF models was 94.1%; the overall accuracy for all 7 outputs was in the range of 86.8-92.5%, Kappa in the range of 84.0-91.0% and F-1 score in the range of 82.1-90.1%. These results showed that the classification was acceptable and not over performed as the accuracies of all classified images were lower than the models. This study also found that irrigation started to apply in early November and irrigated fields were increased and suspended in December and January due to freeze. The newly irrigated fields were found again in March and April when the temperature goes up above zero degree. The area of irrigated fields in the study area were increasing over time with sizes of 98.6, 166.9, 208.0, 292.8, 538.0, 623.1, 653.8 km2 from December to April, accounting for 6.1%, 10.4%, 12.9%, 18.2%, 33.4%, 38.7%, and 40.6% of the total irrigatable land in the study area, respectively.
Copyright:
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