4.7 Article

Multi-year mapping of flood autumn irrigation extent and timing in harvested croplands of arid irrigation district

期刊

GISCIENCE & REMOTE SENSING
卷 59, 期 1, 页码 1598-1623

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/15481603.2022.2126342

关键词

Flood irrigation; transient water bodies; irrigation timing; MODIS; water index; arid agricultural region

资金

  1. National Natural Science Foundation of China [52279038, 51839006, 51779119]
  2. Research Program of the State Key Laboratory of Hydroscience and Engineering, Tsinghua University [2020-KY-01]

向作者/读者索取更多资源

This study proposes a threshold-based method to extract the extent and timing of flood irrigation in the Hetao Irrigation District (HID) in China. The model uses MODIS water indices and robust thresholds to achieve strong generalization capability. Through validation using pixel and field survey data, the model shows an overall accuracy of over 90.0% for the irrigation area.
Flood irrigation after crop harvest, e.g. autumn irrigation (AI), is a common irrigation practice in arid and semi-arid regions like Hetao Irrigation District (HID) in Northwest China to increase soil moisture and leach soil salt. Detailed information about the extent, timing, and amount of AI is imperative for modeling agro-hydrological processes and irrigation management. However, little attention is given to the identification of the above AI factors. There are basically three major difficulties in estimating the annual changes in AI, including a suitable index to identify AI, temporal instability of thresholds, and an effective validation method for irrigation timing. Therefore, this study proposes a simple and effective threshold-based method to extract the extent and timing of AI in the HID using MODIS water indices at a daily timescale. The Multi-Band Water Index (MBWI) time series is first reconstructed using an adaptive weighted Savitzky-Golay filter and then used to identify the AI extent and time. The proposed model has a stronger generalization capability both in time and space due to robust thresholds selected from the Z-score normalized feature variable. The model is validated both at pixels generated by the segmentation of Sentinel-derived MBWI using a threshold-based model and at sampling points from the field survey. Results show that the model performed well with an overall accuracy of more than 90.0% for the irrigation area. The overall accuracies of irrigation timing are 76.4% and 91.7% based on the middle-to-late and whole irrigation periods, respectively. We found a decreasing trend in the AI area and a gradual delay in the starting time of AI in the HID, mainly due to changes in cropping patterns, climate, and irrigation fees. Overall, the model is promising in identifying flood irrigation extent and timing in large irrigation districts and is helpful for irrigation scheduling.

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