Season-Specific CNN and TVDI Approach for Soil Moisture and Irrigation Monitoring in the Hetao Irrigation District, China

Yule Sun, Dongliang Zhang, Ze Miao, Shaodong Yang, Quanming Liu () and Zhongyi Qu ()
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Yule Sun: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Dongliang Zhang: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Ze Miao: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Shaodong Yang: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Quanming Liu: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Zhongyi Qu: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

Agriculture, 2025, vol. 15, issue 18, 1-35

Abstract: We develop a year-round, field-scale framework to retrieve soil moisture and map irrigation in an arid irrigation district where crop phenology and canopy dynamics undermine static, single-season approaches. However, the currently popular TVDI application is limited during non-growing seasons. To address this gap, we introduce a season-stratified TVDI scheme—based on the LST–EVI feature space with phenology-specific dry/wet edges—coupled with a non-growing-season inversion that fuses Sentinel-1 SAR and Landsat features and compares multiple regressors (PLSR, RF, XGBoost, and CNN). The study leverages 2023–2024 multi-sensor image time series for the Yichang sub-district of the Hetao Irrigation District (China), together with in situ topsoil moisture, meteorological records, a local cropping calendar, and district statistics for validation. Methodologically, EVI is preferred over NDVI to mitigate saturation under dense canopies; season-specific edge fitting stabilizes TVDI, while cross-validated regressors yield robust soil-moisture retrievals outside the growing period, with the CNN achieving the highest accuracy (test R 2 ≈ 0.56–0.61), outperforming PLSR/RF/XGBoost by approximately 12–38%. The integrated mapping reveals complementary seasonal irrigation patterns: spring irrigates about 40–45% of farmland (e.g., 43.39% on 20 May 2024), summer peaks around 70% (e.g., 71.42% on 16 August 2024), and autumn stabilizes near 20–25% (e.g., 24.55% on 23 November 2024), with marked spatial contrasts between intensively irrigated southwest blocks and drier northeastern zones. We conclude that season-stratified edges and multi-source inversions together enable reproducible, year-round irrigation detection at field scale. These results provide operational evidence to refine irrigation scheduling and water allocation, and support drought-risk management and precision water governance in arid irrigation districts.

Keywords: soil moisture; spatiotemporal dynamics; multi-source remote sensing; irrigation identification; Hetao irrigation district (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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