Geomatics

Rice field mapping is essential for effective agricultural and water resource management due to high land pressure. This study aims to map paddy rice by combining segmentation techniques and phenological metrics derived from optical time series. Thus, a crop segmentation-based approach was developed using Sentinel-2 imagery (2018–2019) to assess the paddy rice extent in the Senegal River Delta (SRD). Two super-pixel segmentation algorithms were evaluated to optimize the identification of rice plots by integrating spectral and spatial characteristics from the green, red, and near-infrared (NIR) bands. In this study, the Felzenszwalb outperformed the Quickshift algorithm, achieving a median intersection over union (IoU) of 0.25 compared to 0.20 for the segmentation of rice fields. The analysis of NDVI time series enabled the identification of key stages in the rice phenological cycle. Two machine learning algorithms (i.e., Random Forest and XGBoost) were compared for rice crop detection. Random Forest delivered a better performance (AUC = 0.93, OA = 0.98, F1-score = 0.98) than the XGBoost (AUC = 0.92, OA = 0.98, F1-score = 0.98). Overall, the results indicated that the approach could accurately identify paddy rice fields, and thus improve decision making and support food security management in the region.

Flash floods in arid regions dictate a rapid flood inundation mapping for early warning. However, hydrodynamic models, such as HEC-RAS, provide accurate flood mapping but require extensive topographical data and high computational resources. The GeoFlood method offers a rapid alternative for early warning relying on terrain-driven framework and simple hydraulics. This study examined GeoFlood applicability on two arid catchments and tested its sensitivity for different return periods, Manning coefficients, and wadi length segmentations. The original GeoFlood method showed good consistency with HEC-RAS in well-defined wadis but relatively poor performance in flat areas, with segmentation and slope calculation significantly affecting GeoFlood accuracy and robustness. To overcome these limitations, slope calculation was improved using the Theil–Sen trend, and segmentation was automated using the penalized cost approach Continuous Piecewise Optimal Partitioning (CPOP) to detect slope breakpoints. CPOP provides superior and robust performance without prior knowledge of the best segmentation lengths, producing smoother slopes at accurate breakpoints with a Fowlkes–Mallows (FM) index of 0.88 in flat areas and an error bias of 1.05 compared to a variable FM from 0.72 to 0.88 and an error bias from 0.81 to 1.3 for the original GeoFlood. The enhanced GeoFlood provides reliable robust results in arid regions when data are scarce.

Last year signaled a great step forward in my editorial career and, I hope, a good year for the journal [...]