SWOT-based flood descriptors - South Sudan, 2023-2025

Field name 

Description

Use Case Name 

Flooding and health care service disruption in South Sudan.

Dataset Name

SWOT-based Flood Descriptors

Dataset Description 

This dataset contains SWOT-derived flood statistic computed at specific health facilities (HF) across an area of interest spreading 189.856 km2 within South Sudan. Flood statistics were calculated using SWOT raster frames for the 2023-2025 period after a quality filtering step to remove pixels with a) degraded and bad quality measurements for water surface elevation, b) active flags in fields of "value_bad", "geolocation_qual_degraded", and "classification_qual_degraded", c) layover systematic errors above 95th percentile, d) a cross-track distance outside of 10-60 km in both sides of the swath, e) a dark water fraction greater than 0.2, and f) a water fraction lower than 0.15. Flood descriptors were calculated at square buffers around the 279 facilities, with a buffer size of 5.4x5.4km for PHCU type and 3.6x3.6 km for PHCC. Each flood descriptor includes an additional field with an estimation of the uncertainty propagated from the wse_uncert field.

Each file represents a single SWOT frame and contains one record for each health facility located within its footprint. Output files retain the original names of the corresponding SWOT frames.

Temporal Domain 

2023-2025

Spatial Domain 

South Sudan (bounding box ranging from longitude 28.5942ºE to 4.8367ºE and latitude 31.8383ºN to 9.6283ºN, in EPSG:4326).

Key Variables 

Flood fraction: Number of pixels flooded respect the total one within the buffer (0-1). Mean depth. Water surface elevation average among the identified flooded pixels. Median depth. Water surface elevation median value among the identified flooded pixels. Maximum Depth: Maximum water surface elevation value, defined as the 95th percentile to exclude extreme erroneous values and outliers. Distance: The linear distance (meters) from the health facility (patch center point) to the pixel with the value of the 95th percentile.

All units for flood-depth-related descriptors are meters relative to EGM2008 geoid. Each flood descriptor includes an additional field with an estimation of the uncertainty propagated from the wse_uncert field using empirical formulas and bootstrap resampling.

Data Format 

GeoJSON files.

Source Data 

Surface Water and Ocean Topography (SWOT) mission frames of L2_HR_Raster product, along with Health Facilities of HSF Master Facility List (World Health Organization) that are typically unaffected by flooding—based on VIIRS-detected flood events.

Limitations/ Assumptions 

Main limitations of this approach arise from the propagation of noise measurements throughout the workflow, the irregular SWOT revisit time, and the intrinsic characteristics of SWOT observations. Radar imaging artefacts, such as layover effects and complex scattering over heterogeneous water surfaces, can introduce errors in water surface elevation measurements. One of the main intrinsic limitations of SWOT data is the reduced data quality in the nadir (inner swath) region due to high amount of interferometric errors. Although quality filtering based on uncertainty metrics and ancillary flags were applied to mitigate these effects, residual outliers and biases may persist and propagate through the analysis, potentially affecting the estimated temporal variations.

### 字段名 | 描述
用例名称 | 南苏丹洪涝与医疗服务中断
数据集名称 | 基于SWOT的洪涝描述符数据集
数据集说明 | 本数据集包含针对南苏丹境内面积达189.856平方千米的研究区内，特定医疗设施（Health Facilities, HF）计算得到的SWOT衍生洪涝统计量。洪涝统计量基于2023-2025年的SWOT栅格条带计算，计算前已完成质量过滤，剔除以下像素：a) 水面高程的退化与低质量测量值；b) "value_bad"、"geolocation_qual_degraded"及"classification_qual_degraded"字段中带有有效标记的像素；c) 超过95百分位的叠掩系统误差像素；d) 条带两侧跨轨距离超出10-60千米的像素；e) 暗水分数大于0.2的像素；f) 水体分数低于0.15的像素。

针对279个医疗设施，在其周边构建方形缓冲区以计算洪涝描述符：初级卫生保健单元（PHCU）类型的设施缓冲区尺寸为5.4×5.4千米，初级卫生保健中心（PHCC）类型的设施缓冲区尺寸为3.6×3.6千米。每个洪涝描述符均附带一个额外字段，用于存储由wse_uncert字段传播得到的不确定性估计值。

每个文件对应单条SWOT条带，包含其覆盖范围内所有医疗设施的一条记录。输出文件保留对应SWOT条带的原始文件名。

时间范围 | 2023-2025年

空间范围 | 南苏丹（采用EPSG:4326坐标系，边界框为东经28.5942°至4.8367°，北纬31.8383°至9.6283°）

关键变量 | 水体分数：缓冲区中淹没像素数与总像素数之比（取值范围0-1）；平均水深；已识别淹没像素的平均水面高程；中值水深；已识别淹没像素的水面高程中值；最大水深：取95百分位值以排除极端错误值与异常值，定义为水面高程的最大值；距离：医疗设施（斑块中心点）至95百分位值对应像素的直线距离（单位：米）。

所有洪水深度相关描述符的单位均为相对于地球重力模型2008（EGM2008）大地水准面的米数。每个洪涝描述符均附带一个额外字段，通过经验公式与自助重采样方法，基于wse_uncert字段传播得到不确定性估计值。

数据格式 | GeoJSON文件

源数据 | 地表水域与海洋地形（Surface Water and Ocean Topography, SWOT）任务的L2_HR_Raster产品条带，以及世界卫生组织（World Health Organization, WHO）的HSF主医疗设施列表中的医疗设施——这些设施基于可见光红外成像辐射仪套件（Visible Infrared Imaging Radiometer Suite, VIIRS）探测的洪涝事件，被归类为通常不受洪涝影响的设施。

局限性与假设 | 本方法的主要局限性源于工作流程中噪声测量的传播、不规则的SWOT重访时间，以及SWOT观测的固有特性。雷达成像伪影（如叠掩效应与非均匀水域的复杂散射）可能会引入水面高程测量误差。SWOT数据的主要固有局限性之一是星下点（条带内部区域）因大量干涉误差导致数据质量下降。尽管已基于不确定性指标与辅助标记进行质量过滤以缓解这些影响，但仍可能存在残留异常值与偏差，并在分析中传播，进而可能影响估算的时间变化特征。