基于Sentinel-2密集时间序列的年内塑料农业覆盖周期动态追踪研究

《International Journal of Applied Earth Observation and Geoinformation》：Tracking Intra-annual plasticulture covering cycles using dense Sentinel-2 time-series

本研究针对塑料农业覆盖物（PCs）时空动态难以捕捉、季节性覆盖易被遗漏的问题，提出了一种基于规则的塑料覆盖追踪器（PCT）方法。该方法利用Sentinel-2密集时间序列，通过HSL颜色空间变换增强光谱可分性，结合时序滤波和NDVI确认，成功提取了PC覆盖的开始日期、结束日期和持续时间等关键时序指标，并将像元分类为永久性、季节性和非PC类型。结果表明，PCT在时序指标提取和类型分类上均具有高精度，有效填补了现有方法在追踪PC时空动态方面的空白，为环境风险评估和可持续塑料农业管理提供了有力工具。

　　

在当今全球粮食需求持续增长的背景下，集约化农业在满足食物供给的同时，也面临着有限耕地资源的压力。为了提升单位面积产量，塑料农业（Plasticulture）——即使用塑料薄膜覆盖作物和土壤的实践——已被广泛采用。这种实践通过调节土壤温湿度、降低病虫害风险来改善微气候条件，从而显著提高作物产量。自20世纪中期引入以来，塑料农业在全球范围内持续扩张。然而，塑料覆盖物（PCs）的广泛使用也引发了一系列环境担忧，包括塑料残留物在土壤中的积累、化学添加剂向环境的释放、温室气体排放增加以及土壤退化等。

塑料覆盖物的使用形式多样（如温室、隧道和地膜覆盖），覆盖持续时间也因作物类型、气候和管理实践而异。根据年内持续时间，PCs可分为永久性和季节性两类。前者全年存在，后者仅存在数周至数月。季节性PC通常使用更薄的材料且更换更频繁，导致其残留积累率更高。这些残留碎片会逐渐分解成微塑料，并释放邻苯二甲酸酯（PAEs）等添加剂，造成土壤污染。鉴于这些巨大的环境风险，精确绘制PCs的空间分布并准确表征其类别至关重要。

然而，塑料农业实践的高时空变异性给PCs的制图和覆盖周期追踪带来了挑战。尽管先前的研究在PC制图方面取得了进展，探索了基于指数、机器学习和深度学习的方法，但大多数研究侧重于单时相的静态制图，忽略了季节性变化。近年来，一些研究开始利用多时相影像，但主要是为了滤除季节性覆盖而非识别它。另一项近期工作利用年度聚合的时间序列数据进行全年塑料农业的全面制图，但它仍然属于静态制图的范畴，缺乏时序信息。总体而言，现有研究仅提供了特定时间点PC分布的“快照”，这对于全面制图和完整表征塑料农业周期是远远不够的。

随着卫星任务数量的增加和免费开放数据政策的实施，密集时间序列遥感已成为捕捉地表动态的强大工具。密集时间序列遥感凭借其长时序跨度和高时间分辨率，能够检测短暂的地表变化。对于本研究而言，它可以克服捕捉PCs任意部署时间和持续时间的挑战。另一个优势在于，密集时间序列数据能够提取地表动态的关键日期和阶段。越来越多的研究利用密集时间序列数据来表征作物的关键物候期、湖冰的冻融循环、积雪的积累-融化过程以及地表水动态。对于本研究，密集时间序列数据使得提取PC应用的关键时序指标（如开始日期、结束日期和持续时间）成为可能，为解读塑料农业实践和评估环境问题提供了独特的见解。

尽管潜力巨大，但利用密集时间序列数据追踪塑料农业覆盖周期仍面临两大挑战。其一在于地表反射率随时间的高度可变性。特别是在开阔农田的休耕期，PCs与裸土的光谱相似性可能导致误分类。此外，由于其半透明特性，PCs混合了塑料薄膜、下层土壤和作物的信号。因此，卫星数据中PCs的光谱信号受到作物生长状态的影响。另一个挑战涉及追踪覆盖周期的方法选择。深度学习方法已被证明可用于精确的PC制图，但将其扩展到密集时间序列PC追踪需要在时间和空间上都有标注以捕捉时间依赖性，这给实际应用带来了显著障碍。相比之下，基于规则的方法由于其简单性，为大范围制图提供了一种有用的替代方案。然而，如前所述，反射率的时间变化使得难以依赖单一特征进行稳定的PC提取，这凸显了开发更稳健的基于规则策略的必要性。

为此，发表在《International Journal of Applied Earth Observation and Geoinformation》上的研究《Tracking Intra-annual plasticulture covering cycles using dense Sentinel-2 time-series》提出了一种名为塑料覆盖追踪器（PCT）的基于规则的方法。该研究利用密集的Sentinel-2时间序列数据，旨在追踪塑料覆盖周期，并生成包含季节性和永久性PCs的全面地图。

为开展此项研究，研究人员主要运用了几项关键技术方法。首先，研究利用了Sentinel-2 L2A级影像的红、绿、蓝和近红外波段，构建了从2023年5月到2024年9月的密集时间序列数据集，并在Google Earth Engine平台上进行了云雪等噪声去除的预处理。核心方法PCT框架包含四个主要步骤：1. 将RGB图像转换至HSL（色相-饱和度-明度）颜色空间，以增强PCs与背景的光谱可分性；2. 在饱和度-明度二维特征空间中建立决策规则，进行单时相PC分类；3. 对初步分类结果进行时序滤波，去除噪声并填补数据空缺，生成候选PC覆盖期；4. 结合NDVI时间序列分析，检测作物生长周期，确认农业用途的PCs，减少误分类。最后，基于提取的覆盖周期信息，计算关键时序指标（开始日期、结束日期、持续时间）并对每个像元进行分类（永久性PC、季节性PC、非PC）。研究区域选在中国山东省具有PC类型多样性的泰安、潍坊和青岛三个代表性区域，通过野外调查和遥感影像解译收集了样本数据用于方法开发和验证。

4.1. 特征可分性分析

研究首先对RGB和HSL颜色空间中的特征进行了可分性分析。结果显示，在RGB空间中，PCs与其他地物类型（特别是裸土和不透水面）存在显著光谱混淆。转换至HSL空间后，PCs在饱和度（低值）和明度（中高值）组件上表现出更集中的分布，形成了明显的聚类。定量分析采用Jeffries-Matusita (JM)距离进行评估，单个饱和度特征的JM距离为1.34，而饱和度-明度组合的JM距离提升至1.50，显著高于RGB空间中任何特征组合（最高仅为0.50）。这表明HSL变换有效增强了PCs与背景地物的光谱可分性，为后续规则分类奠定了基础。

4.2. PCT各模块对静态分类的贡献评估

通过消融实验评估了PCT框架中各模块（HSL变换、单时相分类、时序滤波、NDVI确认）对静态分类（所有时相的分类结果）的贡献。实验表明，逐步加入模块能持续提升分类性能。仅使用HSL变换和单时相分类时，生产者精度（PA）和用户精度（UA）均较低（PA=0.49, UA=0.72），存在大量漏分和误分。加入时序滤波模块后，PA显著提升至0.93，UA提升至0.80，有效去除了噪声并填补了缺失数据。最后加入NDVI确认模块，UA进一步提升至0.98，虽然PA略有下降至0.91（因过滤了闲置或低NDVI的PC区域），但总体精度（OA）达到0.95，F1分数达到0.95。这表明每个模块都对最终结果的准确性和可靠性至关重要。

4.3. 覆盖周期检测关键指标评估

研究对PCT提取的覆盖开始日期、结束日期和持续时间进行了时间偏差分析。 across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC samples across all PC 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