时序滤波对农作物遥感识别的影响

获取长时序且高质量遥感观测数据是捕捉不同农作物关键物候节律信息，进而获取高精度农作物空间分布信息的关键。受云雨天气影响，卫星遥感易产生低质量观测，其往往不参与或采用时序滤波处理后再用于农作物遥感识别。然而，时序滤波对于农作物遥感识别的影响机制尚未摸清，为高效且高精度农作物遥感制图带来了较大挑战。该研究基于HLS（Harmonized Landsat Sentinel-2，30 m）和MODIS（Moderate-resolution Imaging Spectroradiometer，500 m）两种空间尺度的反射率产品分别构建时间序列数据集，以S-G（Savitzky-Golay）和HANTS（Harmonic Analysis of Time Series）滤波为例，采用随机森林分类器探究时序滤波分别对于30与500 m空间尺度农作物识别的影响。并通过植被指数时序曲线对比，深入分析时序滤波对于两种空间尺度农作物识别关键特征所带来的差异。结果表明，在该研究试验区域及观测时相内，针对中高分辨率HLS影像，相较于未经滤波处理数据，S-G和HANTS滤波农作物识别精度分别下降了1.73和5.17个百分点；而对于中低分辨率MODIS影像，未经滤波处理、S-G滤波和HANTS滤波后的农作物总体识别精度分别为84.73%、85.51%和83.05%，时序滤波前后农作物识别精度没有显著差异。此外，对比不同作物类型的植被指数时序曲线后发现，中高分辨率尺度下，对于大豆与玉米，其较小的类间差异会被时间滤波误认为噪声而进一步弱化，从而降低相似农作物的分类精度，而对于特征差异较明显的水稻与玉米、水稻与大豆，时序滤波则会减小其类内差异从而提高分类精度。对比而言，对于中低分辨率影像的农作物识别，受到混合像元等因素的干扰，同一作物类型中的光谱异质性是影响农作物识别精度的重要因素，因此时序滤波对其时序特征的影响较小。该研究通过深入分析时序滤波处理对不同空间尺度农作物识别的影响，为未来农作物空间分布的获取提供理论参考和技术支撑。

Impacts of temporal smoothing methods on crop type identification

Abstract: Accurate crop type mapping depends mainly on the reliable acquisition of multi-temporal remote sensing data with high quality. Traditionally, some satellite observations can be easily affected by cloudy and rainy weather, thereby generating undesired images. These low-quality images can normally be excluded or reconstructed using the temporal filters for the remote sensing classification in practice. However, temporal filtering can also eliminate some useful information in the temporal trajectory of original images, leading to the uncertain identification of crop types. Meanwhile, the land cover mixtures within a pixel grid can pose a great influence on the performance of temporal filters, with the decrease in the spatial resolution of images. This study aims to comprehensively investigate the impacts of Savitzky-Golay (S-G) and Harmonic Analysis of Time Series (HANTS) filter on the crop type mapping over different spatial resolutions using the random forest classifier, Harmonized Landsat Sentinel-2 (HLS, 30 m), and Moderate-resolution Imaging Spectroradiometer (MODIS, 500 m) data. Specifically, six indices of time-series vegetation were selected to identify three crop types (soybeans, corn, and rice) in Heilongjiang Province in China. According to the derived maps of crop type, the temporal filtering posed a greater impact on the spatial distribution of crop classification in the decametric spatial resolution image (HLS) than that in MODIS. The evaluation results showed that the overall accuracy of the crop type maps derived by S-G and HANTS was reduced by 1.73 and 5.17 percentage points, compared with the original observations for decametric resolution images. By contrast, the overall accuracies were 84.73%, 85.51%, and 83.05% using the original, S-G, and HANTS observations over the low-resolution images, respectively, indicating no significant change in the crop type mapping before and after temporal filtering. Compared with the time-series vegetation indices over different crop types, both the intra- and inter-class differences of crop types changed significantly after temporal filtering in the decametric resolution images, indicating some great impacts on the accuracy of crop type mapping. Specifically, the inter-class difference between two similar crop types was incorrectly identified as the inherent noise by the temporal filtering. In this case, the temporal filtering had reduced the difference for the low accuracy of crop type mapping. For instance, the user''s accuracies of soybeans and corn were reduced by 4.60 and 8.77 percentage points, respectively, before and after the HANTS filter. The temporal filtering had improved the accuracy of crop type identification (e.g., the user''s accuracy of rice was improved after HANTS filter), because the significant intra-class difference of each crop type was further reduced in the time-series vegetation indices, compared with the observations without filtering. In terms of crop type mapping on the low-resolution images (i.e., MODIS), the temporal filtering posed no impacts on the accuracy of crop type identification, particularly for the larger effects of land cover mixtures in the pixel grid. Consequently, there were some impacts of temporal filtering on the performance of crop type mapping over different spatial scales. The finding can provide theoretical references and technical support for the spatial distribution of crop types.