综合时序光学和SAR数据的南方多云雨地区耕地种植强度提取

doi:10.11947/j.AGCS.2024.20230497

摘要/Abstract

摘要：

及时准确地获取耕地种植强度时空分布信息对于调整农业生产布局、制定粮食生产决策具有重要参考价值。目前种植强度提取研究大都利用光学数据和物候知识展开，然而在南方多云雨地区易缺失多季种植耕地的关键物候参数，具有与作物相似物候特点的易混植被难以剔除，像素级结果中椒盐噪声明显。因此，本文基于时序光学和SAR数据，提出一种综合光学物候参数、SAR时序特征及超像素优化的耕地种植强度提取方法。首先利用光学NDVI、LSWI时序曲线获取生长期数量和生长期长度，然后构建SAR时序特征识别早稻移栽灌水信号，最后利用空间上下文信息对种植强度提取结果进行超像素优化。利用2020—2021年洪湖市的时序Sentinel-1/2数据，验证了本文方法的有效性，总体精度和Kappa系数分别达92.02%和0.84。结果表明，引入生长期长度可以有效去除易混植被，SAR时序特征将易被错分的双季水稻正确分类，超像素优化使种植强度结果更加准确和完整，本文方法在多云雨种植制度复杂区域能够获取准确的种植强度分布信息。

关键词: 时序光学, 时序SAR, 种植强度, 物候参数

Abstract:

Timely and accurate acquisition of spatiotemporal distribution information regarding cropping intensity holds significant reference value for adjusting agricultural production layout and making grain production decisions. Current research on cropping intensity extraction primarily relies on optical data and phenological knowledge. However, critical phenological parameters for multi-season cropping cropland are often missing in cloudy and rainy regions of the South China, and confusable vegetation with phenological characteristics similar to crops is difficult to cull, and the salt-and-pepper noise is obvious in the pixel-level results. This paper introduces a novel method for extracting cropping intensity by integrating optical phenological parameters, SAR temporal features, and superpixel optimization based on time-series optical and SAR data. Initially, optical NDVI and LSWI temporal curves are utilized to acquire the number and duration of growth periods. Subsequently, SAR temporal features are employed to identify early-season signals of transplanting and irrigation. Finally, spatial contextual information is utilized for superpixel optimization of the cropping intensity extraction results. The effectiveness of the proposed method is validated using time-series Sentinel-1/2 data from Honghu city in 2020—2021, yielding an overall accuracy of 92.02% and a Kappa coefficient of 0.84. Results indicate that incorporating growth period duration effectively mitigates the influence of mixed vegetation, SAR temporal features accurately classify double-season rice, and superpixel optimization enhances the accuracy and completeness of planting intensity results. This method proves capable of accurately capturing cropping intensity distribution in regions with cloudy and rainy complex cropping pattern.

Key words: time-series optical, time-series SAR, cropping intensity, phenological parameters

中图分类号:

P237

陈启浩, 李广潮, 曹文静, 刘修国. 综合时序光学和SAR数据的南方多云雨地区耕地种植强度提取[J]. 测绘学报, 2024, 53(12): 2361-2374.

Qihao CHEN, Guangchao LI, Wenjing CAO, Xiuguo LIU. Cropland intensity extraction combined using optical and SAR time-series in cloudy and rainy areas of southern China[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(12): 2361-2374.

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方案名	试验设计
方案1	生长期数量提取
方案2	生长期数量提取+生长期长度筛选
方案3	生长期数量提取+生长期长度筛选+SAR时序特征RI判别
本文方法	生长期数量提取+生长期长度筛选+SAR时序特征RI判别+SNIC超像素优化

方案	单季_PA/（%）	单季_UA/（%）	双季_PA/（%）	双季_UA/（%）	三季_PA/（%）	三季_UA/（%）	OA/（%）	Kappa
方案1	96.40	63.77	67.82	97.72	73.93	99.79	77.76	0.60
方案2	98.77	63.89	69.81	97.72	75.33	99.79	79.88	0.63
方案3	93.74	83.92	90.41	95.43	77.44	99.63	91.14	0.82
本文方法	94.07	85.66	91.59	95.70	78.30	99.90	92.02	0.84

方案	单季_PA/（%）	单季_UA/（%）	双季_PA/（%）	双季_UA/（%）	OA/（%）	Kappa
方案1	96.66	50.11	72.73	99.52	77.58	0.51
方案2	96.66	50.15	74.91	99.54	79.31	0.53
方案3	92.06	79.91	93.61	98.37	93.30	0.81
本文方法	94.50	83.90	95.10	98.97	94.98	0.85