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引用本文:

DOI:

10.11834/jrs.20233219

收稿日期:

2023-06-18

修改日期:

2023-09-19

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综合多源遥感信息的水稻纹枯病区域生境适宜性评价方法
田洋洋1, 吴开华1, 李惠紫1, 沈艳艳1, 邱晗潇1, 翟婧2, 张竞成1
1.杭州电子科技大学自动化学院;2.宁波市农业技术推广总站
摘要:

作物病害的发生与其生境的适宜性关系密切,目前病害生境监测和评价通常较为粗放,主要依靠气象信息,对田块间作物生长状态和环境条件等空间异质性因素缺乏精细的描述,难以为病害的精准预测提供有效信息。本研究以水稻中发生面积较大的主要病害水稻纹枯病为研究对象,通过在县域尺度开展多年份病害等级调查获得建模及验证样本,综合运用光学、微波及热红外等多源遥感数据对病害关键生境因素进行监测,关键生境因素主要包括寄主生长状态、稻田水层状态及稻田地表温度生境因素,并在此基础上,结合空间网格化分析和偏最小二乘(PLS)回归方法建立了病害生境适宜性评价模型。结果表明,利用遥感信息能够有效表征病害相关生境因素,同时,基于多源遥感表征的生境因素建立的水稻纹枯病区域生境适宜性评价模型能够得到与实际病害调查空间分布趋势基本一致的结果,模型对病情等级的拟合优度R2为0.60-0.65,RMSE分别为0.72、0.56。基于多源遥感信息能够以空间连续的方式对寄主状态、环境条件等重要病害生境因素进行表征,及实现病害生境适宜性的有效评价,为病害预测、防控提供重要的参考和指导信息。

Evaluation method of regional habitat suitability of rice sheath blight based on multi-source remote sensing information
Abstract:

Crop diseases have a severe impact on food security, and the excessive use of pesticides in crop disease prevention and control is a common issue. The evaluation of disease habitat suitability is able to provide important information for disease forecasting and control. The occurrence of crop diseases is closely associated with factors such as the growth status of host and environmental conditions. While the disease habitat conditions vary significantly due to cultivation practices and microclimate variations in the field. At present, disease habitat monitoring and evaluation are generally coarse, mainly relying on meteorological information and lacking detailed descriptions of spatially heterogeneous factors such as crop growth status and environmental conditions among fields. In this study, rice sheath blight (RSB), a major disease widespread in rice cultivation, was selected as the research object, the disease surveys were conducted at a county level in 2018 and 2019. Multi-source remote sensing data including optical, microwave and thermal infrared images were used for monitoring the key disease habitat factors. Multi-temporal Sentinel-2 optical images were utilized to extract the planting area of the host crop, which solved the problem of confusing the host with other vegetation in single phase images; the growth status of host was indicated by the tasseled cap products of Sentinel-2 optical images; the status of water layer in rice field was extracted by combining Sentinel-1 microwave images and Sentinel-2 optical images, the optical image of rice region was segmented by object-oriented analysis method to obtain the rice plot boundary in order to eliminate the noise of microwave image; and the MODIS land surface temperature (LST) products were utilized to reflect evapotranspiration and respiration status of rice plants. Based on these remote sensing habitat features of the RSB and a spatial gridding analysis, the habitat suitability evaluation model was established using the partial least squares (PLS) regression method. By validating against the disease survey data, the results showed that the remote sensing information can effectively characterize the disease habitat features. The R2 of the habitat suitability evaluation model was 0.60-0.65, and the RMSE was 0.72 and 0.56, respectively, and the output of the model was in good agreement with the actual spatial pattern of the disease. Besides, the hot and cold spots of the disease habitat suitability map were highly consistent with the actual pattern of disease occurrence in the region. Moreover, the rate of habitat suitability under each disease grade was also analyzed, and the results further confirmed the rationality of the evaluation. Therefore, this study demonstrates the feasibility of utilizing multi-source remote sensing data in evaluating the disease habitat suitability. The disease habitat evaluation map can be integrated into some disease epidemic models to develop spatio-temporal dynamic disease forecasting models at regional scale, and multi-source data, such as meteorological data, remote sensing data, and ground sensor networks, can be incorporated to establish a more comprehensive habitat suitability evaluation model, which is expected to be beneficial for large-scale disease control.

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