School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Int J Environ Res Public Health. 2018 Mar 6;15(3):461. doi: 10.3390/ijerph15030461.
Remote sensing can actively monitor heavy metal contamination in crops, but with the increase of satellite sensors, the optimal scale for monitoring heavy metal stress in rice is still unknown. This study focused on identifying the optimal scale by comparing the ability to detect heavy metal stress in rice at various spatial scales. The 2 m, 8 m, and 16 m resolution GF-1 (China) data and the 30 m resolution HJ-1 (China) data were used to invert leaf area index (LAI). The LAI was the input parameter of the World Food Studies (WOFOST) model, and we obtained the dry weight of storage organs (WSO) and dry weight of roots (WRT) through the assimilation method; then, the mass ratio of rice storage organs and roots (SORMR) was calculated. Through the comparative analysis of SORMR at each spatial scale of data, we determined the optimal scale to monitor heavy metal stress in rice. The following conclusions were drawn: (1) SORMR could accurately and effectively monitor heavy metal stress; (2) the 8 m and 16 m images from GF-1 were suitable for monitoring heavy metal stress in rice; (3) 16 m was considered the optimal scale to assess heavy metal stress in rice.
遥感可以主动监测作物中的重金属污染，但随着卫星传感器的增加，监测水稻重金属胁迫的最佳尺度仍不清楚。本研究通过比较不同空间尺度下水稻重金属胁迫的检测能力，重点确定了最佳尺度。研究使用了 2 m、8 m 和 16 m 分辨率的 GF-1（中国）数据和 30 m 分辨率的 HJ-1（中国）数据来反演叶面积指数（LAI）。将 LAI 作为世界粮食研究（WOFOST）模型的输入参数，通过同化方法获得了储存器官的干重（WSO）和根的干重（WRT）；然后，计算了水稻储存器官和根的质量比（SORMR）。通过对各数据空间尺度下 SORMR 的比较分析，确定了监测水稻重金属胁迫的最佳尺度。得出以下结论：（1）SORMR 可以准确有效地监测重金属胁迫；（2）GF-1 的 8 m 和 16 m 图像适用于监测水稻中的重金属胁迫；（3）16 m 被认为是评估水稻重金属胁迫的最佳尺度。
