自2002年以来,利用遥感和深度神经网络重建了大湖地区总的时空空间变化
Hongwei Guo1, Jinhui Jeanne Huang2, Xiaotong Zhu2
1School of Geographic Information and Tourism, Chuzhou University, Chuzhou, 239099, Anhui, China; College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environmental Safety, Nankai University, Tianjin, 300457, China.
Water research
|March 28, 2024
概括
一个深度神经网络模型从卫星数据准确地估计了大湖地区的总 (TP) 度. 这种方法显示,从2002年到2022年,TP显著下降,这与减少的耕地和生态系统改善有关.
科学领域:
- 环境遥感 环境遥感
- 水质监测 监测水质 监测水质
- 机器学习在环境科学中的应用.
背景情况:
- 总 (TP) 对水生生态系统至关重要,但由于其非光学性质,通过遥感进行估计具有挑战性.
- 对TP度 (CTP) 的精确时空监测对于像大湖这样的大水体的管理至关重要.
研究的目的:
- 开发和验证一个深度神经网络 (DNN) 模型,用于使用MODIS衍生远程传感反射率 (Rrs) 估计CTP.
- 重建大湖地区历史的CTP分布,并确定其变化的关键驱动因素.
- 评估模型对其他传感器 (如VIIRS) 的可转移性,以提高数据连续性.
主要方法:
- 在3916次同步现场测量和MODIS Rrs数据上开发了一个DNN模型.
- 从2002年起重建的大湖地区的年度和年内CTP空间分布.
- 将重建的CTP与九个潜在因素 (例如,叶绿素a,融雪,耕地) 相关联,并进行了灵敏度分析.
主要成果:
- 在测试组中,DNN模型在CTP估计中实现了高精度 (R2=0.83,MAE=1.05μg/L,RMSE=2.95μg/L).
- 大湖地区近地表CTP在2002-2022年间显著下降 (p <0.05),主要是由于耕地减少和生态系统改善.
- 该模型证明了对VIIRS传感器的稳定性和可转移性 (R2=0.76),确保了数据连续性.
结论:
- 开发的DNN模型为基于遥感的CTP估计和时空重建提供了可靠和实用的方法.
- 减少的耕地和改善的自然生态系统是推动长期下降的关键因素.
- 这项研究增强了我们对CTP动态的理解,并为大湖地区的水资源管理提供了宝贵的工具.
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