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缩小MODIS陆地表面温度,使用应用到MODIS和哨兵2图像的特拉佩齐尔概念

Zohreh Faraji¹, Abbas Kaviani², Leila Khosravi³

¹Water Science and Engineering Dept., Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazin, Iran.

Environmental monitoring and assessment

|June 27, 2024

概括

新的OPTRAM和TOTRAM模型减少了热图像缩小的错误,特别是在灌的田地. 与传统的DisTRAD模型相比,这些模型提高了陆地表面温度 (LST) 数据的准确性.

科学领域:

遥感遥感遥感遥感
环境科学环境科学
农业科学农业科学

背景情况:

高分辨率的热数据对于环境监测至关重要.
传统的缩放模型,如DisTRAD,由于土壤湿度的变化,可能会在灌地区产生错误.

研究的目的:

评估OPTRAM和TOTRAM模型在降低土地表面温度 (LST) 的有效性,降低灌领域的误差.
为了比较OPTRAM-TOTRAM与DisTRAD模型的性能.

主要方法:

利用哨兵卫星图像将MODIS LST从1000米缩小到20米的分辨率.
使用OPTRAM模型进行土壤湿度估计,并将其与TOTRAM集成用于LST缩小.
使用卡兹文省36个地面点的观测数据验证了结果.

主要成果:

在NDVI>0.6.6.时,OPTRAM模型显示了土壤湿度估计的高精度 (R2>0.88,RMSE <0.06 cm3/cm3).
与Distrad相比,OPTRAM-TOTRAM模型在灌事件中分别减少了平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 约0.4°C和1.2°C.

结论:

OPTRAM-TOTRAM显著提高了LST在灌农田的缩小精度.
该研究建议进一步评估OPTRAM和TOTRAM用于MODIS LST在各种灌环境中的缩小规模.

关键词:

这就是DisTRAD模型.在OPTRAM模型中,土壤水分土壤水分托特拉姆车型模型

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