基于随机森林和卷积神经网络的FY-4A号卫星沙尘监测研究

Sand and Dust Monitoring Using FY-4A Satellite Data based on the Random Forests and Convolutional Neural Networks

利用归一化差值沙尘指数NDDI(Normalized Difference Dust Index)、随机森林RF(Random Forests)和卷积神经网络CNN(Convolutional Neural Networks)算法结合对地静止风云四号气象卫星(FY-4A)中多通道扫描成像辐射计AGRI(Advanced Geostationary Radiation Imager)数据对塔里木盆地沙尘进行监测研究。结果表明,AGRI数据计算得NDDI_(AGRI)沙尘指数在监测沙尘时,需要针对不同时间的AGRI数据取不同的阈值;并且对云和陆地的交错区域,以及一些植被覆盖和荒漠交错区域存在错误识别。RF建立的沙尘监测模型,测试样本精确度(Precision)、召回率(Recall)、F1-score的值都达100%,训练样本的交叉验证精度平均值为99.5%;CNN模型中,训练样本和测试样本的精确值(Accuracy)和损失函数值(Loss)都分别为99.9%和0.1%;因此RF和CNN模型都具有较强的沙尘监测能力。在实际沙尘监测中CNN相比RF在识别沙尘与非沙尘交界处更加精确,RF和CNN在沙尘识别过程中都易将部分沙尘与云混合区域以及戈壁错误识别成沙尘。

Sand and dust is a typical weather disaster which outbreaks in arid and semi-arid areas globally.This natural phenomenon,which is the result of stormy winds,raises a lot of dust from desert surfaces and decreases visibility to less than 1 km.The dust aerosol generated from dust storm dominates the aerosol loading in the troposphere and has comprehensive impacts on the global environment,weather,climate and ecology.Monitoring sand and dust from space using satellite remote sensing has become one of the most important issues in this field.However,sand and dust is difficult to accurately characterize by using single-band and linear models.Feng Yun-4A(FY-4A)imagery provides a good data source for timely and accurate monitoring of sand and dust.The machine learning models are important tools in sand and dust monitoring and forecasting.In this paper,the Normalized Difference Dust Index(NDDI),Random Forests(RF)and Convolutional Neural Networks(CNN)were employed to monitor sand and dust based on the Advanced Geostationary Radiation Imager(AGRI)of geostationary FY-4A meteorological satellite in the Tarim Basin.The results showed that,sand and dust can be identified by NDDI_(AGRI)thresholds calculated using AGRI data.The determination of the NDDI_(AGRI)thresholds were obtained through statistical analysis of pixels,but it is necessary to take different thresholds for different times AGRI data.There are some identification errors in the cross region of cloud and land,and some vegetation coverage and desert by the NDDI_(AGRI)thresholds.The values of Precision,Recall,and F1-score of testing samples were all 100%;and the accuracy of cross validation of training samples was 99.5%for the sand and dust model of RF.The Loss and Accuracy in the estimation obtained using the CNN algorithm were about 0.1%and 99.9%,respectively,versus the training samples and testing samples.Both RF and CNN models have the ability and robustness to be used in sand and dust monitoring.The efficiency of two models had been checked using new dust events.Results show that the CNN algorithm preforms better than RF algorithm in identifying the junction of dust and non-dust.The RF and CNN algorithm have identification errors in some parts of sand and dust monitoring process,such as the mixed area of dust and clouds,and the Gobi area.The research results of this paper provide an important basis application of machine learning combined with FY-4A meteorological satellite data to monitor sand and dust operational.