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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Downscaling Solar-Induced Chlorophyll Fluorescence Based on Convolutional Neural Network Method to Monitor Agricultural Drought
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Downscaling Solar-Induced Chlorophyll Fluorescence Based on Convolutional Neural Network Method to Monitor Agricultural Drought

机译：基于卷积神经网络监测农业干旱的卷积性太阳能诱导的叶绿素荧光

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Agricultural drought is a frequent global phenomenon. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. The global 0.05° spatial resolution data set has been obtained using the data-driven algorithm method. However, the broken farmland is not conducive to regional agricultural drought monitoring. Hence, 0.05° SIF products should be downscaled. On this basis, a convolutional neural network (CNN) downscaled work was conducted in this article to obtain 0.008° spatial resolution SIF results. The downscaled SIF and land surface temperature (LST) data were used to establish the temperature fluorescence dryness index (TFDI). The new TFDI was subsequently used for monitoring agricultural drought in Henan province (China) during the corn-growing season (from June to October 2013–2017). Results showed that the downscaled SIF data exhibit a good correlation with gross primary productivity (GPP) from the Moderate Resolution Imaging Spectroradiometer (MODIS) than 0.05° SIF products. During the study period, the soil moisture fluctuation corresponded well with precipitation, and the value of TFDI had an opposite fluctuation with soil moisture. Meanwhile, the annual averaged TFDI had a high correlation with summer corn yield ( $R = -0.84$ ). In conclusion, the SIF results through the CNN-based downscaled method were reliable, and the new TFDI was suitable for region agricultural drought monitoring.
机译：农业干旱是一种常见的全球现象。太阳能诱导的叶绿素荧光（SIF）是光合作用的副产物，可用于监测植被生长和农业干旱。使用数据驱动算法方法获得了全局0.05°空间分辨率数据集。然而，破碎的农田并不有利于区域农业干旱监测。因此，0.05°SIF产品应缩小。在此基础上，在本文中进行了卷积神经网络（CNN）缩小工作，以获得0.008°空间分辨率SIF结果。较低的SIF和陆地温度（LST）数据用于建立温度荧光干燥指数（TFDI）。新的TFDI随后用于在玉米生长季节（从2013-2017 6月开始）的河南省（中国）的农业干旱。结果表明，较低的SIF数据从中等分辨率成像光谱辐射器（MODIS）与0.05°SIF产品的总初级生产率（GPP）表现出良好的相关性。在研究期间，土壤水分波动与沉淀相对应良好，TFDI的值与土壤水分相反。同时，年平均TFDI与夏季玉米产量的相关性高（<内联XMLNS：MML =“http://www.w3.org/1998/math/mathml”xmlns：xlink =“http：// www .w3.org / 1999 / xlink“> $ r = -0.84 $ ）。总之，通过基于CNN的较低方法的SIF结果可靠，新的TFDI适用于地区农业干旱监测。

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