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    Remote sensing inversion of multi-period winter wheat canopy water content based on a genetic algorithm
    Suyun NIE, Bin YANG, Wei XIA, Yuan ZHANG
    Journal of East China Normal University(Natural Science)    2023, 2023 (3): 71-81.   DOI: 10.3969/j.issn.1000-5641.2023.03.008
    Abstract198)   HTML17)    PDF (8950KB)(56)      

    The remote sensing inversion of crop canopy water content is a valuable for assessing drought stress of wheat fields and implementing precision irrigation. This study aimed to quickly obtain the canopy water content during the growth period for winter wheat in North China by using multi-temporal remote sensing images of Landsat-8 OLI and Sentinel-2 MSI from January to May 2017. The regression relationship was constructed with NDWI and measured water content in a wheat field via the mixed pixel decomposition model. The genetic algorithm was then used to inverse the canopy water content. The proposed method demonstrated better performance compared to ground-truth data, with the coefficient of determination (R2) and the root mean square error (RMSE) of 0.567 and 5.6%, respectively. Additionally, the error was reduced by more than 20% when compared to the direct inversion based on NDWI. This study indicates that quantification of different linear mixing ratios of wheat canopy and background soil can effectively eliminate the influence of soil on wheat water content inversion, and is crucial for the application of remote sensing to wheat growth monitoring.

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    Emergency monitoring of remote sensing for flood inundation region based on SAR texture and LightGBM
    Cheng SUN, Fang SHEN, Rugang TANG
    Journal of East China Normal University(Natural Science)    2023, 2023 (3): 82-92.   DOI: 10.3969/j.issn.1000-5641.2023.03.009
    Abstract207)   HTML20)    PDF (2605KB)(92)      

    In response to the need for high timeliness and accuracy monitoring for inundation region during flood disaster, a new extraction method of water areas based on SAR texture and LightGBM was proposed. Compared with other methods, such as the SDWI water index, SVM, RF and GBDT methods, it shows that the accuracy of water extraction of river, lake and flooded area is beyond 98% and higher than other methods. Meanwhile, the operating efficiency of the proposed method is 20 ~ 100 times higher than other methods, which greatly improves the timeliness of inundation emergency monitoring during flood disaster.

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