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The Optimal Threshold and Vegetation Index Time Series for Retrieving Crop Phenology Based on a Modified Dynamic Threshold Method

机译:基于修改动态阈值方法检索作物候选的最优阈值和植被指数序列

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摘要

Crop phenology is an important parameter for crop growth monitoring, yield prediction, and growth simulation. The dynamic threshold method is widely used to retrieve vegetation phenology from remotely sensed vegetation index time series. However, crop growth is not only driven by natural conditions, but also modified through field management activities. Complicated planting patterns, such as multiple cropping, makes the vegetation index dynamics less symmetrical. These impacts are not considered in current approaches for crop phenology retrieval based on the dynamic threshold method. Thus, this paper aimed to (1) investigate the optimal thresholds for retrieving the start of the season (SOS) and the end of the season (EOS) of different crops, and (2) compare the performances of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in retrieving crop phenology with a modified version of the dynamic threshold method. The reference data included SOS and EOS ground observations of three major crop types in 2015 and 2016, which includes rice, wheat, and maize. Results show that (1) the modification of the original method ensures a 100% retrieval rate, which was not guaranteed using the original method. The modified dynamic threshold method is more suitable to retrieve crop SOS/EOS because it considers the asymmetry of crop vegetation index time series. (2) It is inappropriate to retrieve SOS and EOS with the same threshold for all crops, and the commonly used 20% or 50% thresholds are not the optimal thresholds for all crops. (3) For single and late rice, the accuracies of the SOS estimations based on EVI are generally higher compared to those based on NDVI. However, for spring maize and summer maize, results based on NDVI give higher accuracies. In terms of EOS, for early rice and summer maize, estimates based on EVI result in higher accuracies, but, for late rice and winter wheat, results based on NDVI are closer to the ground records.
机译:作物候选是作物生长监测,产量预测和生长模拟的重要参数。动态阈值方法广泛用于从远程感测的植被指数时间序列中检索植被候选。然而,作物增长不仅由自然条件驱动,而且还通过现场管理活动进行修改。复杂的种植模式,例如多种裁剪,使得植被指数动态更少对称。基于动态阈值方法,在作物候选检索的当前方法中不考虑这些影响。因此,本文旨在(1)研究了检索季节开始(SOS)的最佳阈值和不同作物的季节(EOS),(2)比较归一化差异植被指数的性能( NDVI)和增强植被指数(EVI)用动态阈值方法的修改版本检索作物候选。参考资料包括2015年和2016年三种主要作物类型的SOS和EOS接地观察,其中包括米饭,小麦和玉米。结果表明,(1)原始方法的修改确保了100%的检索速率,使用原始方法保证不保证。修改的动态阈值方法更适合于检索庄稼SOS / EOS,因为它考虑了茶植物指数时间序列的不对称性。 (2)检索具有相同阈值的SOS和EOS对所有作物的阈值是不合适的,并且常用的20%或50%阈值不是所有作物的最佳阈值。 (3)对于单层和晚稻,与基于NDVI的那些,基于EVI的SOS估计的精度通常更高。然而,对于春季玉米和夏季玉米,基于NDVI的结果提供更高的精度。就EOS而言,对于早稻和夏季玉米来说,基于EVI的估计导致更高的准确性,但对于晚稻和冬小麦,基于NDVI的结果更接近地面记录。

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