Remote sensing can be used to monitor cropland phenological characteristics; however, tradeoffs between the spatial and temporal resolutions of cloudless satellite images limit the accuracy of their retrieval. In this study, an improved enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to human-dominated Xiong’an New Area to develop a self-adapting algorithm automating the extraction of main phenological transition points (greenup, maturity, senescence, and dormancy). The analyses of cropland phenological characteristics were performed utilizing the Softmax classification method. By examining three different phases of fusion images, it was found that the improved ESTARFM was more accurate than the original ESTARFM (correlation coefficient > 0.76; relative root mean square error < 0.25; structural similarity index > 0.79). The reconstructed normalized difference vegetation indexes were consistent with that acquired by the Moderate Resolution Imaging Spectroradiometer (average discrepancy: 0.1136, median absolute deviation: 0.0110). The greenup, maturity, senescence, and dormancy points were monitored in 5-day resolution and 50-day length on a 30-m grid scale, and their average day of year (DOY) were 67, 119, 127, and 166 for wheat; 173, 224, 232, and 283 for single-season corn; and 189, 227, 232, and 285 for rotation corn, respectively. The corresponding median absolute deviations were 2, 3, 2, and 2 days for wheat; 2, 5, 3, and 4 days for single-season corn; and 2, 5, 2, and 2 days for rotation corn, respectively, while all coefficients of variation did not exceed 6%. The proposed self-adapting approach can be used for identifying the planting modes at grid level in rotation agroecosystems and cropland phenological dynamics on a global or regional scale.

中文翻译：

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动态融合NDVI图像自适应提取轮作农业生态系统农田物候转变

遥感可用于监测耕地物候特征；然而，无云卫星图像的空间和时间分辨率之间的权衡限制了其检索的准确性。本研究将改进的增强时空自适应反射融合模型（ESTARFM）应用于以人为主导的雄安新区，开发了一种自适应算法，自动提取主要物候转变点（绿化、成熟、衰老、和休眠）。农田物候特征的分析是利用Softmax分类方法进行的。通过检查融合图像的三个不同阶段，发现改进的ESTARFM比原始ESTARFM更准确（相关系数> 0.76；相对均方根误差< 0.25；结构相似指数 > 0.79）。重建的归一化差异植被指数与中分辨率成像光谱仪获得的一致（平均差异：0.1136，中值绝对偏差：0.0110）。在 30 米网格尺度上以 5 天分辨率和 50 天长度监测绿化、成熟、衰老和休眠点，它们的平均年日数 (DOY) 分别为 67、119、127 和 166 天。 ; 单季玉米为 173、224、232 和 283；轮作玉米分别为 189、227、232 和 285。小麦相应的中位数绝对偏差为 2、3、2 和 2 天；单季玉米2、5、3、4天；和轮作玉米分别为 2、5、2 和 2 天，而所有变异系数不超过 6%。