Droughts pose significant economic and ecological concerns, and considering climate change projections, timely monitoring and early warning based on satellite observations must be realized at regional to global scales. Nevertheless, whether data reconstruction is necessary to produce high-quality satellite-based time series data for drought monitoring and the data reconstruction approaches to be applied, if necessary, remain unclear. We attempted to fill this knowledge gap by investigating three widely used data reconstruction approaches, i.e., the Savitzky–Golay filter, harmonic analysis of time series (HANTS) and Whittaker Smoother, across the Lancang–Mekong river basin through the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 and MOD11A2 products for 2001–2018 and Google Earth Engine cloud platform. Several remote sensing drought indices based on the unreconstructed and reconstructed vegetation indices (VIs) and Land Surface Temperature (LST) were retrieved for basin-wide drought detection. The performance of the examined reconstruction approaches was evaluated using three statistical indices (coefficient of determination (R²), standard deviation of bias (BIAS(std)) and mean squared error (MSE)), spatial consistency with the reference dataset and capability to characterize the drought events. Data reconstruction considerably enhanced the drought index performances for drought detection; however, reconstruction was not necessary in all situations. The reconstructed drought indices exhibited higher R² values (by 10.6–10.8%), lower BIAS(std) values (by −1.7– −12.5%), and smaller MSE values (by −5.8– −13.4%) compared to those of unreconstructed indices. For most evaluation indicators, HANTS outperformed the other methods, and Vegetation Condition Index (VCI) and Vegetation Health Index (VHI) outperformed the other drought indices. The findings highlight the importance of data reconstruction to detect and characterize drought events and the dependency of the performance of reconstruction methods on drought indices and evaluation metrics when using MODIS time series data.

干旱引起重大的经济和生态问题，考虑到气候变化预测，必须在区域乃至全球范围内实现基于卫星观测的及时监测和预警。然而，对于产生高质量的基于卫星的时间序列数据进行干旱监测是否需要进行数据重建，以及在必要时将要采用的数据重建方法尚不清楚。我们试图通过中等分辨率成像光谱仪研究三种广泛使用的数据重建方法，即Savitzky-Golay滤波器，时间序列谐波分析（HANTS）和Whittaker平滑器，来填补这一知识空白。适用于2001–2018年的MOD13Q1和MOD11A2产品以及Google Earth Engine云平台。检索了基于未重建和重建植被指数（VIs）和地表温度（LST）的几种遥感干旱指数，用于全流域干旱检测。使用三个统计指标（确定系数（R²），偏差的标准偏差（BIAS（std））和均方误差（MSE），与参考数据集的空间一致性以及表征干旱事件的能力。数据重建大大提高了干旱指标在干旱检测方面的表现；但是，并非在所有情况下都必须进行重建。重建的干旱指数表现出较高的R ^2值与未经重建的指标相比，BIAS（std）值降低了10.6-10.8％，BIAS（std）值降低了（-1.7--12.5％），MSE值降低了（5.8--13.4％）。对于大多数评估指标，HANTS优于其他方法，植被状况指数（VCI）和植被健康指数（VHI）优于其他干旱指数。这些发现强调了数据重建对于检测和表征干旱事件的重要性，以及使用MODIS时间序列数据时重建方法的性能对干旱指数和评估指标的依赖性。