The data quality issue induced by atmospheric and other disturbances can significantly impede the application of remote sensing vegetation indices (VIs). Despite the development of numerous VI reconstruction techniques, two major challenges remain, i.e., the reliance on quality flag inputs and parameter settings. Quality flags are usually necessary as inputs for the different methods to improve the reconstruction accuracy, but mislabeling can be common in the quality flag data, which can directly introduce uncertainties. Furthermore, constant parameter schemes are usually assigned during the reconstruction applications, but the optimal parameters for all the models can show great spatial heterogeneity. Accordingly, in this paper, we propose a parameter-free and flag-free adaptive time-series method based on a variational reconstruction framework (PF-Free) to address the afore-mentioned issues, which can be applied without any parameter or flag inputs. PF-Free makes full use of the time-series temporal smoothness and inter-annual similarity to label the data after time-series rearrangement, and the parameters are adaptively selected using an improved generalized cross-validation (GCV) technique. Simulation and real-data experiments all demonstrate that PF-Free can achieve better and more stable reconstruction results, compared to other comparative methods. The adaptive quality flags can denote the data quality robustly and accurately, while guaranteeing better reconstruction performance, as long as there is any mislabeling in the original flags. Moreover, the adaptive parameter selection strategy considers the great spatial heterogeneity in the optimal parameters on a pixel-by-pixel basis, leading to more stable reconstruction outcomes under complex conditions. Further experiments also prove the effectiveness of PF-Free in processing data without quality flags or severely contaminated data, using Advanced Very High-Resolution Radiometer (AVHRR) data and Moderate Resolution Imaging Spectroradiometer (MODIS) daily normalized difference vegetation index (NDVI) data. This work provides a practical reconstruction method for VI time series, which is both flexible and convenient, without requiring any parameter or flag input, which we believe will advance VI reconstruction applications.

Hyperspectral images (HSIs) are inevitably degraded by a mixture of various types of noise, such as Gaussian noise, impulse noise, stripe noise, and dead pixels, which greatly limits the subsequent applications. Although various denoising methods have already been developed, accurately recovering the spatial-spectral structure of HSIs remains a challenging problem to be addressed. Furthermore, serious stripe noise, which is common in real HSIs, is still not fully separated by the previous models. In this paper, we propose an adaptive hyper-Laplacian regularized low-rank tensor decomposition (LRTDAHL) method for HSI denoising and destriping. On the one hand, the stripe noise is separately modeled by the tensor decomposition, which can effectively encode the spatial-spectral correlation of the stripe noise. On the other hand, adaptive hyper-Laplacian spatial-spectral regularization is introduced to represent the distribution structure of different HSI gradient data by adaptively estimating the optimal hyper-Laplacian parameter, which can reduce the spatial information loss and over-smoothing caused by the previous total variation regularization. The proposed model is solved using the alternating direction method of multipliers (ADMM) algorithm. Extensive simulation and real-data experiments all demonstrate the effectiveness and superiority of the proposed method.

Solar-induced chlorophyll fluorescence (SIF) is an intuitive and accurate way to measure vegetation photosynthesis. Orbiting Carbon Observatory-2 (OCO-2)-retrieved SIF has shown great potential in estimating terrestrial gross primary production (GPP), but the discontinuous spatial coverage limits its application. Although some researchers have reconstructed OCO-2 SIF data, few have considered the uneven spatial and temporal distribution of the swath-distributed data, which can induce large uncertainties. In this article, we propose a spatiotemporal constrained light gradient boosting machine model (ST-LGBM) to reconstruct a contiguous OCO-2 SIF product (eight days, 0.05°), considering the data distribution characteristics. Two spatial and temporal constraining factors are introduced to utilize the relationships between the swath-distributed OCO-2 samples, combining the geographical regularity and vegetation phenological characteristics. The results indicate that the ST-LGBM method can improve the reconstruction accuracy in the missing data areas ($R^{2}= 0.79$), with an increment of 0.05 in$R^{2}$. The declined accuracy of the traditional light gradient boosting machine (LightGBM) method in the missing data areas is well alleviated in our results. The real-data comparison with TROPOspheric Monitoring Instrument (TROPOMI) SIF observations also shows that the results of the ST-LGBM method can achieve a much better consistency, in both spatial distribution and temporal variation. The sensitivity analysis also shows that the ST-LGBM can support stable results when using various input combinations or different machine learning models. This approach represents an innovative way to reconstruct a more accurate globally continuous OCO-2 SIF product and also provides references to reconstruct other data with a similar distribution.

Topography data are essential for land management, hydrology, and earth science applications. The topography in a riparian zone varies with time. Available global digital elevation models (GDEMs) do not satisfy the hydrological and environmental modeling requirements for riparian zones. In this study, a novel waterline method was proposed to acquire the digital elevation model (DEM) of riparian zones. Synthetic aperture radar (SAR) images are used to extract waterlines and corresponding water level is applied to acquire the elevation. The uneven water surfaces are considered to acquire the elevation of waterlines and Shuttle Radar Topography Mission (SRTM) DEM is used to interpolate the elevation of pixels without waterlines. The proposed method is applied in the riparian zone of Three Gorges Reservoir and Dongting Lake to test its feasibility. More than 80 Sentinel-1 SAR images at different water levels within three years are used to detect waterlines. Based on corresponding water level of hydrological stations, the elevation of waterlines are acquired via inverse distance weighting method. The distance layer and slope derived from the original SRTM DEM are used to interpolate the elevation of pixels between waterlines. The DEM generated by the proposed method is compared against the DEM derived in 2017, SRTM DEM, the DEM which does not include the uneven effects, and Ice, Cloud and land Elevation Satellite -2 (ICESat-2) ATL08. The good performance of the proposed method implies its high efficiency in revealing riparian zone topography.

Clouds greatly reduce the available ground information in optical remote sensing images. This paper proposed a double shift network to remove the thick clouds from multitemporal remote sensing images. The proposed networks are divided into two shift steps. In the first shift, the moment match and style transfer play the role of multi-temporal image normalization to obtain more reliable training images. In the second shift, in order to improve the network architecture's ability of capturing global semantics and local details, the shift connection layer and depthwise separable convolutions are introduced into U-Net. These two shift steps can not only improve the visual effect of cloud removal, but also further improve the quantitative evaluation. Experiments prove that the double shift network shows great advantages in cloud removal.