Detang Zhong

High spatiotemporal resolution of terrestrial total water storage plays a key role in assessing trends and availability of water resources. This study presents a two-step method for downscaling GRACE-derived total water storage anomaly (GRACE TWSA) from its original coarse spatiotemporal resolution (monthly, 3-degree spherical cap/~300 km) to a high resolution (daily, 5 km) through combining land surface model (LSM) simulated high spatiotemporal resolution terrestrial water storage anomaly (LSM TWSA). In the first step, an iterative adjustment method based on the self-calibration variance-component model (SCVCM) is used to spatially downscale the monthly GRACE TWSA to the high spatial resolution of the LSM TWSA. In the second step, the spatially downscaled monthly GRACE TWSA is further downscaled to the daily temporal resolution. By applying the method to downscale the coarse resolution GRACE TWSA from the Jet Propulsion Laboratory (JPL) mascon solution with the daily high spatial resolution (5 km) LSM TWSA from the Ecological Assimilation of Land and Climate Observations (EALCO) model, we evaluated the benefit and effectiveness of the proposed method. The results show that the proposed method is capable to downscale GRACE TWSA spatiotemporally with reduced uncertainty. The downscaled GRACE TWSA are also evaluated through in-situ groundwater monitoring well observations and the results show a certain level agreement between the estimated and observed trends.

High‐resolution data of total water storage play a key role in assessing trends and availability of water resources. This study presents an iterative adjustment method based on the Self‐calibration Variance‐Component Model (SCVCM) for spatially downscaling GRACE‐derived Total Water Storage Anomaly (GRACE TWSA) from its original coarse resolution (∼300 km) to a high resolution (5 km) through integrating Land Surface Model (LSM) simulated high‐resolution Terrestrial Water Storage Anomaly (LSM TWSA). The proposed method takes the GRACE TWSA and the LSM TWSA, which includes soil water content, snow water equivalent, and plant water, as inputs with unknown uncertainties. It then establishes an observation system to estimate the unknown TWSA at the high resolution through an iterative adjustment process based on a posteriori variance‐component estimation technique. By applying the method to the coarse‐resolution (∼300 km) GRACE TWSA from the JPL (Jet Propulsion Laboratory) mascon solution and the high‐resolution (5 km) LSM TWSA from the Ecological Assimilation of Land and Climate Observations (EALCO) model, we evaluated its benefit and effectiveness. The results show that the proposed method is capable to downscale GRACE TWSA with improved uncertainties. The downscaled GRACE TWSA are also evaluated through in situ groundwater monitoring well observations and the results show a certain level agreement between the estimated and observed trends.

Time-series for medium spatial resolution satellite imagery are a valuable resource for environmental assessment and monitoring at regional and local scales. Sentinel-2 satellites from the European Space Agency (ESA) feature a multispectral instrument (MSI) with 13 spectral bands and spatial resolutions from 10 m to 60 m, offering a revisit range from 5 days at the equator to a daily approach of the poles. Since their launch, the Sentinel-2 MSI image time-series from satellites have been used widely in various environmental studies. However, the values of Sentinel-2 image time-series have not been fully realized and their usage is impeded by cloud contamination on images, especially in cloudy regions. To increase cloud-free image availability and usage of the time-series, this study attempted to reconstruct a Sentinel-2 cloud-free image time-series using an extended spatiotemporal image fusion approach. First, a spatiotemporal image fusion model was applied to predict synthetic Sentinel-2 images when clear-sky images were not available. Second, the cloudy and cloud shadow pixels of the cloud contaminated images were identified based on analysis of the differences of the synthetic and observation image pairs. Third, the cloudy and cloud shadow pixels were replaced by the corresponding pixels of its synthetic image. Lastly, the pixels from the synthetic image were radiometrically calibrated to the observation image via a normalization process. With these processes, we can reconstruct a full length cloud-free Sentinel-2 MSI image time-series to maximize the values of observation information by keeping observed cloud-free pixels and calibrating the synthetized images by using the observed cloud-free pixels as references for better quality.

A key challenge in developing models for the fusion of surface reflectance data across multiple satellite sensors is ensuring that they apply to both gradual vegetation phenological dynamics and abrupt land surface changes. To better model land cover spatial and temporal changes, we proposed previously a Prediction Smooth Reflectance Fusion Model (PSRFM) that combines a dynamic prediction model based on the linear spectral mixing model with a smoothing filter corresponding to the weighted average of forward and backward temporal predictions. One of the significant advantages of PSRFM is that PSRFM can model abrupt land surface changes either through optimized clusters or the residuals of the predicted gradual changes. In this paper, we expanded our approach and developed more efficient methods for clustering. We applied the new methods for dramatic land surface changes caused by a flood and a forest fire. Comparison of the model outputs showed that the new methods can capture the land surface changes more effectively. We also compared the improved PSRFM to two most popular reflectance fusion algorithms: Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced version of STARFM (ESTARFM). The results showed that the improved PSRFM is more effective and outperforms STARFM and ESTARFM both visually and quantitatively.

Landsat images have been widely used in support of responsible development of natural resources, disaster risk management (e.g., forest fire, flooding etc.), agricultural production monitoring, as well as environmental change studies due to its medium spatial resolution and rich spectral information. However, its availability and usability are largely constrained by its low revisit frequency. On the other hand, MODIS (Moderate Resolution Imaging Spectroradiometer) images for land studies have much more frequent coverage but with a lower spatial resolution of 250–500 m. To take advantages of the two sensors and expand their availability and usability, during the last decade, a number of image fusion methods have been developed for generating Landsat-like images from MODIS observations to supplement clear-sky Landsat imagery. However, available methods are typically effective or applicable for certain applications. For a better result, a new Prediction Smooth Reflectance Fusion Model (PSRFM) for blending Landsat and MODIS images is proposed. PSRFM consists of a dynamic prediction model and a smoothing filter. The dynamic prediction model generates synthetic Landsat images from a pair of Landsat and MODIS images and another MODIS image, either forward or backward in time. The smoothing filter combines the forward and backward predictions by weighted average based on elapsed time or on the estimated prediction uncertainty. Optionally, the smooth filtering can be applied with constraints based on Normalized Difference Snow Index (NDSI) or Normalized Difference Vegetation Index (NDVI). In comparison to some published reflectance fusion methods, PSRFM shows the following desirable characteristics: (1) it can deal with one pair or two pairs of Landsat and MODIS images; (2) it can incorporate input image uncertainty during prediction and estimate prediction uncertainty; (3) it can track gradual vegetation phenological changes and deal with abrupt land-cover type changes; and (4) for predictions using two pairs of input images, the results can be further improved through the constrained smoothing filter based on NDSI or NDVI for certain applications. We tested PSRFM to generate a Landsat-like image time series by using Landsat 8 OLI and MODIS (MOD09GA) images and compared it to two reflectance fusion algorithms: STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) and ESTARFM (Enhanced version of STARFM). The results show that the proposed PSRFM is effective and outperforms STARFM and ESTARFM both visually and quantitatively.

Zero-length spring gravity sensors, such as the Micro-g LaCoste Turnkey Airborne Gravity System (TAGS), are widely used in airborne gravimetry applications due to their virtually drift-free characteristics. However, the quality of the final gravity product depends on the methods chosen to correct for several dynamic effects, including the Eötvös correction, the vertical acceleration correction of the moving aircraft, and the instrument platform tilt correction. For geodetic applications using regional gravity field mapping for geoid determination, the choices of correction method are extremely important because the flights are done uncommonly high and fast. For this reason, and in support of the Gravity for the Redefinition of the American Vertical Datum (GRAV-D) project, the National Oceanic and Atmospheric Administration’s National Geodetic Survey (NGS), and Fugro Geospatial have independently developed airborne gravity processing software packages for TAGS data — called Newton and GravPRO, respectively. We have evaluated the airborne gravity data processing methods that are used in the two software packages. We processed a GRAV-D data set collected by NGS over Louisiana and the Gulf of Mexico in 2008 with both packages, we compared the results for each major data correction applied, and we evaluated unique add-on features and tools. The results determined that the two software packages were very comparable, both yielding an excellent crossover root-mean-square of 1.8 mGal without any crossover adjustment applied. For the worst 14% of survey lines, which had higher levels of aircraft dynamics during flight, GravPRO was able to retain more data due to advantages in its platform tilt correction method. Also, GravPRO’s instrument calibration function, which is based on crossover error analysis, found a 45% improvement in achievable survey precision from approximately 2 mGal to approximately 1 mGal when comparing the test data set to a global gravity model.