Drainage reorganization generally occurs on geological time scales under unstable river network conditions. A few local‐scale studies indicate that recent climatic changes might accelerate the drainage reorganization process. However, large‐scale drainage reorganization during the modern era is rarely documented. This study examines ongoing drainage reorganization on the endorheic Tibetan Plateau (TP) as a primary result of drastic lake expansion. A total of 11 drainage system reorganization events comprising 24 different lake basins and covering a total area of 61,115 km² occurred between 2000 and 2018. Assuming the continued growth rate of TP lakes, we project another 20 basins will be reorganized before 2030. These drainage basin reorganizations not only alter hydrological processes in the endorheic TP but may also cause the endorheic‐exorheic transition, leading to the upstream sprawl of the Yangtze River Basin and posing outburst flooding risks on China's key infrastructure such as the Qinghai‐Tibet Railway.

Understanding the Tibetan Plateau’s role in environmental change has gained increasing scientific attention in light of warming and changes in land management. We examine changes in greenness over the Tibetan Plateau using the Normalized Difference Vegetation Index (NDVI) from the Global Inventory Monitoring and Modeling Study (GIMMS₃g) to identify significant changes over the entire plateau, six ecoregions, and protected areas based on a multiyear time series of July imagery from 1982 to 2015. We also test whether there have been changes in human populations in protected areas. There has been relatively little change in mean NDVI over the Tibetan Plateau or ecoregions, however, there were significant changes at the pixel level. There are sixty-nine protected areas on the Tibetan Plateau; sixty-two protected areas had no significant change in mean NDVI and seven protected areas experienced a significant increase in NDVI. There has been an increase in population within protected areas from 2000 to 2015; however, mean populations significantly increased in two protected areas and significantly decreased in four protected areas. Results suggest a slow greening of the Tibetan Plateau, ecoregions, and protected areas, with a more rapid greening in northern Tibet at the pixel level. Most protected areas are experiencing minor changes in NDVI independent of human population.

Interferometric synthetic aperture radar (InSAR) processing techniques have been widely used to derive surface deformation or retrieve terrain elevation. Over the development of the past few decades, most research has mainly focused on its application, new techniques for improved accuracy, or the investigation of a particular error source and its correction method. Therefore, a thorough discussion about each error source and its influence on InSAR-derived products is rarely addressed. Additionally, InSAR is a challenging topic for beginners to learn due to the intricate mathematics and the necessary signal processing knowledge required to grasp the core concepts. This results in the fact that existing papers about InSAR are easy to understand for those with a technical background but difficult for those without. To cope with the two issues, this paper aims to provide an organized, comprehensive, and easily understandable review of the InSAR error budget. In order to assist readers of various backgrounds in comprehending the concepts, we describe the error sources in plain language, use the most fundamental math, offer clear examples, and exhibit numerical and visual comparisons. In this paper, InSAR-related errors are categorized as intrinsic height errors or location-induced errors. Intrinsic height errors are further divided into two subcategories (i.e., systematic and random error). These errors can result in an incorrect number of phase fringes and introduce unwanted phase noise into the output interferograms, respectively. Location-induced errors are the projection errors caused by the slant-ranging attribute of the SAR systems and include foreshortening, layover, and shadow effects. The main focus of this work is on systematic and random error, as well as their effects on InSAR-derived topographic and deformation products. Furthermore, because the effects of systematic and random errors are greatly dependent on radar wavelengths, different bands are utilized for comparison, including L-band, S-band, C-band, and X-band scenarios. As examples, we used the parameters of the upcoming NISAR operation to represent L-band and S-band, ERS-1 and Sentinel-1 to represent C-band, and TerraSAR-X to represent X-band. This paper seeks to bridge this knowledge gap by presenting an approachable exploration of InSAR error sources and their implications. This robust and accessible analysis of the InSAR error budget is especially pertinent as more SAR data products are made available (e.g., NISAR, ICEYE, Capella, Umbra, etc.) and the SAR user-base continues to expand. Finally, a commentary is offered to explore the error sources that were not included in this work, as well as to present our thoughts and conclusions.

Of the approximately 6700 lakes and reservoirs larger than 1 km² in the Contiguous United States (CONUS), only ~430 (~6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA’s ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for ~6200 overlapping lakes and reservoirs (>1 km²) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine ~19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.

The Slumgullion landslide, located in southwestern Colorado, has been active since the early 1700s and current data suggests that the most active portion of the slide creeps at a rate of ~1.5–2.0 cm/day. Accurate deformation measurement techniques are vital to the understanding of persistent, yet slow-moving landslides like the Slumgullion. The factors that affect slope movements at the Slumgullion are on-time scales that are well suited towards a remotely sensed approach to constrain the 12 different kinematic units that make up the persistent creeping landslide. We derive a time series of motion vectors (magnitude and direction) using subpixel offset techniques from very high resolution TerraSAR-X Staring Spotlight ascending/descending data as well as from a novel high-resolution amalgamation of airborne lidar and unmanned aerial systems (UAS) Structure from Motion (SfM) digital surface model (DSM) hillshades. Deformation rates calculated from the spaceborne and airborne datasets show high agreement (mean difference of ~0.9 mm/day), further highlighting the potential for the monitoring of ongoing mass wasting events utilizing unmanned aircraft systems (UAS) We compare pixel offset results from an 11-day synthetic aperture radar (SAR) pair acquired in July of 2016 with motion vectors from a coincident low-cost L1 only Global Navigation Satellite System (GNSS) field campaign in order to verify the remotely sensed results and to derive the accuracy of the azimuth and range offsets. We find that the average azimuth and range pixel offset accuracies utilizing the methods herein are on the order of 1/18 and 1/20 of their along-track and slant range focused ground pixel spacing values of 16.8 cm and 45.5 cm, respectively. We utilize the SAR offset time series to add a twelfth kinematic unit to the previously established set of eleven unique regions at the site of an established minislide within the main landslide itself. Lastly, we compare the calculated rates and direction from all spaceborne- and airborne-derived motion vectors for each of the established kinematic zones within the active portion of the landslide. These comparisons show an overall increased magnitude and across-track component (i.e., more westerly angles of motion) for the descending SAR data as compared to their ascending counterparts. The processing techniques and subsequent results herein provide for an improved knowledge of the Slumgullion landslide’s kinematics and this increased knowledge has implications for the advancement of measurement techniques and the understanding of globally distributed creeping landslides.