Radar altimetry is now commonly used to provide long-term monitoring of inland water levels in complement to or for replacing disappearing in situ networks of gauge stations. Recent improvements in tracking and acquisition modes improved the quality the water retrievals. The newly implemented Open Loop mode is likely to increase the number of monitored water bodies owing to the use of an a priori elevation, especially in hilly and mountainous areas. The novelty of this study is to provide a comprehensive evaluation of the performances of the past and current radar altimetry missions according to their acquisition (Low Resolution Mode or Synthetic Aperture Radar) and tracking (close or open loop) modes, and acquisition frequency (Ku or Ka) in a mountainous area where tracking losses of the signal are likely to occur, as well as of the recently launched ICESat-2 and GEDI lidar missions. To do so, we evaluate the quality of water level retrievals from most radar altimetry missions launched after 1995 over eight lakes in Switzerland, using the recently developed ALtimetry Time Series software, to compare the performances of the new tracking and acquisition modes and also the impact of the frequency used. The combination of the Open Loop tracking mode with the Synthetic Aperture Radar acquisition mode on SENTINEL-3A and B missions outperforms the classical Low Resolution Mode of the other missions with a lake observability greater than 95%, an almost constant bias of (−0.17 ± 0.04) m, a RMSE generally lower than 0.07 m and a R most of the times higher than 0.85 when compared to in situ gauge records. To increase the number of lakes that can be monitored and the temporal sampling of the water level retrievals, data acquired by lidar altimetry missions were also considered. Very accurate results were also obtained with ICESat-2 data with RMSE lower than 0.06 and R higher than 0.95 when compared to in situ water levels. An almost constant bias (0.42 ± 0.03) m was also observed. More contrasted results were obtained using GEDI. As these data were available on a shorter time period, more analyses are necessary to determine their potential for retrieving water levels.

A total of 13 satellite missions have been launched since 1985, with different types of radar altimeters on board. This study intends to make a comprehensive evaluation of historic and currently operational satellite radar altimetry missions for lake water level retrieval over the same set of lakes and to develop a strategy for constructing consistent long-term water level records for inland lakes at global scale. The lake water level estimates produced by different retracking algorithms (retrackers) of the satellite missions were compared with the gauge measurements over 12 lakes in four countries. The performance of each retracker was assessed in terms of the data missing rate, the correlation coefficient r, the bias, and the root mean square error (RMSE) between the altimetry-derived lake water level estimates and the concurrent gauge measurements. The results show that the model-free retrackers (e.g., OCOG/Ice-1/Ice) outperform the model-based retrackers for most of the missions, particularly over small lakes. Among the satellite altimetry missions, Sentinel-3 gave the best results, followed by SARAL. ENVISAT has slightly better lake water level estimates than Jason-1 and Jason-2, but its data missing rate is higher. For small lakes, ERS-1 and ERS-2 missions provided more accurate lake water level estimates than the TOPEX/Poseidon mission. In contrast, for large lakes, TOPEX/Poseidon is a better option due to its lower data missing rate and shorter repeat cycle. GeoSat and GeoSat Follow-On (GFO) both have an extremely high data missing rate of lake water level estimates. Although several contemporary radar altimetry missions provide more accurate lake level estimates than GFO, GeoSat was the sole radar altimetry mission, between 1985 and 1990, that provided the lake water level estimates. With a full consideration of the performance and the operational duration, the best strategy for constructing long-term lake water level records should be a two-step bias correction and normalization procedure. In the first step, use Jason-2 as the initial reference to estimate the systematic biases with TOPEX/Poseidon, Jason-1, and Jason-3 and then normalize them to form a consistent TOPEX/Poseidon–Jason series. Then, use the TOPEX/Poseidon–Jason series as the reference to estimate and remove systematic biases with other radar altimetry missions to construct consistent long-term lake water level series for ungauged lakes.

Variations in snow cover resulting from global warming inevitably affect alpine vegetation growth on the Tibetan Plateau (TP), but our knowledge of such influences is still limited. Here, we investigated the relationship between snow cover and alpine vegetation during 2003–2020 on the TP using the satellite-derived vegetation green-up date (GUD) and metrics of snow cover phenology, namely the snow cover onset date (SCOD), snow cover end date (SCED), snow cover duration (SCD), and snowmelt onset date (SMOD). In this study, we first analyzed the spatiotemporal changes in the GUD and the snow cover phenology metrics on the TP. Pearson’s correlation, gray relation analysis, and linear regression were then employed to determine the impact of snow cover phenology on the GUD. Overall, with the SCOD, SCED, and SMOD delayed by one day, the GUD was advanced by 0.07 and 0.03 days and was postponed by 0.32 days, respectively, and a one-day extension of the SCD resulted in a 0.04-day advance in the GUD. In addition, the roles of vegetation type, topography, and climate factors (temperature and precipitation) in modulating the relationships between snow cover phenology and the GUD were evaluated. The GUD of alpine steppes was negatively correlated with the SCOD and SCED, contrary to that of the other vegetation types. The GUD of alpine steppes was also more sensitive to snow cover phenology than that of other vegetation types. The increase in elevation generally enhanced the sensitivity of the GUD to snow cover phenology. The GUD showed a stronger negative sensitivity to the SCD in warmer areas and a stronger positive sensitivity to the SMOD in wetter areas. Our findings revealed the essential impact of variation in snow cover phenology on the GUD and indicated the complex interference of environmental factors in the relationship between snow cover and vegetation growth.

The vegetation green-up date (GUD) of the Tibetan Plateau (TP) is highly sensitive to climate change. Accurate estimation of GUD is essential for understanding the dynamics and stability of terrestrial ecosystems and their interactions with climate. The GUD is usually determined from a time-series of vegetation indices (VIs). The adoption of different VIs and GUD extraction methods can lead to different GUDs. However, our knowledge of the uncertainty in these GUDs on TP is still limited. In this study, we evaluated the performance of different VIs and GUD extraction methods on TP from 2003 to 2020. The GUDs were determined from six Moderate Resolution Imaging Spectroradiometer (MODIS) derived VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), phenology index (PI), normalized difference phenology index (NDPI), and normalized difference greenness index (NDGI). Four extraction methods (β_max, CCR_max, G20, and RC_max) were applied individually to each VI to determine GUD. The GUDs obtained from all VIs showed similar patterns of early green-up in the eastern and late green-up in the western plateau, and similar trend of GUD advancement in the eastern and postponement in the western plateau. The accuracy of the derived GUDs was evaluated by comparison with ground-observed GUDs from 19 agrometeorological stations. Our results show that two snow-free VIs, NDGI and NDPI, had better performance in GUD extraction than the snow-calibrated conventional VIs, NDVI and EVI. Among all the VIs, NDGI gave the highest GUD accuracy when combined with the four extraction methods. Based on NDGI, the GUD extracted by the CCR_max method was found to have the highest consistency (r = 0.62, p < 0.01, RMSE = 11 days, bias = −3.84 days) with ground observations. The NDGI also showed the highest accuracy for preseason snow-covered site-years (r = 0.71, p < 0.01, RMSE = 10.69 days, bias = −4.05 days), indicating its optimal resistance to snow cover influence. In comparison, NDII and PI hardly captured GUD. NDII was seriously affected by preseason snow cover, as indicated by the negative correlation coefficient (r = −0.34, p < 0.1), high RMSE and bias (RMSE = 50.23 days, bias = −24.25 days).

An urban spatial cluster (USC) describes one or more geographic agglomerations and the linkages among cities. USCs are conventionally delineated based on predefined administrative boundaries of cities, without considering the dynamic and evolving nature of the spatial extent of USCs. This study uses Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) satellite images to quantitatively detect and characterize the evolution of USCs. We propose a dynamic minimum spanning tree (DMST) and a subgraph partitioning method to identify the evolving USCs over time, which considers both the spatial proximity of urban built-up areas and their affiliations with USCs at the previous snapshot. China is selected as a case study for its rapid urbanization process and the cluster-based economic development strategy. Four DMSTs are generated for China using the urban built-up areas extracted from DMSP/OLS NTL satellite images collected in 2000, 2004, 2008, and 2012. Each DMST is partitioned into various subtrees and the urban built-up areas connected by the same subtree are identified as a potential USC. By inspecting the evolution of USCs over time, three different types of USCs are obtained, including newly emerging, single-core, and multicore clusters. Using the rank-size distribution, we find that large-sized USCs have greater development than medium- and small-sized USCs. A clear directionality and heterogeneity are observed in the expansions of the ten largest USCs. Our study provides further insight for the understanding of urban system and its spatial structures, and assists policymakers in their planning practices at national and regional scales.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has been collecting elevation measurements of the Earth’s surface since its launch in September 2018. Although ICESat-2 was not designed for urban applications, its excellent altimetry capabilities over the globe make it possible to obtain urban height information. However, few studies have been conducted to validate ICESat-2 height measurements in urban areas. In this letter, we evaluate the heights retrieved from 20 months of ATL03 and ATL08 data using airborne LiDAR data collected in New York City (NYC). The results indicated that the heights from ATL03 product have a moderate agreement with airborne LiDAR data with vertical errors around −1.49 m (root mean squared error [RMSE] = 2.89 m, mean absolute error [MAE] = 2.11 m, $R^{2} = 0.98$ , and observations = 910 497). The ATL08 product also performed a fine accuracy in terrain heights estimating (mean error [ME] = −0.01 m, RMSE = 3.63 m, MAE = 2.04 m, and $R^{2} = 0.94$ ). In the three categories of urban environments, ATL03 performs best in urban high-rise dense area, with an average residual error of −1.44 m, followed by urban non-high-rise dense area with an average residual error of −1.49 m. In urban forest area, ATL08 shows its performance in measuring terrain height with an RMSE of 1.78 m. We demonstrated that ICESat-2 can provide a useful source of urban heights and holds great potential to light up more urban applications related to urban change monitoring and 3-D morphology.