For over 40 years, the Landsat satellite series has acted as Earth's “photographer,” archiving a massive library of global surface imagery. Recently, with the rise of cloud-based platforms like Google Earth Engine (GEE), scientists have been able to process these valuable imagery archives to assess the health of rivers and lakes worldwide.However, the accuracy of these assessments has long been compromised by a hidden “visual error”. The standard Landsat Surface Reflectance products typically used in GEE rely on atmospheric correction algorithms developed for land surfaces. When applied to water with very low water-leaving signals, these algorithms often fail, leading to substantial misestimation of water quality.A research team led by Prof. DUAN Hongtao from the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (NIGLAS), with doctoral student YANG Chen as the first author and Dr. SHEN Ming and Prof. DUAN Hongtao as co-corresponding authors, has successfully “corrected” this error on a global scale. By analyzing over 1,000 sets of global field measurements, the researchers identified the root causes of these inaccuracies and proposed an optimized GEE-based solution. The findings were published in the ISPRS Journal of Photogrammetry and Remote Sensing.Through global comparisons, the researchers pointed two critical issues with Landsat surface reflectance data over inland waters.Firstly, when water bodies are near bright objects like clouds or snow, land-based “aerosol correction” algorithms tend to over-compensate. This results in unrealistic “black patches” (artifacts) on the imagery. The hallmark of this error is a negative reflectance value at 443 nm. This issue affects an average of 13% of global inland water pixels. If ignored, relative error in retrieving key indicators, such as suspended particulate matter (SPM) concentration, can exceed 100%.Secondly, cross-sensor inconsistency causes data gaps. Analyzing decades of water quality requires stitching together data from Landsat-5, 7, 8, and 9. However, differences in sensor configuration and algorithms across generations create significant reflectance shifts over inland waters. These inconsistencies introduce significant uncertainties into long-term environmental trend analysis.After testing various optimized algorithms on GEE (including SIAC and MAIN), the researchers concluded that the MAIN algorithm, based on the “Short-Wave Infrared black pixel” assumption, is currently the best choice.By avoiding over-correction, MAIN removes artifacts in images, restoring true spatial patterns. Also, it provides a consistent framework across different satellites, improving temporal consistency by over 90% and enabling a seamless 40-year data record. Already deployed on GEE, the algorithm allows researchers to generate high-quality water reflectance products directly in the cloud without downloading massive datasets.For those who must continue using official products, the researchers suggest a simple preprocessing fix: Discard pixels where CA < 0. This low-cost step significantly reduces artifact contamination.This study presents the first global-scale systematic quantification of inherent errors in official Landsat products for inland water monitoring. By recommending the ready-to-use MAIN algorithm, the research provides a quality-control benchmark and an efficient tool for the scientific community. “Our study ensures that 40 years of Earth archives can be used reliably to monitor water resources and safeguard the global environment.” Said by Prof. Duan

Algal blooms are becoming more frequent and intense in lakes around the world, posing increasing risks to water quality, ecosystem health, and carbon cycling. However, whether stronger blooms also occur earlier or last longer has remained unclear at the global scale.A new study published in Communications Earth & Environment shows that bloom intensity and bloom timing often change independently across global lakes, revealing a more complex response of lake ecosystems to human activities and climate change than previously understood.The study was conducted by researchers from the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, together with collaborators from Southwest University. Using two decades of satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), the team analyzed algal bloom dynamics in 4,085 lakes larger than 20 km² worldwide. The researchers examined both bloom intensity, represented by fractional floating algal cover (FAC), and bloom timing, including the start and end dates of bloom seasons.The results showed that about 71% of lakes exhibited increasing bloom intensity between 2003 and 2022. In contrast, changes in bloom timing were much more spatially heterogeneous. Some lakes showed earlier bloom onset, while others exhibited delayed onset or shifts in bloom ending. This pattern indicates a partial decoupling between bloom intensity and bloom phenology at the global scale.The researchers further found that the drivers of these changes differ. Human-related factors, especially population density, agricultural pressure, and economic development, were more strongly associated with increasing bloom intensity. By comparison, climatic factors such as temperature, wind speed, and precipitation played a greater role in regulating bloom timing, particularly in lakes located in cold and temperate regions.Future projections under different shared socioeconomic pathway scenarios suggest that this divergence may become even more pronounced under continued climate change. Tropical lakes are projected to experience rapid bloom intensification with relatively modest timing shifts, whereas cold-region lakes are expected to show contrasting phenological responses across regions. For example, European lakes may exhibit earlier bloom onset and later bloom ending, while North American lakes may show the opposite tendency.According to the researchers, these findings highlight that global lake algal blooms cannot be understood solely in terms of increasing intensity. Changes in bloom timing may alter food-web structure, ecological stability, and carbon cycling, even when intensity changes are modest. The study therefore provides an important scientific basis for region-specific lake management and risk mitigation under climate change.

Salt lakes are important in global hydrological and biogeochemical cycles, accounting for approximately 44% of the total lake water volume worldwide. Water salinity serves as the key variable regulating physical, ecological, and chemical processes in these systems, playing a decisive role in regional ecological security and the sustainability of water resources.However, both climate change and intensifying human activities are altering the water salinity of these vital ecosystems, according to a comprehensive new study published in Earth-Science Reviews.Led by researcher Prof. SONG Chunqiao from the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, the study presents an interdisciplinary review of the spatiotemporal dynamics, monitoring technologies, and socio-environmental consequences of salt lake salinity.The research team synthesized global records to analyze the evolution of monitoring methods. They noted a significant transformation from traditional point sampling to large-scale, long-term dynamic monitoring systems centered on satellite remote sensing and machine learning. The study reveals a highly heterogeneous spatial pattern of water salinity changes globally. While global salt lakes are predominantly distributed across arid and semi-arid zones, they are currently experiencing strongly divergent evolutionary trajectories.The researchers found that salt lakes in arid regions are frequently undergoing aggravated salinization, primarily driven by agricultural irrigation and increased aridity. Conversely, high-altitude salt lakes, particularly those on the Tibetan Plateau, exhibit significant desalination due to increased water volumes and glacial melt associated with a warming and wetting climate.These dynamic shifts in salinity exert cascading impacts across socio-ecological systems. The study details how altered salinity dynamically reshapes food web structures, regulates physical stratification and elemental cycling, and ultimately exerts severe impacts on soil salinization, drinking water safety, and critical infrastructure across watersheds.“These findings underscore the profound vulnerability of salt lake ecosystems to global environmental changes and local human interventions, emphasizing the need to move beyond mere monitoring toward predictive modeling of future salinity changes.” said by Prof. Song.