Multi-source Satellite Images Research Articles

Monitoring of Spatio-Temporal Variations of Oil Slicks via the Collocation of Multi-Source Satellite Images

Monitoring of Spatio-Temporal Variations of Oil Slicks via the Collocation of Multi-Source Satellite Images

Exploring the potential of multi-source satellite remote sensing in monitoring crop nutrient status: A multi-year case study of cranberries in Wisconsin, USA

A timing and precise diagnosis of crop nutrient status is essential for optimizing management practices that promote environmentally friendly and enhanced crop yields. Although plant tissue analysis has conventionally been employed to evaluate the nutritional status of crops, this method cannot capture the spatial variability of crop nutrients. In contrast, satellite-based remote sensing can monitor the nutrient status of crops across expansive areas. This study explored the capability of multi-source satellite images (PlanetScope-4: 3 m, 4 bands; PlanetScope-8: 3 m, 8 bands; Sentinel-2: 10–60 m, 13 bands; PRISMA: 30 m, 239 bands) in mapping 12 foliar nutrients in cranberries. Three machine learning approaches, including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), were used to relate foliar nutrients to different types of satellite-derived features (SR: surface reflectance; VI: vegetation indices; TF: texture features) or their combinations (SR+VI, VI+TF and SR+VI+TF). Model performance was compared across different foliar nutrients, modelling approaches and combinations of model input features using R2 (the coefficient of determination) and RRMSE (relative root mean square error, = root mean square error/nutrient range × 100 %). Input features that were important to foliar nutrient modelling were identified. The model performance difference among nutrients was consistent between Planet-4 and Sentinel-2, as well as between Planet-8 and PRISMA. In the Planet-4 and Sentinel-2 derived models, N was best predicted (average R2 = 0.77, average RRMSE=15 %), followed by macronutrients S (0.60–0.63, 11 %), Mg (0.58–0.65, 10–11 %), Ca (0.49–0.51, 9 %), Na (0.69, 22 %), P (0.49, 9 %) and K (0.20, 8 %), and then by all micronutrients(i.e., Fe, Mn, B, Cu and Zn: R2 = 0.04–0.61; RRMSE=16–28 %). In the Planet-8 and PRISMA derived models, macronutrients (i.e., N, P, K, Mg, Ca, S and Na) had lower R2 and RRMSE (R2 = 0.06–0.59; RRMSE=7–57 %) than micronutrients (i.e., Fe, Mn, B, Cu and Zn: R2 = 0.18–0.60; RRMSE=19–66 %). The successful retrieval of foliar nutrients from satellite imagery was influenced by many factors, including the intercorrelation between nutrients and model input features, the data availability at critical growth stages, and satellite images characteristics (e.g., spatial and spectral resolutions). Except for foliar nitrogen, foliar nutrients typically do not exhibit distinct absorption features associated with C, H, N, or O molecular bonds in the 400–2500 nm range. Our results indicate that their successful retrieval can be primarily attributed to the association between foliar nutrients and other leaf components (e.g., pigments, water, and dry matter) that do display spectral features within this range. Our study demonstrated the potential of integrating multi-source satellite data for precise nutrient monitoring over large scales.

Dynamic Monitoring and Change Analysis of Lake Area on the Inner-Mongolian Plateau over the Past 22 Years

Lakes are essential components of the terrestrial water cycle. Their size and quantity reflect natural climate change and anthropogenic activities in time. Lakes on the Inner-Mongolian Plateau (IMP) have experienced significant changes in recent decades, but the current situation remains elusive. In this study, we conducted multi-decadal intensive monitoring of lake area and performed comprehensive variation analysis on the IMP. The study involved pre-processing, lake area extraction, post-processing, and lake area analysis procedures using multi-source satellite images. The results reveal the detailed variation in the lake from various aspects. The temporal analysis indicates that the lake area has undergone two distinct periods of decline followed by subsequent increase, and the variation pattern from January to December was diverse. The spatial analysis suggests that the lake area expanded slightly in the western and southern parts, while the area shrank in the other two. Variations in lakes of different sizes exhibited spatial heterogeneity. To some extent, effective actions have led to lake rehabilitation, but it has not yet reached or surpassed the level of 2000. This study provides a substantial data basis and technological guidance for conducting lacustrine surveys. The results can play important roles in fostering further analyses of the water cycle and the carbon cycle.

Historical information fusion of dense multi-source satellite image time series for flood extent mapping

Existing lightweight flood extent mapping (FEM) methods, leveraging satellite imagery, offer respite from the burden of extensive data processing. However, these methods often grapple with suboptimal accuracy of the FEM results. To tackle this challenge, this paper introduces an innovative lightweight historical information fusion framework aimed at enhancing FEM results through the utilization of dense multi-source satellite image time series (MSSITS). Firstly, we propose an ultra-lightweight convolutional neural network comprising fundamental network modules, such as convolution layers, batch normalization layers, and parametric rectified linear unit layers. This network swiftly extracts initial FEM results from dense MSSITS. Secondly, we devise a historical information fusion strategy to refine the initial FEM results of the current image by fusing historical FEM results exhibiting high temporal correlation. During the fusion stage, we harness the complementary attributes of dense MSSITS. Specifically, the fusion of diverse satellite sensor data aids in mitigating false alarms in the initial FEM results, thereby facilitating the acquisition of refined FEM results. This approach effectively diminishes the impact of noise interferences, including terrain shadows and surging flood waves in synthetic aperture radar images, as well as cloud occlusions and cloud shadows in optical images. Experimental findings, based on the dense MSSITS of four authentic flood events, demonstrate that our proposed method yields relatively superior FEM performance while maintaining a low FEM complexity in comparison with commonly used semantic segmentation methods.

A stepwise unmixing model to address the scale gap issue present in downscaling of geostationary meteorological satellite surface temperature images

Land surface temperature (LST) is a critical parameter that drives the response of a variety of ecosystems to environmental and climatic changes. The geostationary satellite brings unique opportunities to monitor the LST at a hemispheric scale with temporal resolutions of up to 5 min. However, the ultra-coarse spatial resolutions ranging from 2 km to 5 km limit its application at local spatial scales. Downscaling the geostationary satellite LST image with the high-resolution low-Earth-orbit satellite images is a cost-effective way to circumvent this dilemma. Yet, the big gap between the observation scales of these satellite data poses a challenge for accurate downscaling. To address this problem, we proposed a stepwise temperature unmixing (TUM) model called ‘UnmixGO’, which downscales hourly LST images of Geostationary Operational Environmental Satellites (GOES-R) from 2 km to 100 m resolution. The spatially adaptive endmembers and the constrained solution space of the TUM model keep the errors in downscaled LSTs from being over-amplified in the stepwise data treatment. We validated the algorithm in six experimental areas and at five flux tower sites across the contiguous United States, revealing that UnmixGO outperformed conventional methods in accuracy by 0.49 K and 1.11 K on average for downscaling the simulated and real GOES-R LST images, respectively. Furthermore, the technical framework employed by UnmixGO is compatible with multi-source satellite images, enhancing the added value of our study in a future where various remote sensing data is increasingly accessible.

Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they lack the ability to quantify the uncertainty of the estimation results. However, TC, as a typical chaotic system, often requires confidence intervals for intensity estimates in real-world emergency decision-making scenarios. Additionally, the use of multi-source image inputs contributes to the uncertainty of the model. Consequently, this study introduces a neural network (MTCIE) that utilizes multi-source satellite images to provide probabilistic estimates of TC intensity. The model utilizes infrared and microwave images from multiple satellites as inputs. It uses a dual-branch self-attention encoder to extract TC image features and provides uncertainty estimates for TC intensity. Furthermore, a dataset for estimating the intensity of multi-source TC remote sensing images (MTCID) is constructed through the registration of latitude, longitude, and time, along with data augmentation. The proposed method achieves a MAE of 7.42 kt in deterministic estimation, comparable to mainstream networks like TCIENet. In uncertain estimation, it outperforms methods like MC Dropout in the PICP metric, providing reliable probability estimates. This supports TC disaster emergency decision making, enhancing risk mitigation in real-world applications.

Assessing Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 Data for Large-Scale Wildfire-Burned Area Mapping: Insights from the 2017–2019 Canada Wildfires

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR’s performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring.

Evaluating the Land Use and Land Cover change of unprotected wetland situated in the Khultabad Tehsil of Aurangabad District, Maharashtra

The study investigated the effects of Land Use and Land Cover (LULC) change dynamics on the state and status of an unprotected wetland in semi-arid regions of India. The effects of the LULC shift on the wetland during a 15-year period were mapped and quantified using the long-term satellite image data collection. Using the supervised analysis approach, a multi-source satellite image analysis was carried out. The complete research area's Landsat data series were utilised to evaluate, map, and track LULC change over time. Wetland detection maps created for the 2007–2013–2016–2022 temporal periods were examined. According to the findings, the amount of agricultural land remained essentially constant during the comparative period, ranging from 29.71% to 31.43%. The percentage of waste land progressively grew from 14.29% to 22.86% between 2007 and 2016. By 2022, the plantation area will have shrunk from 15.43% in 2007 to 5%. Similarly, it is anticipated that the habitat would grow from 8.07% in 2007 to 12.93% in 2022. Farmland and fallow area figures are different because both land uses were determined using different season satellite pictures. But there is no obvious increase or decrease in the total agricultural area. These results may be used to create specialised wetland management plans and perhaps even a framework for restoring wetlands that are not currently under protection

Attention Mechanism-Combined LSTM for Grain Yield Prediction in China Using Multi-Source Satellite Imagery

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a dataset. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (convolutional block attention module, CBAM) is added to extract spatial information in different remote sensing bands to improve the efficiency of the model. Long short-term memory (LSTM) neural networks are added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. After the study, it was found that the LSTM model using a combination of multi-source satellite images and an attention mechanism can effectively predict grain yield in China. Furthermore, the proposed model was tested on data from 2018 to 2020 showing an average R2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner.

SIFNet: A self-attention interaction fusion network for multisource satellite imagery template matching

Multisource satellite images provide abundant and complementary earth observations, while nonlinear radiometric and geometric distortions (such as scale and rotation variations) between these multimodal images pose remarkable challenges for further remote sensing applications, such as change detection. We therefore proposed a template matching algorithm based on self-attention interactive fusion network, named SIFNet, to align multisource satellite images. First, a feature pyramid network was first conducted to extract multiscale features for each pixel, with the template and reference images as the inputs. Then, the extracted features were fused by self-attention layers in Transformer for information interaction. Third, the similarity and semantic matching loss functions were developed to convert satellite imagery registration into regression task, allowing SIFNet aligning multimodal patch images more efficiently based on point-to-point correspondence, instead of globally searching extremums as previous matching strategies did. We performed experiments based on four multimodal datasets (i.e. Google, GF-2, Landsat-8 and optical-SAR images) with various scenes to evaluate the performance and robustness of SIFNet. The results demonstrate the proposed SIFNet performed a comparable accuracy for template matching with other algorithms and was robust to geometric distortions and radiometric variations of multisource remote sensing data.

Semantic Segmentation of Land Cover in Urban Areas by Fusing Multisource Satellite Image Time Series

Semantic Segmentation of Land Cover in Urban Areas by Fusing Multisource Satellite Image Time Series

A Novel Operational Rice Mapping Method Based on Multi-Source Satellite Images and Object-Oriented Classification

Combining optical and synthetic aperture radar (SAR) data for crop mapping has become a crucial way to improve classification accuracy, especially in cloudy and rainy areas. However, the acquisition of optical images is significantly unstable due to the influence of cloudy and rainy weather, which seriously restricts the application of this method in practice. To solve this problem, this study proposed an optical-SAR imagery-based rice mapping method which has the advantages of less dependence on optical images, easy operation and high classification accuracy. To account for the trait of sparse availability of optical images, this method only needs one clear sky optical image in the rice growth period and combined it with multi-temporal SAR images to achieve a high accuracy rice mapping result. Meanwhile, this paper also proposed a comprehensively multi-scale segmentation parameter optimization algorithm, which considers the area consistency, shape error and location difference between the segmented object and reference object, and adopts an orthogonal experiment approach. Based on the optical image, the boundaries of the parcel objects can be segmented, which were subsequently used to perform the object-oriented classification. The results show that the overall accuracy of the proposed method in Yangzhou City is 94.64%. Moreover, according to a random pick test, it is encouraging that the proposed method has strong robustness in response to the instability of the acquisition time of SAR images. A relatively high overall accuracy of 90.09% suggested that the proposed method can provide a reliable rice mapping result in cloudy and rainy areas.

Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping

Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well-suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and one-class classification (OCC). We aim to map forest mortality caused by an outbreak of geometrid moths in a sparsely forested forest-tundra ecotone using multisource satellite images. The images preceding and following the event are collected by Landsat-5 and RADARSAT-2, respectively. Using a recent deep learning method for change-aware image translation, we compute difference images in both satellites’ respective domains. These differences are stacked with the original pre- and post-event images and passed to an OCC trained on a small sample from the targeted change class. The classifier produces a credible map of the complex pattern of forest mortality.

Using multisource satellite products to estimate forest aboveground biomass in Oita prefecture: a novel approach with improved accuracy and computational efficiency

Accurate estimation of forest aboveground biomass (AGB) using satellite information is crucial for quantitatively evaluating forest carbon stock for climate change mitigation. However, with the emergence of an enormous number of identical data, selecting the most valuable dataset for estimation is essential to improve model efficiency and accuracy. Here, we present a novel framework using principal component analysis (PCA)-derived textures to tackle redundant variables and the integration of multisource satellite images using the random forest regression algorithm with recursive feature elimination to map the AGB of deciduous broadleaf forests in Oita prefecture, Japan. Our results highlighted that the model using PCA and derivative features had the best performance [R2 = 0.69, root mean square error (RMSE) = 33.59 Mg/ha, mean absolute error = 25.01 Mg/ha, relative RMSE = 0.27]. In this case, of the 34 variables selected by RFE for the model, 19 were derived from principal components and corresponding textures, indicating a considerable improvement in using our novel framework to estimate AGB. Together, this study provides a valuable strategy for feature selection to lessen multicollinearity and redundancy.

A Two-Step Block Adjustment Method for DSM Accuracy Improvement with Elevation Control of ICESat-2 Data

Digital surface models (DSMs) have been widely utilized in various applications as fundamental geographic information data. Block adjustment is normally performed on satellite images to enhance the geometric accuracy and DSMs are then generated by stereo mapping. However, new errors may be introduced during the stereo mapping processing and geometric discrepancies between DSMs may still exist. In particular, block adjustment is difficult for multisource satellite images. Therefore, this paper presents a two-step block adjustment approach directly performed on DSMs, with high-accuracy ICESat-2 laser altimetry data used as elevation control. In the method, DSM tie-point matching, elevation control/check point selection from ICESat-2 laser points, and planar and elevation block adjustments are performed in sequence. In the experiments, ZY-3 satellite stereo images and corresponding generated DSMs, as well as SRTM and ALOS DSMs, are used for verification. The experimental results show that the absolute elevation accuracy and the relative geometric consistency of the DSMs are both significantly improved after two-step DSM block adjustment and it can efficiently improve the accuracy, not only for DSMs acquired by the same sensor type, but also for DSMs acquired by different sensor types, which demonstrates the feasibility and advantage of the proposed method.

Mapping Morphodynamic Variabilities of a Meso-Tidal Flat in Shanghai Based on Satellite-Derived Data

Morphodynamic variabilities of tidal flats (e.g., the variations of pattern, area, and topography) are a key issue in the management of coastal intertidal zones. In this study, the morphodynamic variabilities of the Lin-gang (Shanghai) tidal flat were investigated using waterlines extracted from multi-source satellite images acquired from 2013 to 2020. The waterlines were evaluated against in situ measurements. The results of our investigation indicated that the tidal flat was in a state of rapid accretion from 2015 to 2018, and in a state of erosion from 2018 to 2020. We found that the accretion of the tidal flat was most likely due to the protection of local vegetation, which prevents the sea bottom from eroding. However, storms have primarily been causing erosion since 2018. The potential mechanisms of the geomorphological variations were further analyzed using the empirical orthogonal function (EOF) method. The analysis revealed that the variation in the tidal flat was dominated by two modes. The first mode accounted for 55% of the variation, while the second mode accounted for 18%. The spatial distribution of the first mode was highly related to the artificial vegetation, indicating that the local variations in the vegetation prevented the sea bottom from eroding, which was dominant in the accretional phase from 2015 to 2018. The second model reflected the extreme meteorological events that resulted in potential changes in the tidal flat’s pattern (i.e., transitioning to an erosion phase from 2018 to 2020). The satellite-derived topographies were demonstrated to be an effective means of mapping the evolution of a meso-tidal flat.

A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images

Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research.

Representing and modeling spatio-temporal uncertainty using belief function theory in flood extent mapping

In our world of rapid change, floods are a growing threat. In this context, flood extent mapping is important for damage and accurate and timely information about flood-affected areas are needed. Due to the suitability of remote sensing in mapping flood inundation, multi-source satellite images have been used in recent years. This paper presents a new approach to representing and modeling uncertainty in spatio-temporal information of satellite images using belief function theory for flooded areas extraction. Interval approaches and the associated arithmetic operations are used for modeling the uncertainty of spatial locations and temporal durations from 2D dimensional model in order to take the direction of flow in the flood. Based on topological spatial relationships, the similarity for the spatio-temporal inundation events is performed and the similar intervals are fused. Detailed examples are given to illustrate each step. A case study of flood risk in Chad Lake from 2003 to 2019 using Sentinel-2, MODIS and Landsat-8 images shows how spatio-temporal uncertainty can be problematic and bias the results of even the simplest flood mapping, and how coupling a Dempster–Shafer approach with topological relationships nevertheless offers a useful solution.

Extraction of Long Time-Series Vegetation Indices from Combined Multisource Satellite Imagery.

Extracting vegetation cover information by combining multisource satellite images can improve the time scale of vegetation cover monitoring, realize encrypted observation in short period, and shorten the regional vegetation remote sensing monitoring cycle. The NDVI and RVI datasets from 2007–2019 were extracted using 9 phases of multisource satellite images (Landsat TM/OLI, Sentinel-2 MSI, and GF-1 PMS) covering Xiaxi, Sichuan. Three typical validation sites representing higher vegetation cover in mountains and no vegetation cover in water bodies in the region, respectively, were selected to extract NDVI and RVI at the corresponding locations. Linear regression and Spearman correlation coefficient (ρ) analysis were used to verify the correlation between NDVI and RVI from multisource images. The results showed that the vegetation indices fluctuated smoothly in the time series within the validation sites, and the vegetation indices of multisource satellite images were good measures of long-term vegetation cover in the region; the vegetation indices of the same satellite images showed significant correlations (both R2 and ρ exceeded 0.8), and the vegetation indices of different satellite images (PSM and MSI, PSM and OLI) showed more significant correlations (both R2 and ρ exceeded 0.7); the smaller the difference between the original resolutions of satellite images, the more significant the correlation between the extracted NDVI and RVI.

Longterm multisource satellite data fusion reveals dynamic expansion of lake water area and storage in a hyperarid basin of China

Lakes play an important role in global hydrologically and provide substantial water resources in hyperarid regions, and they are becoming even more significant as climate change impacts hydrological processes, especially in these sensitive regions. This study proposes a lake-monitoring framework that uses multisource satellite data to automatically estimate the spatiotemporal variations from 1987 to 2020 of lake area, lake water level, and lake water storage (LWS) in the Qaidam Basin (QB), which is a typical hyperarid basin in northwest China. The global surface water dataset provides long-time-series data for lake areas. Multisource satellite images (Landsat 7, Landsat 8, Sentinel 2) and altimetry data (CryoSat 2, ICESat, ICESat 2, Sentinel 3) are used to build hypsometric curves (the fit to the curve of lake area vs lake water level) to reconstruct lake water level before the 21st century. In contrast with traditional methods, we propose to verify the construction of a hypsometric curve to select a reasonable curve and thereby improve the accuracy of the extrapolation of lake water level. On this basis, we estimated the variation of LWS from 1987 to 2020 and to use products of the gravity recovery and climate experiment and of land surface model simulations to estimate the variation in total LWS in the QB for comparative analysis. The results show that the total LWS in the QB has increased by 82.63% over the past 33 years. Compared with the mean value from 2004 to 2009, the total LWS in 2020 increased by 4.41 km3, which is equivalent to 12.53%–19.49% of the increase in terrestrial water storage. This ratio cannot be ignored. The analysis of spatiotemporal variations in lakes shows that large shallow lakes are disappearing, leading to ecological deterioration in some areas. Conversely, there is increased water storage in small deep lakes, which may be beneficial for water conservation in hyperarid basins. In conclusion, the proposed automated processing program should facilitate the rapid monitoring of lake dynamics around the world. We emphasize that the variation of lake water storage in hyperarid basins cannot be ignored, which should stimulate more relevant research.