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Reliable and continuous information on cropping intensity is crucial for assessing cropland utilization and formulating policies regarding cropland protection and management. However, there is still a lack of high-resolution cropping intensity maps for recent years, particularly in fragmented agricultural regions. In this study, we combined Landsat-8 and Sentinel-2 imagery to generate cropping intensity maps from 2019 to 2023 at a 10 m resolution for Shaanxi Province, China. First, the satellite imagery was harmonized to construct 10-day composite enhanced vegetation index (EVI) time series. Then, the cropping intensity was determined by counting the number of valid EVI peaks within a year. Assessment based on 578 sample points showed a high level of accuracy, with overall accuracy and Kappa coefficient values exceeding 0.96 and 0.93, respectively. We further analyzed the spatiotemporal patterns of cropping intensity and generated a map of abandoned cropland in Shaanxi. The results indicated that cropland in Shaanxi Province was mainly utilized for single-cropping (52.9% of area), followed by double-cropping (35.2%), with non-cropping accounting for 11.9%. Cropping intensity tended to be lower in the north and higher in the south. Temporally, the average cropping intensity of Shaanxi increased from 1.1 to over 1.3 from 2019 to 2023. Despite this upward trend, large areas of cropland were abandoned in northern Shaanxi. These results demonstrate the potential of utilizing Landsat-8 and Sentinel-2 imagery to identify cropping intensity dynamics in fragmented agricultural regions and to guide more efficient cropland management. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of the temporal variations in the area and evapotranspiration (ET) of five key lakes on the Qinghai–Tibet Plateau: Qinghai Lake, Hara Lake, Eling Lake, Gyaring Lake, and Ulan Ula Lake. Utilizing remote sensing data from Landsat satellites over the period from 1986 to 2022, we examined the dynamic changes and identified significant correlations and lag effects between lake area and regional ET. Distinct temporal patterns and sudden changes in lake areas and ET were observed, with Qinghai Lake showing an upward trend in the summer area and a mutation in 2020, while ET exhibited a mutation in 2021. Cyclic characteristics were observed, with significant correlations noted during specific periods, indicating a strong positive phase relationship between lake area and ET. This research contributes to the sustainable development and ecological protection efforts in this ecologically fragile region. [ABSTRACT FROM AUTHOR]

Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model's inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The movement of glaciers plays a crucial role in environmental and geological processes, significantly influencing the formation and dynamics of ice bodies. This study leverages feature tracking technology to analyze optical and Synthetic Aperture Radar (SAR) remote sensing imagery, specifically GF-1 optical images and GF-3, Sentinel-1 SAR images, captured during the 2020 to 2021 ablation season in Gongar. The aim was to quantify glacier surface velocities and to evaluate the comparative effectiveness of different remote sensing modalities in capturing these dynamics. Our findings indicate a strong consistency in the spatial distribution of glacier surface velocities derived from diverse remote sensing data sources, with high-precision optical imagery (GF-1) yielding the most accurate velocity measurements, followed by Sentinel-1 SAR data. Notably, large glaciers in Gongar exhibited rapid movements, with an average velocity of 0.16 m/d, primarily at elevations between 4,500 and 6,500 m. The fastest velocities were recorded at approximately 4,500 m elevation. Glaciers with inclines ranging from 10° to 60° displayed the highest velocities within the 20°-30° slope range. It was observed that glaciers on the southeast slope moved faster, exhibiting the highest average surface velocity, in contrast to those on the west slope, which moved more slowly. The surface velocity of the ice tongue region of Krayaylak Glacier that the largest glacier in Pamir, was observed to be lower than 0.6 m/d, indicating a slow movement speed. The study also reveals that the effectiveness of different remote sensing data in detecting glacier velocity in Gongar, with high-resolution data more accurately capturing surface velocities in melting areas or those with slower movement. This study underscores the importance of multi-source remote sensing data in understanding glacier dynamics and contributes valuable insights into the mechanisms driving glacier movements. [ABSTRACT FROM AUTHOR]

With the acceleration of global warming and the intensification of anthropogenic activities, numerous lakes worldwide are experiencing reductions in their water surface areas. Yilong Lake, a typical shallow plateau lake located on the Yunnan–Guizhou Plateau in China, serves as a crucial water resource for local human production, daily life, and ecosystem services. Hence, long-term comprehensive monitoring of its dynamic changes is essential for its effective protection. However, previous studies have predominantly utilized remote sensing data with limited temporal resolution, thus failing to reflect the long-term variations in Yilong Lake's water body. This study employs high temporal resolution monitoring, utilizing multi-source satellite data (e.g., KeyHole, Landsat, HJ-1 A/B) images spanning from 1965 to 2022 to investigate the changes in Yilong Lake's surface area, analyzing the influencing factors and ecological impacts of these changes. The results indicate that from 1965 to 2022, Yilong Lake's water surface area decreased by 8.33 km2, with a maximum surface area of 40.49 km2 on 7 January 1986, and a minimum surface area of 10.64 km2 on 20 April 2013. These changes are characterized by three significant phases: (1) a rapid shrinking phase (1965–1979); (2) a fluctuating shrinking period (1986–2016); and (3) an expanding recovery phase (2016–2022). Spatially, the most significant shrinkage was observed along the southern and southwestern shores of the lake. The driving factors varied across different periods: sunshine duration was the dominant influence during the rapid shrinking phase (1965–1979), accounting for 82% of the changes; population and cropland area were the main drive factors during the fluctuating shrinking period (1986–2016), accounting for 56% of the changes; and during the expanding recovery phase (2016–2022), the population accounted for 75% of the changes in the lake's surface area. Currently, the protection of Yilong Lake depends on water supplementation and strict regulation of outflow, resulting in the lake exhibiting characteristics similar to a reservoir. This long-term investigation provides baseline information for future lake monitoring. Our research findings can also guide decision-makers in urban water resource management and environmental protection, ensuring the scientific and rational use of watershed water resources, effectively curbing the shrinkage of Yilong Lake, and achieving long-term sustainable restoration of the lake's ecology. [ABSTRACT FROM AUTHOR]

Large-scale exploitation of underground mineral resources causes surface collapse, reduces land use efficiency, and brings a series of ecological and environmental problems. This is significantly important for the ecological restoration work of mining areas to accurately extract the subsidence range and depth of coal mine surface and formulate the regulation model suitable for coal mine subsidence areas. In this research, we used Differential Interferometric Synthetic Aperture Radar (D-InSAR) technology to extract the subsidence range of the Zhaogu Mining Area in Henan Province based on multi-source remote sensing data. We constructed the Spectral-Spatial Residual Network (SSRN) to classify the land use information within the subsidence range. Finally, we constructed a fuzzy comprehensive evaluation model based on the improved G1 method that assesses the extent of land damage in the subsidence area. Additionally, a suitable governance model for the subsidence area in the Zhaogu Mining Area is proposed. The results can provide technical support and data reference for the comprehensive treatment of subsidence in the Zhaogu Mining Area. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Forest & Environment is the property of Journal of Forest & Environment Editorial and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Forest Engineering is the property of Forest Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

On August 12, 2015, a catastrophic landslide occurred in Shanyang County, Shaanxi Province, China, resulting in 7 deaths and 53 missing. This study investigates the lifecycle evolution and failure mechanism of the Shanyang landslide with multi-source remote sensing data, emphasizing the critical role of locked segments in the Shanyang landslide. Differential interferometric analysis and deformation decomposition were utilized to reveal the pre-failure deformation pattern of the Shanyang landslide. Creeping deformation was found along the underlying soft layer 4 months prior to the landslide, with the deformation mainly occurring downslope and controlled by the locked segment at the front edge of the slope. The integration of a 1:1000 pre-failure topographic map and a high-precision post-failure digital elevation model determined the landslide volume to be 1.60 × 106 m3 and revealed a maximum travel distance of 500 m. Combining engineering geological zoning with deformation data, the Shanyang landslide was classified as a typical locked-segment-dominated slide in soft-hard interbedded strata, with rainfall as a key deformation influence factor. Finally, using the time series deformation from SBAS-InSAR, the post-failure stability of the landslide area was analyzed. This study demonstrates the potential of integrating multi-temporal remote sensing techniques to identify the entire deformation and destruction process of landslides and their influencing factors, which offers valuable insights for improving early landslide warnings and hazard assessments. [ABSTRACT FROM AUTHOR]

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters. [ABSTRACT FROM AUTHOR]

Copyright of Geology & Exploration is the property of Geology & Exploration Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Characterized by soil moisture content and plant growth, agricultural drought occurs when the soil moisture content is lower than the water requirement of plants. Microwave remote sensing observation has the advantages of all-weather application and sensitivity to soil moisture change. However, microwave remote sensing can only invert 0~5 cm of soil surface moisture, so it cannot effectively reflect the drought situation of farmland. Therefore, this study took Henan Province as the study area, used soil moisture active and passive (SMAP) satellite soil moisture data, employed NDVI, LST, and ET as the independent variables, and took the drought grade on the sample as the dependent variable. Using the 2017–2019 data as the training set and the 2020 data as the testing set, a random forest drought monitoring model with comprehensive influence of multiple factors was constructed based on the training set data. In the process of model training, the cross-validation method was employed to establish and verify the model. This involved allocating 80% of the sample data for model construction and reserving 20% for model verification. The results demonstrated an 85% accuracy on the training set and an 87% accuracy on the testing set. Additionally, two drought events occurring during the winter wheat growing period in Henan Province were monitored, and the validity of these droughts was confirmed using on-site soil moisture and the vegetation supply water index (VSWI). The findings indicated a high incidence of agricultural drought in the southwestern part of Henan Province, while the central and northern regions experienced a lower incidence during the jointing to heading and filling stages. Subsequently, leveraging the results from the random forest drought monitoring, this study conducted a time series analysis using the Mann–Kendall test and a spatial analysis employing Moran's I index to examine the temporal and spatial distribution of agricultural drought in Henan Province. This analysis aimed to unveil trends in soil moisture changes affecting agricultural drought, as observed via the SMAP satellite (NASA). The results suggested a possible significant spatial auto-correlation in the occurrence of agricultural drought. [ABSTRACT FROM AUTHOR]

The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the formation process and optical properties of winter aerosol pollution in Beijing and surrounding areas. The analysis of ground station data shows that compared to 2019 and 2021, the pandemic lockdown policy resulted in a decrease in the total number of pollution days and a decrease in the average concentration of particulate matter in the Beijing area in 2020. The terrain characteristics of the Beijing-Tianjin-Hebei (BTH) made it prone to northeast and southwest winds. The highest incidence of aerosol pollution in Beijing occurs in February and March during the spring and winter seasons. Analysis of a typical heavy aerosol pollution process in the Beijing area from 28 February to 5 March 2019 shows that dust and fine particulate matter contributed to the primary pollution; surface air temperature inversion and an average wind speed of less than 3 m/s were conducive to the continuous accumulation of pollutants, which was accompanied by the oxidation reaction of NO2 and O3, forming photochemical pollution. The heavy aerosol pollution was transmitted and diffused towards the southeast, gradually eliminating the pollution. Our results provide relevant research support for the prevention and control of aerosol pollution. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Long time series land cover classification information is the basis for scientific research on urban sprawls, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the random forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of the sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of the sample points without land class change, determined by counting the sample points of each band of the Landsat time series from 1986 to 2022, was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of the TM and ETM+ sensor data from 2013 to 2022; and (iii) the addition of a mining land cover type increases the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and forest area. Among the land classifications via multi-source remote sensing, the combined variables of Spectral band + Index + Terrain + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and the use of sensors under complex terrain conditions. The use of the GEE cloud computing platform enabled the rapid analysis of remotely sensed data to produce land cover maps with high accuracy and a long time series. [ABSTRACT FROM AUTHOR]

On October 6, 2021, a high-position landslide disaster occurred in Yanwo Village, Rongshan Town, Lizhou District, Guangyuan City, Sichuan Province, around 13: 00. The landslide resulted in the destruction of 4 houses, 3 power lines, the burial of a 170-meter-long rural road, and the blockage of a 350-meter-long river channel. By utilizing a variety of remote sensing information sources, including multi-period satellite data before and after landslide, high-precision UAV aerial images, and airborne LiDAR data, the characteristics of high landslide and its sliding mode are studied by using a four-dimensional analysis approach combining three-dimensional space with time. Starting from the development background of high landslide, the deformation and failure characteristics and sliding mode of high landslide are summarized by analyzing the deformation characteristics before sliding and the dynamic procession of the high landslide. Based on the deduced landslide progression and the stability evaluation of the residual landslide at the rear, three sliding modes for future high-position landslide events are predicted and analyzed: Firstly, the back edge of landslide continues to be disjointed and move, causing a direct shear failure of the front part of the landslide by squeezing. Secondly, the trailing edge of the landslide continues to be disjointed and move, resulting in the sliding landslide occurs along the existing landslide channel. Thirdly, the initiation of the front part of the landslide trigger a tractional landslide in the middle and rear parts of the landslide. High-position landslides are common in the southwest mountainous areas, and during early geological hazard investigations, they should be effectively identified, and disaster prevention and mitigation measures should be strengthened. [ABSTRACT FROM AUTHOR]

The Shishapangma region, situated in the middle of the Himalayas, is rich in glacial lakes and glaciers. Hence, glacial lake outburst floods (GLOFs) have become a top priority because of the severe threat posed by GLOFs to the downstream settlements. This study presents a comprehensive analysis of GLOF hazards using multi-source remote sensing datasets and designs a flood model considering the different breaching depths and release volumes for the Galong Co region. Based on high-resolution optical images, we derived the expanding lake area and volume of glacial lakes. We monitored deformation velocity and long-term deformation time series around the lake dam with Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The glacier thinning trend was obtained from the difference in the Digital Elevation Model (DEM). We identified potential avalanche sources by combining topographic slope and measurable deformation. We then carried out flood modeling under three different scenarios using the hydrodynamic model HEC-RAS for Galong Co, which is formed upstream of Nyalam. The results show that the Nyalam region is exposed to high-intensity GLOFs in all scenarios. The larger breaching depth and release volumes caused a greater flow depth and peak discharge. Overall, the multiple remote sensing approaches can be applied to other glacial lakes, and the modeling can be used as a basis for GLOF mitigation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As one of the world's major forestry countries, accurate forest-type maps in China are of great importance for the monitoring and management of forestry resources. Classifying and mapping forest types on a large scale across the country is challenging due to the complex composition of forest types, the similarity of spectral features among forest types, and the need to collect and process large amounts of data. In this study, we generated a medium-resolution (30 m) forest classification map of China using multi-source remote sensing images and local samples. A mapping framework based on Google Earth Engine (GEE) was constructed mainly using the spectral, textural, and structural features of Sentinel-1 and Sentinel-2 remote sensing images, while local acquisition data were utilized as the mapping channel for training. The proposed method includes the following steps. First, local data processing is performed to obtain training and validation samples. Second, Sentinel-1 and Sentinel-2 data are processed to improve the classification accuracy by using the enhanced vegetation index (EVI) and the red-edge position index (REPI) computed based on the S2A data. Third, to improve classification efficiency, useless bands are removed and important bands are retained through feature importance analysis. Finally, random forest (RF) is used as a classifier to train the above features, and the classification results are used for mapping and accuracy evaluation. The validation of the samples showed an accuracy of 82.37% and a Kappa value of 0.72. The results showed that the total forest area in China is 21,662,261.17 km 2 , of which 1,127,294.42 km 2 of coniferous forests account for 52% of the total area, 981,690.98 km 2 of broad-leaf forests account for 45.3 % of the total area, and 57,275.77 km 2 of mixed coniferous and broad-leaf forests account for 2.6% of the total area. Upon further evaluation, we found that textural and structural features play a greater role in classification compared to spectral features. Our study shows that combining multi-source high-resolution remote sensing imagery with locally collected samples can produce forest maps for large areas. Our maps can accurately reflect the distribution of forests in China, which is conducive to forest conservation and development. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change can induce phytoplankton blooms (PBs) in eutrophic lakes worldwide, and these blooms severely threaten lake ecosystems and human health. However, it is unclear how urbanization and its interaction with climate impact PBs, which has implications for the management of lakes. Here, we used multi‐source remote sensing data and integrated the Virtual‐Baseline Floating macroAlgae Height (VB‐FAH) index and OTSU threshold automatic segmentation algorithm to extract the area of PBs in Lake Dianchi, China, which has been subjected to frequent PBs and rapid urbanization in its vicinity. We further explored long‐term (2000–2021) trends in the phenological and severity metrics of PBs and quantified the contributions from urbanization, climate change, and also nutrient levels to these trends. When comparing data from 2011–2021 to 2000–2010, we found significantly advanced initiation of PBs (28.6 days) and noticeably longer duration (51.9 days) but an insignificant trend in time of disappearance. The enhancement of algal nutrient use efficiency, likely induced by increased water temperature and reduced nutrient concentrations, presumably contributed to an earlier initiation and longer duration of PBs, while there was a negative correlation between spring wind speed and the initiation of PBs. Fortunately, we found that both the area of the PBs and the frequency of severe blooms (covering more than 19.8 km2) demonstrated downward trends, which could be attributed to increased wind speed and/or reduced nutrient levels. Moreover, the enhanced land surface temperature caused by urbanization altered the thermodynamic characteristics between the land and the lake, which, in turn, possibly caused an increase in local wind speed and water temperature, suggesting that urbanization can differently regulate the phenology and severity of PBs. Our findings have significant implications for the understanding of the impacts of urbanization on PB dynamics and for improving lake management practices to promote sustainable urban development under global change. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, there has been growing concern that vegetation changes on the Tibetan Plateau are associated with climate change (temperature and precipitation) and human activities. This study used six types of remote sensing vegetation data, including GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI, MODIS EVI (Enhanced Vegetation Index), SPOT Vegetation (Spot-VGT) NDVI, LAI (Leaf Area Index) and NPP (Net Primary Productivity), and applied the maximum synthesis method, trend analysis, correlation analysis, and multivariate statistical analysis to investigate vegetation change processes since the 1980s. The study showed that the amount of vegetation on the TP had increased significantly since 2000 (p <.01), especially in the northeastern part of the TP. There was no significant change prior to 2000. The different vegetation data sources varied greatly. Four remote sensing indices, MODIS EVI, Spot-VGT NDVI, LAI, and NPP, showed a significant increase in vegetation from 2000, accounting for 16.18%, 44.55%, 30.44% and 8.94% of the total area, respectively (p <.05). Multiple data sources provided a more comprehensive understanding, whereas a single data source had substantial uncertainty. Human activities, such as the implementation of large-scale ecological projects, played a dominant role in increasing vegetation, while climate change played a subsidiary role. The MODIS EVI, Spot-VGT NDVI, LAI, and NPP data showed that the area of increased vegetation caused by human activities accounted for 53.51%, 45.68%, 37.52%, and 31.79% of the total area of the TP, respectively. The relative increase from climate change was 10.28%, 17.49%, 13.15%, and 8.82%, respectively. The current study applied multi-source remotely sensed vegetation data, which effectively reduced the uncertainty caused by individual data sources and provided more rigorous and scientific research conclusions. [ABSTRACT FROM AUTHOR]

Neural network models play an important role in crop extraction based on remote sensing data. However, when dealing with high-dimensional remote sensing data, these models are susceptible to performance degradation. In order to address the challenges associated with multi-source Gaofen satellite data, a novel method is proposed for dimension reduction and crop classification. This method combines the benefits of the stacked autoencoder network for data dimensionality reduction, and the convolutional neural network for classification. By leveraging the advantages of multi-dimensional remote sensing information, and mitigating the impact of dimensionality on the classification accuracy, this method aims to improve the effectiveness of crop classification. The proposed method was applied to the extraction of crop-planting areas in the Yangling Agricultural Demonstration Zone, using multi-temporal spectral data collected from the Gaofen satellites. The results demonstrate that the fusion network, which extracts low-dimensional characteristics, offers advantages in classification accuracy. At the same time, the proposed model is compared with methods such as the decision tree (DT), random forest (RF), support vector machine (SVM), hyperspectral image classification based on a convolutional neural network (HICCNN), and a characteristic selection classification method based on a convolutional neural network (CSCNN). The overall accuracy of the proposed method can reach 98.57%, which is 7.95%, 4.69%, 5.68%, 1.21%, and 1.10% higher than the above methods, respectively. The effectiveness of the proposed model was verified through experiments. Additionally, the model demonstrates a strong robustness when classifying based on new data. When extracting the crop area of the entire Yangling District, the errors for wheat and corn are only 9.6% and 6.3%, respectively, and the extraction results accurately reflect the actual planting situation of crops. [ABSTRACT FROM AUTHOR]

It is crucial to investigate the urban agglomerations spatio-temporal evolution patterns and driving factors for analyzing the urban spatial structure-functional division and promoting the coordinated development of urban agglomerations. In this study, a novel vegetation-building-nighttime light-adjusted index (VBNAI) was established for rapid and effective mapping of urban construction land (UCL) in Central Plains Urban Agglomeration (CPUA), China during 2000–2020 based on Google Earth Engine (GEE) platform. Compared with traditional indices, VBNAI can significantly decrease the blooming effect, Normalized Difference Vegetation Index (NDVI) saturation, and soil background of nighttime light data. In addition, the urban expansion indices and standard deviation ellipse model were synthetically adopted to analyze the spatio-temporal evolution pattern of urban expansion. The gravity model and the geographically weighted regression model were employed to determine the spatial interaction forces and drivers of urban expansion, respectively. The results showed that the VBNAI index has obvious advantages in efficiency and accuracy to extract UCL with the overall accuracy of more than 91%. The UCL of CPUA had increased by 4489.84 km2 during 2000–2020 with the gravity center moving towards southeast continuously. From 2000 to 2010, the urban expansion was in a 'center-hinterland' pattern which had benefit from the favorable effect of the traffic shaft belt. During 2010–2020, the urban network structure had basically established. Urban expansion had been influenced by a variety of socio-economic and demographic factors, and the impact degree varied from region to region. This study could provide scientific references for facilitating the intensive utilization of urban resources and optimizing the spatial development pattern of urban agglomeration. [ABSTRACT FROM AUTHOR]

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Accurate information on dominant tree species and their spatial distribution in subtropical natural forests are key ecological monitoring factors for accurately characterizing forest biodiversity, depicting the tree competition mechanism and quantitatively evaluating forest ecosystem stability. In this study, the subtropical natural forest in northwest Yunnan province of China was selected as the study area. Firstly, an object-oriented multi-resolution segmentation (MRS) algorithm was used to segment individual tree crowns from the UAV RGB imagery and satellite multispectral imagery in the forests with different densities (low (547 n/ha), middle (753 n/ha) and high (1040 n/ha)), and parameters of the MRS algorithm were tested and optimized for accurately extracting the tree crown and position information of the individual tree. Secondly, the texture metrics of the UAV RGB imagery and the spectral metrics of the satellite multispectral imagery within the individual tree crown were extracted, and the random forest algorithm and three deep learning networks constructed in this study were utilized to classify the five dominant tree species. Finally, we compared and evaluated the performance of the random forest algorithm and three deep learning networks for dominant tree species classification using the field measurement data, and the influence of the number of training samples on the accuracy of dominant tree species classification using deep learning networks was investigated. The results showed that: (1) Stand density had little influence on individual tree segmentation using the object-oriented MRS algorithm. In the forests with different stand densities, the F1 score of individual tree segmentation based on satellite multispectral imagery was 71.3–74.7%, and that based on UAV high-resolution RGB imagery was 75.4–79.2%. (2) The overall accuracy of dominant tree species classification using the light-weight network MobileNetV2 (OA = 71.11–82.22%), residual network ResNet34 (OA = 78.89–91.11%) and dense network DenseNet121 (OA = 81.11–94.44%) was higher than that of the random forest algorithm (OA = 60.00–64.44%), among which DenseNet121 had the highest overall accuracy. Texture metrics improved the overall accuracy of dominant tree species classification. (3) For the three deep learning networks, the changes in overall accuracy of dominant tree species classification influenced by the number of training samples were 2.69–4.28%. [ABSTRACT FROM AUTHOR]

With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time. Unfortunately, the domain shift between multi-source remote sensing data from various sensors and regions poses a significant challenge. Segmentation models face difficulty in adapting to incremental domains due to catastrophic forgetting, which can be addressed via incremental learning methods. However, current incremental learning methods mainly focus on class-incremental learning, wherein classes belong to the same remote sensing domain, and neglect investigations into incremental domains in remote sensing. To solve this problem, we propose a domain-incremental learning method for semantic segmentation in multi-source remote sensing data. Specifically, our model aims to incrementally learn a new domain while preserving its performance on previous domains without accessing previous domain data. To achieve this, our model has a unique parameter learning structure that reparametrizes domain-agnostic and domain-specific parameters. We use different optimization strategies to adapt to domain shift in incremental domain learning. Additionally, we adopt multi-level knowledge distillation loss to mitigate the impact of label space shift among domains. The experiments demonstrate that our method achieves excellent performance in domain-incremental settings, outperforming existing methods with only a few parameters. [ABSTRACT FROM AUTHOR]

Due to poor penetrability, optical remote sensing can not identify understory vegetation beneath the canopy. Thus, the vegetation coverage extracted by optical remote sensing alone could not sufficiently capture understory vegetation information to formulate the vegetation coverage factor for soil erosion evaluation. To address this issue, the authors took the Baiyangdian-Daqing River Basin as the research object and considered the photon counting ICESat-2/ATLAS vegetation coverage sampling under different photon point classifications. Based on the measured data, satellite-ground collaborative vegetation coverage sampling was achieved in the study area. The results showed that compared with the inversion results extracted by the traditional NDVI pixel dichotomy, the vegetation coverage estimated by the random forest regression model constructed in this study was more accurate. To a certain extent, the proposed model can monitor the understory vegetation of dense forests and complement the lack of understory vegetation signal in optical remote sensing. In the three error tolerance 0.05, 0.1, and 0.15 ranges, the inversion accuracy of vegetation coverage was increased by –4.1%, 5.3%, and 9.4%, reaching the accuracy of 55.6%, 71.1%, and 94.3%, respectively. [ABSTRACT FROM AUTHOR]

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Leaf area index (LAI) is an important parameter indicating crop growth. At present, spectral technology has developed into a popular means for LAI monitoring, which can provide accurate estimation results by constructing a model with crop spectral information and a ground LAI value. Spectral range and data type may affect the performance of the model, but few studies have compared the estimation accuracy of crop LAI using different spectral sensors, especially in ramie. In this study, we compared the potential to estimate the ramie LAI of a hyperspectral sensor with a multispectral sensor. A handheld hyperspectral sensor and an airborne multispectral sensor were used to collect spectral data from multiple growth stages, and then four machine learning algorithms were used to construct the LAI estimation model. The results showed that the R2 of the hyperspectral model was 0.702, while the R2 of the multispectral model was 0.816. The cropped hyperspectral data was less sensitive to LAI than the multispectral data with the same spectral band, due to the result of radiation area and data type. The accuracy of the ramie LAI estimation model constructed with all stage data was improved (R2 = 0.828). In conclusion, the airborne multi-spectrometer is more suitable for monitoring ramie LAI in the field. [ABSTRACT FROM AUTHOR]

Crop type mapping visualizes the spatial distribution pattern and proportion of planting areas of different crop types, which is the basis for subsequent agricultural applications. Although optical remote sensing has been widely used to monitor crop dynamics, data are not always available due to cloud and other atmospheric effects on optical sensors. Satellite microwave systems such as Synthetic Aperture Radar (SAR) have all-time and all-weather advantages in monitoring ground and crop conditions, combining optical imagery and SAR imagery for crop type classification is of great significance. Our study mainly proposes seven feature combination schemes based on the combination of multi-temporal spectral features and texture features of Sentinel-2 (S2), and radar backscattering features of Sentinel-1 (S1) evaluate the influence of different data sources and different features on classification accuracy, obtains the optimal classification strategy and analyses the contribution of different features to classification result, in the aim of providing a new technical approach for the fine identification of crops from multi-source remote-sensing data. Results show that the crop classification accuracy of combined multi-time series spectral, texture, and radar features is higher than that of combining two types of features. The features subset selected by multi-period spectral, texture, and radar features have the best classification result, the overall accuracy (OA) and kappa coefficients reach 96.40% and 0.93, respectively. The study provides a method reference for future research on larger-scale remote-sensing crop precise extraction. [ABSTRACT FROM AUTHOR]

Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulative temperature data for multiple growth stages of summer maize, respectively. Secondly, the effective plant height (canopy height model (CHM)), effective accumulation temperature (growing degree days (GDD)), canopy vegetation index (mainly spectral vegetation index) and canopy hyperspectral features of maize were extracted, and sensitive features were screened by correlation analysis. Then, based on single-source and multi-source data, multiple linear regression (MLR), partial least-squares regression (PLSR) and random forest (RF) regression were used to construct LAI and SPAD inversion models. Finally, the distribution of LAI and SPAD prescription plots was generated and the trend for the two was analyzed. The results were as follows: (1) The correlations between the position of the hyperspectral red edge and the first-order differential value in the red edge with LAI and SPAD were all greater than 0.5. The correlation between the vegetation index, including a red and near-infrared band, with LAI and SPAD was above 0.75. The correlation between crop height and effective accumulated temperature with LAI and SPAD was above 0.7. (2) The inversion models based on multi-source data were more effective than the models made with single-source data. The RF model with multi-source data fusion achieved the highest accuracy of all models. In the testing set, the LAI and SPAD models' R2 was 0.9315 and 0.7767; the RMSE was 0.4895 and 2.8387. (3) The absolute error between the extraction result of each model prescription map and the measured value was small. The error between the predicted value and the measured value of the LAI prescription map generated by the RF model was less than 0.4895. The difference between the predicted value and the measured value of the SPAD prescription map was less than 2.8387. The LAI and SPAD of summer maize first increased and then decreased with the advancement of the growth period, which was in line with the actual growth conditions. The research results indicate that the proposed method could effectively monitor maize growth parameters and provide a scientific basis for summer maize field management. [ABSTRACT FROM AUTHOR]

Quantitative assessment of the effectiveness of ecological restoration provides timely feedback on restoration efforts, and helps to accurately understand the extent of restoration, while providing scientific support for optimizing restoration programs. In recent decades, the Wuliangsuhai watershed in China's Inner Mongolia Autonomous Region has been affected by anthropogenic activities, resulting in an increasingly unbalanced ecological environment. In order to curb environmental degradation, the local government implemented the "mountain, water, forest, field, lake and grass ecological protection and restoration project of the Wuliangsuhai watershed" from 2018 to 2020. The project has been completed and there is an urgent need for remote sensing monitoring to aid in performance evaluation. We took the ecological protection and restoration area of the Wuliangsuhai watershed in China as the research object, applied multi-source remote sensing imagery and auxiliary data such as meteorology and geographic basic data, extracted information of each evaluation index before and after the implementation of this project, and used the entropy value method to determine the index weights to comprehensively evaluate the ecological restoration effect. The results showed that after the implementation of the ecological restoration project, the vegetation coverage was further improved, the effectiveness of desert management was obvious, soil and water conservation capacity was strengthened, the ecosystem became more stable, and the areas with good environment were mostly located in the central and eastern parts. A total of 37.86% of the areas had obvious ecological restoration effects, and all indicators were further improved. Among the main treatment areas, the restoration effect of the Wuliangsuhai water ecological restoration and biodiversity conservation area was the best. The restoration effect will be further accentuated over time. This study provides a scientific reference for the further management of the ecological environment in the watershed and can provide a reference for the evaluation of the ecological restoration effect in similar areas in the future. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study's results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses. [ABSTRACT FROM AUTHOR]

Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with R 2   = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method. [ABSTRACT FROM AUTHOR]

Lake Dianchi is one of the most eutrophic lakes in China. The decline in water quality and the occurrence of massive algal blooms pose a significant threat to the health and environmental safety of the water ecosystem, making Lake Dianchi a key concern for algal bloom management in China. Obtaining the spatiotemporal dynamics of algal blooms for the longest time possible is crucial to algal bloom management and future prediction. However, it is difficult to acquire a long-term record of algal blooms from a single sensor in order to cover a more extended period of eutrophication in the lake due to the limitation of the spatial and temporal resolution of the sensors. In this study, Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) images were combined with the Floating Algae Index (FAI) to reconstruct a unified time series of bloom areas to analyze the algal bloom dynamics in Lake Dianchi in recent decades. Regarding the interannual variation, the bloom area showed an increasing trend from 1987 to 2021, with larger bloom areas in 1991–1992, 2000–2003, 2012–2013, and 2020–2021. In terms of seasonal characteristics, the bloom area was significantly more prominent in the rainy season compared with the dry season during the year. The spatial distribution of the bloom frequency showed a pattern of higher frequencies in the north and lower frequencies in the south. From 2000 to 2021, the initial bloom time and bloom duration showed a trend of delaying and then advancing and decreasing and then increasing, respectively. We analyzed the importance of long-term records of algal blooms and found that the percentage of rainy season images is an essential factor in reconstructing time series based on different sensors. In addition, the relationship between environmental factors and algal blooms was analyzed. The results show that wind speed and air temperature were the main meteorological factors controlling the interannual variation in algal blooms in Lake Dianchi. Water quality factors such as nutrients have less of an influence on the variation in algal blooms because the algal growth demand has been met. Environmental management measures taken by local governments have led to improvements in the lake's trophic state, and continued strengthening of environmental pollution control is expected to curb the algal blooms in Lake Dianchi. This study provides a long-term record of algal blooms in Lake Dianchi, which provides essential reference information for a comprehensive understanding of the development process of algal blooms in Lake Dianchi and its sustainable development. [ABSTRACT FROM AUTHOR]

Pine Island Glacier (PIG) is one of the largest contributors to sea level rise in Antarctica. Continuous thinning and frequent calving imply significant destabilization of Pine Island Glacier Ice Shelf (PIGIS). To understand the mechanism of its accelerated disintegration and its future development, we conducted a long-term monitoring and comprehensive analysis of PIGIS, including ice flow velocity, ice shelf fronts, ocean water temperature, rifts, and surface strain rates, based on multi-source satellite observations during 1973–2020. The results reveal that: (1) ice flow velocities of PIGIS increased from 2.3 km/yr in 1973 to 4.5 km/yr in 2020, with two rapid acceleration periods of 1995–2009 and 2017–2020, and its change was highly correlated to the ocean water temperature variation. (2) At least 13 calving events occurred during 1973–2020, with four unprecedented successive retreats in 2015, 2017, 2018, and 2020. (3) The acceleration of ice shelf rifting and calving may correlate to the destruction of shear margins, while this damage was likely a response to the warming of bottom seawater. The weakening southern shear margin may continue to recede, indicating that the instability of PIGIS will continue. [ABSTRACT FROM AUTHOR]