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Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands. [ABSTRACT FROM AUTHOR]

Forest stock volume is the main factor to evaluate forest carbon sink level. At present, the combination of multi-source remote sensing and non-parametric models has been widely used in FSV estimation. However, the biodiversity of natural forests is complex, and the response of the spatial information of remote sensing images to FSV is significantly reduced, which seriously affects the accuracy of FSV estimation. To address this challenge, this paper takes China's Baishanzu Forest Park with representative characteristics of natural forests as the research object, integrates the forest survey data, SRTM data, and Landsat 8 images of Baishanzu Forest Park, constructs a time series dataset based on survey time, and establishes an FSV estimation model based on the CNN-LSTM-Attention algorithm. The model uses the convolutional neural network to extract the spatial features of remote sensing images, uses the LSTM to capture the time-varying characteristics of FSV, captures the feature variables with a high response to FSV through the attention mechanism, and finally completes the prediction of FSV. The experimental results show that some features (e.g., texture, elevation, etc.) of the dataset based on multi-source data feature variables are more effective in FSV estimation than spectral features. Compared with the existing models such as MLR and RF, the proposed model achieved higher accuracy in the study area (R2 = 0.8463, rMSE = 26.73 m3/ha, MAE = 16.47 m3/ha). [ABSTRACT FROM AUTHOR]

The evaluation of satellite soil moisture is a big challenge owing to the large spatial mismatch between pixel-based satellite soil moisture products and point-based in situ measurements. Upscaling in situ measurements to obtain the "true value" of soil moisture content at the satellite grid/footprint scale can make up for the scale difference and improve the validation. Many existing upscaling methods have strict requirements regarding the spatial distribution and quantity of soil moisture sensors. However, in reality, soil-moisture-monitoring networks are commonly sparse with low sensor density, which increases the difficulty of obtaining accurate upscaled soil moisture data and limits the validation of satellite products. For this reason, this paper proposes a scheme to upscale in situ measurements using five machine learning methods along with Landsat 8 datasets and DEM data to validate the accuracy of a SMAP-enhanced passive soil moisture product for a sparse network on the Qinghai–Tibet Plateau. The proposed scheme realizes the upscaling of in situ soil moisture data to the pixel scale (30 m × 30 m) and then to the coarse grid scale (9 km × 9 km) by using multi-source remote sensing data as the bridge of scale conversion. The long-time SMAP SM products since April 2015 on the Qinghai–Tibet Plateau were validated based on upscaled soil moisture data. The results show that (1) random forest regression performs the best, and the upscaled soil moisture data reflect the region-average soil moisture conditions that can be used for evaluating SMAP data; (2) the SMAP product meets its scientific measurement requirements; and (3) the SMAP product generally underestimates the soil moisture in the study area. [ABSTRACT FROM AUTHOR]

Analysis of the spatial and temporal variation patterns of surface evapotranspiration is important for understanding global climate change, promoting scientific deployment of regional water resources, and improving crop yield and water productivity. Based on Landsat 8 OIL_TIRS data and remote sensing image data of the lower Yangtze River urban cluster for the same period of 2016–2021, combined with soil and meteorological data of the study area, this paper constructed a multiple linear regression (MLR) model and an extreme learning machine (ELM) inversion model with evapotranspiration as the target and, based on the model inversion, quantitatively and qualitatively analyzed the spatial and temporal variability in surface evapotranspiration in the study area in the past six years. The results show that both models based on feature factors and spectral indices obtained a good inversion accuracy, with the fusion of feature factors effectively improving the inversion ability of the model for ET. The best model for ET in 2016, 2017, and 2021 was MLR, with an R2 greater than 0.8; the best model for ET in 2018–2019 was ELM, with an R2 of 0.83 and 0.62, respectively. The inter-annual ET in the study area showed a "double-peak" dynamic variation, with peaks in 2018 and 2020; the intra-annual ET showed a single-peak cycle, with peaks in July–August. Seasonal differences were obvious, and spatially high-ET areas were mainly found in rural areas north of the Yangtze River and central and western China where agricultural land is concentrated. The net solar radiation, soil heat flux, soil temperature and humidity, and fractional vegetation cover all had significant positive effects on ET, with correlation coefficients ranging from 0.39 to 0.94. This study can provide methodological and scientific support for the quantitative and qualitative estimation of regional ET. [ABSTRACT FROM AUTHOR]

As a particular type of alpine glacier, debris-covered glaciers are essential for local water resources and glacial disaster warnings. The Eastern Tomur Peak Region (EPTR) is the most concentrated glacier in Tien Shan Mountain, China, where the glaciers have not been studied in detail. This paper evaluates the delineation accuracy of Landsat8 OLI, Sentinel-1A, and GF images for debris-covered glaciers in the EPTR. Each image uses the most advanced delineation method for itself to minimize the error of inherent resolutions. The results show that the accuracy of these images for delineating debris-covered glaciers is very high, and the F1 scores are expressed as 96.73%, 93.55%, and 95.81%, respectively. Therefore, Landsat images were selected to analyze the area change of EPTR from 2000 to 2022 over a 5-year time scale. The results indicate that glaciers of the EPTR decreased by 19.05 km2 from 2000 to 2020, accounting for 1.9% (0.08% a−1), and debris increased by 10.8%, which validates the opinion that the presence of debris inhibits glacier melting. The most varied time was 2010–2022, but it was much less than other Tien Shan regions. The lower glacier ablation rate in this area results from the combined effect of decreased bare ice and increased debris. The main reason for the change in debris-covered glaciers is the increase in temperature. [ABSTRACT FROM AUTHOR]

Models for bathymetry retrieval from multispectral images have not considered the errors caused by tidal fluctuation. A rigorous bathymetric model that considers the variation in tide height time series, including the tide height calculation and instantaneous tide height correction at the epoch of satellite flight into the bathymetric retrieval model, is proposed in this paper. The model was applied on Weizhou Island, located in Guangxi Province, China, and its accuracy verificated with four check lines and seven checkpoints. A scene from the Landsat 8 satellite image was used as experimental data. The reference ("true") water depth data collected by a RESON SeaBat 7125 multibeam instrument was used for comparison analysis. When satellite-derived bathymetry is compared, it is found that maximum absolute error, mean absolute error, and RMSE have decreased 54, 45, and 30% relative to that of the traditional model in the entire test field. The accuracy of the water depths retrieved by our model increased 30 and 56% when validated using four check lines and seven checkpoints, respectively. Therefore, it can be concluded that the model proposed in this paper can effectively improve the accuracy of bathymetry retrieved from Landsat 8 images. [ABSTRACT FROM AUTHOR]

To assess the status and change trend of forest in China, an indicator framework was developed using SDG sub-indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDG indicators. The main modification include the use of moderate and high spatial resolution satellite data, as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass (AGB). This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover, while time series Landsat data is used to estimate forest AGB across the whole territory of China. The study calculate two SDG sub-indicators: SDG15.1.1 for forest area and SDG15.2.1 for sustainable forest management. The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021, accounting for about 23.51% of the land area. The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha, and the overall trend of forest AGB change in China was positive, albeit with some spatial differences. [ABSTRACT FROM AUTHOR]

Accurate understanding of daily evapotranspiration (ET) at field scale is of great significance for agricultural water resources management. The operational simplified surface energy balance (SSEBop) model has been applied to estimate field scale ET with Landsat satellite imagery. However, there is still uncertainty in the ET time reconstruction for cloudy days based on limited clear days' Landsat ET fraction (ETf) computed by SSEBop. The Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data can provide daily surface observation over clear-sky areas. This paper presented an enhanced gap-filling scheme for the SSEBop ET model, which improved the temporal resolution of Landsat ETf through the spatio-temporal fusion with SSEBop MODIS ETf on clear days and increased the time reconstruction accuracy of field-scale ET. The results were validated with the eddy covariance (EC) measurements over cropland in northwestern China. It indicated that the improved scheme performed better than the original SSEBop Landsat approach in daily ET estimation, with higher Nash-Sutcliffe efficiency (NSE, 0.75 vs. 0.70), lower root mean square error (RMSE, 0.95 mm·d-1 vs. 1.05 mm·d-1), and percent bias (PBias, 16.5% vs. 25.0%). This fusion method reduced the proportion of deviation (13.3% vs. 25.5%) in the total errors and made the random error the main proportion, which can be reduced over time and space in regional ET estimation. It also evidently improved the underestimation of crop ET by the SSEBop Landsat scheme during irrigation before sowing and could more accurately describe the synergistic changes of soil moisture and cropland ET. The proposed MODIS and Landsat ETf fusion can significantly improve the accuracy of SSEBop in estimating field-scale ET. [ABSTRACT FROM AUTHOR]

In the urbanization of China, construction land is generally distributed as a continuous area, and its change shows distinct spatial aggregation leading to the strong spatial autocorrelation, which is more obvious in remote sensing imagery with a fine spatial resolution. Based on the TripleSat-2 multi-spectral remote sensing images covering Nanjing city in 2016 and 2017, the paper compares and analyzes the performance of remote sensing change detection of construction land after we introduced the spatial autocorrelation index of the change vector as the image feature. Firstly, the three typical spatial autocorrelation indices of local G, Moran's I and Geary's C are extracted, and then the optimal spatial range of Lag and the optimal autocorrelation index suitable for the change detection are determined. The results showed that: (1) The spectral change vector had significant positive correlation. (2) The optimal range of Lag can be determined by global Moran's I and semi-variance. (3) We used the local G and local Moran's I with the spectral change vectors to increase the F1 score of the change detection, and the local G showed the better performance. (4) Using the local G with the optimal range of Lag as additional image features, the F1 scores were 20% higher than that using only the spectral change vectors. Fusing local spatial autocorrelation information especially of the multi- scale local G as the additional image features can effectively improve the change detection accuracy of construction land in remfote sensing imagery. [ABSTRACT FROM AUTHOR]

Tsunami is one of the world's most dangerous marine disaster. In this paper, freely available remote sensing data are applied to study the hazard, vulnerability, and evacuation in the event that a tsunami strikes the district of Tianya in the city of Sanya. Tsunami inundation is calculated using a tsunami numerical model, and the tsunami vulnerability and evacuation in the inundation area are analyzed. Aster Global Digital Elevation Model elevation data are applied to provide input data for the tsunami numerical model and thus obtain tsunami inundation areas, while they are also used to study the surface slope for evacuation. Landsat satellite imagery is used to analyze land–water borders and land cover in both hazard assessment and evacuation analysis. Visible Infrared Imaging Radiometer Suite nighttime lights data provide information of the socioeconomic activity for vulnerability analysis. The analysis results show that the remote sensing data is suitable for tsunami assessment and evacuation analysis of China's county-level region. We can get a general understanding about tsunami vulnerability and evacuation situation. One kind of remote sensing data can accomplish several tasks, avoiding the error caused by different source data. Remote sensing can play an important role in tsunami assessment. [ABSTRACT FROM AUTHOR]

• The landscape pattern of wetland is closely related to the population of cranes. • Landscape pattern has a time-lag effect on crane population. • Regression forecasting model based on Landscape pattern predicts crane population of each year. • Predicting crane populations can provide data support for wetland reserves. Reasonable prediction of the crane population that arrives at Poyang Lake Reserve for overwintering every year is of great significance to protect cranes and to provide data about reserve planning. In this paper, considering the time-lag effect of landscape index on crane population, the Landsat satellite images of Poyang Lake wetland in 19 winter periods from 2001 to 2019 were selected. The land-use types of the images were classified by support vector machine to acquire the landscape pattern index. In addition, the Pearson test was adopted to examine the correlation between crane population dynamics and landscape pattern index during overwintering. Finally, the regression model of the crane population was obtained by multivariate linear regression analysis. The results showed that the landscape index had a delayed impact on the crane population, which was significantly correlated with the crane population in the following five years. This effect on different crane population was disparate. The number of siberian cranes and white-naped crane populations showed a downward trend, while that of hooded cranes and common crane populations showed an upward trend. Furthermore, the landscape index affects the crane population to a certain extent, but the landscape pattern has diverse effects on different crane populations. It is suggested that corresponding protection measures be taken for different crane populations to provide a better habitat for cranes in Poyang Lake. [ABSTRACT FROM AUTHOR]

Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program significantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature (LST). However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM + and channel differences among sensors. Based on these investigations, the concerns are to: (1) show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors; (2) emphasize efforts which should be done for the quantitative applications of Landsat data; and (3) understand the potential challenges for the continuity of Landsat observation ( i.e. , thermal infrared) for global change monitoring, while several climate data record programs being in progress. [ABSTRACT FROM AUTHOR]

Due to the country's rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city. [ABSTRACT FROM AUTHOR]

With the increasing problem of eutrophication, lacustrine ecosystem can undergo complex changes, often resulting in a shift from a clear macrophyte-dominated state to a turbid phytoplankton-dominated state. However, it's not clear how lake transitions occur at regional and global scales. This is due to a lack of long-term monitoring and an effective algorithm that can distinguish floating/emergent aquatic vegetation (FEAV), submerged aquatic vegetation (SAV) and algal bloom (AB). This paper presents a novel three-step classification algorithm based on Landsat imagery, (ie., vegetation and bloom indices (VBI) algorithm): (1) to distinguish between aquatic vegetation (AV) and non-aquatic vegetation (non-AV) extents by an aquatic vegetation index (AVI) derived from tasseled cap transformation; (2) to identify FEAV and SAV within AV extent by using the normalized difference vegetation index (NDVI), and (3) to extract AB from non-AV extent with floating algae index (FAI). The performance of the VBI algorithm was validated using 1307 field samples collected across 22 lakes in the middle and lower reaches of the Yangtze River (MLY) in China, with a pooled overall accuracy of 84.49%. Finally, the VBI algorithm was applied to Landsat data from 1985 to 2021 to quantify the temporal changes of FEAV, SAV and AB in large lakes (i.e., > 50 km2) along the MLY. Within these lakes, AV showed a significant decrease over the past 37 years, mainly due to the decrease of SAV; while AB occurred with higher frequency and in more lakes. The transition from a macrophyte-dominated state to a phytoplankton-dominated aquatic systems is still ongoing in the MLY area. This study shows outstanding innovation in the construction of a new classification index (i.e., AVI), and the method of calculating the adaptive threshold, which makes VBI algorithm have great potential in mapping long-term changes and capturing transformation between lake communities on a global scale. • A novel Landsat-based algorithm was developed for quantifying aquatic communities. • An index for aquatic vegetation (AV) was proposed to classify AV and non-AV. • Long-term algal blooms and AV (floating/emergent/submerged) in MLY lakes were mapped. • AV decreased while algal blooms increased in most MLY lakes from 1985 to 2021. • Shift from macrophyte- to phytoplankton-dominated state is ongoing in MLY lakes. [ABSTRACT FROM AUTHOR]

The aim of this study was to clarify the distribution of irrigated drylands in arid and semi-arid areas, where complex terrain, diverse crops and staggered cultivated lands exist. This paper studied the classification methods of irrigated drylands based on temperature, precipitation, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) from Landsat data in the one-harvest area of the northern Loess Plateau of China by using the Google Earth Engine (GEE) platform. An extraction method was proposed for irrigated drylands in arid and semi-arid regions of northwest China. In addition, the change types of irrigated and rainfed drylands in the two periods were classified, and a method was also put forward to directly classify the change types by using the image differences between the two periods combined with the classification results of each period. It was found that combining the ratio of NDVI and NDWI with the accumulated values of temperature and precipitation of the 30 days before imaging could effectively improve the classification accuracy. Moreover, directly classifying the gaps of remote sensing factors in the time dimension before combining spatial clustering information could yield a more accurate type of change, because the accumulation of errors in the change maps obtained from the overlay analysis of distribution maps of the two periods could be avoided. The accuracy of classification could be improved by introducing the dynamic information of time dimension into the classification of historical periods. This study complements the extraction method for this type of irrigated dryland, and the classification results can improve the accuracy of existing products in terms of spatial resolution, which can fill the shortage of detailed distribution data for irrigated and rainfed drylands in this region. [ABSTRACT FROM AUTHOR]

The normalized difference water indices (NDWIs) were successfully used in map land surface water mapping (LSWM) from Landsat series multispectral images. This paper evaluates the potential of the recent Landsat satellite (Landsat-8) Operational Land Imager (OLI) multispectral images for LSWM using three NDWI models. We tested the accuracy and robustness of the three OLI NDWI models in the Yangtze River Basin and the Huaihe River Basin in China. The results demonstrate that the three OLI NDWI models achieve an overall accuracy of more than 95%, a kappa coefficient of 0.89 and a producer’s accuracy of 95% for LSWM. The results also demonstrate that the NDWI model using the green band (Band 3) and the SWIR1 band (Band 6) (referred to as NDWIO6,3) of the OLI sensor has a higher LSWM accuracy than the other two NDWI models. [ABSTRACT FROM AUTHOR]

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs. [ABSTRACT FROM AUTHOR]