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Covering the full texts of all papers published in MDPI's Remote Sensing between 2009 and 2021, in-depth scientometric analyses were conducted. Trends in publications show an increase in the overall number of papers. A relative increase in papers using SAR sensors and a relative decrease in papers using optical remote sensing can also be seen. The full-text analyses reveal distinctive styles and writing patterns for papers from different sub-fields of remote sensing and for different countries and even cities. While a slight increase in the readability of abstracts is detected over time, the overall readability of papers is decreasing. Institutional co-authorship analysis reveals the ongoing 'scientific decoupling' between China and the USA in remote sensing. Using scientometric full-text analysis, current trends and developments are revealed. [ABSTRACT FROM AUTHOR]

Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords "Sentinel", "deep learning" (DL), and "LSTM" (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images' accuracy. [ABSTRACT FROM AUTHOR]

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]

The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene. [ABSTRACT FROM AUTHOR]

Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification. [ABSTRACT FROM AUTHOR]

Lanzhou City is located in the semi-arid region of northwest China, which experiences serious desertification. Moreover, the high intensity of land development, with the accelerated industrialization and urbanization, causes increasingly aggravated conflict between humans and the environment. Exploring the response of the ecological environment quality to the natural environment and anthropogenic activities is important to protect the sustainable development of urban economic construction and the environment. Based on the Google Earth Engine (GEE) platform, this paper constructed a modified Remote Sensing Ecological Index (MRSEI) model which could reflect the ecological environment quality by integrating the desertification index (DI) into the Remote Sensing Ecological index (RSEI) model. This paper explores the spatiotemporal variation in the environmental quality from 2000 to 2020 in Lanzhou, China, and analyzes the natural and anthropogenic factors affecting the environment quality in terms of temperature, precipitation, gross domestic product (GDP), land use, night lighting, and population. The results showed that the mean value of MRSEI ranged from 0.254 to 0.400. The area undergoing fast growth in ecological quality was in the northwestern part of Lanzhou, and the area of decrease was in the central part. Various factors have different degrees of influence on the ecosystem, with temperature, precipitation, and land use having a greater impact, and GDP and population having a limited impact. Precipitation and temperature showed a strong impact when interacting with other factors, demonstrating that precipitation and temperature were also key factors affecting MRSEI. Overall, climate change and the implementation of ecological restoration projects have led to an improvement in the quality of the ecological environment in Lanzhou. This study provides a reference for understanding the spatiotemporal changes in the ecological environment in semi-arid Lanzhou and is conducive to formulating proper protection strategies. [ABSTRACT FROM AUTHOR]

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha. [ABSTRACT FROM AUTHOR]

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds. [ABSTRACT FROM AUTHOR]

Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simultaneously, dynamical soil evaporation coefficients are generated based on the assimilated SM to improve the simulation accuracy of the PML model. The new scheme was validated at a typical irrigation zone in north China and showed obvious improvements in both SM and ET simulations. Moreover, the effect of the assimilation of SM on the simulation accuracy of ET for different crop growth periods is further analyzed. This research provides a new idea for the coupling simulation of the SM and PML models. [ABSTRACT FROM AUTHOR]

This study investigates the application of hyperspectral image space–spectral fusion technology in lithologic classification, using data from China's GF-5 and Europe's Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space–spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The "GSA+RF" method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions. [ABSTRACT FROM AUTHOR]

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions. [ABSTRACT FROM AUTHOR]

Mountainous terrains are typical over southeast China, with complex and diverse topography, large terrain undulations, rich geographic features, and meteorological variations. Previous studies show that ERA5 meteorological variables are generally accurate with respect to large plains or urban agglomerations, while their applicability to mountainous areas remains inconclusive. In this paper, using high-precision measurements probed by ground-based remote sensing instruments in May–July 2023 at a typical mountainous Shanghuang site in southeast China, the vertical accuracy of the ERA5 reanalysis datasets were comparatively evaluated. Our findings depict that the horizontal wind speeds of the ERA5 reanalysis data show a good performance compared to the Doppler lidar observations. In quantitative terms, ERA5 horizontal wind speeds are about 8% higher than the observed values below a height of 400 m, while above 400 m, an increasing negative bias is observed along as altitude increases. Differing from the horizontal wind speeds, there is a large discrepancy in the vertical wind speeds between the ERA5 and the observations, with a deviation of −150% to 40%. In terms of the thermal variables, the temperature extracted from ERA5 are consistent with the measurements in the low troposphere. Nevertheless, large systematic errors occur at 2000–3000 m, and the overall presentation shows that the errors gradually increase with the increase in altitude. Concerning the relative humidity, the general trend in ERA5 is similar to that observed by the microwave radiometer, but the relative errors from 500 to 2500 m range from 40% to 100%. This study also reveals that ERA5 is poorly representative and requires further improvements during extreme weather events such as rainstorms and typhoons. In particular, the horizontal wind speeds at the middle and lower levels deviate strongly from the observations. Given the importance of atmospheric thermodynamic stratifications in terms of both environmental and climatic issues, the results expand the application of the ERA5 reanalysis datasets in the mountainous areas of southeast China. More importantly, it provides credible reference data for the meteorological predictions and climate modelings in the southeast China mountainous region. [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]

Ponds constitute a pivotal component of aquatic ecosystems. The aquatic ecosystem of the Huai River Basin (HRB) in China was once damaged by severe pollution, and numerous ponds in the basin have not been secured. In this paper, Shenqiu County, a typical county in HRB with many ponds, is selected. Based on high-resolution images with ALOS in 2010, GF-2 in 2016, and GF-1 in 2022, we employed discriminant analysis (DA), classification and regression tree, support vector machine, and random forest to extract the ponds based on object-oriented and further analyzed the spatial-temporal variations of the ponds in this county. The results of the DA in these three years exhibited a higher kappa coefficient (>0.7), and overall accuracy (>75%), signifying superior performance when compared to the other three methods. There were 4625, 5315, and 4748 ponds in 2010, 2016, and 2022, with a total area of 12.87, 11.99, and 9.37 km2, respectively. The number of ponds had a trend of rising in the initial period (2010–2016) and falling later (2016–2022), while the total area revealed a continuous decline. Meanwhile, these ponds showed a clustering phenomenon with three main clustering areas, and the scope of the clustering areas also changed to a certain extent from 2010 to 2022. Our study offers valuable methodological support for the ecological monitoring and management of water environments in regions characterized by a dense concentration of ponds. The crucial data related to ponds in this study will help inform both environmental and social development initiatives within the area. [ABSTRACT FROM AUTHOR]

Land desertification is one of the serious ecological and environmental problems facing mankind today, which threatens the survival and development of human society. China is one of the countries with the most serious land desertification problems in the world. Therefore, it is of great theoretical value and practical significance to carry out accurate identification and monitoring of land desertification and its influencing factors in ecologically fragile areas of China. This is conducive to curbing land desertification and ensuring regional ecological security. Minqin County, Gansu Province, located in northwestern China, is one of the most serious areas of land desertification, which is also one of the four sandstorm sources in China. Based on ENVINet5, this paper constructs a high-precision land desertification identification method with an accuracy of 93.71%, which analyzes the trend and reasons of land desertification in this area, provides suggestions for disaster prevention in Minqin County. and provides a reference for other similar areas to make corresponding desertification control policies. [ABSTRACT FROM AUTHOR]

Continuous evapotranspiration (ET) data with high spatial resolution are crucial for water resources management in irrigated agricultural areas in arid regions. Many global ET products are available now but with a coarse spatial resolution. Spatial-temporal fusion methods, such as the spatial and temporal adaptive reflectance fusion model (STARFM), can help to downscale coarse spatial resolution ET products. In this paper, the STARFM model is improved by incorporating the temperature vegetation dryness index (TVDI) into the data fusion process, and we propose a spatial and temporal adaptive evapotranspiration downscaling method (STAEDM). The modified method STAEDM was applied to the 1 km SSEBOP ET product to derive a downscaled 30 m ET for irrigated agricultural fields of Northwest China. The STAEDM exhibits a significant improvement compared to the original STARFM method for downscaling SSEBOP ET on Landsat-unavailable dates, with an increase in the squared correlation coefficients (r2) from 0.68 to 0.77 and a decrease in the root mean square error (RMSE) from 10.28 mm/10 d to 8.48 mm/10 d. The ET based on the STAEDM additionally preserves more spatial details than STARFM for heterogeneous agricultural fields and can better capture the ET seasonal dynamics. The STAEDM ET can better capture the temporal variation of 10-day ET during the whole crop growing season than SSEBOP. [ABSTRACT FROM AUTHOR]

It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation. [ABSTRACT FROM AUTHOR]

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. [ABSTRACT FROM AUTHOR]

Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on remote sensing interpretation and field investigation, a spatial distribution map of avalanches was established, and the induced and triggering factors in disaster-prone environments were analyzed using the certainty factor model. The degree of influence (E) of the disaster-causing factors on avalanche triggering was quantified, and the main control conditions conducive to avalanche occurrence in different periods were obtained. The RAMMS-avalanche model was used to analyze the activity characteristics at points where multiple avalanches occurred. Research results: (1) The E values of the average temperature, average snowfall, and surface roughness in February were significantly higher than those of other hazard-causing factors, reaching 1.83 and 1.71, respectively, indicating strong control. The E values of the surface cutting degree, average temperature, and average snow depth in March were all higher than 1.8, indicating that these control factors were more prominent than the other factors. In contrast, there were four hazard-causing factors with E values higher than 1.5 in April: the mean temperature, slope, surface roughness, and mean wind speed, with clear control. (2) Under the influence of the different hazard-causing factors, the types of avalanches from February–April mainly included new full-layer avalanches, surface avalanches, and full-layer wet avalanches. (3) In the RAMMS-avalanche simulation test, considering the deposition effect, compared to the previous avalanche movement path, the secondary avalanche flow accumulation area impact range changes were slight, while the movement area within the avalanche path changes was large, as were the different categories of avalanches and their different movement characteristic values. Overall, wet snow avalanches are more hazardous, and the impact force is larger. The new snow avalanches start in a short period, the sliding rate is fast, and the avalanche sliding surface (full-snow surface and face-snow) of the difference is mainly manifested in the differences in the value of the flow height. [ABSTRACT FROM AUTHOR]

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security. [ABSTRACT FROM AUTHOR]

Going beyond previous work, this paper presents a systematic literature review that explores the deployment of satellites, drones, and ground-based sensors for yield prediction in agriculture. It covers multiple aspects of the topic, including crop types, key sensor platforms, data analysis techniques, and performance in estimating yield. To this end, datasets from Scopus and Web of Science were analyzed, resulting in the full review of 269 out of 1429 retrieved publications. Our study revealed that China (93 articles, >1800 citations) and the USA (58 articles, >1600 citations) are prominent contributors in this field; while satellites were the primary remote sensing platform (62%), followed by airborne (30%) and proximal sensors (27%). Additionally, statistical methods were used in 157 articles, and model-based approaches were utilized in 60 articles, while machine learning and deep learning were employed in 142 articles and 62 articles, respectively. When comparing methods, machine learning and deep learning methods exhibited high accuracy in crop yield prediction, while other techniques also demonstrated success, contingent on the specific crop platform and method employed. The findings of this study serve as a comprehensive roadmap for researchers and farmers, enabling them to make data-driven decisions and optimize agricultural practices, paving the way towards a fully digitized yield prediction. [ABSTRACT FROM AUTHOR]

Monitoring mine activities can help management track the status of mineral resource exploration and mine rehabilitation. It is crucial to the sustainable development of the mining industry and the protection of the geological environment in mining areas. To monitor the mining activities of shallow surface outcrops in the arid and semi-arid regions of northwest China, this paper proposes a remote sensing monitoring approach of mining activities based on deep learning and integrated interferometric synthetic aperture radar technique. This approach uses the DeepLabV3-ResNet model to identify and extract the spatial location of the mine patches and then uses object-oriented analysis and spatial analysis methods to optimize the mine patch boundaries. SBAS-InSAR technique is used to obtain the time-series deformation information of the mine patches and is combined with the multi-temporal optical imagery to analyze the mining activities in the study area. The proposed approach has a recognition accuracy of 95.80% for the identification and extraction of mine patches, with an F1-score of 0.727 at the pixel level, and the average area similarity for all patches is 0.78 at the object-oriented level. The proposed approach possesses the capability to analyze mining activities, indicating promising prospects for engineering applications. It provides a reference for monitoring mining activities using multisource satellite remote sensing. [ABSTRACT FROM AUTHOR]

The world's largest macroalgae bloom (also known as green tide) has been reported since the 29th Olympic Games in 2008, which is verified as the fast reproduction of floating green macroalgae (Ulva prolifera). It is helpful to assess the biomass of macroalgae for the government of marine environment protection. In this study, the synchronization cruise experiment was firstly introduced, which aimed to investigate the biomass evaluation of Ulva prolifera in the Yellow Sea of China. The Floating Algae Index by Polarimetric SAR image (FAIPS) was then proposed. Finally, the floating algae biomass evaluation model was demonstrated and verified, which showed an exponential relationship between FAIPS and wet biomass per area (kg/m2) of macroalgae. The model proposed in this paper can be used in the biomass assessment of floating algae in the presence of polarimetric SAR images, regardless of daylight and cloud coverage over the sea surface. [ABSTRACT FROM AUTHOR]

The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics. [ABSTRACT FROM AUTHOR]

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing. [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]

Using a shape model (SM) is a typical method to determine the phenological phases of crops with long-time-series satellite remote sensing data. The average AGDD-based shape model (AAGDD-SM) takes temperature into account compared to SM, however, the commonly used daily average temperature is not sufficient to determine the exact AGDD owing to the possibly significant changes in temperatures throughout the day. In this paper, a modified shape model was proposed for the better estimation of phenological dates and it is incorporated into the continuous AGDD (CAGDD) which was calculated based on temperatures from a continuous 24 h within a day, different from the calendar day or the average AGDD indicators. In this study, the CAGDD replaced the abscissa of the NDVI growth curve over a 5-year period (2014 to 2018, excluding 2015) for a test site of early rice in Jiangxi province of China. Four key phenological phases, including the reviving, tillering, heading and anthesis phases, were selected and determined with reference to the field-observed phenological data. The results show that compared with the AAGDD-SM, the method proposed in this paper has basically improved the prediction of each phenological period. For those cases where the average temperature is lower than the minimum temperatures (K1) but the effective accumulated temperature is not zero, more accurate AGDD can be calculated according to the method in this paper. [ABSTRACT FROM AUTHOR]

In the context of global sustainable development, solar energy is very widely used. The installed capacity of photovoltaic panels in countries around the world, especially in China, is increasing steadily and rapidly. In order to obtain accurate information about photovoltaic panels and provide data support for the macro-control of the photovoltaic industry, this paper proposed a hierarchical information extraction method, including positioning information and shape information, and carried out photovoltaic panel distribution mapping. This method is suitable for large-scale centralized photovoltaic power plants based on multi-source satellite remote sensing images. This experiment takes the three northwest provinces of China as the research area. First, a deep learning scene classification model, the EfficientNet-B5 model, is used to locate the photovoltaic power plants on 16-m spatial resolution images. This step obtains the area that contains or may contain photovoltaic panels, greatly reducing the study area with an accuracy of 99.97%. Second, a deep learning semantic segmentation model, the U2-Net model, is used to precisely locate photovoltaic panels on 2-m spatial resolution images. This step achieves the exact extraction results of the photovoltaic panels from the area obtained in the previous step, with an accuracy of 97.686%. This paper verifies the superiority of a hierarchical information extraction method in terms of accuracy and efficiency through comparative experiments with DeepLabV3+, U-Net, SegNet, and FCN8s. This meaningful work identified 180 centralized photovoltaic power plants in the study area. Additionally, this method makes full use of the characteristics of different remote sensing data sources. This method can be applied to the rapid extraction of global photovoltaic panels. [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]

The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data. [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]

The Wudaoliang–Changshagongma fault is one of the NW-trending faults located within the southern Bayan Har Block of the Tibetan Plateau in China. In this paper, we used high-resolution imagery and digital elevation model data to study the geomorphological and geological characteristics of the fault. Furthermore, the result also determined the fault trace and estimated the average horizontal slip rate of the fault since the late Quaternary to have been 2.6 ± 0.6 mm/a. This slip rate is approximately equivalent to that of the Awancang, Madoi–Garde, and Dari faults, which are also located within the block. Furthermore, the slip rates of these faults obtained by remote sensing and geological methods are consistent with GPS observations. It indicates that tectonic deformation within the block is continuous and diffuse. Using trenching study results and sedimentary radiocarbon dating, we identified four paleoearthquake events that occurred at 42,378–32,975, 33,935–20,663, 5052–4862, and after 673–628 cal BP, respectively. The recurrence intervals of large earthquakes on the faults within the block are much longer than those of the boundary faults, and the slip rates are also smaller, indicating that faults within the block play a regulatory role in the tectonic deformation of the Bayan Har Block. [ABSTRACT FROM AUTHOR]

Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490/Rrs560 and Rrs665/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and −0.75 mg/m3, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors. [ABSTRACT FROM AUTHOR]

Mountain areas in China account for 69% of the total land area; however, it is still an urgent that we grasp the special ecological structure of mountain areas and maximize the resource advantages of mountain areas under the principle of maintaining a certain ecological level. In this paper, Landsat 5, Landsat 8 and Sentinel-2A images were used as data sources to monitor and analyze land development and ecological change in Gui 'an in 2010, 2013, 2016, 2019 and 2021, so as to explore ecological maintenance mechanisms. Firstly, random forest classification based on multi-source remote sensing data was used to classify land, and the five phases of land-use change were assessed using quantitative analysis, in order to analyze the mountain region's land-use-change characteristics at different stages of development. The results show that Gui 'an has the "two-stage", rapid-development, rapid-recovery mode. Each stage includes a development-and-expansion period and a construction-and-protection period. In the construction period, ecological recovery construction will be emphasized, and the change intensity and rate of the second stage are lower than that of the first stage. Secondly, using a remote sensing ecological index, vegetation coverage, and a landscape index, an ecological evaluation model of the study area was constructed to analyze changes in the ecological environment and its protection in the process of land development. The ecological maintenance status of the five stages was quantitatively monitored using the analysis methods of difference change and coefficient of variation. The results showed that in the first stage of land development and expansion, the landscape pattern and ecological quality fluctuated greatly, and the proportion of ecological quality of an excellent grade decreased by 28.46%. However, in the second stage, the change slowed down and remained unchanged, and gradually moved to the middle and upper level. The results show that there is a close relationship between ecological maintenance and the land development mode, and new mountain towns can maintain ecological quality and achieve sustainable development through a reasonable land development mode. At the end of this paper, the factors affecting the ecological maintenance capacity of Gui 'an are discussed, providing effective reference material and development models for the development of mountainous areas. [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]

Timely and accurate crop yield information can ensure regional food security. In the field of predicting crop yields, deep learning techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN) are frequently employed. Many studies have shown that the predictions of models combining the two are better than those of single models. Crop growth can be reflected by the vegetation index calculated using data from remote sensing. However, the use of pure remote sensing data alone ignores the spatial heterogeneity of different regions. In this paper, we tested a total of three models, CNN-LSTM, CNN and convolutional LSTM (ConvLSTM), for predicting the annual rice yield at the county level in Hubei Province, China. The model was trained by ERA5 temperature (AT) data, MODIS remote sensing data including the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and Soil-Adapted Vegetation Index (SAVI), and a dummy variable representing spatial heterogeneity; rice yield data from 2000–2019 were employed as labels. Data download and processing were based on Google Earth Engine (GEE). The downloaded remote sensing images were processed into normalized histograms for the training and prediction of deep learning models. According to the experimental findings, the model that included a dummy variable to represent spatial heterogeneity had a stronger predictive ability than the model trained using just remote sensing data. The prediction performance of the CNN-LSTM model outperformed the CNN or ConvLSTM model. [ABSTRACT FROM AUTHOR]

Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and money is high and it does not meet the needs of spatiotemporal dynamic monitoring. Remote sensing provides an effective tool for TP concentration monitoring spatiotemporally. However, monitoring the TP concentration of small inland waterbodies is challenging for satellite remote sensing due to the inadequate spatial resolution. Recently, unmanned aerial vehicles (UAV) have been applied to quantitatively retrieve the spatiotemporal distribution of TP concentration without the challenges of cloud cover and atmospheric effects. Although state-of-the-art algorithms to retrieve TP concentration have been improved, specific models are only used for specific water quality parameters or regions, and there are no robust and reliable TP retrieval models for small inland waterbodies at this time. To address this issue, six machine learning methods optimized by intelligent optimization algorithms (IOA-ML models) have been developed to quantitatively retrieve TP concentration combined with the reflectance of original bands and selected band combinations of UAV multispectral images. We evaluated the performances of models in terms of coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD). The results showed that the R2 of the six IOA-ML models for training, validation, and test sets were 0.8856–0.984, 0.8054–0.8929, and 0.7462–0.9045, respectively, indicating the methods had high precision and transferability. The extreme gradient boosting optimized by genetic algorithm (GA-XGB) performed best, with the highest precision for the validation and test sets. The spatial distribution of TP concentration of each flight derived from different models had similar distribution characteristics. This paper provides a reference for promoting the intelligent and automatic level of water environment monitoring in small inland waterbodies. [ABSTRACT FROM AUTHOR]

The continuous transformation from biodiverse natural forests and mixed-use farms into monoculture rubber plantations may lead to a series of hazards, such as natural forest habitats fragmentation, biodiversity loss, as well as drought and water shortage. Therefore, understanding the spatial distribution of rubber plantations is crucial to regional ecological security and a sustainable economy. However, the spectral characteristics of rubber tree is easily mixed with other vegetation such as natural forests, tea plantations, orchards and shrubs, which brings difficulty and uncertainty to regional scale identification. In this paper, we proposed a classification method combines multi-source phenology characteristics and random forest algorithm. On the basis of optimization of input samples and features, phenological spectrum, brightness, greenness, wetness, fractional vegetation cover, topography and other features of rubber were extracted. Five classification schemes were constructed for comparison, and the one with the highest classification accuracy was used to identify the spatial pattern of rubber plantations in 2014, 2016, 2018 and 2020 in Xishuangbanna. The results show that: (1) the identification results are in consistent with field survey and rubber plantations area generally shows a first increasing and then decreasing trend; (2) the Overall Accuracy (OA) and Kappa coefficient of the proposed method are 90.0% and 0.86, respectively, with a Producer's Accuracy (PA) and User's Accuracy (UA) of 95.2% and 88.8%, respectively; (3) cross-validation was employed to analyze the accuracy evaluation indexes of the identification results: both PA and UA of the rubber plantations stay stable over 85%, with the minimum fluctuation and best stability of UA value. The OA value and Kappa coefficient were stable in the range of 0.88–0.90 and 0.84–0.86, respectively. The method proposed provides reliable results on spatial distribution of rubber, and is potentially transferable to other mountainous areas as a robust approach for rapid monitoring of rubber plantations. [ABSTRACT FROM AUTHOR]

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry. [ABSTRACT FROM AUTHOR]

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach. [ABSTRACT FROM AUTHOR]

In line with the United Nations Sustainable Development Goal (SDG) 6, the main goal of the Special Issue on "Remote sensing for water resources and environmental management" was to solicit papers from a diverse range of scientists around the world on the use of cutting-edge remote sensing technologies to assess and monitor freshwater quality, quantity, availability, and management to ensure water security. Special consideration was given to scientific manuscripts that covered, but were not limited to, the development of geospatial techniques and remote sensing applications for detecting, quantifying, and monitoring freshwater water resources, identifying potential threats to water resources and agriculture, as well as other themes related to water resources and environmental management at various spatial scales. The Special Issue attracted over thirteen peer-reviewed scientific articles, with the majority of manuscripts originating from China. Most of the studies made use of satellite datasets, ranging from coarse spatial resolution data, such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), to medium spatial resolution data, such as the Landsat series, ERA5, Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), CLSM and NOAH ET, and MODIS (Moderate Resolution Imaging Spectroradiometer). Google Earth Engine (GEE) data, together with big data processing techniques, such as the remote sensing-based energy balance model (ALEXI/DisALEXI approach) and the STARFM data fusion technique, were used for analyzing geospatial datasets. Overall, this Special Issue demonstrated significant knowledge gaps in various big data image processing techniques and improved computing processes in assessing and monitoring water resources and the environment at various spatial and temporal scales. [ABSTRACT FROM AUTHOR]

To protect and restore this downstream ecosystem, the Tarim River Basin Administration Bureau (TBAB) initiated the Ecological Water Compensation (EWC) project from 2000 to 2018. Revealing the mechanism of vegetation-hydroecological response processes in the lower reaches of the Tarim River before and after EWC work is conducive to water resource planning, utilization and protection. In this paper, the spatiotemporal responses of vegetation and groundwater to EWC were examined at the points, lines, and area (PLA) scale by coupling remote sensing techniques and field station observation data collected between 2000 and 2017. The findings indicated that (1) In general, the regional fractional vegetation coverage (FVC) increased significantly, and the average FVC growth rate was 3.5%/year from 2000 to 2017 (R2 > 0.84, p < 0.01, 2-tailed). (2) The regional vegetation restoration process showed obvious fluctuations and stage characteristics, but the spatial scope of the significantly increased vegetation area was limited. Plants grew rapidly within 10 km of the river, while 10 km away from the water channel, no obvious change was observed. (3) Strong coupling relationships were identified among the FVC growth, EWC volume and groundwater depth variations (p < 0.05, 2-tailed). The response times of the regional vegetation and groundwater depth to EWC indicated one-year lags. The above results imply that the regional ecological environment was significantly improved over the study period, thus confirming that the EWC project made remarkable accomplishments. However, the effect of ecological restoration is not sufficiently stable at present. Vegetation restoration has mainly been centralized around the river channel and is greatly dependent on the annual EWC volume. In addition, the local conditions begin to degrade soon after an EWC project is terminated, and vice versa; when EWC commences, the FVC immediately begins to improve. Therefore, the current EWC achievements need to be further consolidated and strengthened in the future. [ABSTRACT FROM AUTHOR]

Land surface reflectance (LSR) data form the basis of quantitatively remotely sensed applications. For accurate LSR retrieval, atmospheric correction has been investigated by many researchers and implemented in typical processing systems, including common atmospheric correction software for various types of datasets and automatic operating systems for application to certain individual data sources. In recent years, China has launched multiple medium–high-resolution satellites but has not provided standard LSR products partly because of the lack of an appropriate operational system. In this paper, a multi-source remote sensing LSR product system for medium- and high-resolution data is proposed, called the "Operational Atmospheric Correction Framework for multi-source Medium-high-resolution Remote Sensing data of China" (ACFrC). The AC algorithm, processing flow, and design of the multi-source LSR system were described in detail. A practical atmospheric correction algorithm was proposed specially for data in only the visible and near-infrared (VNIR) bands. The entire processing chain was divided into modules for multi-source data ingestion, apparent reflectance calculation, cloud and water identification, atmospheric correction, and standard LSR product generation. To date, most types of multi-source data have been tested using the ACFrC system, with reasonable results being obtained. From the preliminary results, the 313 scenes of LSR products from the GaoFen-2 (GF-2) satellite over China for the period from 2015 to 2018 were cross-compared with Landsat-8 LSR acquired on the same day, showing an overall uncertainty less than 0.112 × LSR + 0.0112. Further, the ACFrC data processing efficiency was found to be suitable for automatic operation. System improvement is ongoing and future refinements will include online cloud parallel computing functionality and services, more robust algorithms, and other radiometric processing functions. [ABSTRACT FROM AUTHOR]

Forests are the most important part of terrestrial ecosystems. In the context of China's industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area's tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha−1. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha−1 of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development. [ABSTRACT FROM AUTHOR]

Drought is one of the most common natural threats to agricultural production worldwide. Few studies have studied the effects of agricultural practices on drought mitigation at a regional scale over a long period. This paper analyzes the spatiotemporal characteristics in the agricultural drought-affected area change index (ADAC), which was developed to assess the drought mitigation. The linear regression method was used to investigate the impact factors on the change of ADAC in the three main winter wheat provinces of northern China. The results showed that the average ADAC during the main growing season in the study area was approximately −61.5% over the past 38 years, which indicated a great decrease of the agricultural drought-affected area. The significant decreasing trends of ADAC values across the study area during 1981–2000 could be explained by the area percentage equipped for irrigation (APEI) by 49.2–89.7%. There was a lack of pronounced change trends of ADAC during 2001–2018, implying that the positive effects of irrigation infrastructure in the plain area might reach a plateau under the constraints of available water resources, and other agricultural practices need to be investigated in the future. This research provides helpful decision information on drought adaptation management and water conservation project planning. [ABSTRACT FROM AUTHOR]

Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (DSD) can be used to improve the accuracy of radar quantitative precipitation estimation (QPE) and further understand the microphysical process of precipitation; however, its spatio-temporal characteristics vary with different climates, rain types, and geographical locations. Due to the lack of observations, the DSD characteristics in the Beibu Gulf, especially at the rainfall center of Guangxi in South China, is poorly understood. In this paper, these regional DSD characteristics were analyzed during the warm season with an upgraded version of the OTT Particle Size Velocity (Parsivel) (OTT2) disdrometer. The DSD datasets from June to October 2020 and March to May 2021 were grouped into convective and stratiform precipitation by rain rate (R). The rainfall parameters were calculated from DSDs to further understand the rain characteristics. The results showed that: (1) the regional DSDs feature the lowest concentration of largest-sized drops when compared with the statistical results for other areas such as Zhuhai in South China, Nanjing in East China, Hubei province in Central China and Beijing in North China; (2) the raindrop spectra have an excellent fit with the three-parameter gamma distribution, particularly in regard to the medium-size raindrops; (3) the μ – Λ relation is closer to the coastal regions than the inland area of South China; (4) the localized Z−R relations differ greatly for convective rainfall (Z = 202.542 R1.553) and stratiform rainfall (Z = 328.793 R1.363). This study is the first study on DSDs in the Beibu Gulf region. The above findings will provide a better understanding of the microphysical nature of surface precipitation for different rain types along the Beibu Gulf in southern China, which may improve precipitation retrievals from remote sensing observations. [ABSTRACT FROM AUTHOR]

Simple Summary: Grasshoppers are among the most dangerous agricultural pests of China. However, the monitoring, prediction and control of grasshoppers are complex and difficult. Therefore, it is crucial to detect the key factors affecting the spatial distribution of grasshopper occurrence, understand the role of the environmental factors in grasshopper occurrence, and study whether different laws exist between different grass types. Here we conduct a species–environmental matching model integrated by Maxent model and remote sensing data to identify the spatial distribution of grasshopper occurrence in Inner Mongolia of China, analyze the related environmental variables and detect the most relevant environmental factors for grasshopper occurrence both in typical steppe and meadow steppe. Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland. [ABSTRACT FROM AUTHOR]

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin'anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models. [ABSTRACT FROM AUTHOR]

The geometric registration of mountainous remote sensing images is always a challenging project, as terrain fluctuations increase the complexity. Deep learning, with its superior computing power and data-driven nature, promises to solve this problem. However, the lack of an appropriate dataset limits the development of deep learning technology for mountainous remote sensing image registration, which it still an unsolved problem in photogrammetry and remote sensing. To remedy this problem, this paper presents a manually constructed imagery dataset of mountainous regions, called the MID (mountainous imagery dataset). To create the MID, we use 38 images from the Gaofen-2 satellite developed by China and generated 4093 pairs of reference and sensed image patches, making this the first real mountainous dataset to our knowledge. Simultaneously, we propose a fully unsupervised, convolutional-network-based iterative registration scheme for the MID. First, the large and global deformation of the reference and sensed images is reduced using an affine registration module, generating the coarse alignment. Then, the local and varied distortions are learned and eliminated progressively using a hybrid dilated convolution (HDC)-based encoder–decoder module with multistep iterations, achieving fine registration results. The HDC aims to increase the receptive field without blocking the artifacts, allowing for the continuous characteristics of the mountainous images of a local region to be represented. We provide a performance analysis of some typical registration algorithms and the developed approach for the MID. The proposed scheme gives the highest registration precision, achieving the subpixel alignment of mountainous remote sensing images. Additionally, the experimental results demonstrate the usability of the MID, which can lay a foundation for the development of deep learning technology in large mountainous remote sensing image registration tasks. [ABSTRACT FROM AUTHOR]