GIScience & Remote Sensing最新文献

{"title":"Failure process and three-dimensional motions of mining-induced Jianshanying landslide in China observed by optical, LiDAR and SAR datasets","authors":"Chaoying Zhao, Liquan Chen, Yueping Yin, Xiaojie Liu, Bin Li, Chaofeng Ren, Donglie Liu","doi":"10.1080/15481603.2023.2268367","DOIUrl":"https://doi.org/10.1080/15481603.2023.2268367","url":null,"abstract":"The occurrence of collapses and landslides due to underground mining has its unique failure mechanism, especially in the Karst mountainous regions of China. Spaceborne and airborne remote sensing observations provide rapid and effective tools for assessing surface changes and monitoring surface deformation of such landslides. In this study, we take the Jianshanying landslide, a typical mining-induced and fast-deformed landslide, as an example, and reveal the failure mechanism of such landslide by investigating the historical surface displacement. First, the complete evolution of the landslide surface was investigated from its original state to the overall sliding. The data include the satellite and Unmanned Aerial Vehicle (UAV) optical images, UAV three-dimensional (3-D) real scene models, high-resolution Light Detection and Ranging (LiDAR) DEM, and field survey. The results show that the head region entered the high deformation stage after 2019, the maximum deformation rate was 12.3 m/yr. The landslide morphology was formed after the overall slide occurred in September 2020. Then, the pre-event 3-D surface deformation after the landslide entered the high deformation stage was recovered using Interferometric Synthetic Aperture Radar (InSAR), differential DEM, and SAR/optical offset-tracking techniques. The vertical deformation was recovered around −30 m from 2019 to 2020. In particular, we solved the problem of unequal accuracy of SAR and optical offset-tracking observations in 3-D deformation inversion by employing the Helmert variance component estimation method. The maximum deformation was 6 m and 3 m within 4 months in the NS and EW directions, respectively. Finally, we revealed the failure mechanism of the Jianshanying landslide based on the disparity of horizontal and vertical deformation. That is, underground mining causes a significant subsidence of the rear part of the landslide body, resulting in different stress changes in the rear and front parts of the landslide body, which eventually led to sliding of the front part of the slope along the free surface. This work investigates and monitors the typical underground mining-induced Jianshanying landslide by using multi-sensor remote sensing approaches to trace the pre-event surface motions and to reveal its failure mechanism.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136098233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic variations in thermal regime and surface deformation along the drainage channel for an expanding lake on the Tibetan Plateau","authors":"Zekun Ding, Fujun Niu, Yanhu Mu, Guoyu Li, Mingtang Chai, Zeyong Gao, Ling Chen, Kun Zhang, Yuncheng Mao","doi":"10.1080/15481603.2023.2266661","DOIUrl":"https://doi.org/10.1080/15481603.2023.2266661","url":null,"abstract":"The outburst of Zonag Lake in 2011 triggered a series of floods in the continuous permafrost region of the hinterland of the Qinghai-Tibet Plateau. This re-distributed the surface water in the basin and caused rapid expansion of the tail lake (Salt Lake). To avoid potential overflow of the expanding Salt Lake, a channel was excavated to drain the lake water into a downstream river. In this study, to investigate the permafrost thermal regime and the surface deformation around the expanding Salt Lake and the channel, in-situ monitoring sections were settled from Salt Lake to the downstream of the channel to obtain the permafrost temperature. Additionally, using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), the surface deformation around Salt Lake and the channel was measured. The data showed that the ground temperature at the channel was 0.6°C higher than the natural field and the mean subsidence rate around the channel was 1.5 mm/yr higher than that at Salt Lake. These results show that the permafrost temperature in the study area changed considerably with variations in the distance from the lake/channel, and the deformation in the study area was dominated by subsidence.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136295122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulating urban growth by coupling macro-processes and micro-dynamics: a case study on Wuhan, China","authors":"Yunqi Guo, Limin Jiao, Xianzeng Yang, Jia Li, Gang Xu","doi":"10.1080/15481603.2023.2264582","DOIUrl":"https://doi.org/10.1080/15481603.2023.2264582","url":null,"abstract":"The urban form influences the quality of urban functions and is strongly correlated with the sustaining capabilities of urban development. However, in the context of rapid urbanization, unreasonable land expansion as a universal phenomenon poses a great challenge for urban management. Notably, the urban expansion process is self-organizing, and the evolving macroscopic pattern can be used to predict microscopic behavioral characteristics. Therefore, the analysis of macro- and micro-interactions can provide new ideas for urban modeling. Traditional geographic cellular automata (CA) models often have poor morphological reproducibility, and the few models that combine top-down and bottom-up CA use strict coupling constraints, resulting in inadequate self-organizing natural expressions and poor precision performances. In this study, we proposed a new land growth simulation model based on a soft constraint mechanism that couples micro-dynamics with macro-processes. Specifically, a geographic micro-process model (GMP) based on the meta-process accumulation concept was applied to capture the evolution characteristics of the macro-urban form and spatially deduce the future urban intensity gradient. The soft coupling between the macro and micro levels of the model was supported by a punishment mechanism that was developed for this study. A specially designed index, the morphology similarity (MS) index, was developed to evaluate and understand the heterogeneity of the simulated and real urban forms from a micro-perspective. The model was applied to Wuhan, the largest city in central China, to demonstrate that the proposed model has a high simulation accuracy [with a Kappa value of 0.8506 and a figure-of-merit (FoM) value of 0.3034 in the optimal parameter combination] and imitative ability [maximum sensitivity (MS) value of 0.01341 in the optimal parameter combination vs. MS value of 0.01336 in the true scenario]. The evaluation system developed in this study also demonstrated the high robustness and reliability of the future multi-scenario simulation conducted in this work.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based detection of irrigation in Vojvodina (Serbia) using Sentinel-2 data","authors":"Mirjana Radulović, Sanja Brdar, Branislav Pejak, Predrag Lugonja, Ioannis Athanasiadis, Nina Pajević, Dragoslav Pavić, Vladimir Crnojević","doi":"10.1080/15481603.2023.2262010","DOIUrl":"https://doi.org/10.1080/15481603.2023.2262010","url":null,"abstract":"With rapid population growth and the high influence of climate change on agricultural productivity, providing enough food is the main challenge in the 21st century. Irrigation, as a hydrological artificial process, has an indispensable role in achieving that goal. However, high pressure and demand on water resources could lead to serious problems in water consumption. Knowing information about the spatial distribution of irrigation parcels is essential to many aspects of Earth system science and global change research. To extract this knowledge for the main agricultural region in Serbia located in the moderate continental area, we utilized optical satellite Sentinel-2 data and collected ground truth data needed to train the machine learning model. Both satellite imagery and ground truth data were collected for the three most irrigated crops, maize, soybean, and sugar beet during 3 years (2020–2022) characterized by different weather conditions. This data was then used for training the Random Forest-based models, separately for each crop type, differentiating irrigated and rainfed crops on the parcel level. Finally, the models were run for the whole territory of Vojvodina generating 10 m resolution maps of irrigated three crops of interest. With overall accuracy for crops per year (2020: 0.76; 2021: 0.78; 2022: 0.84) results showed that this method could be successfully used for detecting the irrigation of three crops of interest. This was confirmed by validation with the national dataset from Public Water Management Company “Vode Vojvodine” which revealed that classification maps had an accuracy of 76%. These maps further allow us to understand the spatial dynamics of the most important irrigated crops and can serve for the improvement of sustainable agricultural water management.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135829408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction of a large-scale realistic three-dimensional (3-D) mountain forest scene for radiative transfer simulations","authors":"Xiaohan Lin, Ainong Li, Jinhu Bian, Zhengjian Zhang, Guangbin Lei, Limin Chen, Jianbo Qi","doi":"10.1080/15481603.2023.2261993","DOIUrl":"https://doi.org/10.1080/15481603.2023.2261993","url":null,"abstract":"The realistic three-dimensional (3D) forest scene is an important input to 3D radiative transfer simulations, which are essential for analyzing the reflective properties of forest canopies. Previous studies utilized the voxel as an essential element to reconstruct the 3D forest scene, while they mainly focused on the small flattened areas and ignored the wood components. This study introduces a novel approach for reconstructing a realistic 3D mountain forest scene by incorporating branches into the voxel crown. To determine the optimal voxel size for simulating Bidirectional Reflectance Functions (BRFs) in a temperate deciduous mountain forest, this study reconstructed the forest scene using eight different voxel sizes, ranging from 30 to 100 cm with a step of 10 cm. Two forest scenes were examined to evaluate the impact of branches on radiative transfer simulations: one with branch voxel-based scenes and one without branches. The radiative transfer simulation is conducted using an efficient Monte Carlo path-tracing algorithm and has been implemented in the LargE-Scale remote sensing data and image Simulation framework (LESS) model, facilitating high-quality, large-scale simulations of forested environments. The finding revealed that the optimal voxel size for simulating BRFs in 30 m resolution is approximately 90 cm, smaller than the 100 cm used in flat areas. This study emphasized the significant impact of branches on the BRF simulations and underscored their critical role in scene reconstruction. The impact of branches is two-fold: branches themselves increase the simulated BRFs, whereas their shadows decrease them. Moreover, the effects of branches and their shadows decrease as the voxel size increases. The simulated spectral albedo exhibits maximum deviations of 0.71% and 1.04% in the red and NIR wavebands, respectively, while remaining below 0.2% in the blue waveband. Furthermore, the study suggests that if the precise branch architecture is unknown, constructing branches of the first generation is recommended to achieve better results. Additionally, the results demonstrate that the proposed scene achieves greater accuracy and robustness when compared to both the ellipsoid-based and the boundary-based scenes. The finding of this study can help researchers to better understand the underlying mechanisms driving the reflective properties of forest canopies, which can inform future studies and improve the accuracy of forest monitoring and ecological modeling.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136279541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multitemporal index for the automatic identification of winter wheat based on Sentinel-2 imagery time series","authors":"Yi Xie, Shujing Shi, Lan Xun, Pengxin Wang","doi":"10.1080/15481603.2023.2262833","DOIUrl":"https://doi.org/10.1080/15481603.2023.2262833","url":null,"abstract":"Timely and accurate monitoring of the spatial distribution of wheat is crucial for wheat field management, growth monitoring, yield estimation and prediction. In this study, a multitemporal index, termed the winter wheat mapping index (WWMI), was constructed for automatic winter wheat mapping on the basis of Sentinel-2 enhanced vegetation index (EVI) time series and wheat phenological features. Henan, an important winter wheat production province in China, was selected as the study area. Zhumadian, the primary wheat-growing city in Henan, was the test area. Both empirical and automatic threshold (Otsu) methods were adopted to calculate the optimal threshold of the WWMI. The performance of WWMI in winter wheat mapping was compared at object-oriented and pixel-based levels. The proposed WWMI separated winter wheat and nonwinter wheat areas well, thus achieving highly accurate winter wheat mapping. In Zhumadian, the empirical threshold method performed better than the Otsu method, but the former relied on official statistics to iteratively adjust the WWMI threshold. In Henan, the mapping accuracy achieved by the Otsu method was higher than that achieved by the empirical threshold method, with mean relative errors (MREs) of 6.78% and 19.87% at the municipal and county levels, respectively. This was because, compared with the empirical threshold method, the Otsu method did not rely on official statistics and adaptively determined the optimal threshold of the WWMI for each city in Henan, thus fully considering wheat growth state differences in different cities. In addition, the object-oriented WWMI performed better than the pixel-based WWMI in wheat mapping. The results further demonstrated the feasibility of combining the WWMI with the Otsu method for automatic winter wheat mapping at large extents, which will provide a theoretical basis for identifying other food crops.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136279528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coral reef applications of Landsat-8: geomorphic zonation and benthic habitat mapping of Xisha Islands, China","authors":"Mingjun He, Junyu He, Yajun Zhou, Liyuan Sun, Shuangyan He, Cong Liu, Yanzhen Gu, Peiliang Li","doi":"10.1080/15481603.2023.2261213","DOIUrl":"https://doi.org/10.1080/15481603.2023.2261213","url":null,"abstract":"Being one of the most significant and valuable coral reef systems in the South China Sea, the Xisha Islands has undergone rapid transformation due to increasing stressors from human impacts and climate change in recent years. However, as indispensable information for coral reef monitoring and management, the detailed reef extent, geomorphic zonation, or benthic composition of the Xisha Islands is not well documented. Considering limited access to the Xisha Islands, the rapid development of optical remote sensing technology provides us with a feasible mean for coral reef observation. This study adopted a water depth substitution index – probabilistic inundation (PI) – combined with depth-invariant index (DII) to achieve reef extent exploration, geomorphologic and benthic habitat types classification with unsupervised classification algorithms based on Landsat-8 time-series satellite data. Compared with two open-access datasets, the extent of each independent reef extracted from PI exhibited higher similarity with the actual boundary conditions displayed in RGB (Red-Green-Blue) composite images from Landsat-8. Based on PI and derived slope, we obtained geomorphic zonation classification results, and similarly benthic compositions were retrieved based on PI, DII, and reflectance. The overall accuracy of geomorphic zonation and benthic habitat classification results were 72% and 86%, respectively. We also interestingly discovered that corals of the Xisha Islands may be capable of an ability to resist chronic heat stress as a growth trend of reef area after two successive stress events in 2014–2015 were observed at most reefs. The proposed mapping framework of this study provides a repeatable and flexible scheme in depicting the comprehensive situation of coral reefs at Xisha Islands based only on publicly available remote sensing data without complicated pre-set parameters, which could be easily extended to coral reef research around the world. Simultaneously, the findings also provide requisite information supporting the sustainable management and conservation of coral reef ecosystems in the Xisha Islands.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance, effectiveness and computational efficiency of powerline extraction methods for quantifying ecosystem structure from light detection and ranging","authors":"Yifang Shi, W. Daniel Kissling","doi":"10.1080/15481603.2023.2260637","DOIUrl":"https://doi.org/10.1080/15481603.2023.2260637","url":null,"abstract":"National and regional data products of the ecosystem structure derived from airborne laser scanning (ALS) surveys with Light Detection And Ranging (LiDAR) technology are essential for ecology, biodiversity, and ecosystem monitoring. However, noises like powerlines often remain, hindering the accurate measurement of 3D ecosystem structures from LiDAR. Currently, there is a lack of studies assessing powerline noise removal in the context of generating data products of ecosystem structures from ALS point clouds. Here, we assessed the (1) performance and accuracy, (2) effectiveness, and (3) time efficiency and execution time of three powerline extraction methods (i.e. two point-based methods based on deep learning and eigenvalue decomposition, respectively, and one hybrid method) for removing powerline noise when quantifying 3D ecosystem structures in landscapes with varying canopy heights and vegetation openness. Twenty-five LiDAR metrics representing three key dimensions of the ecosystem structure (i.e. vegetation height, cover, and vertical variability) across 10 study areas in the Netherlands were used for our assessment. The deep learning method had the best performance and showed the highest accuracy of powerline removal across various landscape types (average F1 score = 96%), closely followed by the hybrid method (average F1 score = 95%). In contrast, the accuracy of the eigenvalue decomposition method was lower (average F1 score = 82%) and depended on landscape context and vegetation composition (e.g. the F1 score decreased from 96% to 63% when the average canopy height increased across landscapes). Powerline noise removal had the highest effectiveness (i.e. generating LiDAR metrics closest to those derived from manually labeled ground truth data) for LiDAR metrics capturing height and cover of low- and high-vegetation layers. Time efficiency (processed points per second) was highest for the eigenvalue decomposition method, yet the hybrid method reduced the execution time by > 50% compared to the deep learning method (ranging from 20% to 89% in study areas with different landscape composition). Based on our findings, we recommend the hybrid method for upscaling powerline removal on multi-terabyte ALS datasets to a regional or national extent because of its high accuracy and computational efficiency. Remaining misclassifications in LiDAR metrics could be further minimized by improving the training dataset for deep learning models (e.g. including various shapes of transmission towers from different datasets). Our findings provide novel insights into the performance of different powerline extraction methods, how their effectiveness varies for improving vegetation metrics and mapping the 3D ecosystem structure from LiDAR, and their computational efficiency for upscaling powerline removal in multi-terabyte ALS datasets to a national extent.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The superiority of the Adjusted Normalized Difference Snow Index (ANDSI) for mapping glaciers using Sentinel-2 multispectral satellite imagery","authors":"Babak Mohammadi, Petter Pilesjö, Zheng Duan","doi":"10.1080/15481603.2023.2257978","DOIUrl":"https://doi.org/10.1080/15481603.2023.2257978","url":null,"abstract":"Accurate monitoring of glaciers’ extents and their dynamics is essential for improving our understanding of the impacts of climate and environmental changes in cold regions. The satellite-based Normalized Difference Snow Index (NDSI) has been widely used for mapping snow cover and glaciers around the globe. However, mapping glaciers in snow-covered areas using existing indices remains a challenging task due to their incapabilities in separating snow, glaciers, and water. This study aimed to evaluate a new satellite-based index and apply machine learning algorithms to improve the accuracy of mapping glaciers. A new index based on satellite data from Sentinel-2 was tested, which we call the Adjusted Normalized Difference Snow Index (ANDSI). ANDSI (besides NDSI) was used with five different machine learning algorithms, namely Artificial Neural Network, C5.0 Decision Tree Algorithm, Naive Bayes classifier, Support Vector Machine, and Extreme Gradient Boosting, to map glaciers, and their performance was evaluated against ground reference data. Four glacierized regions in different countries (Canada, China, Sweden, and Switzerland-Italy) were selected as study sites to evaluate the performance of the proposed ANDSI. Results showed that the proposed ANDSI outperformed the original NDSI, and the C5.0 classifier showed the best overall accuracy and Kappa among the selected five machine learning classifiers in the majority of cases. The original NDSI yielded results with an average overall accuracy of (around) 91% and the proposed ANDSI with (around) 95% for glacier mapping across all models and study regions. This study demonstrates that the proposed ANDSI serves as a superior and improved method for accurately mapping glaciers in cold regions.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agricultural drought dynamics in China during 1982–2020: a depiction with satellite remotely sensed soil moisture","authors":"Hao Sun, Qian Xu, Yunjia Wang, Zhiyu Zhao, Xiaohan Zhang, Hao Liu, Jinhua Gao","doi":"10.1080/15481603.2023.2257469","DOIUrl":"https://doi.org/10.1080/15481603.2023.2257469","url":null,"abstract":"Agricultural drought (AD) is a serious threat to food security for many regions worldwide. Understanding the dynamics of AD contributes to preventing or mitigating its adverse impacts. Soil moisture (SM) anomaly is a relatively straightforward indicator of AD. However, most of the previous studies on AD dynamics of China were conducted with non-remotely sensed SM indicators due to the lack of long-term and spatial-continuous SM datasets. Here, such an SM dataset was created by enhancing a satellite remote sensing SM dataset with a machine learning method XGBoost, various remote sensing datasets, and some surface or meteorological parameters from reanalysis data. The new SM dataset has a period of 1982–2020, a spatial resolution of 0.25°, and a temporal resolution of 1 month. Furthermore, Standardized SM Index at one-month scale (SSMI1) was calculated, and AD events were identified using the SSMI1 and a 3-dimensional clustering method. Results demonstrated that 1) the new SM presented comparable or even better performances with the original SM as evaluated with spatial distributions, in-situ SM observations, and manufactured data gaps. 2) The AD was most frequent in North China, followed by the western parts of East China, Northeast, and Southwest China. The centroids of identified AD events were found chiefly in the Northeast, North, Southwest, and western parts of East China. 3) The severity of AD events presented a decreasing trend from 1982 to 2020, while significant drying trends were found mostly in the southern parts of North China, western parts of East China, and Southwest China. 4) The AD dynamics revealed in this study are basically consistent with other studies but also have unique features such as more space details and less drought frequency and count than that of meteorological drought. Further studies are expected to create a long-term satellite SM with faster timeliness, higher resolution, and greater depth.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135014172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}