International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"Estimating canopy nitrogen content by coupling PROSAIL-PRO with a nitrogen allocation model","authors":"Dong Li ,&nbsp;Yapeng Wu ,&nbsp;Katja Berger ,&nbsp;Qianliang Kuang ,&nbsp;Wei Feng ,&nbsp;Jing M. Chen ,&nbsp;Wenhui Wang ,&nbsp;Hengbiao Zheng ,&nbsp;Xia Yao ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Tao Cheng","doi":"10.1016/j.jag.2024.104280","DOIUrl":"10.1016/j.jag.2024.104280","url":null,"abstract":"<div><div>Nitrogen is one of the most important macronutrients for plant growth and timely estimation of canopy nitrogen content (CNC) is crucial for agricultural applications. Remote sensing has emerged as an important tool to quantify CNC using either empirically or physically based methods. Most empirical methods use chlorophyll related spectral indices and are dependent on the relationship between nitrogen and chlorophyll, which varies with vegetation types and growth stages. In contrast, physically based methods use the full-range reflectance data and retrieve CNC from coupled leaf and canopy radiative transfer models (such as PROSPECT-PRO + 4SAIL, PROSAIL-PRO). However, the subtle absorption features of nitrogen and protein in fresh leaves hinder the accurate estimation of CNC. Therefore, this study proposed an efficient and mechanistic framework to estimate CNC (PROSAIL-NAM) by coupling PROSAIL-PRO with a nitrogen allocation model, which divided the total nitrogen into non-photosynthetic nitrogen (NPN) and photosynthetic nitrogen (PN). At the canopy level, PN and NPN are assumed to be proportional to canopy chlorophyll content (CCC) and canopy dry matter content (CDM), respectively. The PROSAIL-PRO model was first used to estimate CCC and CDM, and then the resulting CCC and CDM were fed to the nitrogen allocation model to estimate CNC. The estimation accuracy of CNC was assessed with six diverse datasets: four from field crop experiments across geographic sites, one from multiple ecosystems, and one from a satellite-ground joint experiment. Our results showed that satisfactory estimations of CNC were obtained when CCC and CDM were estimated using a model inversion method (RMSE = 0.54–1.56 g/m²) and a hybrid retrieval method (RMSE = 0.49–2.25 g/m²). The model inversion method was comparable with the hybrid retrieval method for ground platforms, but performed better for airborne and satellite platforms. In addition, the traditional protein-nitrogen conversion model obtained CNC from the canopy protein content and led to clear overestimations of CNC with RMSE &gt; 1.95 g/m². This study represents a first attempt to develop a robust approach by coupling PROSAIL-PRO with a nitrogen allocation model for accurate estimation of CNC across geographic sites, ecosystems, and platforms. These finding will advance the monitoring of CNC from regional to global scales.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104280"},"PeriodicalIF":7.6,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating overlapping deformation patterns of the Beijing Plain by independent component analysis of InSAR observations","authors":"Shangjing Lai ,&nbsp;Jinxin Lin ,&nbsp;Jie Dong ,&nbsp;Jianzhong Wu ,&nbsp;Xinlei Huang ,&nbsp;Mingsheng Liao","doi":"10.1016/j.jag.2024.104279","DOIUrl":"10.1016/j.jag.2024.104279","url":null,"abstract":"<div><div>Due to policies such as groundwater extraction restrictions, water diversion, and water replenishment, groundwater levels in the Beijing Plain have generally risen. This has effectively alleviated ground subsidence, with some regions even experiencing uplift. Under these new water conditions, strata deformation also shows spatiotemporal heterogeneity, and the overlap of these multiple deformation patterns hinders the interpretation of ground deformation. In this study, we employed Satellite Interferometric Synthetic Aperture Radar (InSAR) to investigate the spatiotemporal heterogeneous deformation patterns and their influencing factors in the Beijing Plain. We utilized Independent Component Analysis (ICA) to effectively separate overlapping ground deformation patterns from InSAR-derived ground surface deformation. Four ground deformation patterns—approximately linear subsidence, approximately linear uplift, decelerating subsidence, and periodic deformation—were extracted. Their overlapping status and influencing factors were explored based on external hydrogeological data. Approximately linear subsidence represents the continuous compression of deep strata in areas with severe subsidence. Approximately linear uplift represents the rebound of shallow strata caused by water replenishment. Periodic deformation represents the ground’s response to precipitation and irrigation pumping. The ICA results benefit the estimation of the time lag between groundwater rise and strata rebound in the Beijing Plain, as well as the estimation of the increase in groundwater storage capacity in the Miyun-Huairou-Shunyi region. This study provides a new perspective for extracting deformation information from InSAR observations, gaining a deeper understanding of deformation patterns related to hydrogeological phenomena, and assessing the effectiveness of groundwater management and land subsidence control policies.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104279"},"PeriodicalIF":7.6,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach for snow depth retrieval in forested areas by integrating horizontal and vertical canopy structures information","authors":"Shanna Yue ,&nbsp;Liyun Dai ,&nbsp;Jie Deng ,&nbsp;Yanxing Hu ,&nbsp;Lin Xiao ,&nbsp;Tao Che","doi":"10.1016/j.jag.2024.104278","DOIUrl":"10.1016/j.jag.2024.104278","url":null,"abstract":"<div><div>Snow cover significantly influences the Earth’s climate system and global hydrological cycle through its thermal insulation properties and high albedo, and is an important component of the cryosphere. Currently, the most efficient means of quantifying snow depth at both global and regional scales is through passive microwave remote sensing. However, the accuracy of passive microwave remote sensing inversion of snow depth in forested areas is affected by the forest canopy. In this study, a normalized maximum stem volume (NMSV) index was constructed by combining canopy height and tree cover data obtained through remote sensing techniques. The NMSV index was then incorporated into development of snow depth retrieval algorithm to improve accuracy of passive microwave snow depth estimation in forested areas. Compared to the Chang algorithm and the AMSR-E snow depth product, this study demonstrated higher accuracy in the mid- to high-latitude forested areas of Eurasia, with an R value approximately twice as high and a reduction in the overall root mean square error (RMSE) by 2.3 cm and 7.2 cm, respectively. The relative mean bias of this study in the Western Russia, the Eastern Siberian Mountains and the Northeast China is significantly reduced than that of the existing remote sensing algorithms. Against the ERA5 and GlobSnow datasets, with the exception of the Western Russia, the performance of this study in the mid- to high-latitude forested areas of Eurasia is comparable to the ERA5 dataset and superior to the other datasets. Based on the performance of the algorithms in different NMSV values, we observe a decline in the accuracy of this algorithm when the value exceeds 0.8, which was caused by small size of high NMSV values among the ground observation sites involved in the development of snow depth retrieval algorithm. Overall, the NMSV index proposed in this study, which integrates information from both the horizontal and vertical structures of forest, can better characterize the microwave radiation properties of sparse and moderately dense forests, facilitating improvements in the accuracy of passive microwave snow depth retrieval in global forested areas.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104278"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geological remote sensing interpretation via a local-to-global sensitive feature fusion network","authors":"Sheng Wang,&nbsp;Xiaohui Huang,&nbsp;Wei Han,&nbsp;Xiaohan Zhang,&nbsp;Jun Li","doi":"10.1016/j.jag.2024.104258","DOIUrl":"10.1016/j.jag.2024.104258","url":null,"abstract":"<div><div>Interpreting surface geological elements (such as rocks, minerals, soils, and water bodies) is the main task of geological survey, which plays a crucial role in geological environment remote sensing (GERS). However, the characteristics of geological elements, including high variabilities, various morphology, complicated boundaries and imbalanced class distribution, make it still a challenge for deep learning methods to interpret GERS images. Considering the correlations of geological elements as the regionalized variables in geostatistics, the sensitive features of GERS interpretation mainly include three aspects: tonal, textural and structural characteristics within a singular-class elements, spatial and spectral correlations of adjacent elements, and their global tectonic or spatial distribution. Thus, to simulate the manual interpretation process of geologists from local to global and promote GERS interpretation performance, we propose a local-to-global multi-scale feature fusion network (LGMSFNet). A geological object context represents the intra-class semantic dependencies of pixel sets with the same class. And a local feature aggregation module models the channel and spatial association. Then discriminative features are integrated by a global feature fusion module. For the model optimization, we focus on hard examples during the training process to achieve the balanced optimization of various categories. Two research areas that include large-scale rocks, soils and water exposed on the surface are selected. Massive experiments demonstrate the superiority of the LGMSFNet in GERS interpretation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104258"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-dimensional multiscale fast SAR image registration method","authors":"Jiamu Li ,&nbsp;Wenbo Yu ,&nbsp;Zijian Wang ,&nbsp;Jiaxin Xie ,&nbsp;Xiaojie Zhou ,&nbsp;Yabo Liu ,&nbsp;Zhongjun Yu ,&nbsp;Meng Li ,&nbsp;Yi Wang","doi":"10.1016/j.jag.2024.104266","DOIUrl":"10.1016/j.jag.2024.104266","url":null,"abstract":"<div><div>Synthetic aperture radar (SAR) has developed in leaps and bounds over the past decades, which makes rapid revisit and high-frequency coverage feasible. However, accurate and efficient registration of the SAR image is still a challenging task. Many existing SAR image registration methods major in describing detected features in a unique, identifiable, but maybe complex way. These descriptors are usually high-dimensional, resulting in increased computational complexity. To this end, a low-dimensional multiscale fast method for SAR image registration is proposed in this paper. First, the candidate points are detected in the modulus map of phase congruency (PC). This operation is robust to speckle noise in SAR images and improves the repeatability of feature points. Second, circular neighbourhoods of each point are extracted in multiple scales to describe their features with the maximum index map (MIM). Note that we condense the feature information of candidate points in the whole neighbourhood in an intensity-order way, which significantly reduces the dimensionality of the descriptors. Overall, the proposed method focuses on efficient representation of point features, thus allowing more feature points to be detected and involved in the subsequent high-speed feature matching. Experiments on raw SAR images with neither prior information nor any pre-processing measure like terrain correction and de-speckling demonstrate the efficacy of the proposed method over other state-of-the-art SAR image registration algorithms. Some analyses concerning the factors affecting feature matching and invariance of PC map and MIM are also studied.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104266"},"PeriodicalIF":7.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards accurate L4 ocean colour products: Interpolating remote sensing reflectance via DINEOF","authors":"Christian Marchese ,&nbsp;Simone Colella ,&nbsp;Vittorio Ernesto Brando ,&nbsp;Maria Laura Zoffoli ,&nbsp;Gianluca Volpe","doi":"10.1016/j.jag.2024.104270","DOIUrl":"10.1016/j.jag.2024.104270","url":null,"abstract":"<div><div>Ocean colour (OC) remote sensing benefits society by providing continuous biological and ecological parameters relevant to sustainable marine resource exploitation. It enhances our understanding of climate change and allows us to monitor oceanographic phenomena over various scales of variability. However, significant data gaps occur daily due to cloud cover, atmospheric correction failures, sun-glint contamination, and satellite coverage limitations. Level 4 (L4) gap-free images are generally created by averaging over specific periods (e.g., weekly, monthly, seasonal) or re-gridding data with coarser resolution to overcome these limitations. These approaches, however, often fail to capture anomalous events or fine-scale resolution processes, calling for more advanced methods. The Data Interpolating Empirical Orthogonal Function (DINEOF) method has proved effective in reconstructing missing OC data and capturing smaller-scale features in noisy fields. To the best of authors knowledge, DINEOF is here used for the first time to interpolate multispectral Remote Sensing Reflectance (Rrs) to produce a consistent and gap-free L4 Rrs dataset, minimizing errors in inferred ocean products, such as Chlorophyll-a (Chl), the most widely used proxy for phytoplankton biomass. Specifically, using a multivariate approach, we assessed the DINEOF technique’s capability to reconstruct Rrs, focusing on six bands (412, 443, 490, 510, 555, and 670 nm) and validating the results using extensive <em>in situ</em> datasets. Our outcomes show that this “upstream interpolation” method can generate a consistent Rrs dataset, thereby improving the accuracy of L4 Chl predictions when used as input in algorithms for remote Chl estimation. We anticipate further improvements in L4 Rrs accuracy using richer spectral information from upcoming hyperspectral satellite missions. This study highlights the effectiveness of using Rrs as a standalone dataset for DINEOF interpolation. Operationally, it can derivate various gap-free and consistent biogeochemical parameters with reduced uncertainty, thus providing a more reliable and versatile method.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104270"},"PeriodicalIF":7.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying urban function accessibility and its effect on population mobility based on function-associated population mobility network","authors":"Xinrui Liu,&nbsp;Rui Li,&nbsp;Jing Cai,&nbsp;Bosen Li,&nbsp;Yanhao Li","doi":"10.1016/j.jag.2024.104273","DOIUrl":"10.1016/j.jag.2024.104273","url":null,"abstract":"<div><div>Due to rapid urbanization and globalization, urban functions are increasingly segregated in cities comprising centers of population aggregation and economic activity. Urban development yields intertwining and interdependent functional areas for residence, commerce, and education, which leads to complex but regular population mobility patterns. Population mobility spaces can effectively represent the actual service scopes of urban functions. Therefore, in this study, we propose a function-associated population mobility network (FPMN) to dynamically delineate the service capacities of urban functions, and to quantify the driving effects of urban functions on population mobility under spatial interaction conditions. By integrating the cumulative opportunities of geographic nodes with the potential opportunities reflected by population mobility, k-step function accessibility with population mobility (k-step PFA) is defined in FPMN to characterize the accessibility relationships between geographic nodes and function nodes. Based on k-step PFA, considering the complementarity and intervention opportunities, the function-driven index (FDI) is proposed to quantify the driving effects of urban functions on population mobility. Our experimental results indicated that FPMN could effectively represent the actual service scopes and capabilities of urban functions. Compared with general location-based accessibility indicators, k-step PFA has a higher numerical distribution and less spatial disparity, and it aligns more closely with the actual service scopes of urban functions. In addition, compared with intervening opportunities models, FDI delineates the functional driving effects of population mobility with greater precision, as well as revealing the travel propensity traits of a population relative to various urban functions through propensity parameters.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104273"},"PeriodicalIF":7.6,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying node-corridor-network of tourist flow and influencing factors using GPS big data: A case study in Gansu and Qinghai provinces, China","authors":"Zhiyu Zhang ,&nbsp;Fuyuan Wang ,&nbsp;Longtao Deng","doi":"10.1016/j.jag.2024.104271","DOIUrl":"10.1016/j.jag.2024.104271","url":null,"abstract":"<div><div>Due to constraints in data and technological approaches, there is a deficiency in the analysis of spatial patterns and formation mechanisms of large-scale destination tourist flows at the provincial level. This study leverages open GPS trajectory big data and employs grid units to meticulously characterize the spatial patterns and associated formation mechanisms of regional-scale tourist flows in Qinghai and Gansu Provinces. The findings reveal the following: (1) Regional tourist flows exhibit a distinct “point-axis-ring” agglomeration distribution pattern. (2) The “Gansu-Qinghai Tourist Grand Loop” has emerged as a predominant regional tourism corridor. Within this loop, there are smaller, high-density sub-loops centered on specific tourist attractions. (3) Ecology, service and scenic area are the three major influencing mechanisms for spatial differentiation of tourist flow in Gansu-Qinghai region. The findings can provide significant insights for the prioritization of regional tourism route marketing and planning, the configuration of tourism service facilities, etc.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104271"},"PeriodicalIF":7.6,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial intelligence framework for BTS infrastructure planning toward universal internet access target in Indonesia","authors":"Anjar Dimara Sakti ,&nbsp;I Gusti Ayu Andani ,&nbsp;Anissa Dicky Putri ,&nbsp;Muhammad Rizky Zakiar ,&nbsp;Ismail Al Faruqi ,&nbsp;Cokro Santoso ,&nbsp;Rezzy Eko Caraka ,&nbsp;Pitri Rohayani ,&nbsp;Fabian Surya Pramudya ,&nbsp;Arie Wahyu Wijayanto ,&nbsp;Angga Setiyadi ,&nbsp;Wervyan Shalannanda","doi":"10.1016/j.jag.2024.104274","DOIUrl":"10.1016/j.jag.2024.104274","url":null,"abstract":"<div><div>Equitable internet coverage has emerged as a key global priority, which is essential for promoting inclusive and sustainable development. The Indonesian government aims to provide universal internet access by 2024, particularly in remote regions. This study introduces a novel machine-learning-based approach to identify the priority areas for deploying Base Transceiver Station (BTS) towers, which are crucial for achieving the internet access targets of the government. A BTS Network Priority Index was developed by integrating the internet demand estimates with a BTS suitability index derived from key predictors: proximity to fiber optic stations, physical–environmental suitability, and infrastructure–economic readiness. The model identified areas with high internet demand and high BTS suitability as the most critical for immediate development, covering 20 km². Additionally, future BTS development should target areas with high demand but medium suitability (900 km²) and medium demand but high suitability (280 km²). To validate the methodology, the Random Forest model was employed, which achieved an area under the curve value of 0.7315, indicating strong predictive performance. For the BTS Deployment Suitability parameter, the median was 0.65, with the lower and upper quartiles at 0.44 and 0.85, respectively, confirming that most proposed locations are highly suitable for development. This systematic approach provides data-driven insights for the equitable distribution of BTS towers to ensure efficient internet infrastructure expansion across Indonesia. Furthermore, the study offers a framework that can be adapted by other countries aiming to improve their digital infrastructure and achieve comprehensive, equitable internet access.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104274"},"PeriodicalIF":7.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"El Nino Southern Oscillation and Indian Ocean Dipole teleconnection to the wetness and drought trend of Bhutan using time series (1983-2022) PERSIANN rainfall data","authors":"Dibyendu Dutta ,&nbsp;Manoj Kumar Nanda ,&nbsp;Ramprasad Kundu ,&nbsp;Saurabh Tewari ,&nbsp;Pragyan Jain ,&nbsp;Bidyut Kumar Bhadra ,&nbsp;Tanmay Khemka ,&nbsp;Ankur Naik ,&nbsp;Angshu Chakraverty","doi":"10.1016/j.jag.2024.104228","DOIUrl":"10.1016/j.jag.2024.104228","url":null,"abstract":"<div><div>The agrarian economy of Bhutan is highly vulnerable to rainfall uncertainties for its typical geographic location and rugged topography. Rainfall time series is also constrained by inadequate rain gauge stations in the country. To complement rainfall data obtained from 12 distinct satellite sources is validated against surface measurements from 12 ground stations. The rainfall obtained from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) exhibited a strong correlation with the measured rainfall with minimal bias and error. The seasonal mean rainfall is lowest in the winter (3.62%) with high variability (89.18%) and highest in the summer (60.36%) with low variability (33.93%). Using 3- and 12-month accumulation periods, the Standardized Precipitation Index (SPI) was computed to examine the frequency of wetness and drought. In all time frames, the severity of wetness and drought are of the “moderate” type. The majority of the districts witnessed higher levels of wetness in 1987, 1988, 2012, 2015, and 2017, while severe to extreme droughts were found over varying time periods in 1984–86, 1990–93, 1996, 1999, and 2019. In general, the frequency of wetness is 1.17 and 1.33 times higher than drought during June-August and 12-month periods, respectively. A strong positive correlation was observed between the Oceanic Niño Index (ONI) of October-December and the SPI of following June-August as well as the 12-month period. Additionally, the Indian Ocean Dipole Mode Index (DMI) showed a strong positive correlation with SPI, especially during June to August. The low amplitude and rapid fluctuation of DMI implies that the impact of Indian Ocean Dipole may be more seasonal; conversely, ENSO exhibited more noticeable fluctuations on an inter-annual basis. The results also suggest<del>s</del> a complex interplay between the East Asian and Indian summer monsoons, the Tibetan plateau, and the local orography of Bhutan, which determines the wetness and drought. A district-level Mann-Kendall test of SPI showed the trends in wetness and dryness in a spatial context. Three districts at the 12-month scales and four districts at the 3-month scale (September-November) exhibited a significant upward trend, indicating increasing wetness. On the other hand, only one district showed a significant downward trend between September and November, suggesting an increasing drought incidence.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104228"},"PeriodicalIF":7.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}