Jing Guo , Ziti Jiao , Anxin Ding , Siyang Yin , Yidong Tong , Chenxia Wang
{"title":"通过角度信息深入了解北极地区的POLDER数据库","authors":"Jing Guo , Ziti Jiao , Anxin Ding , Siyang Yin , Yidong Tong , Chenxia Wang","doi":"10.1016/j.jag.2025.104863","DOIUrl":null,"url":null,"abstract":"<div><div>Snow and ice are among the most important components of the cryosphere; therefore, accurate identification of snow and ice is key to improve the understanding of the influence of various studies on global warming, e.g., studies on global water cycling, climate change and the radiation budget balance. The spectral information at single-view, e.g., the normalized difference snow index (NDSI) has been widely used in the recognition of snow cover at both local and global scales. However, special information sources related to the angular pattern of snow reflectance have rarely been considered in such applications. In this study, we proposed an angular index named the snow anisotropic reflectance index (SARI) based on the RossThick–LisparseReciprocal–Snow (RTLSRS) bidirectional reflectance distribution function (BRDF) model to achieve snow identification. The POLarization and Directionality of the Earth’s Reflectances (POLDER) database is one of the most widely-used multiangular database because of the abundant measurements and the pixels marked as “snow” are served as the data foundation in this study. The purpose of this study is to explore the potential of multiangular information to differentiate these pixels over Arctic in various situations. First, we preprocess the POLDER snow database to select the pixels with a good BRDF sampling distribution. Then, the SARI is used to preliminarily classify these POLDER datasets as snow and nonsnow, together with the classification result derived by single-view reflectances as comparison in the red and NIR bands. Next, the classification result is validated by using the MODIS MOD10A2 product. Finally, further analysis of the BRDF variations, regarding the SARI, for the classification results is performed by using the ArcticDEM and the Arctic Vegetation Map, a more detailed snow BRDF database over Arctic is obtained. The main results are as follows: (1) The potential of multiangular information (SARI) in recognizing snow had been confirmed according to MOD10A2 product as indirect validation data, the overall accuracy can reach to 86.9%, higher than the conventional single-view method (81.7%). (2) Within–pixel variations in surface terrain and components have a significant influence on the variability in BRDF shapes in the forward and backward scattering directions; in turn, these variations can be easily captured by using several simple thresholds for subcategorization for this POLDER snow database in Arctic Circle. Finally, this study provides a detailed POLDER database over Arctic and explain BRDF variations in various situations, facilitates the future potential applications of the remote sensing community.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104863"},"PeriodicalIF":8.6000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An insight into POLDER database over Arctic through the angular information\",\"authors\":\"Jing Guo , Ziti Jiao , Anxin Ding , Siyang Yin , Yidong Tong , Chenxia Wang\",\"doi\":\"10.1016/j.jag.2025.104863\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Snow and ice are among the most important components of the cryosphere; therefore, accurate identification of snow and ice is key to improve the understanding of the influence of various studies on global warming, e.g., studies on global water cycling, climate change and the radiation budget balance. The spectral information at single-view, e.g., the normalized difference snow index (NDSI) has been widely used in the recognition of snow cover at both local and global scales. However, special information sources related to the angular pattern of snow reflectance have rarely been considered in such applications. In this study, we proposed an angular index named the snow anisotropic reflectance index (SARI) based on the RossThick–LisparseReciprocal–Snow (RTLSRS) bidirectional reflectance distribution function (BRDF) model to achieve snow identification. The POLarization and Directionality of the Earth’s Reflectances (POLDER) database is one of the most widely-used multiangular database because of the abundant measurements and the pixels marked as “snow” are served as the data foundation in this study. The purpose of this study is to explore the potential of multiangular information to differentiate these pixels over Arctic in various situations. First, we preprocess the POLDER snow database to select the pixels with a good BRDF sampling distribution. Then, the SARI is used to preliminarily classify these POLDER datasets as snow and nonsnow, together with the classification result derived by single-view reflectances as comparison in the red and NIR bands. Next, the classification result is validated by using the MODIS MOD10A2 product. Finally, further analysis of the BRDF variations, regarding the SARI, for the classification results is performed by using the ArcticDEM and the Arctic Vegetation Map, a more detailed snow BRDF database over Arctic is obtained. The main results are as follows: (1) The potential of multiangular information (SARI) in recognizing snow had been confirmed according to MOD10A2 product as indirect validation data, the overall accuracy can reach to 86.9%, higher than the conventional single-view method (81.7%). (2) Within–pixel variations in surface terrain and components have a significant influence on the variability in BRDF shapes in the forward and backward scattering directions; in turn, these variations can be easily captured by using several simple thresholds for subcategorization for this POLDER snow database in Arctic Circle. Finally, this study provides a detailed POLDER database over Arctic and explain BRDF variations in various situations, facilitates the future potential applications of the remote sensing community.</div></div>\",\"PeriodicalId\":73423,\"journal\":{\"name\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"volume\":\"144 \",\"pages\":\"Article 104863\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2025-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569843225005102\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843225005102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
摘要
雪和冰是冰冻圈最重要的组成部分;因此,准确识别冰雪是提高对各种研究对全球变暖影响的认识的关键,例如全球水循环、气候变化和辐射收支平衡研究。单视点的光谱信息,如归一化积雪指数(NDSI),已广泛应用于局部和全球尺度的积雪识别。然而,在此类应用中很少考虑与雪反射率角型有关的特殊信息源。本研究基于RTLSRS双向反射分布函数(BRDF)模型,提出了雪各向异性反射指数(SARI)来实现雪的识别。POLDER (POLarization and Directionality of Earth’s reflectance)数据库是目前应用最广泛的多角度数据库之一,因为POLDER数据库测量量丰富,且以“雪”像素为数据基础。本研究的目的是探索多角度信息在北极不同情况下区分这些像元的潜力。首先,对POLDER积雪数据库进行预处理,选择BRDF采样分布良好的像素点。然后,利用SARI将这些POLDER数据集初步分类为雪和非雪,并将单视点反射率的分类结果在红光和近红外波段进行比较。接下来,使用MODIS MOD10A2产品对分类结果进行验证。最后,利用ArcticDEM和Arctic Vegetation Map对SARI分类结果的BRDF变化进行进一步分析,得到了北极地区更详细的积雪BRDF数据库。主要研究结果如下:(1)以MOD10A2产品作为间接验证数据,证实了多角度信息(SARI)在积雪识别中的潜力,总体精度可达86.9%,高于常规单视角方法(81.7%)。(2)地表地形和成分的像元内变化对BRDF正反向形状的变异性有显著影响;反过来,这些变化可以通过使用几个简单的阈值对北极圈的POLDER积雪数据库进行子分类来轻松捕获。最后,本研究提供了详细的北极POLDER数据库,并解释了各种情况下BRDF的变化,促进了未来遥感界的潜在应用。
An insight into POLDER database over Arctic through the angular information
Snow and ice are among the most important components of the cryosphere; therefore, accurate identification of snow and ice is key to improve the understanding of the influence of various studies on global warming, e.g., studies on global water cycling, climate change and the radiation budget balance. The spectral information at single-view, e.g., the normalized difference snow index (NDSI) has been widely used in the recognition of snow cover at both local and global scales. However, special information sources related to the angular pattern of snow reflectance have rarely been considered in such applications. In this study, we proposed an angular index named the snow anisotropic reflectance index (SARI) based on the RossThick–LisparseReciprocal–Snow (RTLSRS) bidirectional reflectance distribution function (BRDF) model to achieve snow identification. The POLarization and Directionality of the Earth’s Reflectances (POLDER) database is one of the most widely-used multiangular database because of the abundant measurements and the pixels marked as “snow” are served as the data foundation in this study. The purpose of this study is to explore the potential of multiangular information to differentiate these pixels over Arctic in various situations. First, we preprocess the POLDER snow database to select the pixels with a good BRDF sampling distribution. Then, the SARI is used to preliminarily classify these POLDER datasets as snow and nonsnow, together with the classification result derived by single-view reflectances as comparison in the red and NIR bands. Next, the classification result is validated by using the MODIS MOD10A2 product. Finally, further analysis of the BRDF variations, regarding the SARI, for the classification results is performed by using the ArcticDEM and the Arctic Vegetation Map, a more detailed snow BRDF database over Arctic is obtained. The main results are as follows: (1) The potential of multiangular information (SARI) in recognizing snow had been confirmed according to MOD10A2 product as indirect validation data, the overall accuracy can reach to 86.9%, higher than the conventional single-view method (81.7%). (2) Within–pixel variations in surface terrain and components have a significant influence on the variability in BRDF shapes in the forward and backward scattering directions; in turn, these variations can be easily captured by using several simple thresholds for subcategorization for this POLDER snow database in Arctic Circle. Finally, this study provides a detailed POLDER database over Arctic and explain BRDF variations in various situations, facilitates the future potential applications of the remote sensing community.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.