Automated rice mapping using multitemporal Sentinel-1 SAR imagery using dynamic threshold and slope-based index methods

IF 3.8 Q2 ENVIRONMENTAL SCIENCES
Aishwarya Hegde A. , Pruthviraj Umesh , Mohit P. Tahiliani
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引用次数: 0

Abstract

Rice cultivation plays a crucial role in food security and economic development, particularly in regions like India, due to its vast population and position as the top rice producer globally. This work introduces a novel framework, the Rice Mapping Method (RMM), which leverages Multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery for automated rice mapping. Contrary to the traditional approaches, RMM combines the Dynamic Threshold Method (DTM) for robust rice field identification and a slope-based index for classifying single and double cropping practices. By analyzing VH backscatter patterns and employing specific thresholds, DTM separates rice pixels from the other background pixels. The DTM, which relies on VH backscatter values during the growing season, has been tested across various rice cultivation landscapes, demonstrating high accuracy up to 0.95. DTM is also tested on different rice-growing areas such as the hilly Kodagu district, with an F1 Score of 0.96, and in the flooded delta region of Kuttanad, achieving an F1 Score of 0.93. The Slope-based Index I(r,c) is introduced to differentiate the single and double cropping pixels by calculating the index for the second season of cropping and gives F1 Score of 0.81. The DTM’s effectiveness in rice field identification is evaluated by comparing it to the classification of the Bi-directional Gated Recurrent Unit (Bi-GRU) network. Similarly, the Slope-based Index is compared with other established automated rice mapping methods to assess its accuracy in distinguishing cropping patterns. RMM was successfully applied in mapping rice-growing areas in the Udupi district for 2021, estimating Kharif and Rabi season areas, the estimated rice area is compared to official statistics by the Directorate of Economics and Statistics, Karnataka State. The proposed RMM approach offers a robust solution for mapping rice fields, particularly in regions with complex cropping landscapes, and enhances agricultural monitoring and decision-making processes contributing to sustainable rice production and food security initiatives.
利用动态阈值和基于坡度的指数方法,利用Sentinel-1多时相SAR图像自动绘制水稻图
水稻种植在粮食安全和经济发展中发挥着至关重要的作用,特别是在印度等地区,因为印度人口众多,而且是全球最大的水稻生产国。这项工作引入了一种新的框架,即水稻制图方法(RMM),该方法利用多时相哨兵-1合成孔径雷达(SAR)图像进行自动水稻制图。与传统方法不同,RMM结合了动态阈值法(DTM)和基于坡度的单作和双作分类指数。通过分析VH后向散射模式并采用特定阈值,DTM将大米像元与其他背景像元分离开来。DTM依赖于生长季节的VH反向散射值,已经在不同的水稻种植景观中进行了测试,显示出高达0.95的高精度。DTM还在不同的水稻种植区进行了测试,如丘陵Kodagu区,F1得分为0.96,在Kuttanad的洪水三角洲地区,F1得分为0.93。引入基于坡度的指数I(r,c),通过计算第二季的指数来区分单次和双次种植像元,F1得分为0.81。通过与双向门控循环单元(Bi-GRU)网络的分类进行比较,评价了DTM在稻田识别中的有效性。同样,将基于坡度的指数与其他已建立的自动化水稻制图方法进行比较,以评估其区分种植模式的准确性。RMM成功地应用于绘制2021年Udupi地区的水稻种植区域,估计了Kharif和Rabi季节的面积,估计的水稻面积与卡纳塔克邦经济和统计局的官方统计数据进行了比较。拟议的RMM方法为绘制稻田地图提供了一个强有力的解决方案,特别是在种植景观复杂的地区,并加强了农业监测和决策过程,有助于可持续水稻生产和粮食安全倡议。
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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