2022-2023 年巴基斯坦旁遮普省多源遥感土壤水分产品的性能

IF 2.8 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Saba ul Hassan, Munawar Shah, Rasim Shahzad, Bushra Ghaffar, Bofeng Li, José Francisco de Oliveira‑Júnior, Khristina Maksudovna Vafaeva, Punyawi Jamjareegulgarn
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In this study, we addressed this lack of reliability and robustness by comprehensively assessing the SSM retrievals from CYclone Global Navigation Satellite System (CYGNSS) data with the satellite-based microwave radiometry products Soil Moisture Active Passive (SMAP) and Modern Era Retrospective-Analysis for Research and Applications (MERRA2) over Punjab in various seasons. ERA5 model-based products for the same period in 2022–2023. Our study reveals a distinct seasonal average SSM variation during autumn (0.20 cm<sup>3</sup>/cm<sup>3</sup>), followed by winter values of 0.19 cm<sup>3</sup>/cm<sup>3</sup>. Subsequently, the minimum SSM values are observed during summer (0.11 cm<sup>3</sup>/cm<sup>3</sup>) and an increase in spring to 0.13 cm<sup>3</sup>/cm<sup>3</sup>. Moreover, a strong positive linear relationship (0.74) is evident between SMAP and ERRA 5 in contrast to a low correlation (0.03) between MERRA2 and both the SMAP and ERRA 5. 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引用次数: 0

摘要

全球导航卫星系统反射测量(GNSS-R)已成为陆地遥感应用的重要工具,特别是在陆地表面土壤湿度(SSM)探测方面。GNSS-R 的高分辨率能力是对传统卫星主动和被动任务的补充,但由于缺乏有效的检索算法,产品的可靠性和稳健性评估仍然缺失。在这项研究中,我们通过全面评估 CYclone 全球导航卫星系统(CYGNSS)数据与卫星微波辐射测量产品在旁遮普不同季节的土壤水分主动被动(SMAP)和现代研究与应用回顾分析(MERRA2)的 SSM 检索,解决了可靠性和稳健性不足的问题。2022-2023 年同一时期基于 ERA5 模型的产品。我们的研究显示,秋季的 SSM 平均值(0.20 立方厘米/立方厘米)有明显的季节性变化,其次是冬季的 0.19 立方厘米/立方厘米。随后,夏季的 SSM 值最小(0.11 立方厘米/立方厘米),春季则增至 0.13 立方厘米/立方厘米。此外,SMAP 与 ERRA 5 之间明显存在较强的正线性关系(0.74),而 MERRA2 与 SMAP 和 ERRA 5 之间的相关性较低(0.03)。此外,SMAP 与 CYGNSS 和 MERRA2 分别显示出 0.53 和 0.03 的中度和弱相关性。CYGNSS 与 ERRA 5 和 SMAP 呈中度相关(0.46),与 MERRA2 的相关性较弱(0.14)。我们的分析结论是,MERRA2(Bias = 0.20 cm³/cm³,ubRMSD = 0.25 cm³/cm³,RMSE = 0.12 cm³/cm³,SD = 0.13 cm³/cm³,MAE = 0.04 cm³/cm,R = 0.03)与 SMAP(Bias = 0.03 cm³/cm³,ubRMSD = 0.03 cm³/cm³, RMSE = 0.04 cm³/cm³, SD = 0.05 cm³/cm³, MAE = 0.03 cm³/cm³, R = 0.74)和 CYGNSS(偏差 = -0.01 cm³/cm³, ubRMSD = 0.09 cm³/cm³, RMSE = 0.07 cm³/cm³, SD = 0.06 cm³/cm³, MAE = 0.05 cm³/cm³, R = 0.46)产品相比,SSM 产品表现较差。这项研究为 SSM 的未来预测提供了准确的依据,同时划定了 GNSS-R 与遥感和模型值相比的局限性。这项研究的结果对推动 GNSS-R 在农业和作物管理方面的应用也具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Performance of multi-source remote sensing soil moisture products over Punjab Pakistan during 2022–2023

Performance of multi-source remote sensing soil moisture products over Punjab Pakistan during 2022–2023

The Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a valuable tool for terrestrial remote sensing applications, particularly in the context of land Surface Soil Moisture (SSM) detection. The high-resolution capability of GNSS-R complements traditional satellite-based active and passive missions but the product reliability and robustness evaluations are still absent due to an efficient retrieval algorithms. In this study, we addressed this lack of reliability and robustness by comprehensively assessing the SSM retrievals from CYclone Global Navigation Satellite System (CYGNSS) data with the satellite-based microwave radiometry products Soil Moisture Active Passive (SMAP) and Modern Era Retrospective-Analysis for Research and Applications (MERRA2) over Punjab in various seasons. ERA5 model-based products for the same period in 2022–2023. Our study reveals a distinct seasonal average SSM variation during autumn (0.20 cm3/cm3), followed by winter values of 0.19 cm3/cm3. Subsequently, the minimum SSM values are observed during summer (0.11 cm3/cm3) and an increase in spring to 0.13 cm3/cm3. Moreover, a strong positive linear relationship (0.74) is evident between SMAP and ERRA 5 in contrast to a low correlation (0.03) between MERRA2 and both the SMAP and ERRA 5. Additionally, SMAP demonstrates moderate and weak correlation of 0.53 and 0.03 with CYGNSS and MERRA2, respectively. The CYGNSS exhibits moderate correlations (0.46) with ERRA 5 and SMAP and a weaker association (0.14) with MERRA2. Our analysis concluded that MERRA2 (Bias = 0.20 cm³/cm³, ubRMSD = 0.25 cm³/cm³, RMSE = 0.12 cm³/cm³, SD = 0.13 cm³/cm³, MAE = 0.04 cm³/cm, R = 0.03) SSM product performs poorly as compared to SMAP (Bias = 0.03 cm³/cm³, ubRMSD = 0.03 cm³/cm³, RMSE = 0.04 cm³/cm³, SD = 0.05 cm³/cm³, MAE = 0.03 cm³/cm³, R = 0.74) and CYGNSS (Bias = -0.01 cm³/cm³, ubRMSD = 0.09 cm³/cm³, RMSE = 0.07 cm³/cm³, SD = 0.06 cm³/cm³, MAE = 0.05 cm³/cm³, R = 0.46) products. This study provides accurate future predictions of SSM with delineating the limitations of GNSS-R in comparison to remote sensing and model values. The findings from this study have also significant implications for the advancement of GNSS-R applications in agriculture and crop management.

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来源期刊
Theoretical and Applied Climatology
Theoretical and Applied Climatology 地学-气象与大气科学
CiteScore
6.00
自引率
11.80%
发文量
376
审稿时长
4.3 months
期刊介绍: Theoretical and Applied Climatology covers the following topics: - climate modeling, climatic changes and climate forecasting, micro- to mesoclimate, applied meteorology as in agro- and forestmeteorology, biometeorology, building meteorology and atmospheric radiation problems as they relate to the biosphere - effects of anthropogenic and natural aerosols or gaseous trace constituents - hardware and software elements of meteorological measurements, including techniques of remote sensing
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