Modeling and Verification of Microwave Scattering Characteristics of Typical Global Tropical Rainforests

Wang Yitong; Hu Xiuqing; Shang Jian; Gu Lingjia; Yin Honggang

doi:10.11898/1001-7313.20240308

Vol.35, NO.3, 2024

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Wang Yitong, Hu Xiuqing, Shang Jian, et al. Modeling and verification of microwave scattering characteristics of typical global tropical rainforests. J Appl Meteor Sci, 2024, 35(3): 350-360. DOI: 10.11898/1001-7313.20240308.

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Wang Yitong, Hu Xiuqing, Shang Jian, et al. Modeling and verification of microwave scattering characteristics of typical global tropical rainforests. J Appl Meteor Sci, 2024, 35(3): 350-360. DOI: 10.11898/1001-7313.20240308.

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Modeling and Verification of Microwave Scattering Characteristics of Typical Global Tropical Rainforests

DOI: 10.11898/1001-7313.20240308

Wang Yitong¹,
Hu Xiuqing^{2, 3, 4
,
,},
Shang Jian^{2, 3, 4},
Gu Lingjia⁵,
Yin Honggang^{2, 3, 4}

1.
Chinese Academy of Meteorological Sciences, Beijing 100081
2.
National Satellite Meteorological Center/National Center for Space Weather, Beijing 100081
3.
Innovation Center for Fengyun Meteorological Satellite, Beijing 100081
4.
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, CMA, Beijing 100081
5.
Jilin University, Changchun 130012

Received Date: 2023-12-03
Rev Recd Date: 2024-03-11
Publish Date: 2024-05-31

Abstract

Abstract

To ensure the microwave scatterometer is accurately calibrated, natural targets with stability, homogeneity, and isotropy are selected as references. The broad and continuous spatial distribution of the tropical rainforest, along with its relatively consistent vegetation cover, makes it an ideal choice.A tropical rainforest optimal stable area selection algorithm, combining mean, standard deviation, and relative standard deviation, is proposed using measurements from the advanced scatterometer (ASCAT) onboard the second Meteorological Operational satellite (MetOp-B) from 2019 to 2021. It is used to identify stable areas within the Amazon, Congo, and Southeast Asian rainforests. Results show that the Amazon rainforest has a larger stable area compared to the Congo and Southeast Asian rainforests, indicating more consistent backscatter across space. However, the Southeast Asian rainforest exhibits scattered stable areas and unstable backscatter properties.To accurately model the intrinsic characteristics of targets within stable areas, influences of seasonal variations, incidence angles and azimuth angles are comprehensively considered. The scatterometer, as an independently measured remote sensing instrument, is not affected by seasonal variations on the earth and experiences minimal temperature-related fluctuations. Therefore, seasonal characteristics of backscatter coefficients in rainforests can be modeled to reduce their impact. Different incidence and azimuth angles can cause variations in the backscatter coefficient. To address this issue, responses to these aspects are also modeled. It is observed that daytime data, with lower model errors, shows greater stability in the stable areas of the Amazon and Congo rainforests. Therefore, daytime data from these areas should be selected to assess instrument stability.A stability verification of ASCAT measurements from the stable areas of the Amazon and Congo rainforest on MetOp-C, covering the period from 1 July 2019 to 31 October 2023, is carried out based on model coefficients derived from the continuous three-year data of ASCAT on MetOp-B. The calibration stability verification quantifies the magnitude of variations in ASCAT measurements over different periods. Through analysis, it's found that measurements from the ASCAT on MetOp-C shows regular fluctuations of about 0.05 dB, indicating relatively stable characteristics.
- scatterometer;
- tropical rainforest;
- optimal stable area selection;
- feature modeling;
- verification

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References(37)

Figures and Tables

Fig. 1 Stable area masks for tropical rainforests

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Fig. 2 Monthly average time series of backscatter coefficient in stable areas (ascending orbits occur at night, while descending orbits occur in the daytime)

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Fig. 3 Monthly average time series of backscatter coefficient in stable areas after time correction

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Fig. 4 Backscatter coefficient varying with incident angle for stable areas (the black solid line denotes fitting curve)

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Fig. 5 Backscatter coefficient varying with azimuth angle for stable areas (the black solid line denotes fitting curve)

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Fig. 6 Validation curve of ASCAT scatterometer onboard MetOp-C satellite

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Table 1 Thresholds for each mask determination method

时段	平均值法/dB	相对标准差法/dB	标准差法/dB
白天	±0.5	1.0	0.2
夜间	±0.5	1.0	0.2

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Table 2 Evaluation indices in different stable areas

地区	时段	均方根误差/dB	平均绝对误差/dB	决定系数
亚马逊雨林	夜间	0.182	0.145	0.960
亚马逊雨林	白天	0.157	0.120	0.971
刚果雨林	夜间	0.175	0.138	0.961
刚果雨林	白天	0.159	0.122	0.970
东南亚雨林	夜间	0.317	0.254	0.893
东南亚雨林	白天	0.280	0.222	0.913

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Table 3 Results of each evaluation index under different incident angle and azimuth angle responses

地区	模型参数项	时段	均方根误差/dB	平均绝对误差/dB	决定系数
亚马逊雨林	无入射角调制	夜间	0.717	0.554	0.410
	无入射角调制	白天	0.711	0.540	0.431
	仅含线性入射角调制	夜间	0.203	0.162	0.951
	仅含线性入射角调制	白天	0.191	0.151	0.958
	无方位角调制	夜间	0.191	0.152	0.955
	无方位角调制	白天	0.167	0.130	0.966
	仅含一阶方位角调制	夜间	0.183	0.146	0.959
	仅含一阶方位角调制	白天	0.160	0.123	0.969
刚果雨林	无入射角调制	夜间	0.717	0.551	0.390
	无入射角调制	白天	0.739	0.562	0.409
	仅含线性入射角调制	夜间	0.200	0.159	0.951
	仅含线性入射角调制	白天	0.195	0.155	0.957
	无方位角调制	夜间	0.181	0.143	0.959
	无方位角调制	白天	0.166	0.129	0.968
	仅含一阶方位角调制	夜间	0.176	0.139	0.961
	仅含一阶方位角调制	白天	0.161	0.124	0.970

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