基于卫星遥感数据的Noah-MP地表反照率关键参数优化

陈进燕; 赵龙; 阳坤; 田佳鑫; 潘金梅; 张可

doi:10.11728/cjss2023.06.2023-0086

基于卫星遥感数据的Noah-MP地表反照率关键参数优化

doi: 10.11728/cjss2023.06.2023-0086 cstr: 32142.14.cjss2023.06.2023-0086

陈进燕^{1, 2,},
赵龙^{1, 2, 3, ,},
阳坤³,
田佳鑫³,
潘金梅⁴,
张可^{1, 2}

1.
西南大学地理科学学院重庆金佛山喀斯特生态系统国家野外科学观测研究站　重庆　400715
2.
西南大学地理科学学院遥感大数据应用重庆市工程研究中心　重庆　400715
3.
清华大学地球系统科学系地球系统数值模拟教育部重点实验室　北京　100084
4.
中国科学院空天信息创新研究院遥感科学国家重点实验室　北京　100101

基金项目: 国家自然科学基金项目（42271491），重庆市自然科学基金项目（CSTB2022NSCQ-MSX1568），地球系统数值模拟教育部重点实验室（清华大学）开放基金项目和西南大学研究生科研创新项目（SWUS23079）共同资助

详细信息

作者简介:

陈进燕：E-mail：chenjy010704@163.com

通讯作者:

赵龙，E-mail：zhaol04@swu.edu.cn

中图分类号: P714
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出版历程
- 收稿日期: 2023-08-16
- 修回日期: 2023-11-09
- 网络出版日期: 2023-12-07

Toward Optimization of Key Parameters in Noah-MP Surface Albedo Using Satellite Remote Sensing Products

CHEN Jinyan^{1, 2
,},
ZHAO Long^{1, 2, 3
, ,},
YANG Kun³,
TIAN Jiaxin³,
PAN Jinmei⁴,
ZHANG Ke^{1, 2}

1.
Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715
2.
Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715
3.
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084
4.
State Key Laboratory Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101

摘要

摘要: 地表反照率是影响地–气相互作用的关键因子，而准确描述地表反照率是改进陆面模型水热模拟能力的关键。当前Noah-MP (the Noah land surface model with Multiple Parameterizations) 土壤反照率估算主要依赖于查找表方法，该方法基于土壤颜色获得不同土壤类型的反照率，但在区域尺度上土壤颜色等级尚未得到有效率定，直接影响了区域反照率模拟水平。此外，裸土反照率的计算还高度依赖于土壤水分。针对这一问题，以同化得到的土壤水分数据作为输入，计算得到不同土壤颜色等级对应的反照率时间序列。在此基础上，以MODIS反照率为参照，同时排除高植被覆盖和积雪的影响，逐步筛选得到青藏高原区域0.25°格点尺度下最优的土壤颜色等级。评估结果表明，优化得到的土壤颜色等级空间分布规律符合土壤质地与反照率之间的物理规律，且改进了研究区域70%空间网格内的Noah-MP模型反照率估计。
- 地表反照率 /
- Noah-MP /
- 土壤颜色等级 /
- 参数优化
Abstract: Surface albedo is a key factor affecting land-air interactions. The accurate estimate of surface albedo is of great value for improving land model’s capability in hydrothermal simulation. In the Noah-MP (the Noah land surface model with multiple parameterizations) land surface model, estimation of soil albedo mainly relies on a look-up table-based method that characterize the albedo of different soil types with the so-called soil color. However, the soil color has not yet been calibrated at the regional or global scale, which greatly hinders the regional albedo simulation. In addition, the calculation of bare soil albedo is highly sensitive to surface soil moisture. To this end, this study first produces an ensemble of albedo time series with regard to different soil types with data assimilation generated soil moisture as input. Then, the optimal 0.25° soil color for the Tibetan Plateau region were screened by referring MODIS albedo and excluding the impacts from dense vegetation and snow cover. The evaluation results show that the spatial distribution of optimized soil color can reasonably reflect the relationship between soil texture and albedo, and improved Noah-MP albedo estimation in over 70% of the grid cells in the study area.
- Land surface albedo /
- Noah-MP /
- Soil Color (SC) /
- Parameter optimization
注释:

1) 1http://www.bnu.data.center.com

2) 2https://data.tpdc.ac.cn/zh-hans/data/c3b7d80c-a43b-4b0b-a09e-a38f837fb921

3) 3https://data.tpdc.ac.cn/zh-hans/data/b8c448ab-9c50-43fe-9b5d-2a5888658fe6

HTML全文

图 1 2016－2020年5－9月全球地表反照率最大值空间分布

Figure 1. Spatial distribution of the maximum global land surface albedo from 2016 to 2020 (Only for May to September)

下载: 全尺寸图片幻灯片

图 2 土壤颜色等级优化与评估流程

Figure 2. Schematic of the soil color optimization

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图 3 所有网格单元的排名最高和后19位土壤颜色等级对应的RMSE之间差异的累积分布函数(CDF)

Figure 3. Cumulative Distribution Function (CDF) of statistics differences overall grid-cells in terms of RMSE with regard to different soil colors

下载: 全尺寸图片幻灯片

图 4 最优土壤颜色等级空间分布

Figure 4. Spatial distribution of optimized soil color

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图 5 基于MODIS反照率数据的最优土壤颜色等级评估结果

Figure 5. Evaluation results of optimal soil color based on MODIS albedo data

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图 6 最优土壤颜色的频率分布(a)及其与不同土壤质地的关系(b～d)

Figure 6. Statistics of optimal soil color (a) and its relationship between different soil texture properties (b～d)

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图 7 不同输入条件下模拟反照率与MODIS数据的RMSE空间分布

Figure 7. RMSE spatial distribution between simulated albedo with different input and MODIS products

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图 8 基于MODIS反照率数据的排除其他因素影响阈值的评估结果

Figure 8. Evaluation results of the threshold that excludes other factors based on MODIS albedo product

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图 9 Lawrence土壤颜色等级优化结果与本研究结果的对比

Figure 9. Comparison between the optimization results of Lawrence soil color grade and the results of this study

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图 10 基于地表温度卫星数据的最优土壤颜色等级评估结果

Figure 10. Evaluation results of optimal soil color based on land surface temperature dataset

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表 1 Noah-MP默认土壤颜色等级

Table 1. Soil color in Noah-MP

Soil color	1	2	3	4	5	6	7	8
SAT-VIS	0.15	0.11	0.10	0.09	0.08	0.07	0.06	0.05
SAT-NIR	0.30	0.22	0.20	0.18	0.16	0.14	0.12	0.10
DRY-VIS	0.27	0.22	0.20	0.18	0.16	0.14	0.12	0.10
DRY-NIR	0.54	0.44	0.40	0.36	0.32	0.28	0.24	0.20

下载: 导出CSV

表 2 扩展后的土壤颜色等级

Table 2. Soil color extended

Soil color	SAT-VIS	SAT-NIR	DRY-VIS	DRY-NIR
1	0.25	0.50	0.36	0.61
2	0.23	0.46	0.34	0.57
3	0.21	0.42	0.32	0.53
4	0.20	0.40	0.31	0.51
5	0.19	0.38	0.30	0.49
6	0.18	0.36	0.29	0.48
7	0.17	0.34	0.28	0.45
8	0.16	0.32	0.27	0.43
9	0.15	0.30	0.26	0.41
10	0.14	0.28	0.25	0.39
11	0.13	0.26	0.24	0.37
12	0.12	0.24	0.23	0.35
13	0.11	0.22	0.22	0.33
14	0.10	0.20	0.20	0.31
15	0.09	0.18	0.18	0.29
16	0.08	0.16	0.16	0.27
17	0.07	0.14	0.14	0.25
18	0.06	0.12	0.12	0.23
19	0.05	0.10	0.10	0.21
20	0.04	0.08	0.08	0.16
注　SAT-VIS为饱和土壤可见光波段反照率， SAT-NIR为饱和土　壤近红外波段反照率，DRY-VIS为干土可见光波段反照率，　　DRY-NIR为干土近红外波段反照率。

下载: 导出CSV

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