基于特征加权CatBoost模型的重力卫星反演地下水储量变化降尺度方法

doi:10.11947/j.AGCS.2026.20250359

测绘学报 ›› 2026, Vol. 55 ›› Issue (3): 490-501.doi: 10.11947/j.AGCS.2026.20250359

基于特征加权CatBoost模型的重力卫星反演地下水储量变化降尺度方法

侯文韬¹^,²(), 肖云²^,³(), 曹杰⁴, 王宇康¹^,², 曹春婷¹^,², 王翰¹^,²

^1.长安大学地质工程与测绘学院，陕西　西安　710054
^2.空间基准全国重点实验室，陕西　西安　710054
^3.西安测绘研究所，陕西　西安　710054
^4.61363部队，陕西　西安　710054

收稿日期:2025-09-25 修回日期:2026-03-21 出版日期:2026-04-16 发布日期:2026-04-16
通讯作者: 肖云 E-mail:2024126031@chd.edu.cn;2262164268@qq.com
作者简介:侯文韬（2002—），男，硕士生，研究方向为卫星重力的水文学应用。E-mail：2024126031@chd.edu.cn
基金资助:
国家重点研发计划(2021YFB3900604)

A downscaling method for gravity satellite derived groundwater storage changes based on a feature-weighted CatBoost model

Wentao HOU¹^,²(), Yun XIAO²^,³(), Jie CAO⁴, Yukang WANG¹^,², Chunting CAO¹^,², Han WANG¹^,²

^1.School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
^2.National Key Laboratory of Spatiotemporal Datum, Xi'an 710054, China
^3.Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China
^4.Troops 61363, Xi'an 710054, China

Received:2025-09-25 Revised:2026-03-21 Online:2026-04-16 Published:2026-04-16
Contact: Yun XIAO E-mail:2024126031@chd.edu.cn;2262164268@qq.com
About author:HOU Wentao (2002—), male, postgraduate, majors in hydrological applications of satellite gravity. E-mail: 2024126031@chd.edu.cn
Supported by:
The National Key Research and Development Program of China(2021YFB3900604)

摘要/Abstract

摘要：

GRACE与GRACE-FO重力卫星为全球地下水储量（GWS）变化监测提供了一种手段，但其空间分辨率较低，难以满足区域水资源精细化管理需求。本文针对华北平原地下水储量监测问题，提出一种基于特征加权CatBoost模型的降尺度方法，融合多源数据集，以提升GRACE（GRACE-FO）数据的空间分辨率。首先，利用贝叶斯三角帽方法（BTCH）对6组GRACE-FO数据进行加权融合，构建稳定可靠的区域地下水储量基础数据；然后，基于偏最小二乘回归（PLSR）提取各特征变量的重要性投影值（VIP）并构建特征加权机制，提升模型对关键特征变量的响应能力；最后，结合具有有序目标编码和有序梯度提升机制的CatBoost模型开展降尺度建模，将GWS数据的空间分辨率尺度从1°降至0.25°，并通过实测水井水位数据进行对比验证。与降尺度前GWS数据相比，降尺度结果与实测水井水位数据的相关性显著提高，且展示出更加丰富的细节。与传统的随机森林（RF）和XGBoost方法相比，本文方法在空间趋势一致性与物理真实性方面均表现更优，显著提升了GRACE（GRACE-FO）地下水反演结果的可靠性与实用性。

关键词: GRACE（GRACE-FO）, 地下水储量变化, 数据融合, 特征加权, 降尺度

Abstract:

The GRACE and GRACE-FO gravity satellite missions have provided a new approach for monitoring global groundwater storage (GWS) anomaly. However, their coarse spatial resolution limits their effectiveness in supporting fine-scale regional water resource management. Focusing on the issue of GWS monitoring in the North China Plain, this study proposes a downscaling method that integrates multi-source satellite data, variable weighting, and machine learning modeling to enhance the spatial resolution of GRACE (GRACE-FO) data. The Bayesian Three-Cornered Hat (BTCH) method is first applied to weight and fuse six GRACE-FO solutions, producing a robust regional GWS baseline dataset. Variable Importance in Projection (VIP) scores are then calculated using Partial Least Squares Regression (PLSR) to establish a feature-weighting mechanism that enhances the model's responsiveness to key variables. Finally, a CatBoost model with ordered target encoding and ordered boosting is employed to downscale the GWS data from 1° to 0.25° resolution. The results were validated through comparison with in-situ well water level measurements. Compared with the original GWS data before downscaling, the downscaled GWS data exhibit significantly higher correlation with well water level observations and reveal much richer spatial details. Compared with traditional Random Forest and XGBoost methods, the proposed approach demonstrates superior performance in spatial trend consistency and physical reliability, significantly enhancing the robustness and practical applicability of GRACE (GRACE-FO) GWS inversion results.

Key words: GRACE (GRACE-FO), groundwater storage anomaly, data fusion, feature weighting, downscaling

中图分类号:

P228

侯文韬, 肖云, 曹杰, 王宇康, 曹春婷, 王翰. 基于特征加权CatBoost模型的重力卫星反演地下水储量变化降尺度方法[J]. 测绘学报, 2026, 55(3): 490-501.

Wentao HOU, Yun XIAO, Jie CAO, Yukang WANG, Chunting CAO, Han WANG. A downscaling method for gravity satellite derived groundwater storage changes based on a feature-weighted CatBoost model[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(3): 490-501.

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参数	含义	取值
task type	训练的器件GPU	—
loss function	损失函数RMSE	—
iterations	迭代次数	450
depth	树身	4
learning_rate	学习率	0.01
l2_leaf_reg	L2正则系数	0.05

数据	2018-06—2020-12	2021-01—2022-12
降尺度前	－4.5±1.0	8.1±1.2
降尺度后	－4.4±1.0	7.9±1.2

数据	负相关（－∞，0]	正相关范围
数据	负相关（－∞，0]	（0，0.2]	（0.2，0.6]	（0.6，1.0]
降尺度前	15.03%	10.40%	34.69%	39.88%
Random Forest	15.02%	6.36%	36.42%	42.20%
XGBoost	13.87%	5.20%	35.84%	45.09%
PLSR-CatBoost	12.14%	5.78%	24.28%	57.80%

地区	降尺度前	降尺度后
保定市	0.23	0.45
沧州市	0.49	0.69
邯郸市	0.64	0.65
衡水市	0.45	0.50
廊坊市	－0.04	0.19
秦皇岛市	0.33	0.51
石家庄市	0.09	0.20
唐山市	0.49	0.61
邢台市	0.61	0.62
雄安新区	－0.15	0.07

基于特征加权CatBoost模型的重力卫星反演地下水储量变化降尺度方法

A downscaling method for gravity satellite derived groundwater storage changes based on a feature-weighted CatBoost model

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