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自然资源遥感  2025, Vol. 37 Issue (5): 267-277    DOI: 10.6046/zrzyyg.2024201
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
修正水分胁迫的NPP反演结果与典型高原盆地土壤水分关系探究
杨赈1,2(), 杨明龙1,2(), 李国柱1,3, 夏永华1,2, 俞婷4, 严正飞1,2, 李万涛1,2
1.昆明理工大学国土资源工程学院,昆明 650093
2.云南省高校高原山区空间信息测绘技术应用工程研究中心,昆明 650093
3.云南海钜地理信息技术有限公司,昆明 650000
4.云南省水利水电科学研究院,昆明 650228
Relationship between modified water stress-based NPP inversion and soil moisture in typical plateau basins
YANG Zhen1,2(), YANG Minglong1,2(), LI Guozhu1,3, XIA Yonghua1,2, YU Ting4, YAN Zhengfei1,2, LI Wantao1,2
1. Faculty of Land Resources Engineering,Kunming University of Science and Technology,Kunming 650093,China
2. Surveying and Mapping Geo-informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education,Kunming 650093,China
3. Yunnan Haiju Geographic Information Technology Co.,Ltd.,Kunming 650000,China
4. Yunnan Institute of Water Resources and Hydropower Research,Kunming 650228,China
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摘要 云南省高原蜻蛉河灌区(海拔1 515~1 876 m)为典型亚热带高山气候区,为探究其土壤水分和植被净初级生产力(net primary productivity,NPP)的变化,该研究基于快速、长时序的遥感监测手段,首先结合地表温度(land surface temperature,LST)、归一化植被指数(normalized difference vegetation index,NDVI)为解释变量,并通过随机森林自适应窗口回归算法将SMAP L4土壤水分产品降尺度为30 m土壤水分空间分布;随后通过地表水分指数(land surface water index,LSWI)修正CASA模型的水分胁迫参数,修正后的模型融合地表反射等多源遥感数据并估算NPP,经空间重采样后获得30 m级NPP空间分布;最后构建有林地、水田、水浇地等多场景,引用皮尔逊相关系数定量评价研究区土壤水分与NPP的空间相关关系。结果表明:研究区土壤水分空间分布呈现夏季北多南少,冬季西北低、东南和南高的特点;对比实测样本反演后的NPP值R2>0.7,RMSE<0.3,夏季、冬季、年均栅格像元NPP值均呈现逐年升高的趋势;在空间维度上,水田灌区、有林地等场景下相关系数均超过0.5,其中有林地对水分胁迫最不敏感,水田和水浇地最受影响。该研究形成了对研究区季节-空间角度土壤水分与NPP平衡关系的监测反馈机制。
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杨赈
杨明龙
李国柱
夏永华
俞婷
严正飞
李万涛
关键词 高原灌区多源遥感数据土壤水分降尺度分析NPP    
Abstract

This study aims at investigating variations in soil moisture and vegetation net primary productivity (NPP) in the Qingling River Irrigation Area,Yunnan (elevation 1 515~1 876 m),a typical subtropical alpine climate region. To this end,initially,this study recognized land surface temperature (LST) and normalized difference vegetation index (NDVI) as explanatory variables,leveraging remote sensing technology for rapid and long-term sequential monitoring. Subsequently,the SMAP L4 soil moisture product was downscaled to a 30 m spatial resolution using the random forest adaptive window regression algorithm. Then,the water stress parameter of the CASA model was modified using the land surface water index (LSWI),which integrated multi-source remote sensing data,such as surface reflectance,to estimate NPP. Following spatial resampling,a 30 m resolution NPP spatial distribution was achieved. Finally,multiple land cover scenarios,including forest land,paddy fields,and irrigated farmland,were established. The Pearson correlation coefficient was introduced for the quantitative evaluation of the spatial relationship between soil moisture and NPP in the study area. In terms of the spatial distribution of soil moisture,the study area exhibited higher values in the north and lower values in the south during summer,while lower values in the northwest and higher values in the southeast and south during winter. Compared to field measurements,the inverted NPP results showed a R2>0.7 and a RMSE<0.3. Both summer,winter,and annual average NPP values at the pixel level showed an increasing trend over time. Spatially,scenarios such as paddy fields and forested land presented correlation coefficients exceeding 0.5. Among these,forest land was least sensitive to water stress,while paddy fields and irrigated farmland were most affected. This study establishes a monitoring and feedback mechanism for the soil moisture-NPP balance from seasonal and spatial perspectives in the study area.

Key wordsplateau irrigation area    multi-source remote sensing data    soil moisture    downscaling analysis    net primary productivity (NPP)
收稿日期: 2024-06-12      出版日期: 2025-10-28
ZTFLH:  TP79  
基金资助:国家自然科学基金项目“禄丰环状构造的UAV数字地貌建模及地表特征测量模拟分析”(62266026)
通讯作者: 杨明龙(1982-),男,博士,副教授,主要从事3S集成应用、三维激光扫描技术的研究。Email:20130051@kust.edu.cn
作者简介: 杨 赈(1998-),男,硕士研究生,主要从事土壤墒情监测、遥感影像处理的研究。Email:yangz@stu.kust.edu.cn
引用本文:   
杨赈, 杨明龙, 李国柱, 夏永华, 俞婷, 严正飞, 李万涛. 修正水分胁迫的NPP反演结果与典型高原盆地土壤水分关系探究[J]. 自然资源遥感, 2025, 37(5): 267-277.
YANG Zhen, YANG Minglong, LI Guozhu, XIA Yonghua, YU Ting, YAN Zhengfei, LI Wantao. Relationship between modified water stress-based NPP inversion and soil moisture in typical plateau basins. Remote Sensing for Natural Resources, 2025, 37(5): 267-277.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2024201      或      https://www.gtzyyg.com/CN/Y2025/V37/I5/267
Fig.1  研究区地理分布、土地利用类型及样本分布状况
数据类型 数据名称 数据来源 空间分辨率/m 范围
遥感数据
MODIS产品
MOD09A1/Terra 8 d
500

[-100,16 000]
MYD09A1/Terra 8 d
光合利用转换数据 气象数据 Terra-Climate/monthly 4 638.3
土地覆盖类型 MCD12Q1 500
土壤水分产品数据及降尺度数据 土壤水分产品 NASA SMAP L4/3-hourly 9 000 [0,0.9]%
NDVI MOD13A2/Terra 16 d 500 [-2 000,10 000]
LST MOD11A1/Terra 1 d 1 000 [-7 000,65 535]K
Tab.1  数据源信息
Fig.2  研究流程图
Fig.3  RF自适应回归法构建降尺度模型
Fig.4  研究区30 m分辨率SMAP L4产品降尺度结果分布
采样点 RF降尺度后的产品 原始产品
夏季 冬季 夏季 冬季
r RMSE r RMSE r RMSE r RMSE
NP 0.69 0.021 0.75 0.018 0.19 0.016 0.22 0.011
SP 0.58 0.035 0.66 0.037 0.25 0.052 0.28 0.048
EP 0.36 0.020 0.43 0.008 0.22 0.012 0.19 0.015
WP 0.47 0.019 0.59 0.033 0.14 0.028 0.21 0.017
均值 0.53 0.023 0.61 0.024 0.20 0.027 0.23 0.023
Tab.2  样本点验证及精度评价表
Fig.5  样本点回归验证图
Fig.6  研究区2019—2023年NPP夏季、冬季、年均分布格局
Fig.7  研究区2019—2023夏季、冬季、年均NPP与土壤水分相关性空间分布
Fig.8  多场景构建NPP与土壤水分相关变化特征
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