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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (5) : 267-277     DOI: 10.6046/zrzyyg.2024201
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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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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.
Keywords plateau irrigation area      multi-source remote sensing data      soil moisture      downscaling analysis      net primary productivity （NPP）     
ZTFLH:  TP79  
Issue Date: 28 October 2025
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Zhen YANG
Minglong YANG
Guozhu LI
Yonghua XIA
Ting YU
Zhengfei YAN
Wantao LI
Cite this article:   
Zhen YANG,Minglong YANG,Guozhu LI, et al. Relationship between modified water stress-based NPP inversion and soil moisture in typical plateau basins[J]. Remote Sensing for Natural Resources, 2025, 37(5): 267-277.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024201     OR     https://www.gtzyyg.com/EN/Y2025/V37/I5/267
Fig.1  Geographical distribution，land use types and distribution of samples of the study area
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  Information of data source
Fig.2  Research flow chart
Fig.3  A downscaling model constructed based on RF adaptive regression method
Fig.4  Downscaling results of SMAP L4 product with 30 m resolution in the study area
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  Sample point verification and accuracy evaluation table
Fig.5  Regression validation plots of sample points
Fig.6  Distribution of NPP in summer，winter，and annual average in study area from 2019 to 2023
Fig.7  The spatial distribution of the correlation between NPP and soil moisture in summer，winter，and annual average in the study area from 2019 to 2023
Fig.8  Features of NPP and soil moisture-related change constructed in multi-scenario
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