Estimation of European Terrestrial Ecosystem NEP Based on an Improved CASA Model

Qiu, Siyi; Liang, Liang; Wang, Qianjie; Geng, Di; Wu, Junjun; Wang, Shuguo; Chen, Bingqian

doi:10.1109/jstars.2022.3233128

Cited by 13 publications

(6 citation statements)

References 31 publications

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“…Numerous research efforts have been undertaken to explore the spatial and temporal changes in NEP using remote sensing techniques. However, most of the studies were based on MODIS remote sensing data [11,12,44,54]. MODIS data have been available since 2000, limiting the examination of carbon source/sink dynamics over extended temporal periods.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020

Chen,

Xu,

Chen

et al. 2023

Remote Sensing

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Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m−2. Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m−2·a−1. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect.

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Section: Discussionmentioning

confidence: 99%

Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020

Chen,

Xu,

Chen

et al. 2023

Remote Sensing

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“…The LUE model is widely used because of its simplicity and precision, particularly the CASA model established by Potter et al [51] in 1993, which has become a standard application in NPP estimation. In recent studies, some researchers have improved the CASA model by optimizing parameters such as optimum temperature, maximum light efficiency (εmax) [52], and fPAR [53], leading to better estimation of NPP at large scales.…”

Section: E Further Directionsmentioning

confidence: 99%

Near-Infrared Radiance of Vegetation is More Sensitive Than Vegetation Indices for Monitoring NPP of Winter Wheat Under Water Stress

Zhao,

Liu,

Shen

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The quantification of NPP plays a crucial role in ensuring food security. Previous studies have established a noteworthy correlation between the near-infrared radiance of vegetation (NIRv,Rad) and NPP. However, limited research has been conducted to investigate the impact of droughgt on the relationship between NPP and NIRv,Rad. To address this gap, we constructed four distinct water stress gradients (well-watered, mild, moderate, and severe) to assess the effectiveness of NIRv,Rad in monitoring drought-induced variations in NPP for winter wheat. The results indicated that NIRv,Rad exhibited higher sensitivity to water stress compared to traditional vegetation indices (VIs) like the NIR reflectance of vegetation (NIRv,Ref), enhanced vegetation index-2 (EVI2), and normalized difference vegetation index (NDVI), which provided confidence in the capacity of NIRv,Rad as a tool for drought monitoring. NIRv,Rad can accurately estimate NPP at both hourly and daily scales under different water stresses. This advantage was more pronounced at hourly scales compared to NIRv,Ref, EVI2 and NDVI. NIRv,Rad proved to be a superior indicator of NPP compared to VIs under water stresses. Our work introduces a novel approach for accurately and easily estimating NPP under different water stress levels and time scales.

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“…NPP, which can be directly estimated by CASA model, is the focus of most studies [41][42][43] [44]. However, after estimating Rh must be subtracted in order to calculate the NEP value that can characterize vegetation carbon sinks.…”

Section: Nep Estimations Of the Coupled Modelmentioning

confidence: 99%

“…However, after estimating Rh must be subtracted in order to calculate the NEP value that can characterize vegetation carbon sinks. [2][4] [44]. Thus, in this paper, the NPP and Rh are estimated and then the NEP in China is calculated on this basis.…”

Section: Nep Estimations Of the Coupled Modelmentioning

confidence: 99%

NEP Estimation of Terrestrial Ecosystems in China Using an Improved CASA Model and Soil Respiration Model

Liang,

Wang,

Qiu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Net ecosystem productivity (NEP) is a critical indicator of the CO2 capture capacity of vegetation ecosystems. Based on the land classification and clumping index (CI) datasets, the key parameters of the CASA model, including fraction of photosynthetic active radiation (FPAR) and maximum light use efficiency (ε max ) , were optimized. Then, the NEP of China's terrestrial ecosystems was estimated, using the improved CASA coupled with a soil respiration model. Finally, the accuracy of NEP estimation was evaluated by observation data from ChinaFLUX station. The research results indicated the RMSE of the improved NEP estimations decreased from 21.139 to 10.179 (unit: gC•m −2 •month −1 ), and the R 2 value increased from 0.413 to 0.832, indicating that optimizing the parameters ε max and FPAR are both effective methods to improve the model. The spatiotemporal variation of China's NEP was analyzed using the optimized results. The NEP value of China shows a decreasing distribution pattern from southeast to northwest, and the values of different regions are in the order South > North > Qinghai-Tibet > Northwest. The monthly NEP variation in different regions of China is a unimodal curve, reaching the maximum in summer. This study optimized the NEP estimation, which can better characterize the distribution pattern of carbon sinks/sources in China's terrestrial ecosystems and lay a scientific foundation for developing regional carbon neutrality schemes.

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Estimation of European Terrestrial Ecosystem NEP Based on an Improved CASA Model

Cited by 13 publications

References 31 publications

Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020

Exploring the Spatiotemporal Dynamics and Driving Factors of Net Ecosystem Productivity in China from 1982 to 2020

Near-Infrared Radiance of Vegetation is More Sensitive Than Vegetation Indices for Monitoring NPP of Winter Wheat Under Water Stress

NEP Estimation of Terrestrial Ecosystems in China Using an Improved CASA Model and Soil Respiration Model

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