Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland

Wu, Chunlin; Chen, Kelong; Chongyi, E; You, Xue-yi; He, Dongcai; Hu, Liangbai; Liu, Baokang; Wang, Runke; Shi, Yaya; Li, Chengxiu; Liu, Fumei

doi:10.5194/gmd-15-6919-2022

Cited by 35 publications

(6 citation statements)

References 41 publications

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“…Based on the Carnegie-Ames-Stanford Approach (CASA) model, remote sensing, meteorological, and field survey data were used to estimate the NPP in the study area. The calculation method is as follows [20]:…”

Section: Primary Productivitymentioning

confidence: 99%

Spatiotemporal Variation Characteristics and Driving Force Analysis of Precipitation Use Efficiency at the North Foot of Yinshan Mountain

Yang,

Liu,

Tao

et al. 2023

Water

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The northern foothills of Yinshan Mountain are situated in northern China’s agricultural and pastoral ecotone, serving as a crucial ecological barrier. To comprehensively assess the impact of grassland resource restoration in this region since the initiation of the Grain-for-Green conversion project in 2000, this study analyzes the spatiotemporal characteristics of precipitation use efficiency (PUE) and investigates climate-driven factors during 2001–2021. The results showed that the grassland types at the north foot of Yinshan could be divided into four categories: warm-arid, warm subtropical semidesert (WSS), warm temperate-arid, warm temperate zonal semidesert (WZS), warm temperate-semiarid, warm temperate typical steppe (WTS), and warm temperate-subhumid forest steppe (WFT). The NPP of the four grassland species were 151.34 (WSS), 196.72 (WZS), 283.33 (WTS), and 118.06 gC·m−2 (WFT), and correspondingly, the PUE of the four grassland species were 0.66 (WSS), 0.66 (WZS), 0.80 (WTS), and 0.57 gC·m−2·mm−1 (WFT). From 2001 to 2021, PUE in grassland showed an overall upward trend, rising from 0.57 to 0.99 gC·m−2·mm−1. The trend analysis found that the vegetation ecological area of the northern foot of Yinshan became better, of which 54.36% was improved and 15.72% was degraded. It is worth pointing out that WSS had the highest degree of improvement, while WFT was in a degraded state. The climate driving force analysis shows that the regional contribution of precipitation is 19.57%, temperature is 28.33%, potential evapotranspiration is 13.65%, wind speed is 10.79%, and saturated vapor pressure is 27.66%.

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Section: Primary Productivitymentioning

confidence: 99%

Spatiotemporal Variation Characteristics and Driving Force Analysis of Precipitation Use Efficiency at the North Foot of Yinshan Mountain

Yang,

Liu,

Tao

et al. 2023

Water

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“…LUE models are designed to simulate the absorption and conversion ability of plants to solar radiation under varying climatic conditions [15][16][17]. Commonly used models include the Carnegie-Ames-Stanford Approach (CASA) model [18][19][20][21][22], the EC-LUE model [23], the GLO-PEM [24], and the MuSyQ-NPP model [25]. As a representative of LUE models, the CASA model is distinguished for its robust process-based framework, efficient data integration, and continuous refinement.…”

Section: Introductionmentioning

confidence: 99%

Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model

Lv,

Li,

Fang

et al. 2024

Remote Sensing

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Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000–2019 using the Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann–Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC/(m2·a) during 2000–2019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors—particularly precipitation—having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies.

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“…This model is simple and practical and can obtain an important parameter, the photosynthetically active radiation absorption ratio (FPAR), from remote sensing data. Thus, it is widely used [22]. Jia et al [23] used CASA to quantify the vegetation NPP of the Ordos area from 2000 to 2019 and verified it using measured data.…”

Section: Introductionmentioning

confidence: 99%

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

Fang

Zhou

et al. 2023

Remote Sensing

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Aerosols impact vegetation productivity by increasing diffuse radiation and changing temperature and humidity conditions. In this study, climate simulations of the Jinghe River Basin in 2020 based on aerosol and aerosol−free scenarios were carried out using the control variable method and the aerosol optical depth parameter as the external input data of Weather Report Forecast (WRF)−solar. These two output results were used as input data for the Carnegie Ames Stanford Approach (CASA) model to calculate the impact of aerosols on vegetation productivity. The results showed that WRF−solar accurately simulated changes in meteorological factors such as temperature, rainfall, solar radiation, and relative humidity in the Jinghe River Basin, with a correlation coefficient above 0.85. Aerosols significantly change the ratio of diffuse to direct radiation, act as a cooling function to reduce temperature, and affect rainfall by interacting with clouds. The scenario simulation results showed that under the influence of aerosols, the total solar radiation was reduced by 224.98 MJ/m2, accounting for 3.44% of the total annual radiation. Correspondingly, the average net primary productivity of vegetation in the Jinghe River Basin in 2020 decreased by 26.64 gC/m2, which was not conducive to vegetation photosynthesis and carbon fixation in the basin.

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Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland

Cited by 35 publications

References 41 publications

Spatiotemporal Variation Characteristics and Driving Force Analysis of Precipitation Use Efficiency at the North Foot of Yinshan Mountain

Spatiotemporal Variation Characteristics and Driving Force Analysis of Precipitation Use Efficiency at the North Foot of Yinshan Mountain

Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

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