Analyzing NPP Response of Different Rangeland Types to Climatic Parameters over Mongolia

Nanzad, Lkhagvadorj; Zhang, Jiahua; Gantsetseg, Batdelger; Sharma, Til Prasad Pangali; Koju, Upama Ashish; Wang, Jingwen; Nabil, Mohsen

doi:10.3390/agronomy11040647

Cited by 10 publications

(15 citation statements)

References 81 publications

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“…Thus, rather than directly adopting satellite products, researchers prefer to develop specific models for the study area to obtain more robust estimates. These models mainly belong to the above approaches and require numerous parameters to drive and calibrate [79][80][81][82][83]. However, similar to the problems in [45], most parameters are uncertain and sparse and are difficult to obtain by same measuring methods.…”

Section: Primary Productivitymentioning

confidence: 99%

“…On this basis, the combination of ground and remote sensing data is fully utilized in many studies to calibrate the process-based biogeochemical models and LUE models. Nanzad et al [79] combined satellite products and meteorological data to drive a boreal ecosystem productivity simulator model. You et al [80] introduced NDVI-driven phenological indices to improve the Biome-BGC model and obtained an increase of 0.08 in R 2 compared to the original model (R 2 = 0.84).…”

Section: Primary Productivitymentioning

confidence: 99%

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Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.

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Section: Primary Productivitymentioning

confidence: 99%

Section: Primary Productivitymentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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“…A clear understanding of the responses of vegetation productivity or ecosystem carbon uptake to the above climatic factors is helpful to extreme events monitoring, mechanism model establishment, etc. Previous studies evaluated the vegetation response to changes in air temperature [30,31], precipitation [32][33][34], and solar radiation [35,36], but fewer climatic variables were involved. However, since the response periods of vegetation to different climatic factors are diverse, it is also necessary to concurrently consider the relationships between grasslands and multiple climatic factors [37].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Seasonality and Response Period on Qualifying the Relationship between Ecosystem Productivity and Climatic Factors over the Eurasian Steppe

Liu

Meng

et al. 2021

Remote Sensing

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As climate change intensifies, surface vegetation productivity and carbon exchange between terrestrial ecosystems and the atmosphere are significantly affected by the variation of climatic factors. Due to the sensitivity of grasslands to these climatic factors, it is crucial to understand the response of vegetation greenness, or carbon exchange within grasslands, to environment factor dynamics. In this study, we used solar-induced chlorophyll fluorescence (SIF), precipitation (P), vapor pressure deficit (VPD), evaporative stress (ES), and root zone soil moisture (RSM) derived from remote sensing, reanalysis, and assimilation datasets to explore the response of vegetation greenness within Eurasian Steppe to climatic factors. Our results indicated deseasonlization based on the Seasonal-Trend decomposition using Loess (STL) method, which was an effective means to remove the seasonality disturbances that affect the qualification of the relationship between SIF and the four climatic factors. The response of SIF had a time lag effect on these climatic factors, and the longer the response period, the greater the impact on the correlation of SIF with P, VPD, ES, and RSM. We also found, among the four factors, that the response of SIF to ES was the timeliest. The findings of this study emphasized the impact of the seasonality and time lag effect on the dynamic response between variables, and provided references to the attribution and monitoring of vegetation greenness and ecosystem productivity.

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“…The approaches chosen were empirical [1][2][3] providing evidence for dose-response relationships for management effects and environmental changes. These are essential to derive parameters for process-based modelling as shown by Giannitsopoulos [4], as well as for understanding ecological phenomena and implementing observed changes in functional groups [3] and functional types [5].…”

mentioning

confidence: 99%

“…Experimentalists should feel encouraged to use this type of model for further work to develop and test research hypotheses and for consultancy purposes. Nanzad and his team demonstrate in their study for Mongolia that models are a crucial component in the analysis of groundand space-based evidence [5]. Magandana et al should follow up their nice experimental results [3] by collaborating with mathematical modellers to raise their analysis to another level, to enable an impact assessment at the sub-continental scale.…”

mentioning

confidence: 99%