Generating Spatiotemporally Continuous Grassland Aboveground Biomass on the Tibetan Plateau Through PROSAIL Model Inversion on Google Earth Engine

Xie, Jiangliu; Wang, Changjing; Ma, Dujuan; Chen, Rui; Xie, Qiaoyun; Xu, Baodong; Zhao, Wei; Yin, Gaofei

doi:10.1109/tgrs.2022.3227565

Cited by 8 publications

(1 citation statement)

References 53 publications

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“…This is because, in winter, herders store a large amount of hay for livestock consumption; thus, in the dormant period, the grass AGB is not zero [64]. The reason for the decreasing trend may be because the grass consumed during grazing is greater than the average carrying capacity of the plateau during the dormant period [66,67]. In summary, the grass AGB estimation method developed in this paper provides a good reflection of the trends in climatic change and the spatial characteristics of the grassland AGB in the Zoigê Plateau.…”

Section: Spatial and Temporal Dynamics Of Grassland Agb In The Zoigê ...mentioning

confidence: 99%

Integrating the PROSAIL and SVR Models to Facilitate the Inversion of Grassland Aboveground Biomass: A Case Study of Zoigê Plateau, China

Wang,

He,

et al. 2024

Remote Sensing

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Grasslands play a vital role in the global ecosystem. Efficient and reproducible methods for estimating the grassland aboveground biomass (AGB) are crucial for understanding grassland growth, promoting sustainable development, and assessing the carbon cycle. Currently, the available methods are limited by their computational inefficiency, model transfer, and sampling scale. Therefore, in this study, the estimation of grassland AGB over a large area was achieved by coupling the PROSAIL model with the support vector machine regression (SVR) method. The ill-posed inverse problem of the PROSAIL model was mitigated through kernel-based regularization using the SVR model. The Zoigê Plateau was used as the case study area, and the results demonstrated that the estimated biomass accurately reproduced the reference AGB map generated by zooming in on on-site measurements (R2 = 0.64, RMSE = 43.52 g/m2, RRMSE = 15.13%). The estimated AGB map also maintained a high fitting accuracy with field sampling data (R2 = 0.69, RMSE = 44.07 g/m2, RRMSE = 14.21%). Further, the generated time-series profiles of grass AGB for 2022 were consistent with the trends in local grass growth dynamics. The proposed method combines the advantages of the PROSAIL model and the regression algorithm, reduces the dependence on field sampling data, improves the universality and repeatability of grassland AGB estimation, and provides an efficient approach for grassland ecosystem construction and planning.

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Section: Spatial and Temporal Dynamics Of Grassland Agb In The Zoigê ...mentioning

confidence: 99%

Integrating the PROSAIL and SVR Models to Facilitate the Inversion of Grassland Aboveground Biomass: A Case Study of Zoigê Plateau, China

Wang,

He,

et al. 2024

Remote Sensing

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Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review

2023

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Synergizing sustainability: a critical review on harnessing agroforestry for biomass, carbon sequestration, and water-food-energy nexus

Barman,

Dutta,

Bera

et al. 2024

Energ. Ecol. Environ.

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Generating Spatiotemporally Continuous Grassland Aboveground Biomass on the Tibetan Plateau Through PROSAIL Model Inversion on Google Earth Engine

Cited by 8 publications

References 53 publications

Integrating the PROSAIL and SVR Models to Facilitate the Inversion of Grassland Aboveground Biomass: A Case Study of Zoigê Plateau, China

Integrating the PROSAIL and SVR Models to Facilitate the Inversion of Grassland Aboveground Biomass: A Case Study of Zoigê Plateau, China

Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review

Synergizing sustainability: a critical review on harnessing agroforestry for biomass, carbon sequestration, and water-food-energy nexus

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