A case study of the RothC soil carbon model with potential evapotranspiration and remote sensing model inputs

Maas, Ellen D.v.L.; Lal, Rattan

doi:10.1016/j.rsase.2022.100876

Cited by 1 publication

(1 citation statement)

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“…As a result, most of the current studies have preferred a modelbased approach for large-scale estimation of GPP values using remote sensing data. These methods typically include light use efficiency models, process-based models, and climate productivity models [5][6][7]. These models integrate different data sources, including satellite imagery, climate data, and land surface characteristics, to estimate GPP at regional to global scales.…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal change analysis of gross primary productivity of Xilingol grassland based on remote sensing technology

Zhao,

Cao

2024

Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024)

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As an important part of China's grassland ecosystem, it is crucial to use remote sensing for high-resolution, long-term ecological monitoring and management of the Xilingol Grassland. This study uses Landsat data from May to September for the years 2013 to 2022 to estimate GPP, with a spatial resolution of 30m, and utilizes Slope and F-test methods to discern the spatial-temporal variations of GPP in Xilingol Grassland. The results indicate that over the past decade: 1) The multi-year average GPP value for Xilingol Grassland stands at 458.55 g C•m -2 •year -1 , with high-value areas mainly located in the northeastern and southern parts of the grassland, and low-value areas mainly located in the southwestern region. 2) The inter-annual average changes in GPP in Xilingol Grassland showed a pattern of initial decline followed by an increase. Among them, the highest proportion without significant change is 54.92%. 3) In particular, the meadow grassland mainly shows an increase or no significant change, accounting for 37.28% and 52.82% respectively. The typical grassland mostly shows no significant change, accounting with 61.8%. And desert steppes mainly showed a decreasing trend with 62.83%.

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Section: Introductionmentioning

confidence: 99%