| Authors |
Cong Xu, JinchenWu, Yuqing Liang, Pengyu Zhu, Siyang Wang, Fangming Wu, Wei Liu, Xin Mei, Zhaoju Zheng, Yuan Zeng, Yujin Zhao, Bingfang Wu, Dan Zhao |
| Intro |
Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity
and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in
hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided
new opportunities for grassland AGB monitoring, but current research remains predominantly
focused on data-driven machine learning models. The black-box nature of such
approaches resulted in a lack of clear interpretation regarding the coupling relationships
between these two data types in grassland AGB estimation. For grassland aboveground
fresh biomass, the theoretical estimation can be decomposed into either the product of
planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland
community volume. Based on this theory, our study developed a semi-mechanistic
remote sensing model for grassland AGB estimation by integrating hyperspectral-derived
biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we
built hyperspectral estimation models for both PD and SD of grassland fresh AGB using
PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and
community volume measurements, respectively, we achieved quadrat-scale remote sensing
estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of
both methods across different phenological stages to evaluate their performance differences.
Our results demonstrated that SD, which incorporated structural features, could be more
precisely estimated (R2 = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral
data compared to PD (R2 = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant
differences observed in their respective responsive spectral bands. PD showed greater
sensitivity to shortwave infrared regions, while SD exhibited stronger associations with
visible, red-edge, and near-infrared bands. Although both methods achieved comparable
overall AGB estimation accuracy (PD-based: R2 = 0.79, nRMSE = 10.19%, Bias% = −7.25%;
SD-based: R2 = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively
mitigated the underestimation of high biomass values caused by spectral saturation effects
and also demonstrated superior and more stable performance across different growth
periods (R2 > 0.6). This work provided concrete physical meaning to the integration of
hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the
potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings
offered theoretical foundations for developing large-scale grassland AGB monitoring
models using airborne and spaceborne remote sensing platforms. |
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