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Senescence is an important phenological stage in the life cycle of grassland plants, and estimating grass senescence in alpine grasslands is particularly crucial for animal husbandry and grazing the utilization of grassland resources for grazing. However, the application of proximal sensing technologies to capture the variations in forage growth parameters in the senescent stage, especially regarding some key stoichiometric ratios, still faces challenges associated with indiscernible spectral properties and indeterminate absorption and reflectance features. The present study aims to demonstrate the potential and effectiveness of applying hyperspectral data to assess the forage carbon-nitrogen (C:N) ratio and calcium-phosphorus (Ca:P) ratio during the senescent stage (September to November). A progressive variable dimensionality reduction strategy based on a genetic algorithm (GA) and hybrid optimization method (ant optimization colony (ACO) and extreme learning machine (ELM)) coupled with multiple linear and nonlinear statistical methods is applied to develop C:N ratio and Ca:P ratio estimation models via combination with data from 205 sample sites collected from six field campaigns (2016–2019) on the eastern Tibetan Plateau. The results show that the important spectral bands favorable for forage C:N ratio and Ca:P ratio retrieval are generally in the red and shortwave infrared (SWIR) regions, and the proposed model presents satisfactory performance in the estimation of the forage C:N ratio (V-R2 = 0.81, V-RMSE = 6.44) and Ca:P ratio (V-R2 = 0.64, V-RMSE = 2.54) during senescence. Moreover, the model can effectively overcome the spatial differences among sampling areas and perform optimally in capturing the variations in the forage C:N ratio and Ca:P ratio in the early and middle stages of senescence. Overall, our study demonstrates that it is feasible and promising to estimate these stoichiometric ratios using hyperspectral feature bands during grass senescence at the canopy level, with potential applications that may further enhance the monitoring of forage quality and quantity. • The integration of GA and ACO performs well in eliminating uninformative variables. • It is feasible to estimate C:N and Ca:P using hyperspectral data in grass senescence. • The red and SWIR regions contribute to forage C:N and Ca:P estimation in grassland. • The proposed model accounts for 81% and 64% of the variations in forage C:N and Ca:P. [ABSTRACT FROM AUTHOR]

Small-scale patchiness intensified the spatial heterogeneous distribution of soil and vegetation organic carbon (C) and total nitrogen (N), thereby inducing large uncertainty in estimating ecosystem C and N stocks. However, patchiness at this scale was difficult to identify by satellite remote sensing and measure in situ. Aerial photographing can provide high-precision images and can potentially improve estimating accuracy in ecosystem C and N stocks. In this study, using combined methods of field sampling, aerial photographing and satellite images, we investigated the characteristics of patchiness, quantified ecosystem C and N stocks in different patches at plot scale (~ 1000 m 2 ) and eventually estimated the uncertainty in ecosystem C and N stocks caused by small-scale patchiness at basin scale. Results showed that: 1) mosaic of vegetation and bald patches coexisted for swamp meadow and alpine meadow, whereas vegetation presented as spot for steppe meadow and alpine steppe; 2) average ecosystem respiration, C and N stocks in intact grassland patches were 2.05 and 1.65, 1.66 and 1.65, 2.01 and 1.69 times higher than those of large and medium bare patches ( P < 0.05); and 3) neglecting the bare patches caused approximate 30%, 15% and 10% overestimation in estimations of ecosystem C emission, C and N stocks. We concluded that small-scale patchiness would result in ecosystem C loss due to decreasing in C input from vegetation and increasing in C output from soil erosion. Our study suggested that integration of field sampling, aerial photographing and satellite images could improve precision in estimation of ecosystem C emission, C and N stocks at basin scale. [ABSTRACT FROM AUTHOR]

Active-layer thickness's distribution is a key parameter for permafrost area studies. In the past, the factors of vegetation, soil water content usually were employed to forecast the spatial distribution of active-layer thickness. But in coarse material distribution area, the above method is often unsatisfactory due to the failure in accurate acquisition of biomass and soil water content. In this paper, a simple method was recommended to be used for calculating of active layer thickness distribution, which is suitable for all kinds of soil texture. Our study analyzed the relationship between ground surface layer permittivity and the thawing depth of the active layer in a permafrost area that was undisturbed by subsurface water flow or local melting. Ground surface layer permittivity is confirmed as having a strong correlation with soil moisture content and biomass of the vegetation cover. We used the direct ground wave velocities from GPR detecting results to compute the dielectric permittivity. Our conclusion is that in our specific study area, a power function relationship existed between the ground surface layer permittivity and the thawing depth of the active layer. [ABSTRACT FROM AUTHOR]