Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau

Abstract

The applicability of hyperspectral remote sensing models for forage nitrogen (N) retrieval during different growth periods is limited. This study aims to develop a multivariate model feasible for estimating the forage N for the growth periods (June to November) in an alpine grassland ecosystem. The random forest (RF) algorithm is employed to determine the optimum combinations of 38 spectral variables capable of capturing dynamic variations in forage N. The results show that (1) throughout the growth period, the red-edge first shifts toward longer wavelengths and then shifts toward shorter wavelengths, the amplitude (AMP) and absorption depth (AD) gradually decrease, and the absorption position (AP) changes slightly; (2) the importance of spectral variables for forage N estimation differs during the different growth periods; (3) the multivariate model achieves better results for the first four periods (June to October) than for the last period (when the grass is completely senesced) (V-R2: 0.58–0.68 versus 0.23); and (4) for the whole growth period (June to November), the prediction accuracy of the general N estimation model validated by the unknown growth period is lower than that validated by the unknown location (V-R2 is 0.28 and 0.55 for the validation strategies of Leave-Time-Out and Leave-Location-Out, respectively). This study demonstrates that the changes in the spectral features of the red wavelength (red-edge position, AMP and AD) are well coupled with the forage N content. Moreover, the development of a multivariate RF model for estimating alpine grasslands N content during different growth periods is promising for the improvement of both the stability and accuracy of the model.

Prediction of Biomass and N Fixation of Legume–Grass Mixtures Using Sensor Fusion

“…To our best knowledge, our study is the first extracting texture features from TLS data in agricultural grasslands and, furthermore, combining them with spectral information. Texture detects other characteristics of plant structure than CSH and MS, especially differences in plant growth stages (Gao et al, 2019) and yield levels (Yue et al, 2019). Therefore, this supplementary information improves biomass and N Fix estimation.…”

“…In our study, the MS sensor covered specific wavelengths of green, red, red edge, and NIRS region. As the red edge region shifts to longer wavelengths for senescent material compared with green vegetation (Gao et al, 2019), a hyperspectral sensor can cover a much broader area of wavelengths. However, this approach needs more cost-intensive equipment and knowledge compared with MS sensors.…”

“…A spectral sensor mounted on a low-cost UAV serves as an interesting and simple tool for grassland monitoring. VIs were already successfully used to estimate grassland biomass, N content, N Fix (Cho et al, 2007;Gao et al, 2019;Grüner et al, 2020) and are highly correlated to leaf area index (LAI) (Mutanga and Skidmore, 2004;He et al, 2006;Darvishzadeh et al, 2008). However, the sole application of VIs for biomass estimation is affected by soil background color at low biomass levels (Huete et al, 1985) and saturates at high biomass and LAI levels (> 2-3) (Carlson and Ripley, 1997;Mutanga and Skidmore, 2004), as it captures reflectance only of the top surface of the canopy.…”

Mapping the Forage Nitrogen-Phosphorus Ratio Based on Sentinel-2 MSI Data and a Random Forest Algorithm in an Alpine Grassland Ecosystem of the Tibetan Plateau

“…For example, the P content of eight forages in the Alxa Desert steppe show regular seasonal dynamics; that is, the P content gradually decreases with the advancement of the growth period [66], and the N content in the aboveground part of plants decreases during the plant growth season (May to September) [67]. The forage N content gradually decreases as the forage grows in alpine grassland, especially the forage N that decreases rapidly during the senescing period [68]. In the late growth season, with the expansion of plants, the forage gradually withers, cell senescence occurs, and fiber material increases, resulting in the dilution effect of elements [69].…”

“…In terms of practical applications, it is of more practical significance to use remote sensing data to monitor forage growth in this stage than in other stages at a regional scale. In general, the nutrient content of forage decreases and the quality deteriorates during the senescing period, and the canopy spectrum is easily affected by soil and other background factors, which will reduce the sensitivity of the spectral bands and VIs to forage N and P [68,71]. According to our research results, the integrated forage N:P ratio model performs better in the senescing period than in the vigorous growth period, which is potentially attributable to the limited number of sample sites, the optimization of model parameters, and large spatial heterogeneity of grassland.…”