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

Attributing Atmospheric Phosphorus in the Himalayas: Biomass Burning vs Mineral Dust

Atmospheric phosphorus is a vital nutrient for ecosystems whose sources and fate are still debated in the fragile Himalayan region, hindering our comprehension of its local ecological impact. This study provides novel insights into atmospheric phosphorus based on the study of total suspended particulate matter at the Qomolangma station. Contrary to the prevailing assumptions, we show that biomass burning (BB), not mineral dust, dominates total dissolved phosphorus (TDP, bioavailable) deposition in this arid region, especially during spring. While total phosphorus is mainly derived from dust (77% annually), TDP is largely affected by the transport of regional biomass-burning plumes from South Asia. During BB pollution episodes, TDP causing springtime TDP fluxes alone accounts for 43% of the annual budget. This suggests that BB outweighs dust in supplying bioavailable phosphorus, a critical nutrient, required to sustain Himalayas' ecological functions. Overall, this first-hand field evidence refines the regional and global phosphorus budget by demonstrating that BB emission, while still unrecognized, is a significant source of P, even in the remote mountains of the Himalayas. It also reveals the heterogeneity of atmospheric phosphorus deposition in that region, which will help predict changes in the impacted ecosystems as the deposition patterns vary.

Mapping the forage nitrogen, phosphorus, and potassium contents of alpine grasslands by integrating Sentinel-2 and Tiangong-2 data

Nitrogen (N), phosphorus (P), and potassium (K) contents are crucial quality indicators for forage in alpine natural grasslands and are closely related to plant growth and reproduction. One of the greatest challenges for the sustainable utilization of grassland resources and the development of high-quality animal husbandry is to efficiently and accurately obtain information about the distribution and dynamic changes in N, P, and K contents in alpine grasslands. A new generation of multispectral sensors, the Sentinel-2 multispectral instrument (MSI) and Tiangong-2 moderate-resolution wide-wavelength imager (MWI), is equipped with several spectral bands suitable for specific applications, showing great potential for mapping forage nutrients at the regional scale. This study aims to achieve high-accuracy spatial mapping of the N, P, and K contents in alpine grasslands at the regional scale on the eastern Qinghai-Tibet Plateau. The Sentinel-2 MSI and Tiangong-2 MWI data, coupled with multiple feature selection algorithms and machine learning models, are applied to develop forage N, P, and K estimation models from data collected at 92 sample sites ranging from the vigorous growth stage to the senescent stage. The results show that the spectral bands of both the Sentinel-2 MSI and Tiangong-2 MWI have an excellent performance in estimating the forage N, P, and K contents (the R² values are 0.68-0.76, 0.54-0.73, and 0.74-0.82 for forage N, P, and K estimations, respectively). Moreover, the model integrating the spectral bands of these two sensors explains 78%, 74%, and 84% of the variations in the forage N, P, and K contents, respectively. These results indicate that the estimation ability of forage nutrients can be further improved by integrating Tiangong-2 MWI and Sentinel-2 MSI data. In conclusion, integration of the spectral bands of multiple sensors is a promising approach to map the forage N, P, and K contents in alpine grasslands with high accuracy at the regional scale. This study offers valuable information for growth monitoring and real-time determination of forage quality in alpine grasslands.

Characterizing Leaf Nutrients of Wetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data

In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops in the uMfolozi floodplain system (UFS). Nutrient concentrations in different floodplain plant species were estimated using Sentinel-2 multispectral data derived vegetation indices in concert with the random forest regression. The results showed a mean R2 of 0.87 and 0.86 for the dry winter and wet summer seasons, respectively. However, copper, sulphur, and magnesium were poorly correlated (R2 ≤ 0.5) with vegetation indices during the summer season. The average % relative root mean square errors (RMSE’s) for seasonal nutrient estimation accuracies for crops and wetland vegetation were 15.2 % and 26.8%, respectively. There was a significant difference in nutrient concentrations between the two plant types, (R2 = 0.94 (crops), R2 = 0.84 (vegetation). The red-edge position 1 (REP1) and the normalised difference vegetation index (NDVI) were the best nutrient predictors. These results demonstrate the usefulness of Sentinel-2 imagery and random forests regression in predicting seasonal, nutrient concentrations as well as the accumulation of chemicals in wetland vegetation and crops.