Horizontal and vertical distributions of estuarine soil total organic carbon and total nitrogen under complex land surface characteristics

Blue carbon benefits from global saltmarsh restoration

Coastal saltmarshes are found globally, yet are 25%-50% reduced compared with their historical cover. Restoration is incentivised by the promise that marshes are efficient storers of 'blue' carbon, although the claim lacks substantiation across global contexts. We synthesised data from 431 studies to quantify the benefits of saltmarsh restoration to carbon accumulation and greenhouse gas uptake. The results showed global marshes store approximately 1.41-2.44 Pg carbon. Restored marshes had very low greenhouse gas (GHG) fluxes and rapid carbon accumulation, resulting in a mean net accumulation rate of 64.70 t CO2 e ha-1 year-1 . Using this estimate and potential restoration rates, we find saltmarsh regeneration could result in 12.93-207.03 Mt CO2 e accumulation per year, offsetting the equivalent of up to 0.51% global energy-related CO2 emissions-a substantial amount, considering marshes represent <1% of Earth's surface. Carbon accumulation rates and GHG fluxes varied contextually with temperature, rainfall and dominant vegetation, with the eastern coasts of the USA and Australia particular hotspots for carbon storage. While the study reveals paucity of data for some variables and continents, suggesting need for further research, the potential for saltmarsh restoration to offset carbon emissions is clear. The ability to facilitate natural carbon accumulation by saltmarshes now rests principally on the action of the management-policy community and on financial opportunities for supporting restoration.

The spatial variation and driving factors of soil total carbon and nitrogen in the Heihe River source region

Soil carbon and nitrogen levels are key indicators of soil fertility and are used to assess ecological value and safeguard the environment. Previous studies have focused on the contributions of vegetation, topography, physical and chemical qualities, and meteorology to soil carbon and nitrogen change, but there has been little consideration of landscape and ecological environment types as potential driving forces. The study investigated the horizontal and vertical distribution and influencing factors of total carbon and total nitrogen in soil at 0-20 and 20-50 cm depths in the source region of the Heihe River. A total of 16 influencing factors related to soil, vegetation, landscape, and ecological environment were selected, and their individual and synergistic effects on the distributions of total carbon and total nitrogen in soil were assessed. The results show gradually decreasing average values of soil total carbon and total nitrogen from the surface layer to the bottom layer, with larger values in the southeast part of the sampling region and smaller values in the northwest. Larger values of soil total carbon and total nitrogen at sampling points are distributed in areas with higher clay and silt and lower soil bulk density, pH, and sand. For environmental factors, larger values of soil total carbon and total nitrogen are distributed in areas with higher annual rainfall, net primary productivity, vegetation index, and urban building index, and lower surface moisture, maximum patch index, boundary density, and bare soil index. Among soil factors, soil bulk density and silt are most closely associated with soil total carbon and total nitrogen. Among surface factors, vegetation index, soil erosion, and urban building index have the greatest influence on vertical distribution, and maximum patch index, surface moisture, and net primary productivity have the greatest influence on horizontal distribution. In conclusion, vegetation, landscape, and soil physical properties all have a significant impact on the distribution of soil carbon and nitrogen, suggesting better strategies to improve soil fertility.

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.