A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau

Improving the spatial and temporal estimation of ecosystem respiration using multi-source data and machine learning methods in a rainfed winter wheat cropland

The investigation of ecosystem respiration (RE) and its vital influential factors along with the timely and accurate detection of spatiotemporal variations in RE are essential for guiding agricultural production planning. RE observation in the plot region is primarily based on the laborious chamber method. However, upscaling the spatial-temporal estimates of RE at the canopy scale is still challenging. The present study conducted a field experiment to determine RE using the chamber method. A multi-rotor unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to acquire the canopy spectral data of wheat during each RE test experiment. Moreover, the agronomic indicators of aboveground plant biomass, leaf area index, leaf dry mass as well as agrometeorological and soil data were measured simultaneously. The study analyzed the potential of multi-information for estimating RE at the field scale and proposed two strategies for RE estimation. In addition, a semiempirical, yet Lloyd and Taylor-based, remote sensing model (LT1-NIR_V) was developed for estimating RE observed across different growth stages with a small margin of error (coefficient of determination [R²] = 0.60-0.64, root-mean-square error [RMSE] = 285.98-316.19 mg m^-2 h^-1). Further, five machine learning (ML) algorithms were utilized to independently estimate RE using two different datasets. The rigorous analyses, which included statistical comparison and cross-validation for estimating RE, confirmed that the XGBoost model, with the highest R² and lowest RMSE (R² = 0.88 and RMSE = 172.70 mg m^-2 h^-1), performed the best among the evaluated ML models. The LT1-NIR_V model was less effective in estimating RE compared with the other ML models. Based on this comprehensive comparison analysis, the ML model can successfully estimate variations in wheat field RE using high-resolution UAV multispectral images and environmental factors from the wheat cropland system, thereby providing a valuable reference for monitoring and upscaling RE observations.

Soil diazotrophic abundance, diversity, and community assembly mechanisms significantly differ between glacier riparian wetlands and their adjacent alpine meadows

Global warming can trigger dramatic glacier area shrinkage and change the flux of glacial runoff, leading to the expansion and subsequent retreat of riparian wetlands. This elicits the interconversion of riparian wetlands and their adjacent ecosystems (e.g., alpine meadows), probably significantly impacting ecosystem nitrogen input by changing soil diazotrophic communities. However, the soil diazotrophic community differences between glacial riparian wetlands and their adjacent ecosystems remain largely unexplored. Here, soils were collected from riparian wetlands and their adjacent alpine meadows at six locations from glacier foreland to lake mouth along a typical Tibetan glacial river in the Namtso watershed. The abundance and diversity of soil diazotrophs were determined by real-time PCR and amplicon sequencing based on nifH gene. The soil diazotrophic community assembly mechanisms were analyzed via iCAMP, a recently developed null model-based method. The results showed that compared with the riparian wetlands, the abundance and diversity of the diazotrophs in the alpine meadow soils significantly decreased. The soil diazotrophic community profiles also significantly differed between the riparian wetlands and alpine meadows. For example, compared with the alpine meadows, the relative abundance of chemoheterotrophic and sulfate-respiration diazotrophs was significantly higher in the riparian wetland soils. In contrast, the diazotrophs related to ureolysis, photoautotrophy, and denitrification were significantly enriched in the alpine meadow soils. The iCAMP analysis showed that the assembly of soil diazotrophic community was mainly controlled by drift and dispersal limitation. Compared with the riparian wetlands, the assembly of the alpine meadow soil diazotrophic community was more affected by dispersal limitation and homogeneous selection. These findings suggest that the conversion of riparian wetlands and alpine meadows can significantly alter soil diazotrophic community and probably the ecosystem nitrogen input mechanisms, highlighting the enormous effects of climate change on alpine ecosystems.

Spatiotemporal Changes and Driver Analysis of Ecosystem Respiration in the Tibetan and Inner Mongolian Grasslands

Ecosystem respiration (RE) plays a critical role in terrestrial carbon cycles, and quantification of RE is important for understanding the interaction between climate change and carbon dynamics. We used a multi-level attention network, Geoman, to identify the relative importance of environmental factors and to simulate spatiotemporal changes in RE in northern China’s grasslands during 2001–2015, based on 18 flux sites and multi-source spatial data. Results indicate that Geoman performed well (R2 = 0.87, RMSE = 0.39 g C m−2 d−1, MAE = 0.28 g C m−2 d−1), and that grassland type and soil texture are the two most important environmental variables for RE estimation. RE in alpine grasslands showed a decreasing gradient from southeast to northwest, and that of temperate grasslands showed a decreasing gradient from northeast to southwest. This can be explained by the enhanced vegetation index (EVI), and soil factors including soil organic carbon density and soil texture. RE in northern China’s grasslands showed a significant increase (1.81 g C m−2 yr−1) during 2001–2015. The increase rate of RE in alpine grassland (2.36 g C m−2 yr−1) was greater than that in temperate grassland (1.28 g C m−2 yr−1). Temperature and EVI contributed to the interannual change of RE in alpine grassland, and precipitation and EVI were the main contributors in temperate grassland. This study provides a key reference for the application of advanced deep learning models in carbon cycle simulation, to reduce uncertainties and improve understanding of the effects of biotic and climatic factors on spatiotemporal changes in RE.

Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data

Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.

Remotely monitoring ecosystem respiration from various grasslands along a large-scale east-west transect across northern China

BACKGROUND

Grassland ecosystems play an important role in the terrestrial carbon cycles through carbon emission by ecosystem respiration (R_e) and carbon uptake by plant photosynthesis (GPP). Surprisingly, given R_e occupies a large component of annual carbon balance, rather less attention has been paid to developing the estimates of R_e compared to GPP.

RESULTS

Based on 11 flux sites over the diverse grassland ecosystems in northern China, this study examined the amounts of carbon released by R_e as well as the dominant environmental controls across temperate meadow steppe, typical steppe, desert steppe and alpine meadow, respectively. Multi-year mean R_e revealed relatively less CO₂ emitted by the desert steppe in comparison with other grassland ecosystems. Meanwhile, C emissions of all grasslands were mainly controlled by the growing period. Correlation analysis revealed that apart from air and soil temperature, soil water content exerted a strong effect on the variability in R_e, which implied the great potential to derive R_e using relevant remote sensing data. Then, these field-measured R_e data were up-scaled to large areas using time-series MODIS information and remote sensing-based piecewise regression models. These semi-empirical models appeared to work well with a small margin of error (R² and RMSE ranged from 0.45 to 0.88 and from 0.21 to 0.69 g C m^-2 d^-1, respectively).

CONCLUSIONS

Generally, the piecewise models from the growth period and dormant season performed better than model developed directly from the entire year. Moreover, the biases between annual mean R_e observations and the remotely-derived products were usually within 20%. Finally, the regional R_e emissions across northern China's grasslands was approximately 100.66 Tg C in 2010, about 1/3 of carbon fixed from the MODIS GPP product. Specially, the desert steppe exhibited the highest ratio, followed by the temperate meadow steppe, typical steppe and alpine meadow. Therefore, this work provides a novel framework to accurately predict the spatio-temporal patterns of R_e over large areas, which can greatly reduce the uncertainties in global carbon estimates and climate projections.

Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison

While a number of machine learning (ML) models have been used to estimate RE, systematic evaluation and comparison of these models are still limited. In this study, we developed three traditional ML models and a deep learning (DL) model, stacked autoencoders (SAE), to estimate RE in northern China’s grasslands. The four models were trained with two strategies: training for all of northern China’s grasslands and separate training for the alpine and temperate grasslands. Our results showed that all four ML models estimated RE in northern China’s grasslands fairly well, while the SAE model performed best (R2 = 0.858, RMSE = 0.472 gC m−2 d−1, MAE = 0.304 gC m−2 d−1). Models trained with the two strategies had almost identical performances. The enhanced vegetation index and soil organic carbon density (SOCD) were the two most important environmental variables for estimating RE in the grasslands of northern China. Air temperature (Ta) was more important than the growing season land surface water index (LSWI) in the alpine grasslands, while the LSWI was more important than Ta in the temperate grasslands. These findings may promote the application of DL models and the inclusion of SOCD for RE estimates with increased accuracy.

MODIS-Derived Estimation of Soil Respiration within Five Cold Temperate Coniferous Forest Sites in the Eastern Loess Plateau, China

Soil respiration (Rs) is seldom analyzed using remotely sensed data because satellite technology has difficulty monitoring various respiratory processes in the soil. We investigated the potential of remote sensing data products to estimate Rs, including land surface temperature (LST) and spectral vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS), using a nine-year (2007–2015) field measurement dataset of Rs and soil temperature (Ts) at five forest sites at the eastern Loess Plateau, China. The results indicate that soil temperature is the primary factor influencing the seasonal variation of Rs at the five sites. The accuracy of the model based on the observed data is not significantly different from the model based on MODIS-derived nighttime LST values. There was a significant difference with the model based on MODIS-derived daytime LST values. Therefore, nighttime LST was the optimum LST for estimation of Rs. The normalized difference vegetation index (NDVI) consistently exhibited a stronger correlation with Rs when compared to the green edge chlorophyll index and enhanced vegetation index. Further analysis showed that adding the NDVI into the model considering only Ts or nighttime LST could significantly improve the simulation accuracy of Rs. The models depending on nighttime LST and NDVI showed comparable accuracy with the models based on the in situ Ts and NDVI. These results suggest that models based entirely on remote sensing data from MODIS have the potential to estimate Rs at the cold temperate coniferous forest sites. The performance of the model in other vegetation types or regions has also been proved. Our conclusions further confirmed that it is feasible for large-scale estimates of Rs by means of MODIS data in temperate coniferous forest ecosystems.

Increasing Dairy Sustainability with Integrated Crop–Livestock Farming

Dairy farms are predominantly carbon sources, due to high livestock emissions from enteric fermentation and manure. Integrated crop–livestock systems (ICLSs) have the potential to offset these greenhouse gas (GHG) emissions, as recycling products within the farm boundaries is prioritized. Here, we quantify seasonal and annual greenhouse gas budgets of an ICLS dairy farm in Wisconsin USA using satellite remote sensing to estimate vegetation net primary productivity (NPP) and Intergovernmental Panel on Climate Change (IPCC) guidelines to calculate farm emissions. Remotely sensed annual vegetation NPP correlated well with farm harvest NPP (R2 = 0.9). As a whole, the farm was a large carbon sink, owing to natural vegetation carbon sinks and harvest products staying within the farm boundaries. Dairy cows accounted for 80% of all emissions as their feed intake dominated farm feed supply. Manure emissions (15%) were low because manure spreading was frequent throughout the year. In combination with soil conservation practices, ICLS farming provides a sustainable means of producing nutritionally valuable food while contributing to sequestration of atmospheric CO2. Here, we introduce a simple and cost-efficient way to quantify whole-farm GHG budgets, which can be used by farmers to understand their carbon footprint, and therefore may encourage management strategies to improve agricultural sustainability.

Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review

Peatlands store large amounts of terrestrial carbon and any changes to their carbon balance could cause large changes in the greenhouse gas (GHG) balance of the Earth's atmosphere. There is still much uncertainty about how the GHG dynamics of peatlands are affected by climate and land use change. Current field-based methods of estimating annual carbon exchange between peatlands and the atmosphere include flux chambers and eddy covariance towers. However, remote sensing has several advantages over these traditional approaches in terms of cost, spatial coverage and accessibility to remote locations. In this paper, we outline the basic principles of using remote sensing to estimate ecosystem carbon fluxes and explain the range of satellite data available for such estimations, considering the indices and models developed to make use of the data. Past studies, which have used remote sensing data in comparison with ground-based calculations of carbon fluxes over Northern peatland landscapes, are discussed, as well as the challenges of working with remote sensing on peatlands. Finally, we suggest areas in need of future work on this topic. We conclude that the application of remote sensing to models of carbon fluxes is a viable research method over Northern peatlands but further work is needed to develop more comprehensive carbon cycle models and to improve the long-term reliability of models, particularly on peatland sites undergoing restoration.