Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

Crop Mapping Based on Sentinel-2 Images Using Semantic Segmentation Model of Attention Mechanism

Using remote sensing images to identify crop plots and estimate crop planting area is an important part of agricultural remote sensing monitoring. High-resolution remote sensing images can provide rich information regarding texture, tone, shape, and spectrum of ground objects. With the advancement of sensor and information technologies, it is now possible to categorize crops with pinpoint accuracy. This study defines crop mapping as a semantic segmentation problem; therefore, a deep learning method is proposed to identify the distribution of corn and soybean using the differences in the spatial and spectral features of crops. The study area is located in the southwest of the Great Lakes in the United States, where corn and soybean cultivation is concentrated. The proposed attention mechanism deep learning model, A2SegNet, was trained and evaluated using three years of Sentinel-2 data, collected between 2019 and 2021. The experimental results show that this method is able to fully extract the spatial and spectral characteristics of crops, and its classification effect is significantly better than that of the baseline method, and it has better classification performance than other deep learning models. We cross verified the trained model on the test sets of different years through transfer learning in both spatiotemporal and spatial dimensions. Proving the effectiveness of the attention mechanism in the process of knowledge transfer, A2SegNet showed better adaptability.

Water, energy and climate benefits of urban greening throughout Europe under different climatic scenarios

Urban greening is an effective mitigation option for climate change in urban areas. In this contribution, a European Union (EU)-wide assessment is presented to quantify the benefits of urban greening in terms of availability of green water, reduction of cooling costs and CO₂ sequestration from the atmosphere, for different climatic scenarios. Results show that greening of 35% of the EU’s urban surface (i.e. more than 26,000 km²) would avoid up to 55.8 Mtons year⁻¹ CO₂ equivalent of greenhouse gas emissions, reducing energy demand for the cooling of buildings in summer by up to 92 TWh per year, with a net present value (NPV) of more than 364 billion Euro. It would also transpire about 10 km³ year⁻¹ of rain water, turning into “green” water about 17.5% of the “blue” water that is now urban runoff, helping reduce pollution of the receiving water bodies and urban flooding. The greening of urban surfaces would decrease their summer temperature by 2.5–6 °C, with a mitigation of the urban heat island effect estimated to have a NPV of 221 billion Euro over a period of 40 years. The monetized benefits cover less than half of the estimated costs of greening, having a NPV of 1323 billion Euro on the same period. Net of the monetized benefits, the cost of greening 26,000 km² of urban surfaces in Europe is estimated around 60 Euro year⁻¹ per European urban resident. The additional benefits of urban greening related to biodiversity, water quality, health, wellbeing and other aspects, although not monetized in this study, might be worth such extra cost. When this is the case, urban greening represents a multifunctional, no-regret, cost-effective solution. 

Meta-models for rapid appraisal of the benefits of urban greening in the European context

STUDY REGION

This study considers daily time series of 14 years of weather parameters (temperature, wind speed, rainfall, vapor pressure and radiation) for 671 functional urban areas (FUA) across Europe, from a latitude of 35° (Cyprus) to 65° (Finland).

STUDY FOCUS

Quantification of urban greening effects usually requires relatively complex and integrated models. In this contribution, we apply well-established hydrological, biomass and energy balance equations to derive meta-models for the estimation of runoff reduction, urban surface heating and thermal protection of buildings, in order to quantify the effects of the greening of 1 m2 of impervious surface (e.g. roofs, sealed ground surfaces and underground parking lots).

NEW HYDROLOGICAL INSIGHTS FOR THE REGION

We propose empirical meta-models for the quick appraisal of urban greening benefits including: urban runoff reduction due to soil water retention and evapotranspiration, land surface temperature reduction, reduction of the indoor temperature beneath the greened surface, dry biomass growth. We show that the choice of vegetation growth parameters has a limited effect on the results, although the amount of produced bulk biomass obviously depends on vegetation type. The proposed meta-models can be applied for the assessment of urban greening benefits at the stage of policy evaluation, land planning and the programming of investments at regional or continental scale, before undertaking more detailed and site-specific calculations as required in the design phase.

Attributing Global Land Carbon Loss to Regional Agricultural and Forestry Commodity Consumption

One of the critical components in climate change mitigation lies in meeting the challenge of reducing global land carbon loss, as human demand increases. Yet, it is unclear which region and which form of commodity consumption were responsible for the greatest loss of land carbon. Here we assumed a uniform lifespan (20-year) for managed land and took the managed land in 2010 as a reference to estimate the land carbon loss for region-commodity on a global scale. The estimates and multi-region input-output table were then combined to identify the regions and commodities that contributed the most to global land carbon loss from the consumption side. The results show that during the lifespan, global consumption for agricultural and forestry commodities excluding wood fuel lost a total of 15.6 Pg in land carbon annually, of which 29% and 25% were attributed to beef and wood consumption, respectively. Land carbon loss per capita consumption was highest in high-income regions (Australia, the United States, Canada, Japan, and Europe) primarily due to the high consumed quantity for commodities per capita in these areas. Further, the net importers for land carbon were usually high-income regions (they held lower land carbon loss per unit of production), which was not conducive to reducing global land carbon loss. The research could contribute to discussions of climate responsibility and then inform climate mitigation policies.

Simulating long-term carbon nitrogen and phosphorus biogeochemical cycling in agricultural environments

Understanding how agricultural practices alter biogeochemical cycles is vital for maintaining land productivity, food security, and other ecosystem services such as carbon sequestration. However, these are complex, highly coupled long-term processes that are difficult to observe or explore through empirical science alone. Models are required that capture the main anthropogenic disturbances, whilst operating across regions and long timescales, simulating both natural and agricultural environments, and shifts among these. Many biogeochemical models neglect agriculture or interactions between carbon and nutrient cycles, which is surprising given the scale of intervention in nitrogen and phosphorus cycles introduced by agriculture. This gap is addressed here, using a plant-soil model that simulates integrated soil carbon, nitrogen and phosphorus (CNP) cycling across natural, semi-natural and agricultural environments. The model is rigorously tested both spatially and temporally using data from long-term agricultural experiments across temperate environments. The model proved capable of reproducing the magnitude of and trends in soil nutrient stocks, and yield responses to nutrient addition. The model has potential to simulate anthropogenic effects on biogeochemical cycles across northern Europe, for long timescales (centuries) without site-specific calibration, using easily accessible input data. The results demonstrate that weatherable P from parent material has a considerable effect on modern pools of soil C and N, despite significant perturbation of nutrient cycling from agricultural practices, highlighting the need to integrate both geological and agricultural processes to understand effects of land-use change on food security, C storage and nutrient sustainability. The results suggest that an important process or source of P is currently missing in our understanding of agricultural biogeochemical cycles. The model could not explain how yields were sustained in plots with low P fertiliser addition. We suggest that plant access to organic P is a key uncertainty warranting further research, particularly given sustainability concerns surrounding rock sources of P fertiliser.

Harmonized global maps of above and belowground biomass carbon density in the year 2010

Remotely sensed biomass carbon density maps are widely used for myriad scientific and policy applications, but all remain limited in scope. They often only represent a single vegetation type and rarely account for carbon stocks in belowground biomass. To date, no global product integrates these disparate estimates into an all-encompassing map at a scale appropriate for many modelling or decision-making applications. We developed an approach for harmonizing vegetation-specific maps of both above and belowground biomass into a single, comprehensive representation of each. We overlaid input maps and allocated their estimates in proportion to the relative spatial extent of each vegetation type using ancillary maps of percent tree cover and landcover, and a rule-based decision schema. The resulting maps consistently and seamlessly report biomass carbon density estimates across a wide range of vegetation types in 2010 with quantified uncertainty. They do so for the globe at an unprecedented 300-meter spatial resolution and can be used to more holistically account for diverse vegetation carbon stocks in global analyses and greenhouse gas inventories.

Spatial-temporal consistency between gross primary productivity and solar-induced chlorophyll fluorescence of vegetation in China during 2007-2014

Accurately estimating spatial-temporal patterns of gross primary production (GPP) is important for the global carbon cycle. Satellite-based light use efficiency (LUE) models are regarded as an efficient tool in simulating spatial-temporal dynamics of GPP. However, the accuracy assessment of GPP simulations from LUE models at both spatial and temporal scales remains a challenge. In this study, we simulated GPP of vegetation in China during 2007-2014 using a LUE model (Vegetation Photosynthesis Model, VPM) based on MODIS (moderate-resolution imaging spectroradiometer) images with 8-day temporal and 500-m spatial resolutions and NCEP (National Center for Environmental Prediction) climate data. Global Ozone Monitoring Instrument 2 (GOME-2) solar-induced chlorophyll fluorescence (SIF) data were used to compare with VPM simulated GPP (GPP_VPM) temporally and spatially using linear correlation analysis. Significant positive linear correlations exist between monthly GPP_VPM and SIF data over a single year (2010) and multiple years (2007-2014) in most areas of China. GPP_VPM is also significantly positive correlated with GOME-2 SIF (R² > 0.43) spatially for seasonal scales. However, poor consistency was detected between GPP_VPM and SIF data at yearly scale. GPP dynamic trends have high spatial-temporal variation in China during 2007-2014. Temperature, leaf area index (LAI), and precipitation are the most important factors influence GPP_VPM in the regions of East Qinghai-Tibet Plateau, Loss Plateau, and Southwestern China, respectively. The results of this study indicate that GPP_VPM is temporally and spatially in line with GOME-2 SIF data, and space-borne SIF data have great potential for evaluating LUE-based GPP models.

The global impact factors of net primary production in different land cover types from 2005 to 2011

With the seriously polluted environment due to social development, the sustainability of net primary production (NPP), which is used to feed most lives on the earth, has become one of the biggest concerns that we have to consider for the sake of food shortage. There have been many researches analyzing one or two potential impact factors of NPP based on field observation data, which brings about many uncertainties for further calculation. Moreover, the frequently used process-based models heavily depend on the understandings of researchers about the NPP process. The premises of such models hinder the impact factor analysis from being objective and confident. To overcome such shortages, we collected 27 potential impact factors of global NPP in terms of eight land cover types. The feature variables include atmosphere, biosphere, anthroposphere and lithosphere parameters, which can be obtained from public available remote sensed products. The experiment shows that latitude, irradiance ultraviolet and normalized difference vegetation index are dominant factors impacting global NPP. Anthropogenic activities, precipitation and surface emissivity are influencing NPP calculation largely. However, some commonly used biosphere parameters in process-based models are actually not playing that important roles in NPP estimation. This work provides a new insight in analyzing NPP impact factors, being more objective and comprehensive compared with frequently used process-based models.