Climate Change Monitoring Research Articles

Climate change adaptation in ex-ante assessment of legal acts – a proposal of indicators for Poland

This article is aimed at developing a proposal of indicators to integrate climate change adaptation into the process of designing legislation and assessing the effectiveness of this legislation in adapting the society, the economy and the environment to climate change. Based on a review of climate change adaptation monitoring indicators used in selected European countries, an assessment of potential indicators according to the proposed criteria was carried out and a proposed list of indicators for monitoring climate change adaptation in selected economic sectors was developed for use in the ex-ante assessment of legislation.
 The proposed set of indicators can be used for ex-ante assessment of legal acts in terms of implementation of climate change adaptation requirements - both to determine the values of indicators selected for a given sector that are relevant to climate change adaptation, and then to monitor changes in these indicators to assess the effects of implementing legal regulations in terms of supporting the state's climate change adaptation policy.

Spatial Assessment of Temperature, Rainfall and Land Cover Change as Climate Change Monitoring Techniques in Okigwe and Its Environs

Climate change is an alarming global environmental change phenomena that is constituting a serious threat to natural, social, cultural, and human systems. Its associated risks require a wide range of policy responses and techniques at all levels from local to global. This study sought to evaluate the changes in climatic parameters over three decades as monitoring techniques in Okigwe, Imo State, Nigeria, based on evaluated land cover and temperature variation using Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 ETM+ satellite data of 1986, 2003, and 2020 respectively. Precipitation data from 1990-2020 of the study area was used to evaluate rainfall variation as climatic change parameters over three decades. Results of the geospatial analysis indicates that climate change has become a fallout from developmental activities taking place in Okigwe, which obviously is impacting on all facets of human activities thereby militating against sustainable development. Implicitly, changes in built-up areas or surfaces significantly produced a corresponding effect in escalating urban heat in the city. This study recommends the enforcement of dequate land use planning via adopting green city planning techniques in infrastructural development.

Electrochemical Sensor Mediated Assessment of Carbonate Moieties in Soil Matrix

Carbon sequestration is a major global phenomenon that is particularly relevant now with the effect of climate change monitoring and regenerative agriculture. It refers to different practices that contribute to the absorption and storage of carbon in the soil. While the impact of sequestration of carbon is majorly from organic sources, the effect from soil inorganic carbon accumulation is not trivial especially in arid and semi-arid regions where the inorganic carbon (IC) share in sequestration is significant. Movement of Atmospheric Carbon Dioxide (CO2) into soil resources via photosynthesis and root respiration with the subsequent formation of bicarbonate in soil, which exists as bicarbonate in solution phase in groundwater sources or precipitates into CaCO3 in soil phase. Current soil carbon monitoring is either based on extensive soil sampling for analyzing in the laboratory or using proximal sensing methods, which are less accurate. Thus, implementation of novel soil sensors for accurate in-situ measurements could fundamentally improve soil carbon assessment and monitoring. Comparatively, the level and depth of assessing inorganic carbon in soil is significantly lower than that of soil organic carbon (SOC) monitoring. Electrochemistry as a transduction mode is highly viable in this regard due to its ease of applicability in various environments including the complex-soil matrix is a vital component that holds together the Earth’s ecosystem. The key mechanism of releasing the inorganic carbon relies on capturing the different pools that encompass the inorganic/ mineral carbon in soil including- carbonate ions in soil from Calcium (Calcite) which is the major pool of the IC in soil contributing to around 90% of this source and Magnesium (Dolomite). The other minor fraction that is present in the IC pool arises from Bicarbonates (HCO3-).In this sensor development study, we have designed a modified 3-electrode system which is functionalized with a correlated ion-capture film that is functional to changes in carbonate moieties in the soil electrolyte test system. Then this composite sensor was introduced into test soil setups with varying carbonate content to measure the sensor response- wherein, cyclic voltammetry (DC based) and Electrochemical Impedance Spectroscopy was used to determine signal output as a function of increasing dose. It was seen that the peak current around 0.4-0.5V on the positive axis was modulated to increase with carbonate content (% or ppm) and correspondingly- changes in the Rct (charge-transfer resistance) and Zw (Warburg impedance) along with the capacitive element shift was noticeable in the EIS spectra. The experimental cycle was performed using an in-house designed portable potentiostat device which was integrated into a probe head setup that could be inserted into soil for testing. The results from these experiments yielded linearity metrics from regression fits as R2 > 0.97 and measurable sensing range from 0.01% (100 ppm) to 1% (10,000 ppm). Therefore, a first-of-a-kind sensor system was developed for determining carbonate content in real soil samples using an electrochemical mode which will be subsequently tested in-field to survey field deployable and point-of-use capability of the system. Figure 1

The Estimation of the Total Freeboard of Arctic Sea Ice in Winter Using Passive Microwave Satellite Measurements

Abstract A method for estimating the total freeboard hf of the sea ice in the Arctic basin was developed in this study. To utilize the dielectric properties of microwave measurements on the sea ice freeboard, we adopted the spectral difference between the microwave frequencies (e.g., the gradient ratio). Satellite lidar altimetry data were utilized as a reference, and two pairs of gradient ratios [GR(36.5, 18.7) and GR(10.7, 6.9)] and an optional brightness temperature for first-year ice [that is, TBH(6.9)] were converted from passive microwave sensors into hf using the multiple linear regression equation. Using this method, we estimated hf without direct altimetry measurements. The developed method was evaluated using Operation IceBridge data and the relationship between the regressed hf and Operation IceBridge hf had a correlation coefficient R of 0.761 and a nearly unbiased (approximately −0.4 cm) pattern. Because passive microwave measurements are taken in the Arctic daily, the approach presented in this study has the potential to enable daily hf estimations for the Arctic. Significance Statement Arctic sea ice is one of the most critical indicators when monitoring climate change. Precise and continuous observations of sea ice thickness are essential to understand Arctic sea ice. This study attempts to estimate sea ice thickness using passive microwave satellite data. Passive microwave satellite observations are advantageous because of their wide spatial coverage and long-term records. Therefore, the suggested method in this study can be used for filling in gaps in coverage between sea ice thickness estimates from L-band radiometry and radar/lidar altimetry. The total freeboard is proportional to the thickness of sea ice, which is converted into thickness using the hydrostatic equation. The estimated total freeboard during two winter periods (2018/19 and 2019/20) demonstrates a plausible geographical distribution over the Arctic and indicates good agreement with airborne measurements.

Comparative Study of Electric Vehicle Battery Systems with Lithium-Ion and Solid State Batteries

Due to mechanical advancements, a continued emphasis on sustainable power, and the typical decrease of transportation's impact on ecological change and other regular difficulties, EVs are currently seeing a renaissance. Electric automobiles are portrayed by Project Drawdown as one of the top 100 modern solutions for monitoring climate change. Despite the traction battery specialty systems utilized for modern (or wearing) automobiles, an electric-vehicle battery (EVB) is a battery used to control the stimulus plan of an electric vehicle (BEVs). These batteries are typically assistance (battery-controlled) batteries, and they typically include lithium ions. Forklifts, electric golf trucks, riding floor scrubbers, electric bicycles, electric cars, trucks, vans, and other types of vehicles all employ traction batteries, which are categorically arranged with a high ampere-hour limit.

Deep Learning-Based Downscaling of Temperatures for Monitoring Local Climate Change Using Global Climate Simulation Data

The impact of climate change on the environment has become increasingly visible today, and foreseeing future climate events, which involves long-term prediction of climate variables (e.g., temperature, wind speed, precipitation, etc.) at a local small scale in a local region, is crucial for disaster risk management. General Circulation Models (GCMs) allow for the simulation of multiple climate variables, decades into the future (often till the year 2100). GCM simulations, however, are at a global large scale (from 100 km to 600 km) and are too coarse to monitor climate change at the local small scale. Statistical downscaling approaches are often applied to the GCM simulations to allow the evaluation of the GCM outputs at the local scale. Machine learning-based techniques are popular approaches for statistical downscaling. In this paper, we provide an overview of GCM downscaling with machine learning and present a case study that leverages deep learning to downscale weekly averages of the daily minimum and maximum temperatures in the Hackensack–Passaic watershed in New Jersey.

Integration of Short-Lived Climate Pollutant and air pollutant mitigation in nationally determined contributions

ABSTRACT Limiting global temperature increases to 1.5 °C while respecting ‘the right to health’ requires substantial reductions in Short-Lived Climate Pollutants (SLCPs), including methane, black carbon and hydrofluorocarbons, and co-emitted air pollutants. This study evaluates the inclusion of SLCP and air pollutant mitigation within Nationally Determined Contributions (NDCs) that were submitted between 2015 and 2022. Between pre- and post-2020 NDCs, explicit reference to SLCPs and air pollutant mitigation as priorities more than doubled, indicating a rise in policy attention to these pollutants. There was also a large increase in the percentage of countries including methane and HFCs within the scope of their overall GHG reduction targets, and three countries include explicit black carbon reduction targets. With respect to policy, there was a 45% increase in the number of specific mitigation measures included in NDCs post-2020. Hence, the number of countries with implicit reductions in SLCPs and other air pollutants covered in their NDCs is now also substantially larger compared to pre-2020, due to greater inclusion of mitigation measures that reduce SLCPs and air pollutants alongside (other) GHGs.

Battery-free wireless imaging of underwater environments

Imaging underwater environments is of great importance to marine sciences, sustainability, climatology, defense, robotics, geology, space exploration, and food security. Despite advances in underwater imaging, most of the ocean and marine organisms remain unobserved and undiscovered. Existing methods for underwater imaging are unsuitable for scalable, long-term, in situ observations because they require tethering for power and communication. Here we describe underwater backscatter imaging, a method for scalable, real-time wireless imaging of underwater environments using fully-submerged battery-free cameras. The cameras power up from harvested acoustic energy, capture color images using ultra-low-power active illumination and a monochrome image sensor, and communicate wirelessly at net-zero-power via acoustic backscatter. We demonstrate wireless battery-free imaging of animals, plants, pollutants, and localization tags in enclosed and open-water environments. The method’s self-sustaining nature makes it desirable for massive, continuous, and long-term ocean deployments with many applications including marine life discovery, submarine surveillance, and underwater climate change monitoring.

Identification of species of the genus Populus L. based on the data of hyperspectral crown survey for climate change monitoring

Solving the problem of global climate change requires an integrated approach, including the use of the phytochorological method. Remote sensing is the best option for monitoring changes in the boundaries of plant ranges. However, the problem of species identification based on RS data has not yet been solved. Therefore, the aim of the study was to assess the possibility of using vegetation indices (VIs) calculated from the data of hyperspectral imaging (HSI) of crowns of woody plants to determine their species. The objects of the study were samples of the species Populus tremula L., P. alba L., P. simonii Carriere. The survey was carried out with a Cubert UHD-185 hyperspectral camera in five periods with an interval of 7–10 days. 80 VIs were calculated. Sample sets were analyzed using Analysis of Variance (ANOVA), Principal component analysis (PCA), Decision Tree (DT) and Random Forest (RF) methods. The PCA and RF methods consistently distinguish all three species from each other throughout all five periods of the experiment. The most informative VIs are: Double Peak Index (DPI), Derivative index 1 (D1), Datt index 3 (Datt3) and Vogelmann index (Vogelmann).

Monitoring of natural and climate changes in the distribution area of the rare medicinal species Adonis Tianschanica Lipsch (Adolf)

The article monitors climatic changes in the distribution zones of the rare, medicinal species Adonis tianschanica Lipsch using the GIS method. Distribution areas of the species: population 1 – Kegen Pass, population 2 – Dzungarian Alatau (Tekeli), population 3 – Terskei Alatau. The article discusses 19 different databases describing the climate, and provides a link to all databases. To compare the two databases, CHELSEA 2.1 (CA) and WorldClim v1, v2 (WC) were selected. The description of the database took into account spatial resolution (data tolerance and spatial scale), periods (covered periods and frequency of data), the main important climatic information (temperature, precipitation). When the data set was available by month, long-term averages were calculated for comparison (1980-1999, 2000-2018). To reduce the number of maps, only annual values were compared. According to the average temperature and the amount of precipitation for these periods (1980-1999, 2000-2018), 16 maps were compiled for 2 databases in which the data were compared and evaluated. According to the air temperature – the average and maximum average, information is given about the percentage of differences, maximum, minimum temperatures. According to the amount of precipitation 1980-1999, 2000-2018, information on precipitation on average for the period is provided. We have to say that such a comparison is not a database check. According to expert knowledge, we can compare and represent only variables with the appropriate distribution and avoid databases that differ significantly from others (for example, WorldClim v2 for temperature). Key words: Adonis tianschanica Lipsch (Adolf), endem, population, climate, Dzungarian Alatau, Terskey Alatau, temperature, precipitation.

Atlas of ACE spectra of clouds and aerosols

Clouds and aerosols play a vital role in the Earth's climate. Detecting polar mesospheric clouds, polar stratospheric clouds and aerosols is useful for monitoring climate change and atmospheric chemistry. ACE (Atmospheric Chemistry Experiment) satellite data are used to provide an infrared spectral atlas of polar mesospheric clouds, three types of polar stratospheric clouds (nitric acid trihydrate, sulfuric/nitric acid ternary solutions, and ice), cirrus clouds, stratospheric smoke from fires and sulfate aerosols. Most of the example spectra have been modeled using the appropriate optical constants and the calculated extinction of sunlight by the particles.

Selected Topics of the Past Thirty Years in Ocean Acoustics

This paper reviews some of the highlights of selected topics in ocean acoustics during the thirty years that have passed since the founding of the Journal of Theoretical and Computational Acoustics. Advances in computational methods and computers helped to make computational ocean acoustics a vibrant area of research during that period. The parabolic equation method provides an unrivaled combination of accuracy and efficiency for propagation problems in which the bathymetry, sound speed, and other environmental parameters vary in the horizontal directions. The extension of this approach to cases involving layers that support shear waves has been an active area of research throughout the thirty year period. Interest in basin-scale and global-scale propagation was stimulated by the Heard Island Feasibility Test for monitoring climate change in terms of changes in travel time that occur as the temperature of the ocean rises. Diminishing ice cover in the Arctic, which is one of the consequences of climate change, has stimulated renewed interest in Arctic acoustics during the past decade. Reverberation is a challenging problem that was the topic of a major research program during the beginning of the thirty year period. An innovative approach for making it feasible to solve such problems was applied to data for reverberation from the seafloor and from schools of fish, and some of the findings were featured in Science and Nature. Source localization is one of the core problems in ocean acoustics. When applied on a 2-D array of receivers, an approach based on the eigenvectors of the covariance matrix is capable of separating the signals from different sources from each other, determining when this partitioning step is successful, and tracking sources that cross each other in bearing; one of the advantages of this approach is that it does not require environmental information or solutions of the wave equation. Geoacoustic inversion for estimating the layer structure, wave speeds, density, and other parameters of ocean bottoms has also been a topic of interest throughout the thirty year period.

Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement

Long-term land surface temperature (LST) variation is vital for the study of climate change and environmental monitoring. Change detection methods provide access to recovery trajectories of trend and seasonality and detect abrupt changes in LST time series, but a comprehensive evaluation of the published methods is lacking. In this study, simulated LST data with a temporal resolution of 8 days under different scenarios were used to evaluate the performance of three commonly used methods: Detecting Breakpoints and Estimating Segments in Trend (DBEST), Breaks for Additive Seasonal and Trend (BFAST), and Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). The results obtained using the simulated data indicated that BEAST was the best method for decomposing LST time series into trend and seasonality (mean RMSEs were 0.28 K and 0.27 K, respectively) and for detecting abrupt changes in these two components (mean F1 scores were 0.83 and 0.95, respectively). BFAST was less robust to high-complexity data (F1: 0.56 and 0.52, RMSE: 1.34 K and 1.46 K). 0.91 K and 1.29 K). DBEST is recommended to capture component details because it yields the least generalized output (F1 for trend: 0.37, RMSE: 0.64 K and 1.37 K). Both BFAST and DBEST exhibited reduced accuracy when the time-series data has long-lasting continuous missing data. An application using the 20-year MODIS LST time series supports the results obtained using the simulated data. BEAST exhibited the highest detection accuracy for land cover change (13 correct detections among 15 true changes), followed by DBEST (9) and BFAST (7). All three methods were ineffective for detecting low-magnitude disturbances: wildfires, heatwaves, and cold spells due to their low intensity or short duration. To reduce the non-negligible commission error of BEAST, this study proposes an improved BEAST, which eliminates the false breakpoints in BEAST using a set of thresholds. Compared with BEAST, the user accuracy of the improved BEAST was significantly increased by 13.9% in the simulated data, resulting in an F1 increase of 0.04, and 15 false breakpoints were eliminated among 53 detected disturbances in the MODIS LST time series. This study outlines commonly used change detection methods and offers guidance for choosing the optimal method to detect changes in LST time series. Furthermore, suggestions on the determination of parameters and false breakpoints elimination in the improved BEAST enable it more practical.

Underwater wireless optical communications links: perspectives, challenges and recent trends

AbstractUnderwater wireless optical communication (UOWC) systems have lately garnered a significant amount of attention for both academic purposes and trial applications. Although the idea is not new, the fact that seawater has a smaller window of absorption for blue-green light has reawakened interest in it, and it has grown an essential attraction because of its high bandwidth, it can cover a wide variety of underwater activities as compared to radio frequency and acoustic technologies. To monitor pollution, maintain oil pipelines, monitor climate change, conduct offshore investigations, and conduct oceanography research, the wireless transmission of information underwater technology is of importance to the military, industrial, and scientific organizations all around the world. The use of wavelengths of visible light to transmit secure data across point-to-point connections in underwater optical wireless communication (UOWC) compares well with the usage of free-space optical (FSO) communications. However, UWOC systems also have a huge amount of absorption and scattering introduced by the aquatic channels. Different from standard terrestrial free-space optical communication, many unique system design strategies have been investigated in recent years to solve these technological issues. This article presents a vision as well as various obstacles in the domain of underwater optical wireless communication, a detailed overview, and comparison of underwater communications techniques (UOWC) links, basic modulation technique techniques, and c pursuits on UWOC.

Spatio-temporal vegetation cover analysis to determine climate change in Papua New Guinea

PurposeThe climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.Design/methodology/approachThe investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.FindingsThe yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.Originality/valueThis study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.

Using an acoustic complexity index to help monitor climate change effects on avian diversity

Passive recording technologies have become a valuable tool for ecologists to accumulate large data sets on vocalizing species assemblages and to study the response of biodiversity to regional and global environmental change. However, the process of interpreting these recordings and identifying species can be time-consuming. To automate this process, and to derive diversity metrics directly from the audio files, soundscape ecologists developed and published many bioacoustics indices, which are calculated using computer algorithms that mathematically summarize the acoustic energy in a recording. Of these indices, the acoustic complexity index (ACI) is perhaps the most widely proposed surrogate of bird species richness. Although several studies have shown the ACI to have positive correlations with species richness, it remains unknown how well the ACI will perform over large, habitat-diverse, regions. Our aim, here, was to examine whether site-level bird species richness (alpha diversity) correlated with the ACI across a large heterogenous mountain region. Further, we explored whether the elevational trends for alpha diversity and the ACI exhibited similar unimodal patterns with corresponding maximums and whether any shifts in the elevation of these maximums could be tracked over time in response to climate change. We deployed recording devices along two extensive transects on the Pacific Crest Trail in California, USA; these included a 689-km trail section in northern California and a 531-km section in southern California. Point counts for birds from the field were combined with the interpretation of the recordings to estimate alpha diversity using a Bayesian multispecies occupancy model. We found that alpha diversity exhibited a positive linear relationship with the ACI on both trail sections. However, these relationships were not strong (low R2 values). Similar mid-elevation maximums for alpha diversity and the ACI were observed along the northern trail section. Power analysis revealed that with repeated annual surveys using our protocols, we could detect an average shift in the elevation of either maximum species richness or ACI of approximately 100 m over 20 years (i.e., 5 m/yr), which could be improved in precision to 50 m (i.e., 2.5 m/yr) with a 5-fold increase in survey effort. Although there was considerable unexplained variation in our diversity- and ACI-elevation trends, which we suspect was due to microhabitat and microclimate effects, we concluded that the ACI was a useful, albeit coarse, surrogate of alpha diversity across large regions. The ACI warrants consideration by ecologists and land managers for application in large-scale monitoring programs.

Fusion of CMONOC and ERA5 PWV Products Based on Backpropagation Neural Network

Data fusion is an effective method to obtain high-precision and high-spatiotemporal-resolution precipitable water vapor (PWV) products, which play an important role in understanding climate change and meteorological monitoring. However, existing fusion methods have some shortcomings, such as ignoring the applicability of the model space or the high complexity of model operation. In this study, the high-precision and high-temporal-resolution Global Navigation Satellite System (GNSS) PWV was used to calibrate and optimize the ERA5 PWV product of the European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) with high spatial resolution to improve its accuracy, and its applicability was verified at the spatiotemporal scale. First, this study obtained accurate GNSS PWV from meteorological data from stations and used it as the true value to analyze the distribution of the ERA5 PWV in mainland China. The results showed that the ERA5 PWV showed significant spatial and temporal differences. Then, a backpropagation neural network (BPNN) fusion correction model with additional constraints was established. The correction results showed that the bias of the ERA5 PWV mainly fluctuated near 0, the correlation between the ERA5 PWV and GNSS PWV was increased to 0.99, and the positive improvement rate of the root-mean-square error (RMSE) was 95%. In the temporal scale validation, the RMSE of the ERA5 PWV decreased from 2.05 mm to 1.67 mm, an improvement of 18.54%. In the spatial scale validation, the RMSE of the four seasons decreased by 0.26–80% (spring), 0.28–70.71% (summer), 0.28–45.23% (autumn), and 0.30–40.75% (winter). Especially in the summer and plateau mountainous areas where the ERA5 PWV performance was poor, the model showed suitable stability. Finally, the fusion model was used to generate a new PWV product, which improved the accuracy of ERA5 PWV on the basis of ensuring the spatial resolution.

LAI-Based Phenological Changes and Climate Sensitivity Analysis in the Three-River Headwaters Region

Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen’s trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October.

Optical Design of a Novel Wide-Field-of-View Space-Based Spectrometer for Climate Monitoring.

We report on a near-infrared imaging spectrometer for sensing the three most prominent greenhouse gases in the atmosphere (water vapor, carbon dioxide and methane). The optical design of the spectrometer involves freeform optics, which enables achieving exceptional performance and allows progressing well beyond the state-of-the-art in terms of compactness, field-of-view, and spatial resolution. The spectrometer is intended to be launched on a small satellite orbiting at 700 km and observing the Earth with a wide field-of-view of 120° and a spatial resolution of 2.6 km at nadir. The satellite will ultimately allow for improved climate change monitoring.

Aligning citizen science and remote sensing phenology observations to characterize climate change impact on vegetation

Phenology observations are essential indicators to characterize the local effects of climate change. Citizen participation in the collection of phenological observations is a potential approach to provide data at both high temporal scale and fine grain resolution. Traditional observation practices of citizen science (CS), although precise at the species scale, are limited to few observations often closely located to an observer’s residence. These limitations hinder coverage of the great variability of vegetation phenology across biomes and improvement of the knowledge of vegetation changes due to climate change impacts. This study presents a new approach to overcome these limitations by improving CS guidance and feedback as well as expanding phenology report sites and observations across different habitats and periods to contribute to monitoring climate change. This approach includes: (a) a new methodology focused on harmonizing remote sensing phenology products with traditional CS phenology observations to direct volunteers to active phenology regions and, (b) a new protocol for citizen scientists providing tools to guide them to specific regions to identify, collect and share species phenological observations and their phenophases. This approach was successfully tested, implemented and evaluated in Catalonia with more than 5000 new phenologically interesting regions identified and more than 200 observations collected and Sentinel-2 derived phenometrics were demonstrated as of good quality.