Fractional Vegetation Cover Research Articles

Evaluating crop-specific responses to salinity and drought stress from remote sensing

Food security is projected to be threatened by increasing co-occurring stresses (e.g., drought and salinity) under global climate change. To mitigate major impacts on food production, the tolerances and vulnerabilities of crops to these threats need to be characterized. The aim of this research is to assess the tolerances of crops to the combination of drought and salinity stress across plant functions under real-life settings. Using five traits, we evaluated the impacts of drought and salinity tolerance on a multitude of crops across throughout the United States. We assessed the dominant stress as well as the onset of combined and individual effects of drought and salinity from March to October. We indeed observed that stress impacts strongly depended on time. In addition, we observed that crops were more sensitive to combined salinity and drought than to individual stresses, although stress impacts significantly varied between time and species. Of the individual traits, leaf Area Index (LAI) was triggered first by stresses, followed by the fraction of vegetation cover (FVC) and the fraction of absorbed photosynthetically active radiation (FAPAR), and leaf water content (Cw) and leaf chlorophyll content (Cab) were the last to respond to stresses. In comparison to other species, almond demonstrated greater resilience to combined drought and salinity, whereas soybean and maize were more drought tolerant. In combination, our study provides a way of assessing the tolerance of various crops to co-occurrent stresses both independently and in combination. By allowing applications to other co-occurring stresses and vegetation types, our approach creates a quantitative foundation to inform sustainable food production.

Effects of hydrothermal factors and human activities on the vegetation coverage of the Qinghai-Tibet Plateau

A systematic understanding of the spatio-temporal changes and driving factors in the Qinghai-Tibet Plateau holds significant scientific reference value for the future of ecological sustainable development. This paper utilizes MODIS normalized difference vegetation index (NDVI) and meteorological data to investigate the spatio-temporal changes and driving factors of vegetation coverage in the Qinghai-Tibet Plateau from 2001 to 2020. Methods employed include the dimidiate pixel model, trend analysis, partial correlation analysis, and residual analysis. The results demonstrate a generally fluctuating upward trend in vegetation coverage across the Tibetan Plateau over the past two decades, with spatial expansion occurring from northwest to southeast. Vegetation coverage exhibits a positive correlation with climate factors. Approximately 60.7% of the area showed a positive correlation between vegetation fractional cover (FVC) and precipitation, with 8.66% of the area demonstrating extremely significant (p < 0.05) and significant (p < 0.01) positive correlation. Human activities, on the whole, have contributed to the enhancement of vegetation cover in the Qinghai-Tibet Plateau. The areas where human activities have positively impacted vegetation cover are primarily situated in north-central Qinghai and north of Ngari, while areas experiencing degradation include certain grassland regions in central-eastern Yushu, Nagqu, and Lhasa.

Residential greenspace and anxiety symptoms among Australian women living in major cities: A longitudinal analysis

Despite an increasing number of studies demonstrating the potential mental health benefits of greenspace, few longitudinal studies have been reported. We aimed to assess associations between two metrics of residential greenspace exposure and anxiety symptoms in a cohort of Australian women living in major cities.Our study comprised 3,938 women, born between 1973 and 78. Anxiety symptoms in the previous month were assessed at follow-up every three years during 2003–2018, using the anxiety subscale of the Goldberg Anxiety and Depression Scale. The three-month seasonal average normalized difference vegetation index (NDVI) was estimated from 30 m Landsat satellite images in a 500 m buffer (NDVI500m) around participants’ address at each survey (higher NDVI indicates more green vegetation). The annual fractional cover of non-photosynthesising vegetation was estimated (fNPV500m, with higher values indicating greater levels of dead leaf litter or dry grass).A generalised estimating equation assessed associations between greenspace measures and anxiety symptoms (as odds ratios [ORs]), adjusting for repeated outcomes and individual-level covariates. Additional analyses focused on women experiencing substantial changes in between-survey exposure due to moving, and adjusting for nitrogen dioxide (NO2) and fine particulate matter (PM2.5) exposure, among others.A standard deviation (SD) (0.12 units) increase in NDVI500m was significantly associated with lower odds of anxiety symptoms (OR: 0.96, 95% CI: 0.93–0.99) in the adjusted model. An SD (5.2%) increase in annual fNPV500m was consistent with the direction expected for that exposure, but not significant in the adjusted model (OR: 1.03, 95% CI: 0.99–1.07). NDVI500m was modestly attenuated when air pollutants were adjusted for, while fNPV500m only became significant when PM2.5 was adjusted for. Between-survey contrasts had no clear effect for NDVI500m. Moving to a higher fNPV500m area, compared with a similar fNPV500m area, was consistently associated with anxiety symptoms (OR: 1.15, 95% CI: 1.02–1.31). NDVI500m was generally associated with lower odds of anxiety symptoms, while fNPV500m was generally associated with higher odds of anxiety, depending on the covariates considered.

Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms

Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)–particle swarm optimization (PSO), GPR–genetic algorithm (GA), GPR–tabu search (TS), and GPR–simulated annealing (SA) hyperparameter-optimized algorithms were developed and compared against kernel-based machine learning regression algorithms and artificial neural network (ANN) and random forest (RF) algorithms. The accuracy of the proposed algorithms was assessed using digital hemispherical photography (DHP) data and destructive measurements performed during the growing season of silage maize in agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019. The results on biophysical variables against validation data showed that the developed GPR-PSO algorithm outperformed other algorithms under study in terms of robustness and accuracy (0.917, 0.931, 0.882 using R2 and 0.627, 0.078, and 1.99 using RMSE in LAI, fCover, and biomass of Sentinel-2 20 m, respectively). GPR-PSO also possesses the unique ability to generate pixel-based uncertainty maps (confidence level) for prediction purposes (i.e., estimated uncertainty level &lt;0.7 in LAI, fCover, and biomass, for 96%, 98%, and 71% of the total study area, respectively). Altogether, GPR-PSO appears to be the most suitable option for mapping biophysical variables at the local scale using Sentinel-2 images.

Assessing Typhoon Soulik-induced morphodynamics over the Mokpo coastal region in South Korea based on a geospatial approach

Abstract. The inner shelf and coastal region of the Yellow Sea along the Korean Peninsula are frequently impacted by typhoons. The Mokpo coastal region in South Korea was significantly affected by Typhoon Soulik in 2018, the deadliest typhoon strike to the southwestern coast since Typhoon Maemi in 2003. Typhoon Soulik overran the region, causing extensive damage to the coast, shoreline, vegetation, and coastal geomorphology. Therefore, it is important to investigate its impact on the coastal ecology, landform, erosion/accretion, suspended-sediment concentration (SSC), and associated coastal changes along the Mokpo region. In this study, the net shoreline movement (NSM), normalized difference vegetation index (NDVI), fractional vegetation coverage (FVC), coastal-landform change model, normalized difference suspended-sediment index (NDSSI), and SSC–reflectance relation have been used to analyze the coastal morphodynamics over the typhoon periods. We used pre- and post-typhoon Sentinel-2 MultiSpectral Instrument (MSI) images for mapping and monitoring the typhoon effect and recovery status of the Mokpo coast through short- and medium-term coastal-change analysis. The findings highlighted the significant impacts of typhoons on coastal dynamics, wetland vegetation, and sediment resuspension along the Mokpo coast. It has been observed that typhoon-induced SSC influences shoreline and coastal morphology. The outcome of this research may provide databases to manage coastal environments and a long-term plan to restore valuable coastal habitats. In addition, the findings may be useful for post-typhoon emergency response, coastal planners, and administrators involved in the long-term development of human life.

Extraction and monitoring of vegetation coverage based on uncrewed aerial vehicle visible image in a post gold mining area

Vegetation coverage reflects the degree of environmental degradation. Timely and effective monitoring of vegetation conditions is the basis for promoting vegetation protection and improving the ecological environment of mining areas. Exploring vegetation coverage extraction methods and selecting the optimal vegetation index in mining areas can provide scientific reference for estimating vegetation coverage based on vegetation index in mining areas. Uncrewed aerial vehicles (UAVs) are widely used because of their fast real-time performance, high spatial resolution, and easy accessibility. In this study, the performances of nine visible vegetation indices and two threshold segmentation methods for extracting vegetation coverage in a post-gold mining area in the Qinling Mountains were comprehensively compared using visible spectrum UAV images. Of the nine indices, the excess green index (EXG) and visible-band difference vegetation index (VDVI) were the most effective in discriminating between vegetation and non-vegetation by visual interpretation. In addition, the accuracy of the bimodal histogram threshold method in extracting vegetation coverage was higher than that of Otsu’s threshold method. The bimodal histogram threshold method combined with EXG yielded optimal extraction results. Based on optimal methods, the total percentages of fractional vegetation coverage in 2019, 2020, and 2021 were 31.47%, 34.08%, and 42.77%, respectively, indicating that the vegetation in the mining area improved. These results provide valuable guidance for extracting vegetation information and evaluating vegetation restoration in mining areas.

A non-linear theoretical dry/wet boundary-based two-source trapezoid model for estimation of land surface evapotranspiration

ABSTRACT It is well known that the dry/wet boundaries of land surface temperature fractional vegetation coverage (LSTfc) trapezoid framework vary linearly with vegetation coverage (fc). In this study, the theoretical end-members algorithm is modified to continuously estimate the dry/wet end-members under varying vegetation conditions, causing the theoretical dry/wet boundaries to become non-linear. The findings revealed that the non-linear dry/wet boundaries were generally below the conventional linear dry/wet boundaries. Furthermore, the non-linear boundary scheme adopted herein provided better performance in estimating the latent heat flux (LE) and vegetation latent heat flux fraction (LEv/LE) compared to the linear boundary scheme. The parametric schemes of aerodynamic and thermodynamic roughness length and the aerodynamic resistance were the major drivers that result in dry/wet boundaries characteristics being highly non-linear. This study enhanced the physical process description in the LST-fc trapezoid framework and improved the prediction accuracy of regional LE and its components.

Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas.

Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data

Aim of study: Natural climate change is a central driver of global ecosystem and forest change. Climate change and topographical factors have had the greatest impact on different types of forests around the world. We used remote sensing technology to detect and analyze the temporal and spatial changes of forest vegetation to provide reference for regional management. Area of study: Maoershan Forest Farm, China. Material and methods: The Landsat images were preprocessed using ArcGIS and ENVI software. The normalized difference vegetation index (NDVI) was calculated to identify vegetation changes from 2000 to 2020. In addition, the vegetation fraction cover (VFC) was calculated using the pixel binary model. The driving factors and their influences on vegetation changes in this region were determined using the random forest algorithm and Pearson correlation analysis method. Main results: From 2000 to 2020, the NDVI showed an overall increasing trend. The results indicated that compared with the climatic factors, topographic factors were more important to vegetation growth in the study area. Among the topographic factors, elevation was the most important factor affecting vegetation growth and both showed a negative correlation. Among the climatic factors, relative humidity was the primary driving factor affecting vegetation growth and both showed a positive correlation. Research highlights: Accurate and timely assessment of vegetation change and its relationship to climate and topographical changes can provide very useful information for policy makers, governments and planners in formulating management policies.

Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine

Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current vegetation states and dynamics of our planet. Although significant efforts have been made, there is still a lack of global and consistently derived multi-temporal trait maps that are cloud-free. Here we present the processing chain for the spatiotemporally continuous production of four EVTs at a global scale: (1) fraction of absorbed photosynthetically active radiation (FAPAR), (2) leaf area index (LAI), (3) fractional vegetation cover (FVC), and (4) leaf chlorophyll content (LCC). The proposed workflow presents a scalable processing approach to the global cloud-free mapping of the EVTs. Hybrid retrieval models, named S3-TOA-GPR-1.0-WS, were implemented into Google Earth Engine (GEE) using Sentinel-3 Ocean and Land Color Instrument (OLCI) Level-1B for the mapping of the four EVTs along with associated uncertainty estimates. We used the Whittaker smoother (WS) for the temporal reconstruction of the four EVTs, which led to continuous data streams, here applied to the year 2019. Cloud-free maps were produced at 5 km spatial resolution at 10-day time intervals. The consistency and plausibility of the EVT estimates for the resulting annual profiles were evaluated by per-pixel intra-annually correlating against corresponding vegetation products of both MODIS and Copernicus Global Land Service (CGLS). The most consistent results were obtained for LAI, which showed intra-annual correlations with an average Pearson correlation coefficient (R) of 0.57 against the CGLS LAI product. Globally, the EVT products showed consistent results, specifically obtaining higher correlation than R&gt; 0.5 with reference products between 30 and 60° latitude in the Northern Hemisphere. Additionally, intra-annual goodness-of-fit statistics were also calculated locally against reference products over four distinct vegetated land covers. As a general trend, vegetated land covers with pronounced phenological dynamics led to high correlations between the different products. However, sparsely vegetated fields as well as areas near the equator linked to smaller seasonality led to lower correlations. We conclude that the global gap-free mapping of the four EVTs was overall consistent. Thanks to GEE, the entire OLCI L1B catalogue can be processed efficiently into the EVT products on a global scale and made cloud-free with the WS temporal reconstruction method. Additionally, GEE facilitates the workflow to be operationally applicable and easily accessible to the broader community.

A Three-Dimensional Conceptual Model for Estimating the Above-Ground Biomass of Winter Wheat Using Digital and Multispectral Unmanned Aerial Vehicle Images at Various Growth Stages

The timely and accurate estimation of above-ground biomass (AGB) is crucial for indicating crop growth status, assisting management decisions, and predicting grain yield. Unmanned aerial vehicle (UAV) remote sensing technology is a promising approach for monitoring crop biomass. However, the determination of winter wheat AGB based on canopy reflectance is affected by spectral saturation effects. Thus, constructing a generic model for accurately estimating winter wheat AGB using UAV data is significant. In this study, a three-dimensional conceptual model (3DCM) for estimating winter wheat AGB was constructed using plant height (PH) and fractional vegetation cover (FVC). Compared with both the traditional vegetation index model and the traditional multi-feature combination model, the 3DCM yielded the best accuracy for the jointing stage (based on RGB data: coefficient of determination (R2) = 0.82, normalized root mean square error (nRMSE) = 0.2; based on multispectral (MS) data: R2 = 0.84, nRMSE = 0.16), but the accuracy decreased significantly when the spike organ appeared. Therefore, the spike number (SN) was added to create a new three-dimensional conceptual model (n3DCM). Under different growth stages and UAV platforms, the n3DCM (RGB: R2 = 0.73–0.85, nRMSE = 0.17–0.23; MS: R2 = 0.77–0.84, nRMSE = 0.17–0.23) remarkably outperformed the traditional multi-feature combination model (RGB: R2 = 0.67–0.88, nRMSE = 0.15–0.25; MS: R2 = 0.60–0.77, nRMSE = 0.19–0.26) for the estimation accuracy of the AGB. This study suggests that the n3DCM has great potential in resolving spectral errors and monitoring growth parameters, which could be extended to other crops and regions for AGB estimation and field-based high-throughput phenotyping.

Retrieval and validation of the dawn-dusk land surface temperature from FY-3E MERSI-LL

ABSTRACT Land surface temperature (LST) is an important physical parameter that reflects changes in surface processes. Remote sensing makes it possible to obtain global or regional LSTs with high spatial and temporal resolution. Feng-Yun 3E (FY-3E) is the world’s first civilian dawn-dusk orbiting meteorological satellite on board the Medium Resolution Spectral Imager – Low-Light (MERSI-LL) sensor capable of acquiring LST at 250 m spatial resolution at dawn and dusk. By complementing with existing polar-orbiting satellites, FY-3E can fill the satellite observation gap within the 6-hour assimilation window and further enhance the observation capability of LST. To retrieve accurate LST from MERSI-LL, the generalized split-window (GSW) method was developed. The GSW coefficients were derived from the simulation dataset produced by Moderate Resolution Transmittance Code 5.2 with an atmospheric profile database. As a key input to the GSW algorithm, the land surface emissivity was dynamically estimated by the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset in combination with the fractional vegetation cover. The water vapour content is calculated in real time by the split-window covariance/variance ratio method. The comparison between retrieved LSTs and in situ LSTs shows that the GSW algorithm can effectively retrieve the LSTs of vegetation cover surfaces. The root mean square error ranged from 0.79 to 1.79 K at dawn and from 1.32 to 1.92 K at dusk.

Impact of coastal island restoration engineering and subsequent tourism on migratory waterbirds: a 3‐year case from Southern China

Coastal engineering poses a significant threat to the survival of migratory waterbirds worldwide. However, the mechanisms through which engineering affects waterbirds are still unclear. To gain a better understanding of this issue, we conducted a three‐year survey of waterbirds on Shanxinsha Island, which underwent restoration engineering, along the East Asian–Australasian Flyway. We compared the seasonal migratory change among different species groups through and after the island restoration engineering. We observed a total monthly maximum count of 118,506 individuals from 61 waterbird species over the span of 38 months, including eight globally threatened species and five species that exceeded 1% of the flyway population. Throughout the survey periods, the average number of total waterbirds and small shorebirds observed during the migrating season decreased by 52.7 and 48.6%, respectively. The massive loss of high‐tide roosting areas was the primary factor contributing to this decline. The combined effects of increased vegetation and deeper water levels resulted in a 38.8% reduction in exposed tidal flat area, as determined through land cover verification and fractional vegetation cover calculations. While tourism activities exhibited fewer negative consequences compared to island restoration engineering, they had a greater impact on small breeding shorebirds. Our study showed that small shorebirds were particularly susceptible to island restoration engineering, whereas large shorebirds and swimming birds were more flexible in their use of roosting or foraging sites. We suggest that the impact of future coastal engineering requires more detailed assessment and monitoring, especially for small migratory shorebirds.

Divergent responses of ecosystem services to afforestation and grassland restoration in the Tibetan Plateau

Afforestation and grassland restoration have been proposed as important pathways for nature-based solutions. However, the effects of different ecological restoration projects on multiple ecosystem services are poorly understood, inhibiting our ability to maximize ecosystem services for further restoration. Here, we provide a comprehensive assessment of the impact of different ecological projects on ecosystem services (carbon storage, water conservation, soil retention), using a pairwise comparative study of samples from 90 project–control pairs in the Tibetan Plateau. Our results found that afforestation increased carbon storage (31.3%) and soil retention (37.6%), but the effects of grassland restoration on services were mixed, while the overall changes in water conservation were negligible. Prior land use/measures and the age of project implementation were key factors in regulating ecosystem service responses. For example, afforestation on bare land increased carbon storage and soil retention but indirectly decreased water conservation by influencing vegetation cover, while cropland afforestation increased water and soil retention. Ecosystem services increased with project age after afforestation. For grassland restoration, short-term recovery increased carbon storage but was not effective in improving water and soil retention. Climate and topography also directly or indirectly controlled the response of ecosystem services by affecting the changes in total nitrogen, total porosity, clay and fractional vegetation cover following the projects. This study improves our current understanding of the mechanisms underlying the responses of ecosystem services to afforestation and grassland restoration. Our results suggest that sustainable restoration management taking into account prior land use/measures, implementation age, climate, topography and other resources is critical for optimizing ecosystem services.

Sensitivity of Grassland Coverage to Climate across Environmental Gradients on the Qinghai-Tibet Plateau

Grassland cover is strongly influenced by climate change. The response of grassland cover to climate change becomes complex with background climate. There have been some advances in research on the sensitivity of grassland vegetation to climate change around the world, but the differences in climate sensitivity among grassland types are still unclear in alpine grassland. Therefore, we applied MODIS NDVI data and trend analysis methods to quantify the spatial and temporal variation of grassland vegetation cover on the Qinghai-Tibet Plateau. Then, we used multiple regression models to analyze the sensitivity of fractional vegetation cover (FVC) to climatic factors (Temperature, Precipitation, Solar radiation, Palmer drought severity index) and summarized the potential mechanisms of vegetation sensitivity to different climatic gradients. Our results showed (1) a significant increasing trend in alpine desert FVC from 2000–2018 (1.12 × 10−3/a, R2 = 0.56, p &lt; 0.001) but no significant trend in other grassland types. (2) FVC sensitivity to climatic factors varied among grassland types, especially in the alpine desert, which had over 60% of the area with positive sensitivity to temperature, precipitation and PDSI. (3) The sensitivity of grassland FVC to heat factors decreases with rising ambient temperature while the sensitivity to moisture increases. Similarly, the sensitivity to moisture decreases while the sensitivity to thermal factors increases along the moisture gradient. Furthermore, the results suggest that future climate warming will promote grassland in cold and wet areas of the Qinghai-Tibet Plateau and may suppress vegetation in warmer areas. In contrast, the response of the alpine desert to future climate is more stable. Studying the impact of climate variation at a regional scale could enhance the adaptability of vegetation in future global climates.

Wildland-urban interface typologies prone to high severity fires in Spain

Due to complex interactions between climate and land use changes, large forest fires have increased in frequency and severity over the last decades, impacting dramatically on biodiversity and society. In southern European countries affected by demographic challenges, fire risk and danger play special relevance at the wildland-urban interfaces (WUIs), where decision-making and land management have strong socio-ecological implications. WUIs have been historically typified according to both fire occurrence probability and settlement vulnerability, but those classifications lack generality regarding fire regime components. We aim to develop an integrated and comprehensive scheme for identifying the WUI typologies most at risk to fire severity across large territories. We selected fourteen large wildfires (over than 500 ha) occurred in Spain (2016–2021) containing different WUI scenarios. First, based on a building cartography and a multi-temporal series of Sentinel-2 imagery, each WUI was delimited and spatially characterized according to building density and pre-fire fuel characteristics (type, amount, and structure). Afterwards, a decision tree regression model was applied to identify the most relevant pre-fire vegetation parameters driving burn severity. The combined effect of the selected pre-fire vegetation drivers and the building density patterns on fire severity was evaluated using linear mixed models. Finally, the WUI typologies most prone to high burn severity were recognized using Tukey post-hoc tests. Results indicated that building density, land cover class and vegetation cover fraction determined fire severity in areas close to human settlements. Specifically, isolated, scattered and sparsely clustered buildings enclosed in a high-cover shrub matrix were the WUI typologies most susceptible to high-severity fires. These findings contribute to the development of appropriate strategies to minimize the risk of severe fires in WUIs and avoid potential losses of multiple ecosystem services valuable for society.

Changing characteristics of ecosystem and water storage under the background of warming and humidification in the Qilian Mountains, China

The Qilian Mountains provide an ecological security barrier, and the region is an important river runoff area in China. Water resources play an essential role in the natural environment of Northwest China. This study used daily temperature and precipitation data from meteorological stations in the Qilian Mountains from 2003 to 2019, Gravity Recovery and Climate Experiment, and Moderate Resolution Imaging Spectroradiometer satellite data. Additionally, we used the Gravity Recovery and Climate Experiment satellite's monthly gravity field model data. Furthermore, we analyzed the characteristics of climate warming and humidification in the eastern, central, and western sections of the Qilian Mountains based on spatial precipitation interpolation and linear trend analysis. Finally, we examined the relationship between water storage changes and precipitation and its impact on vegetation ecology. The results revealed a significant warming and humidification trend in the western Qilian Mountains. The temperature increased significantly, and the increased precipitation rate in summer reached 1.5–3.1 mm/10a. Water storage in the Qilian Mountains displayed an increasing trend, with an increase of approximately 14.3 × 108 m3 over the 17 years study period, averaging an increase of 8.4 mm/year. The spatial distribution of water storage in the Qilian Mountains increased from north to south and east to west. There were noticeable seasonal differences, with the largest surplus occurring in the western Qilian Mountains (71.2 mm in summer). The fractional vegetation coverage in 95.2 % of the western Qilian Mountains and net primary productivity in 90.4 % of the area displayed an increasing trend, and vegetation ecology improved significantly. This study aims to investigate the characteristics of ecosystem and water storage changes in the Qilian Mountain area under the background of climate warming and humidification. The results obtained from this study provided an assessment of the vulnerability of alpine ecosystems and helped in making spatially explicit decisions for the rational utilization of water resources.

Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya

This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya’s lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions.

High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI

China’s FY-4B satellite, launched on 3 June 2021, is a new-generation geostationary meteorological satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4B has 15 spectral channels, including 2 visible (470 and 650 nm), 1 near infrared (825 nm), and 3 shortwave infrared (1379, 1610, and 2225 nm) bands, which can be used to observe the Earth system with the highest spatial resolution of 500 m and 15 min temporal resolution. In this study, FY-4B/AGRI observations were applied for the first time to monitor cyanobacterial blooms in Lake Taihu, China. The AGRI reflectance at visible and near-infrared bands was first corrected to surface reflectance using the 6S radiative transfer model. Due to the similar spectral reflectance characteristics to those of land-based vegetation, the normalized difference vegetation index (NDVI) and some other remote sensing vegetation indices are usually used for the retrieval of cyanobacterial blooms. The fractional vegetation cover (FVC) of algae, defined as the fraction of green vegetation in the nadir view, was adopted to depict the status and trend of cyanobacterial blooms. NDVI and FVC, the two remote sensing indices developed for the retrieval of land vegetation, were used for the detection of cyanobacteria blooms in Lake Taihu. Finally, the FVC derived from AGRI measurements was compared with that obtained from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite to validate the effectiveness of our method. It was found that atmospheric correction can substantially improve the determination of the normalized difference vegetation index (NDVI) values of cyanobacterial blooms in the lake. As a proof of the robustness of the algorithm, the NDVIs are both derived from both AGRI and AHI and their magnitudes are similar. In addition, the distribution of cyanobacterial blooms derived from AGRI FVC is highly consistent with that derived from FY-3D/MERSI and EOS/MODIS. While a lower spatial resolution of FY-4B/AGRI might restrict its capability in capturing some spatial details of cyanobacterial blooms, the high-frequency measurements can provide information for the timely and effective management of aquatic ecosystems and help researchers better quantify and understand the dynamics of cyanobacterial blooms. In particular, AGRI can provide greater details on the diurnal variation in the distribution of cyanobacterial blooms owing to the high temporal resolution.

Relationship Between Urban Spatial Pattern and Thermal Environment Response in Summer: A Case Study of Hefei City

With the continuous expansion of cities, the land cover type of the region is transformed, a large number of natural landscapes are replaced by man-made landscapes, and the environmental temperature rises. The study of the response relationship between urban spatial pattern and thermal environment provides some guidance for improving the ecological environment and optimizing the urban spatial layout. Based on the Landsat 8 series remote sensing image data of Hefei City in 2020 and analysis platforms such as ENVI and ARCGIS, Pearson correlation and profile lines were used to reflect the correlation between the two. Then, the three spatial pattern components with the greatest correlation were selected to construct multiple regression functions to investigate the influence of urban spatial pattern on urban thermal environment and its mechanism of action. The results showed that:① the high temperature area of Hefei City increased significantly with the advance of time during 2013-2020. For different seasons, the urban heat island effect showed that summer>autumn>spring>winter. ② In the central urban area, the building occupancy, building height, imperviousness occupancy, and population density were significantly higher than those in the suburbs, whereas fractional vegetation coverage presented a higher suburban than urban area and mainly showed a point distribution in the urban area and an irregular distribution of water bodies. ③ The urban high-temperature zone was mainly distributed in various development zones in urban areas, whereas other places in urban areas were dominated by medium-high temperature and above-temperature zoning, and suburban areas were dominated by medium-low temperature. ④ The Pearson coefficients between the spatial pattern of each element and the thermal environment were positively correlated with the building occupancy (0.395), impervious surface occupancy (0.333), population density (0.481), and building height (0.188) and negatively correlated with fractional vegetation coverage (-0.577) and water occupancy (-0.384). The coefficients of the constructed multiple regression functions, including building occupancy, population density, and fractional vegetation coverage, were 8.372, 0.295, and -5.639 respectively, with a constant of 38.555. The results of this study can provide a reference basis for optimizing urban spatial layouts and improving urban living quality.