Satellite-based Normalized Difference Vegetation Index Research Articles

Wetting or greening? Probing the global trends in Vegetation Condition Index (VCI)

Vegetation Condition Index (VCI), as a widely used drought index for monitoring vegetation drought stress and estimating drought trends, is constructed by normalizing the long-term satellite-based Normalized Difference Vegetation Index (NDVI) data. However, under global greening, vegetation across different regions has shown an increasing trend in greenness, which may cause VCI to inherit the greening trend in NDVI and further hamper its ability in drought trend analysis. Therefore, this study quantitatively explored the underlying relationship among VCI, global greening, and drought from 2001 to 2021 to examine the utility of VCI in a changing environment. Multi-source indicators were employed as surrogates for vegetation greenness and drought, respectively. Particularly, the Leaf Area Index (LAI) and Net Primary Production (NPP) were used as proxies for vegetation greenness while the Root-Zone Soil Moisture (RZSM), Palmer Drought Severity Index (PDSI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were used for drought indices. Based on Sen’s slope estimator, Mann-Kendall (MK) test, and partial correlation analysis, our results show that the proportion of pixels with an increasing trend in VCI is approximately 38.5%, which is similar to the greenness-related indices (i.e., NPP [26.47%] and LAI [59.14%]), and is significantly higher than the other drought indices (i.e., RZSM [19.83%], PDSI [10.38%], and SPEI [9.32%]). Furthermore, our results demonstrate that the VCI, as a drought index, exhibits a closer correlation with greenness-related indicators (LAI and NPP) than other drought indices (RZSM, PDSI, and SPEI). These results reveal the potential limitations in the practical applications of VCI and could enhance our understanding of vegetation dynamics and drought trends, especially in the current greening globe. Additionally, this study serves as a cautionary note for the scientific community involved in drought monitoring, emphasizing that the VCI may not be a suitable tool for evaluating long-term trends in drought impacts on vegetation.

Green space exposure and blood DNA methylation at birth and in childhood – A multi-cohort study

Green space exposure has been associated with improved mental, physical and general health. However, the underlying biological mechanisms remain largely unknown. The aim of this study was to investigate the association between green space exposure and cord and child blood DNA methylation.Data from eight European birth cohorts with a total of 2,988 newborns and 1,849 children were used. Two indicators of residential green space exposure were assessed: (i) surrounding greenness (satellite-based Normalized Difference Vegetation Index (NDVI) in buffers of 100 m and 300 m) and (ii) proximity to green space (having a green space ≥ 5,000 m2 within a distance of 300 m). For these indicators we assessed two exposure windows: (i) pregnancy, and (ii) the period from pregnancy to child blood DNA methylation assessment, named as cumulative exposure. DNA methylation was measured with the Illumina 450 K or EPIC arrays. To identify differentially methylated positions (DMPs) we fitted robust linear regression models between pregnancy green space exposure and cord blood DNA methylation and between cumulative green space exposure and child blood DNA methylation. Two sensitivity analyses were conducted: (i) without adjusting for cellular composition, and (ii) adjusting for air pollution. Cohort results were combined through fixed-effect inverse variance weighted meta-analyses. Differentially methylated regions (DMRs) were identified from meta-analysed results using the Enmix-combp and DMRcate methods.There was no statistical evidence of pregnancy or cumulative exposures associating with any DMP (False Discovery Rate, FDR, p-value < 0.05). However, surrounding greenness exposure was inversely associated with four DMRs (three in cord blood and one in child blood) annotated to ADAMTS2, KCNQ1DN, SLC6A12 and SDK1 genes. Results did not change substantially in the sensitivity analyses.Overall, we found little evidence of the association between green space exposure and blood DNA methylation. Although we identified associations between surrounding greenness exposure with four DMRs, these findings require replication.

Surrounding greenness is associated with lower risk and burden of low birth weight in Iran

The nexus between prenatal greenspace exposure and low birth weight (LBW) remains largely unstudied in low- and middle-income countries (LMICs). We investigated a nationwide retrospective cohort of 4,021,741 live births (263,728 LBW births) across 31 provinces in Iran during 2013–2018. Greenness exposure during pregnancy was assessed using satellite-based normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). We estimated greenness-LBW associations using multiple logistic models, and quantified avoidable LBW cases under scenarios of improved greenspace through counterfactual analyses. Association analyses provide consistent evidence for approximately L-shaped exposure-response functions, linking 7.0–11.5% declines in the odds of LBW to each 0.1-unit rise in NDVI/EVI with multiple buffers. Assuming causality, 3931–5099 LBW births can be avoided by achieving greenness targets of mean NDVI/EVI, amounting to 4.4–5.6% of total LBW births in 2015. Our findings suggest potential health benefits of improved greenspace in lowering LBW risk and burden in LMICs.

Crop NDVI time series construction by fusing Sentinel-1, Sentinel-2, and environmental data with an ensemble-based framework

Satellite-based Normalized Difference Vegetation Index (NDVI) time series data are conducive for near-real-time (NRT) monitoring of crop progress, but they commonly suffer from data gaps due to cloudy weather conditions. Fortunately, Synthetic Aperture Radar (SAR) data, which are cloud-insensitive, has high potentials to fill these data gaps. However, the relationship between NDVI and SAR data is not generic and affected by various factors (e.g., crop canopy structure and phenology, soil exposure and humidity). Therefore, it is worth exploring to evaluate the relationships and develop a more robust SAR-NDVI fusion method for NDVI time series construction. In this study, we proposed and evaluated an ensemble-based data fusion framework that accounts for these factors, based on which dense NDVI time series for crop monitoring were constructed. The framework consisted of three steps: (1) NDVI was predicted from Sentinel-1 SAR data and auxiliary environmental factors using a random forest (RF) model; (2) an improved feature importance measurement method was proposed to reveal the different contributions of multisource data to the modeling, and suggestions for selecting optimal input parameters were presented; (3) the uncertainty of the predicted data was quantified by an ensemble-based method and incorporated into a Weighted Least Squares (WLS) method to construct dense NDVI time series. Results showed that the RF models were generally improved by the auxiliary data and achieved a satisfactory accuracy of NDVI estimation (R2 > 0.93, RMSE < 0.075) for both corn and soybean crops in Southwestern Ontario, Canada. The proposed method performed well in filling data gaps in both the vegetative and reproductive stages for corn and soybean, providing a practical and promising solution for continuous crop monitoring.

Mapping crop evapotranspiration with high-resolution imagery and meteorological data: insights into sustainable agriculture in Prince Edward Island

Soil moisture variability caused by soil erosion, weather extremes, and spatial variations in soil health is a limiting factor for crop growth and productivity. Crop evapotranspiration (ET) is significant for irrigation water management systems. The variability in crop water requirements at various growth stages is a common concern at a global level. In Canada’s Prince Edward Island (PEI), where agriculture is particularly prominent, this concern is predominantly evident. The island’s most prominent business, agriculture, finds it challenging to predict agricultural water needs due to shifting climate extremes, weather patterns, and precipitation patterns. Thus, accurate estimations for irrigation water requirements are essential for water conservation and precision farming. This work used a satellite-based normalized difference vegetation index (NDVI) technique to simulate the crop coefficient (Kc) and crop evapotranspiration (ETc) for field-scale potato cultivation at various crop growth stages for the growing seasons of 2021 and 2022. The standard FAO Penman–Monteith equation was used to estimate the reference evapotranspiration (ETr) using weather data from the nearest weather stations. The findings showed a statistically significant (p &amp;lt; 0.05) positive association between NDVI and tabulated Kc values extracted from all three satellites (Landsat 8, Sentinel-2A, and Planet) for the 2021 season. However, the correlation weakened in the subsequent year, particularly for Sentinel-2A and Planet data, while the association with Landsat 8 data became statistically insignificant (p &amp;gt; 0.05). Sentinel-2A outperformed Landsat 8 and Planet overall. The Kc values peaked at the halfway stage, fell before the maturity period, and were at their lowest at the start of the season. A similar pattern was observed for ETc (mm/day), which peaked at midseason and decreased with each developmental stage of the potato crop. Similar trends were observed for ETc (mm/day), which peaked at the mid-stage with mean values of 4.0 (2021) and 3.7 (2022), was the lowest in the initial phase with mean values of 1.8 (2021) and 1.5 (2022), and grew with each developmental stage of the potato crop. The study’s ET maps show how agricultural water use varies throughout a growing season. Farmers in Prince Edward Island may find the applied technique helpful in creating sustainable growth plans at different phases of crop development. Integrating high-resolution imagery with soil health, yield mapping, and crop growth parameters can help develop a decision support system to tailor sustainable management practices to improve profit margins, crop yield, and quality.

Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI

Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day−1, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images.

Urban overall and visible greenness and diabetes among older adults in China

Neighborhood greenness has been shown to reduce diabetes risk, however, no studies have compared the effects of overall greenness with visible greenness, which is crucial for understanding how greenness influences diabetes risk. Our study aims to explore the associations between greenness matrix and diabetes, as well as the potential effect modifications and mediating factors. We used logistic regressions to examine the cross-sectional associations of the satellite-based Normalized Difference Vegetation Index (NDVI) and street view-based Green View Index (GVI) with diabetes in 3,924 urban older adults enrolled in the 2017–2018 wave of Chinese Longitudinal Healthy Longevity Survey (CLHLS). We conducted the stratified analyses by age, sex, household income and education. Mediation analyses were also performed to see whether physical activity, BMI, air pollution, and social interaction mediate the associations. Significant associations with diabetes were only observed for NDVI but not for GVI. Participants in the highest quartile of NDVI and GVI had 52% (95 % CI: 48%, 63%) and 14% (-10%, 44%) lower odds of reporting having diabetes diagnosed by a doctor. The protective effects of NDVI were more pronounced in the young old (≥75–95 years) and high-education or high-income groups. No difference between males and females were observed. Air pollution (e.g., PM2.5, NO2 and O3) partially mediated the associations, but physical activity, BMI, and social interaction may not mediate the associations. Our findings indicate beneficial associations between overall greenness but not visible greenness surrounding residences with diabetes in older urban residents in China, especially for old adults with higher education or household income levels. Environmental factors (e.g., air pollution) but not individual behavioural characteristics are the potential underlying mechanisms.

The response and sensitivity of global vegetation to water stress: A comparison of different satellite-based NDVI products

Normalized Difference Vegetation Index (NDVI) is an effective proxy for vegetation activity and terrestrial productivity, and the widely used global NDVI products include the third generation Global Inventory Modelling and Mapping Studies Advanced Very High-Resolution Radiometer NDVI (NDVI3g), Satellite Pour l’Observation de la Terre VEGETATION NDVI (NDVIsp) and Terra Moderate Resolution Imaging Spectroradiometer NDVI (NDVItr). Here, for the first time, we investigated the differences between the three NDVIs in response and sensitivity to water stress represented by root zone soil moisture, vapor pressure deficit, self-calibrating Palmer Drought Severity Index and Standardized Precipitation Evapotranspiration Index across climate zones, humidity gradients, vegetation types and tree cover gradients. Over the temperate and arid climates region, the three NDVIs show higher consistent response fractions (CRFs) to water stress in the water-limited regions, with their sensitivity being in the order of NDVI3g < NDVIsp < NDVItr. The three NDVIs tend to exhibit inconsistent responses to water stress in the areas with higher humidity and tree cover, and their sensitivities to water stress fall as humidity and tree cover increase. In addition, the three NDVIs display higher CRFs and sensitivities to water stress over the non-forested areas. Our results also show that, in the cold climates region, the three NDVIs respond in the opposite way to water stress as that in water-limited areas, and NDVI3g has higher sensitivity to water stress than NDVIsp and NDVItr. This study highlights the differences between three NDVIs products for qualifying and quantifying their response and sensitivity to water stress.

Drought-vulnerable vegetation increases exposure of disadvantaged populations to heatwaves under global warming: A case study from Los Angeles

Urban vegetation is valuable in alleviating local heatwaves. However, drought may decrease vegetation health and limit this cooling effect. Here we use satellite-based Normalized Difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI) to investigate the sensitivity of urban vegetation to drought in Coastal Greater Los Angeles (CGLA) from 2001 to 2020. We applied four statistical models to analyze the relations between 15 socioeconomic variables and the vegetation's sensitivity to drought. We then examined the changes in the cooling effect of the urban vegetation during drought and non-drought periods using remotely sensed land surface temperature (LST) data. The results suggest that economically disadvantaged areas with higher proportions of Hispanics and Blacks are typified by vegetation more sensitive to drought, which is likely linked to inequality in water use. Moreover, these populations experience a lower degree of vegetation cooling effects and higher exposure to heatwaves. The findings of this study imply that the potential of a community's vegetation in mitigating heatwaves is significantly influenced by the socioeconomic conditions of the community. Increasing the resilience of urban vegetation to drought in disadvantaged communities may help promote environmentally sustainable and socially resilient cities under a warming climate.

No Signs of Long-term Greening Trend in Western Mongolian Grasslands

Trends for increased vegetation greenness based on satellite-derived data have been repeatedly published for the temperate grassland biome (including forest steppes) of eastern Inner Asia since 1982. Although this greening trend has been attenuated or partially reversed by drought in the early twenty-first century, linear increases in the Normalized Difference Vegetation Index (NDVI) or other parameters of vegetation greenness are nevertheless evident when the period since 1982 is regarded. However, the question arises whether these trends are part of a long-term trend driven by climate change, as simultaneously forests in the region show widespread drought-induced growth reductions and mortality outbreaks. Therefore, we hypothesized that the post-1982 greening trend was neither part of a long-term trend nor unprecedented. To test this hypothesis, we analyzed monthly maximum NDVI data from AVHRR time series and correlated these data with standardized tree-ring data of Larix sibirica from two regions of western Mongolia. We used linear regression to model the NDVI from tree-ring anomalies and to reconstruct the NDVI since 1940. These reconstructions show that the availability of satellite-based NDVI data coincidentally began during a dry period of low vegetation greenness in the early 1980s and was followed by a wet phase in the 1990s, producing the linear greening trend. No positive long-term trend in the reconstructed NDVI was observed from 1940 to 2010. This result rules out a recent climate change-driven greening trend for the grasslands and forest steppes of western Mongolia and calls into question its existence for all of eastern Inner Asia.

Association between greenness and cardiovascular risk factors: Results from a large cohort study in Thailand

BackgroundThe risk of cardiovascular diseases may be reduced by residing in green environments. However, there are relatively few longitudinal cohort studies, especially in Southeast Asia, that focused on the health benefits of long-term greenness exposure in young adults. The present study examined the association between long-term exposure to residential greenness and self-reported morbidities in participants of the Thai Cohort Study (TCS) in Thailand from 2005 to 2013. MethodsThe self-reported outcomes, including high blood pressure, high blood cholesterol, and diabetes, were reported in 2005, 2009, and 2013, where the study participants provided the exact year of disease occurrence. Greenness was assessed by the satellite-based Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), with a spatial resolution of 250 m. Long-term exposure to NDVI and EVI of each participant's sub-district was averaged over the period of person-time. We used Cox proportional hazards models to examine the association between greenness and health outcomes. Associations with self-reported morbidity were measured using hazard ratios (HRs) per interquartile range (IQR) increase in NDVI and EVI. ResultsAfter adjusting for potential confounders, we observed that an IQR increase in NDVI was associated with lower incidence of high blood pressure (HR = 0.92, 95% CI: 0.89, 0.97) and high blood cholesterol (HR = 0.89, 95%CI: 0.87, 0.92), but not significantly associated with diabetes (HR = 0.93, 95%CI: 0.85, 1.01). EVI was also inversely associated with self-reported high blood pressure (HR = 0.92, 95%CI: 0.88, 0.96), high blood cholesterol (HR = 0.89, 95%CI: 0.87, 0.91), and diabetes (HR = 0.92, 95%CI: 0.85, 0.99). ConclusionsLong-term exposure to residential greenness was inversely associated with self-reported high blood pressure, high blood cholesterol, and diabetes in participants of TCS. Our study provides evidence that greenness exposure may reduce cardiovascular disease risk factors in adult population.

The Effect of Psychological Burden on Dyslipidemia Moderated by Greenness: A Nationwide Study from China.

Globally, dyslipidemia is now become a leading risk factor for many adverse health outcomes, especially in the middle-aged and elderly. Recent evidence suggests that exposure to greenness and the relief of a psychological burden may decrease the prevalence of dyslipidemia. The objective of our study was to examine whether a green space can moderate the association between mental health status and dyslipidemia. Our study selected the datasets of depression symptoms, dyslipidemia from the China Health and Retirement Longitudinal Study (CHARLS), and the satellite-based normalized difference vegetation index (NDVI) from the 30 m annual maximum NDVI dataset in China in 2018. Ultimately, a total of 10,022 middle-aged and elderly Chinese were involved in our study. Multilevel logistic regressions were performed to examine the association between symptoms of depression and dyslipidemia, as well as the moderate effect of greenness exposure on the association. Our research suggested that adults diagnosed with depression symptoms were more likely to suffer from dyslipidemia. In addition, the NDVI was shown to moderate the effect of depression on dyslipidemia significantly, though the effect was attenuated as depression increased. Regarding the moderate effect of the NDVI on the above association across age, gender, and residence, the findings presented that females, the elderly, and respondents living in urban areas were at a greater risk of having dyslipidemia, although the protective effect of the NDVI was considered. Likewise, the moderate effect of the NDVI gradually decreased as the level of depression increased in different groups. The current study conducted in China provides insights into the association between mental health, green space, and dyslipidemia. Hence, improving mental health and green spaces can be potential targets for medical interventions to decrease the prevalence of dyslipidemia.

Associations and pathways between residential greenness and hyperuricemia among adults in rural and urban China

BackgroundResidential greenness may decrease the risk for hyperuricemia in rural areas, but the urban-rural disparities in this association and underlying pathways have not been studied. ObjectivesTo investigate the associations and potential pathways between residential greenness and hyperuricemia in urban and rural areas. MethodsThe baseline survey of the China Multi-Ethnic Cohort (CMEC) was used. Hyperuricemia was defined as serum uric acid (SUA) > 417 μmol/L for men and >357 μmol/L for women. The satellite-based normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were used to capture residential greenness. A propensity score inverse-probability weighting method was used to assess urban-rural differences in the associations between residential greenness and hyperuricemia, with possible mediation effects of physical activity (PA), body mass index (BMI), PM2.5, and NO2 examined by causal mediation analyses. ResultsA total of 72,372 participants were included. The increases in the EVI500m and NDVI500m residential greenness were associated with a decreased risk for hyperuricemia and the SUA level in both urban and rural areas. For example, each 0.1-unit increase in EVI500m was associated with a decreased hyperuricemia risk of 7% (OR = 0.93 [0.91, 0.96]) and a decreased SUA level of −1.77 μmol/L [-2.60, −0.93], respectively; such associations were stronger in urban areas for both the risk for hyperuricemia (OR = 0.84 [0.83, 0.86]) and SUA level (−7.18 μmol/L [-7.91, −6.46]). The subgroup analysis showed that the greenness-hyperuricemia/SUA association varied by age, sex, and annual household income. The percentage of the joint mediation effect of PA, BMI, PM2.5, and NO2 on the association between EVI500m and the risk for hyperuricemia was higher in urban (34.92%) than rural areas (15.40%). BMI, PM2.5, and PA showed significantly independently mediation effects for the greenness-hyperuricemia association in both rural and urban areas. ConclusionsExposure to residential greenness was associated with a decreased risk for hyperuricemia, partially through the pathways of PA, BMI, PM2.5, and NO2, which varied in urban and rural areas.

Association between greenspace exposure and different domains of cognitive function in the French CONSTANCES cohort

BACKGROUND AND AIM Greenspace has been suggested to be associated with better cognitive function in adults. We assessed the association between residential surrounding greenspace and different domains of cognition in the French CONSTANCES cohort. METHODS We included 76,482 participants aged 45 and older, who performed tests at enrollment (2012-2019) on three cognitive domains: episodic memory (fast free and cued recall), language skills (semantic and lexical fluency), and executive functions (digit-symbol substitution, trail making test part A and B [TMT-A and TMT-B]), and a global cognitive score (a composite of these six tests). Residential surrounding greenspace was quantified using satellite-based Normalized Difference Vegetation Index (NDVI) at 300m buffer around the participant’s home in the enrollment year. We modeled the cross-sectional associations for urban, suburban, isolated cities, rural, and Paris separately, using multiple linear regressions on standardized outcomes (TMT-A and TMT-B were log-transformed) adjusted for age, sex, education, recruitment center, neighborhood deprivation, and air pollution. RESULTS The participants were 57.6&amp;#xb1;7.2 years old, 54.9% women, and 49.7% with university education. Average NDVI at 300m buffer (IQR: 0.26) was 0.58&amp;#xb1;0.14, 0.67&amp;#xb1;0.12, 0.71&amp;#xb1;0.11, 0.81&amp;#xb1;0.08, and 0.35&amp;#xb1;0.09 for urban, suburban, isolated cities, rural, and Paris respectively. We found significant beneficial association between residential surrounding greenspace and semantic fluency at urban, suburban, isolated cities, and rural areas (ß=0.048 [95% CI: 0.018, 0.079], 0.068 [0.037, 0.098], 0.105 [0.033, 0.177], 0.096 [0.033, 0.158] respectively). We also observed similar beneficial associations for executive functions in urban and suburban areas. No associations with greenspace were found for episodic memory, lexical fluency, and global cognitive score in most communes of residence. CONCLUSION Exposure to greenspace could be beneficial for cognition, especially semantic and executive functions, in adults. Urban and suburban residents probably gain more cognitive benefits from greenspace. KEYWORDS Natural environment; Cognitive function; Alzheimer’s disease; Environmental epidemiology

Greenspace exposure and depressive symptoms: findings from the French CONSTANCES cohort

Background and Aim: The association between greenspace exposure and different dimensions of depression is yet to be established. We aimed to explore this association in a large population-based cohort. Methods: Data from the enrollment phase of the French CONSTANCES cohort (2012-2020) were analyzed cross-sectionally. Depressive symptoms were measured by the Centre of Epidemiologic Studies Depression (CES-D) questionnaire allowing to compute a total CES-D score ( &amp;#x2265;19 used as a cutoff score to identify individuals at risk for clinical depression), and including four dimensions (depressed affect, disturbed interpersonal relations, low positive affect, somatic complaints). Residential surrounding greenspace exposure was quantified using the satellite-based Normalized Difference Vegetation Index (NDVI) at 300m buffer during the enrollment year. Adjusted multiple logistic and linear regression models were developed, reported as odds ratio [OR] for dichotomized total depressive symptoms, and ß for dimension-scores, with a 95% confidence interval [CI] for an interquartile range [IQR] increase in exposure. Analyses stratified by personal and socioeconomic variables were performed. Results: In total 185,781 participants were included (age: 46.80&amp;#xb1;13.61 years, 53.5% women). Residential surrounding greenness (average NDVI: 0.53; IQR=0.28) was significantly associated with lower odds of having depressive symptoms (OR [95%CI]: 0.94 [0.90-0.99]). This exposure was also associated with a lower scores for all dimensions (ß [95% CI]: -0.043 [-0.053: -0.033]; -0.016 [-0.026:-0.006]; -0.020 [-0.030:-0.010]; and -0.019 [-0.029:-0.009] for depressed affect, disturbed interpersonal relations, low positive affect, and somatic complaints, respectively). We found suggestions for potentially stronger protective associations in men and participants with lower income. Conclusion: Residential surrounding greenspace was associated with lower depressive symptoms in general, and all four dimensions especially depressed affect and somatic complaints. Considering depression prevalence, its burden, and the increasing trend of urbanization, our finding is of importance for policymakers. Keywords: Nature; Greenspace; Mental health; CONSTANCES Cohort

Investigating and predicting spatiotemporal variations in vegetation cover in transitional climate zone: a case study of Gansu (China)

Vegetation ecosystems are sensitive to large-scale climate variability in climate transition zones. As a representative transitional climate zone in Northwest China, Gansu is characterized by a sharp climate and vegetation gradient. In this study, the spatiotemporal variations of vegetation over Gansu are characterized using the satellite-based normalized difference vegetation index (NDVI) observations during 2000–2020. Results demonstrate that a significant greening trend in vegetation over Gansu is positively linked with large-scale climate factors through modulating the water and energy dynamics. As a climate transition zone, the northern water-limited and southern energy-limited regions of Gansu are affected by water and energy dynamics, differently. In the water-limited region, a weakening Asian monsoon along with colder Central Pacific (CP) and warmer North Pacific (NP) Oceans enhances prevailing westerlies which bring more atmospheric moisture. The enhanced atmospheric moisture and rising temperature promote the local vegetation growth. In contrast, large-scale climate variations suppress the southwest monsoon moisture fluxes and reduce precipitation in southern energy-limited regions. In these energy-limited regions, temperature has more effects on vegetation growth than precipitation. Therefore, the greenness of vegetation is because of more available energy from higher temperatures despite overall drying conditions in the region. Based on the above mechanism, future scenarios for climate impacts on vegetation cover over Gansu region are developed based on the two latest generation from coupled climate models (Coupled Model Intercomparison Project Phase 5 and Phase 6; CMIP5 and CMIP6). In the near-term future (2021–2039), the vegetation is likely to increase due to rising temperature. However, the vegetation is expected to decrease in a long-term future (2080–2099) when the energy-limited regions become water-limited due to increasing regional temperatures and lowering atmospheric moisture flux. This study reveals an increasing desertification risk over Gansu. Similar investigations will be valuable in climate transition regions worldwide to explore how large-scale climate variability affects local ecological services under different future climate scenarios.

A Global 250-m Downscaled NDVI Product from 1982 to 2018

Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (Pearson’s R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson’s R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments.

Associations between residential greenness and blood lipids in Chinese elderly population.

Previous studies on residential green space were inconsistent with blood lipid levels and hyperlipidemia. Thus, our study aims to explore the relationship between urban residential greenness and the blood lipid level and hyperlipidemia of the Chinese elderly population. A total of 59,865 older adults were collected from the Shenzhen healthy aging Research (SHARE). Blood lipid levels [total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C)] were measured. Participants' exposure to residential greenness was measured by the satellite-based normalized difference vegetation index (NDVI). Generalized linear mixed models (GLMMs) and logistic regression were performed to assess the associations of residential greenness with lipid levels and dyslipidemia (high TG, high TC, low HDL-C, and high LDL-C). Each per 1 standard deviation (SD) increase in NDVI250-m was associated with a higher HDL-C level (β = 0.003, 95% (confidence interval, CI):0.001-0.005) and lower TG level (β = -0.005, 95% CI -0.141-0.121), after fully adjusting for covariates. Each increment in per interquartile range (IQR)-unit increase in NDVI250-m was associated with lower odds of high TG (odds ratio, OR 0.94, 95% CI 0.91-0.97) and low HDL-C (OR 0.96, 95% CI 0.94-0.99). The NDVI250-m has the highest protective effect on the incidence of high TG and low HDL-C, followed by NDVI500-m and NDVI1250-m. Stratified analyses showed that association between residential greenness and hyperlipidemia was modified by sex, age, BMI, household registration, and physical activity. Higher greenness exposure was beneficially associated with lipid levels and dyslipidemia among Chinese city-dwelling older adults.

Multi-sensor and multi-platform consistency and interoperability between UAV, Planet CubeSat, Sentinel-2, and Landsat reflectance data

ABSTRACT Unmanned aerial vehicle (UAV) and satellite data have considerable complementarity for platform inter-operability, data fusion studies, calibration and validation efforts, and various multiscale analyses. To optimize cross-platform synergies between field-deployable UAV and space-based satellite systems, an understanding of spectral characteristics and compatibility is required. Here, we present the assessment of spectral consistency, undertaking a pixel-to-pixel similarity assessment of co-registered reflectance maps using corresponding spectral bands from UAV and satellite multispectral imagery. A high-resolution centimeter-scale UAV-mounted MicaSense RedEdge-MX sensor is intercompared against variable-resolution multi-spectral sensors on-board PlanetScope, Sentinel-2 and Landsat 8 platforms. Sampling from within an urban environment that covers a range of both natural and man-made surfaces, we employ ground-based spectroradiometer data to evaluate pixel-level responses, using regression analysis and measurements of relative root mean square error (rRMSE) to assess for factors such as spatial and spectral misalignment. Using two radiometric correction approaches for the UAV data, we found that a vicarious radiometric correction was more accurate than a linear empirical line method, with the former improving rRMSE by between 1.6% and 20.11% when assessed against spectroradiometer measurements. Spectral band misalignment between the UAV and satellite sensors affected their spectral consistency, causing different reflectance values for the same object in the corresponding UAV and satellite bands, with the issue amplified over specific land-cover classes (e.g. grass in the red edge part of the spectrum). Using the standard deviation of a UAV-derived normalized difference vegetation index (NDVI) as a metric of spatial heterogeneity, larger differences between the UAV and satellite-based NDVI were observed for different ground features in response to both land-cover boundary and shadow effects. Interestingly, higher spatial heterogeneity did not necessarily lead to higher spectral inconsistencies. It was also determined that as spatial scale differences between the UAV and satellite platforms increased, the lower was the impact of geometric misregistration on their consistency. Indeed, the rRMSE between the reflectance values of the UAV-based spectral bands and the corresponding satellite imagery was smaller at lower resolution (e.g. Landsat 8) than higher resolution (e.g. PlanetScope). Overall, the study provides insight into the collective effect of spectral and spatial misalignments on the degree of spectral consistency that can be expected between UAV and satellite data, guiding robust radiometric intercalibration efforts and the potential for improved synergy and interoperability between UAV and satellite data.

Associations of residential greenness with obesity and BMI level among Chinese rural population: findings from the Henan Rural Cohort Study.

In recent years, increasing evidence supports the notion that obesity risk is affected by residential greenness. However, limited studies have been established in low- and middle-income countries, especially in China. The study aimed to evaluate the associations of residential greenness with obesity and body mass index (BMI) level in Chinese rural-dwelling adults. A total of 39,259 adults from the Henan Rural Cohort Study (HRCS) were included in the analyses. According to the guideline for prevention and control of overweight and obesity in Chinese adults, obesity was defined as BMI ≥ 28kg/m2. Residential greenness was measured by satellite-based normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Generalized linear mixed models were used to study the associations between exposure to residential greenness with obesity and BMI level. Higher residential greenness was significantly correlated with lower odds of obesity and BMI level. For example, in the full-adjusted analyses, an interquartile range (IQR) increase in EVI500-m was linked with reduced odds of obesity (OR = 0.77, 95%CI 0.72-0.82) and BMI level (β = - 0.41kg/m2, 95%CI - 0.48 to - 0.33kg/m2). Mediation analyses showed air pollution and physical activity could be potential mediators in these associations. Besides, we found that the association of NDVI500-m with BMI was stronger in females and low-income populations. Higher residential greenness was associated with a lower prevalence of obesity and BMI level, particularly among females and the low-income population. These relationships were partially mediated by reducing air pollution and increasing physical activity.