Peak Normalized Difference Vegetation Index Research Articles

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Is deeper always better? Evaluating deep learning models for yield forecasting with small data

Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements—public and global records of satellite data in an application ready format with near real time updates—and can be transferred to any country with reliable yield statistics. Three-dimensional histograms of normalized difference vegetation index (NDVI) and climate data are used as input to the 2D model, while simple administrative-level time series averages of NDVI and climate data to the 1D model. The best model architecture is automatically identified during efficient and extensive hyperparameter optimization. To demonstrate the relevance of this approach, we hindcast (2002–2018) the yields of Algeria’s three main crops (barley, durum and soft wheat) and contrast the model’s performance with machine learning algorithms and conventional benchmark models used in a previous study. Simple benchmarks such as peak NDVI remained challenging to outperform while machine learning models were superior to deep learning models for all forecasting months and all tested crops. We attribute the poor performance of deep learning to the small size of the dataset available.

Negative relationship between photosynthesis and late-stage canopy development and senescence over Tibetan Plateau.

Canopy greening, which is associated with significant canopy structure changes, is the most notable signal of ecosystem changes in response to anthropogenic climate change. However, our knowledge of the changing pattern of canopy development and senescence, and its endogenous and climatic drivers is still limited. Here, we used the Normalized Difference Vegetation Index (NDVI) to quantify the changes in the speed of canopy development and senescence over the Tibetan Plateau (TP) during 2000-2018, and used a solar-induced chlorophyll fluorescence dataset as a proxy for photosynthesis, in combination with climate datasets to decipher the endogenous and climatic drivers of the interannual variation in canopy changes. We found that the canopy development during the early green-up stage (April-May) is accelerating at a rate of 0.45-0.8 × 10-3 month-1 year-1 . However, this accelerating canopy development was largely offset by a decelerating canopy development during June and July (-0.61 to -0.51 × 10-3 month-1 year-1 ), leading to the peak NDVI over the TP increasing at a rate of only one fifth of that in northern temperate regions, and less than one tenth of that in the Arctic and boreal regions. During the green-down period, we observed a significant accelerating canopy senescence during October. Photosynthesis was found to be the dominant driver for canopy changes over the TP. Increasing photosynthesis stimulates canopy development during the early green-up stage. However, slower canopy development and accelerated senescence was found with larger photosynthesis in late growth stages. This negative relationship between photosynthesis and canopy development is probably linked to the source-sink balance of plants and shifts in the allocation regime. These results suggest a sink limitation for plant growth over the TP. The impact of canopy greening on the carbon cycle may be more complicated than the source-oriented paradigm used in current ecosystem models.

Nonlinear effects of thermokarst lakes on peripheral vegetation greenness across the Qinghai-Tibet Plateau using stable isotopes and satellite detection

Thermokarst lakes (TLs) widespread in thawing permafrost can degrade vegetation through thermomechanical erosion and waterlogging. However, whether TLs change water sources and alleviate water stress for plants is unknown, and previous knowledge about regional TL effects on surrounding vegetation greenness is rare. Here, we synthesized field investigations, stable isotopes, and remote sensing images from an unmanned aerial vehicle and Sentinel-2 to determine the effects of over 160,000 TLs on their surrounding growing-season normalized difference vegetation index (NDVI) in the Qinghai-Tibet Plateau. The results are as follows: 1) With the largest water source shifting from 0 to 10 cm soil water to lake water, TL-affected plants can grow better than TL-unaffected plants, which is associated with soil texture and the development stage of the lake. 2) Overall, the median affecting distances of TLs on surrounding surface moisture (tasseled cap wetness, TCW) and NDVI were 50 m with their 25th–75th percentiles of 40–70 m and 40–60 m, respectively; compared with the unaffected areas, TLs increased peripheral TCW but reduced NDVI with their median change rates of 19.84% and −10.42%, respectively. 3) However, the nonlinear response of NDVI to the TCW gradient was dominant, which was featured by a local peak NDVI due to the coexistence of the physical destruction and improved water availability, and both TCW and NDVI at the peak area were explicitly larger than those at the unaffected area. 4) Along southeast-northwest environmental gradients (topographic, climatic, and edaphic factors), the affected range and degradation ratio of NDVI were greater under drier climate, larger lake area, sand-richer soil, fewer soil nutrients, and worse vegetation type (e.g., alpine desert). Briefly, the net adverse consequence on NDVI suggests that TLs primarily represent permafrost-supported ecosystems deteriorating, but the clear nonlinear effects advance the knowledge of this landscape; additionally, these local vegetation changes can help interpret the regional greening/browning and promote the research on the biogeochemical interaction among TL, soil, and plants under climate warming and permafrost thawing.

Differential effects of topography on the timing of the growing season in mountainous grassland ecosystems

• What is the overall scientific problem and why did you study it? • Topography is a key driver of the timing of ecosystem processes in mountainous areas. • How did you address the problem, and which spheres are included? • Different aspects of topography affect different stages of annual growth in grasslands. • What was the major method used? • Remote sensing revealed timing of annual grassland growth in relation to topography. • Major finding(s) • Elevation affects the timing of the start, aspect the end of the growing season. • Take home message • Elevation and aspect do not lead to equivalent responses in grassland phenology. Grasslands in mountainous areas often show distinct responses in the timing of their growing season in relation to topographical variation. However, it is unclear which factors of topography (elevation, aspect, and slope) affect which phases of the timing in seasonal growth (start, peak, end, length of growing season). Here we investigated these relationships between topography and growing season timing in the three key grassland types in mountainous areas in South Island, New Zealand. From a near-daily NDVI (Normalized Difference Vegetation Index) dataset over a 16 year period (2001–2016), we extracted five annual land surface phenology indices: start, end, length, peak of the growing season and peak NDVI. Averages in these phenology indices were correlated with three topographical factors. The start of growing season occurred later by 7.1, 5.2 and 3.5 days per 100 m elevation in the three grassland types (Alpine, Tall Tussock and Low Producing grasslands). The end of the season occurred earlier by 1.8, 1.6 days and later by 0.4 days per 10-degree more south-facing (colder) aspects in the three grasslands. A longer growing season was observed at lower elevation and on north-facing (sunny) slopes in alpine grasslands. A later season peak occurred at higher elevation and on north-facing slopes in alpine grasslands and at higher elevations and on steeper slopes in non-alpine grasslands. Higher peak NDVI was detected at the lower elevation. Our results show that different facets of a landscape's topography affect different stages of a grassland's growing season, and these responses also differ between grassland types. This highlights the importance of considering all topographical features when relationships between the physical environment and biological responses are investigated.

Off-peak NDVI correction to reconstruct Landsat time series for post-fire recovery in high-latitude forests

Collecting long-term satellite image series in high latitudes has been challenging due to its short growing season. For off-peak imagery, its reflective properties need to be corrected to maintain the spectral consistency. This study compares three statistical approaches to reconstructing a 30-year normalized difference vegetation index (NDVI) series for forest recovery assessment after the 1987 Black Dragon Fire in the Greater Hinggan Mountains Forest, Northeast China. To correct the off-peak NDVI to peak NDVI, the Landsat paired regression takes advantage of the scene-to-scene linear relationship between the two images, the GIMMS booster approach compensates the NDVI increment rate based on the 15-day AVHRR NDVI3g products between the two dates, and the climatic adjustment approach compensates the absolute NDVI change relying on the non-linear climatic influence on forest growth. The results find that the Landsat paired regression achieves the best performance with the image-wide residues within ± 0.2. The climatic adjustment picks the first-level NDVI under climatic control, while the GIMMS booster is heavily affected by the NDVI3g data quality. All approaches agree that the early-season (May-June) images are better sources for NDVI series reconstruction. The late-season images (especially October) are subject to fall senescence and early snowfall and therefore, are not recommended for satellite image series in high-latitude forests. The reconstructed NDVI series effectively corrects the off-season troughs along the trajectory. NDVI in burned forests increase rapidly in five years, and the heavily burned forests have the highest rate. Forest greenness could recover back to normal in ten years. This study confirms the feasibility of off-peak correction for building sparse image series. With advanced data availability such as the 5-day Sentinel-2 (10–60 m), daily MODIS imagery (500–1000 m), and hourly climatic reanalysis dataset (1–10 km), all three proposed approaches in this study could be improved for better application for post-fire monitoring of high-latitude forests.

Impacts of 319 wind farms on surface temperature and vegetation in the United States

The development of wind energy is essential for decarbonizing energy production. However, the construction of wind farms changes land surface temperature (LST) and vegetation by modifying land surface properties and disturbing land–atmosphere interactions. In this study, we used moderate resolution imaging spectroradiometer satellite data to quantify the impacts on local climate and vegetation of 319 wind farms in the United States. Our results indicated insignificant impacts on LST during the daytime but significant warming of 0.10 °C of annual mean nighttime LST averaged over all wind farms, and 0.36 °C for those 61% wind farms with warming. The nighttime LST impacts exhibited seasonal variations, with stronger warming in winter and autumn, up to 0.18 °C, but weaker effects in summer and spring. We observed a decrease in peak normalized difference vegetation index (NDVI) for 59% of wind farms due to infrastructure construction, with an average reduction of 0.0067 compared to non-wind farm areas. The impacts of wind farms depended on wind farm size, with winter LST impacts for large and small wind farms ranging from 0.21 °C to 0.14 °C, and peak NDVI impacts ranging from −0.009 to −0.006. The LST impacts declined with the increasing distance from the wind farm, with detectable impacts up to 10 km. In contrast, the vegetation impacts on NDVI were only evident within the wind farm locations. Wind farms built in grassland and cropland showed larger warming effects but weaker vegetation impact than those built on forests. Furthermore, spatial correlation analyses with environmental factors suggest limited geographical controls on the heterogeneous wind farm impacts and highlight the important role of local factors. Our analyses based on a large sample offer new evidence for wind farm impacts with improved representativeness compared to previous studies. This knowledge is important to fully understand the climatic and environmental implications of energy system decarbonization.

Increasing control of climate warming on the greening of alpine pastures in central Asia

High-altitude environments are among the most sensitive to climate change, where impacts are amplified. To clarify alpine pasture dynamics in three typical mountains (i.e., Altay, Tianshan, and Kunlun) in central Asia, the change trends in the long-term summer peak Normalized Difference Vegetation Index (NDVI) were examined by applying three satellite sources: Landsat ETM+/OLI (1999–2020), Global Inventory Modeling and Mapping Studies (1981–2013), and Moderate Resolution Imaging Spectroradiometer (2000–2020). Satellite-derived NDVIs are reliable because similar variations can be observed during overlapping periods. The results show that the alpine pastures experienced overall greening. The most significant greening was observed in Kunlun Mountains and the high-altitude areas of Tianshan and Altay Mountains, where the annual average temperature is lower. Relationships between NDVI and potential driving factors, such as average precipitation (AP), surface soil water content (SSWC), total water storage (TWS), snow cover frequency, growing degree days (GDD), and average temperature (AT), were further analyzed. Over the last two decades, the optimal habitat of alpine pastures became warmer and wetter, as the AT, GDD, SSWC, TWS all showed increased trends. However, AP decreased at a rate of –0.625 mm/year, indicating that melting ice materials under current warming helped preserve moisture and soil water availability for pasture growth. Additionally, the primary factor regulating greenness patterns shifted from AP during 2002–2011 to GDD during 2012, 2014, and 2016–2020, indicating that the alpine pastures in central Asia have been increasingly dependent on climate warming. Our study highlights that the response of alpine pastures to climatic factors might change under ongoing climate change.

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

Using MODIS-NDVI Time Series to Quantify the Vegetation Responses to River Hydro-Geomorphology in the Wandering River Floodplain in an Arid Region

Vegetation, hydrology and geomorphology are three major elements of the floodplain ecosystem on Earth. Although the normalized difference vegetation index (NDVI) has been used extensively to characterize floodplain vegetation growth, vigour and biomass, methods for quantifying the various distinct responses of floodplain vegetation to hydro-geomorphological changes in different lateral belts in arid regions are still needed. In this study, the Linhe reach was divided into four lateral belts based on their hydro-geomorphological characteristics, and the Moderate Resolution Imaging Spectroradiometer (MODIS)-NDVI time series statistical indicators were used to characterise the distinct changing the patterns of vegetation growth in different belts. The response of floodplain vegetation to river hydro-geomorphology in each belt was analysed. The result showed that the average maximum NDVI value in the regular inundation area was 0.23 and higher than that in the other lateral belts. The correlation between the water persistence time and peak NDVI value in the regular water inundation area was significant (ρ = 0.84), indicating that in contrast to highly frequent or extremely rare water inundation, regular water inundation provides significant benefits to floodplains. Continuous or highly frequent inundation may cause decreased vegetation productivity. Overall, our results suggest that the vegetation greenness response to the river hydro-geomorphology is different from the river to the edge of the floodplain. Thus, a better understanding of the interactions between the floodplain vegetation and river hydro-morphology and river water resource management in arid-region floodplains.

A slow rainy season onset is a reliable harbinger of drought in most food insecure regions in Sub-Saharan Africa.

Since 2015, Sub-Saharan Africa (SSA) has experienced an unprecedented rise in acute food insecurity (AFI), and current projections for the year 2020 indicate that more than 100 million Africans are estimated to receive emergency food assistance. Climate-driven drought is one of the main contributing factors to AFI, and timely and appropriate actions can be taken to mitigate impacts of AFI on lives and livelihoods through early warning systems. To support this goal, we use observations of peak Normalized Difference Vegetation Index (NDVI) as an indicator of seasonal drought conditions following a rainy season to show that delays in the onset of the rainy season (onset date) can be an effective early indicator of seasonal drought conditions. The core of this study is an evaluation of the relationship of the onset dates and peak NDVI, stratified by AFI risks, calculated using AFI reports by the United States Agency of International Development (USAID)-funded Famine Early Warning Systems Network (FEWS NET). Several parts of SSA, mostly located in East Africa (EA), reported the “Crisis” phase of AFI—requiring emergency food assistance—at least one-third of the time between April 2011 to present. The results show that the onset date can effectively explain much of the interannual variability in peak NDVI in the regions with the highest AFI risk level, particularly in EA where the median of correlation (across all the Administrative Unit 2) varies between -0.42 to -0.68. In general, an onset date delay of at least 1 dekad (10 days) increases the likelihood of seasonal drought conditions. In the regions with highest risks of AFI, an onset delay of just 1 dekad doubles the chance of the standardized anomaly of peak NDVI being below -1, making a -1 anomaly the most probable outcome. In those regions, a 2-dekads delay in the onset date is associated with a very high probability (50%) of seasonal drought conditions (-1 standardized anomaly of NDVI). Finally, a multivariate regression analysis between standardized anomaly and onset date anomaly further substantiates the negative impacts of delay in onset date on NDVI anomaly. This relationship is statistically significant over the SSA as a whole, particularly in the EA region. These results imply that the onset date can be used as an additional critical tool to provide alerts of seasonal drought development in the most food-insecure regions of SSA. Early warning systems using onset date as a tool can help trigger effective mid-season responses to save human lives, livestock, and livelihoods, and, therefore, mitigate the adverse impacts of drought hazards.

A narrow window of summer temperatures associated with shrub growth in Arctic Alaska

Warming in recent decades has triggered shrub expansion in Arctic and alpine tundra, which is transforming these temperature-limited ecosystems and altering carbon and nutrient cycles, fire regimes, permafrost stability, land-surface climate-feedbacks, and wildlife habitat. Where and when Arctic shrub expansion happens in the future will depend in part on how different shrub communities respond to warming air temperatures. Here, we analyze a shrub ring-width network of 18 sites consisting of Salix spp. and Alnus viridis growing across the North Slope of Alaska (68–71°N; 164–149°W) to assess shrub temperature sensitivity and compare radial growth patterns with satellite NDVI (normalized difference vegetation index) data since 1982. Regardless of site conditions and taxa, all shrubs shared a common year-to-year growth variability and had a positive response to daily maximum air temperatures (Tmax) from ca. May 31 (i.e. Tmax ∼6 °C) to early July (i.e. Tmax ∼12 °C), two-thirds of which were significant correlations. Thus, the month of June had the highest shrub growth-temperature sensitivity. This period coincides with the seasonal increase in temperature and phenological green up on the North Slope indicated by both field observations and the seasonal cycle of NDVI (a proxy of photosynthetic activity). Nearly all of the sampled shrubs (98%) initiated their growth after 1960, with 74% initiated since 1980. This post-1980 shrub-recruitment pulse coincided with ∼2 °C warmer June temperatures compared to prior periods, as well as with positive trends in shrub basal area increments and peak summer NDVI. Significant correlations between shrub growth and peak summer NDVI indicate these radial growth patterns in shrubs reflect tundra productivity at a broader scale and that tundra vegetation on the North Slope of Alaska underwent a greening trend between 1980 and 2012.

Early high‐moisture wheat harvest improves double‐crop system: II. Soybean growth and yield

AbstractDouble cropping soybean [Glycine max (L.) Merr.] after winter wheat (Triticum aestivum L.) increases total food production without additional land. However, double‐crop soybean usually yields less than full‐season soybean, mainly due to late planting. We evaluated double‐crop soybean growth and yield as affected by early planting immediately after high‐moisture wheat harvest across 20 site‐years in five Mid‐Atlantic states during 2015–2017. At each site, six soybean cultivars from relative maturity group (rMG) 3.1–5.9 were planted at three to five dates in a 4‐ to 14‐d interval. Soybean growth, measured by normalized difference vegetation index (NDVI) across the growing season, was affected only by planting date. Although NDVI peaked near the R5 stage, it took 9–27 more days to reach the peak NDVI (0.84–0.98) for early‐planted soybean than for late‐planted soybean. Relative yield declined with planting dates, which explained 41–81% of the relative yield variability. The yield loss from delayed planting was greater in the north (33–80%; Pennsylvania, Maryland, and Delaware) than in the south (20–27%; Virginia, North Carolina) due to longer delay in planting and shorter growing season in the north. Soybean NDVI from the R1–R6 stages was associated with yield, with the strongest association (R2 = .55–.57) at the R2 and R3 stages. The area under the NDVI curve (AUNDVIC) was also strongly associated (R2 = .77) with relative yield, indicating an excellent tool for explaining double‐crop soybean yield loss due to poor growth. High‐moisture wheat harvest facilitated soybean planting 4–21 d earlier, which increased growth and yield.

The environmental drivers of annual variation in forest greenness are variable in the northern Intermountain West, USA

AbstractPeak snowpack in the western USA has decreased 21% since the mid‐20th century, and these trends are forecasted to continue over the next century. In water‐limited systems, forest productivity during the growing season is assumed to be linked with snowpack during the previous winter, but this linkage has proven not to be universal in studies across varying ecosystems. We compared peak Normalized Difference Vegetation Index (NDVI, a proxy for vegetation productivity) at relatively fine scales (30‐m pixel) to point measurements of snow water equivalent (SWE) at 169 locations scattered among the northern Intermountain West for the 25‐yr period between 1993 and 2017. We further compared NDVI to additional environmental variables including topographical variables (elevation, aspect, and slope), and climatic variables (Palmer Drought Severity Index, snowmelt date, and snowmelt length). We hypothesized that peak SWE would be a strong predictor of peak NDVI, but the strength of the SWE–NDVI relationships would vary spatially in our topographically complex study area. We observed weak relationships between snowpack and vegetation productivity, with only 10% of the locations displaying statistically significant correlations between annual peak SWE and peak NDVI. Furthermore, we determined that the response of NDVI to snowpack and environmental variables differed among subregions of our study area. For example, we observed strong SWE–NDVI associations in the continental‐most portion of our study area and weak correlations in other subregions of our study area. Our results highlight that in regions characterized by complex topography, (1) environmental drivers of NDVI are highly variable and (2) fine‐resolution studies more accurately capture the spatial heterogeneity of NDVI compared to course‐resolution studies. However, one consequence of fine‐resolution studies may be that microclimate effects at each site swamp the signal of predictor‐ecological response relationships, whereas these relationships may be more discernable in larger scale remote sensing studies.

Satellite-derived foresummer drought sensitivity of plant productivity in Rocky Mountain headwater catchments: spatial heterogeneity and geological-geomorphological control

Long-term plot-scale studies have found water limitation to be a key factor driving ecosystem productivity in the Rocky Mountains. Specifically, the intensity of early summer (the ‘foresummer’ period from May to June) drought conditions appears to impose critical controls on peak ecosystem productivity. This study aims to (1) assess the importance of early snowmelt and foresummer drought in controlling peak plant productivity, based on the historical Landsat normalized-difference vegetation index (NDVI) and climate data; (2) map the spatial heterogeneity of foresummer drought sensitivity; and (3) identify the environmental controls (e.g. geomorphology, elevation, geology, plant types) on drought sensitivity. Our domain (15 × 15 km) includes four drainages within the East Water watershed near Gothic, Colorado, USA. We define foresummer drought sensitivity based on the regression slopes of the annual peak NDVI against the June Palmer Drought Severity Index between 1992 and 2010. Results show that foresummer drought sensitivity is spatially heterogeneous, and primarily dependent on the plant type and elevation. In support of the plot-based studies, we find that years with earlier snowmelt and drier foresummer conditions lead to lower peak NDVI; particularly in the low-elevation regions. Using random forest analysis, we identify additional key controls related to surface energy exchanges (i.e. potential net radiation), hydrological processes (i.e. microtopography and slope), and underlying geology. This remote-sensing-based approach for quantifying foresummer drought sensitivity can be used to identify the regions that are vulnerable or resilient to climate perturbations, as well as to inform future sampling, characterization, and modeling studies.

Monitoring the Seasonal Hydrology of Alpine Wetlands in Response to Snow Cover Dynamics and Summer Climate: A Novel Approach with Sentinel-2

Climate change in the European Alps during recent years has led to decreased snow cover duration as well as increases in the frequency and intensity of summer heat waves. The risk of drought for alpine wetlands and temporary pools, which rely on water from snowmelt and provide habitat for specialist plant and amphibian biodiversity, is largely unknown and understudied in this context. Here, we test and validate a novel application of Sentinel-2 imagery aimed at quantifying seasonal variation in water surface area in the context of 95 small (median surface area <100 m2) and shallow (median depth of 20 cm) alpine wetlands in the French Alps, using a linear spectral unmixing approach. For three study years (2016–2018), we used path-analysis to correlate mid-summer water surface area to annual metrics of snowpack (depth and duration) and spring and summer climate (temperature and precipitation). We further sought to evaluate potential biotic responses to drought for study years by monitoring the survival of common frog (Rana temporaria) tadpoles and wetland plant biomass production quantified using peak Normalized Difference Vegetation Index (NDVI). We found strong agreement between citizen science-based observations of water surface area and Sentinel-2 based estimates (R2 = 0.8–0.9). Mid-summer watershed snow cover duration and summer temperatures emerged as the most important factors regulating alpine wetland hydrology, while the effects of summer precipitation, and local and watershed snow melt-out timing were not significant. We found that a lack of summer snowfields in 2017 combined with a summer heat wave resulted in a significant decrease in mid-summer water surface area, and led to the drying up of certain wetlands as well as the observed mortality of tadpoles. We did not observe a negative effect of the 2017 summer on the biomass production of wetland vegetation, suggesting that wetlands that maintain soil moisture may act as favorable microhabitats for above treeline vegetation during dry years. Our work introduces a remote sensing-based protocol for monitoring the surface hydrology of alpine wetland habitats at the regional scale. Given that climate models predict continued reduction of snow cover in the Alps during the coming years, as well as particularly intense warming during the summer months, our conclusions underscore the vulnerability of alpine wetlands in the face of ongoing climate change.

Optimal Soybean (Glycine max L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images

Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability.

Phenology Patterns Indicate Recovery Trajectories of Ponderosa Pine Forests After High-Severity Fires

Post-fire recovery trajectories in ponderosa pine (Pinus ponderosa Laws.) forests of the southwestern United States are increasingly shifting away from pre-burn vegetation communities. This study investigated whether phenological metrics derived from a multi-decade remotely sensed imagery time-series could differentiate among grass, evergreen shrub, deciduous, or conifer-dominated replacement pathways. We focused on 10 fires that burned ponderosa pine forests in Arizona and New Mexico, USA before the year 2000. A total of 29 sites with discernable post-fire recovery signals were selected within high-severity burn areas. At each site, we used Google Earth Engine to derive time-series of normalized difference vegetation index (NDVI) signals from Landsat Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imager data from 1984 to 2017. We aggregated values to 8- and 16-day intervals, fit Savitzky–Golay filters to each sequence, and extracted annual phenology metrics of amplitude, base value, peak value, and timing of peak value in the TIMESAT analysis package. Results showed that relative to post-fire conditions, pre-burn ponderosa pine forests exhibit significantly lower mean NDVI amplitude (0.14 vs. 0.21), higher mean base NDVI (0.47 vs. 0.22), higher mean peak NDVI (0.60 vs. 0.43), and later mean peak NDVI (day of year 277 vs. 237). Vegetation succession pathways exhibit distinct phenometric characteristics as early as year 5 (amplitude) and as late as year 20 (timing of peak NDVI). This study confirms the feasibility of leveraging phenology metrics derived from long-term imagery time-series to identify and monitor ecological outcomes. This information may be of benefit to land resource managers who seek indicators of future landscape compositions to inform management strategies.

Association between residential greenness and exposure to volatile organic compounds

Residential proximity to vegetation and plants is associated with many health benefits, including reduced risk of cardiovascular disease, diabetes and mental stress. Although the mechanisms by which proximity to greenness affects health remain unclear, plants have been shown to remove particulate air pollution. However, the association between residential-area vegetation and exposure to volatile organic chemicals (VOCs) has not been investigated. We recruited a cohort of 213 non-smoking individuals and estimated peak, cumulative, and contemporaneous greenery using satellite-derived normalized difference vegetation index (NDVI) near their residence. We found that the urinary metabolites of exposure to VOCs - acrolein, acrylamide, acrylonitrile, benzene, 1-bromopropane, propylene oxide were inversely associated (7–31% lower) with 0.1 higher peak NDVI values within 100 m radius of the participants’ home. These associations were significant at radii ranging from 25 to 300 m. Strongest associations were observed within a 200 m radius, where VOC metabolites were 22% lower per 0.1 unit higher NDVI. Of the 18 measured urinary metabolites, 7 were positively associated with variation of greenness within a 200 m radius of homes. The percent of tree canopy and street trees around participants’ residence were less strongly associated with metabolite levels. The associations between urinary VOC metabolites and residential NDVI values were stronger in winter than in summer, and in participants who were more educated, White, and those who lived close to areas of high traffic. These findings suggest high levels of residential greenness are associated with lower VOC exposure, particularly in winter.

Assessment of Wheat Straw Cover and Yield Performance in a Rice-Wheat Cropping System by Using Landsat Satellite Data

Proper straw cover information is one of the most important inputs for agroecosystem and environmental modeling, but the availability of accurate information remains limited. However, several remote-sensing (RS)-based studies have provided a residue cover estimation and provided spatial distribution mapping of paddy rice areas in a constant field condition. Despite this, the performance of rice crops with straw applications has received little attention. Furthermore, there are no methods currently available to quantify the wheat straw cover (WSC) percentage and its effect on rice crops in the rice-wheat cropping region on a large scale and a continuous basis. The novel approach proposed in this study demonstrates that the Landsat satellite data and seven RS-based indices, e.g., (i) normalized difference vegetation index (NDVI), (ii) Normalized difference senescent vegetation index (NDSVI), (iii) Normalized difference index 5 (NDI5), (iv) Normalized difference index 7 (NDI7), (v) Simple tillage index (STI), (vi) Normalized difference tillage index (NDTI), and (vii) Shortwave red normalized difference index (SRNDI), can be used to estimate the WSC percentage and determine the performance of rice crops over the study area in Changshu county, China. The regression model shows that the NDTI index performed better in differentiating the WSC at sampling points with a coefficient of determination (R2 = 0.80) and root mean squared difference (RMSD = 8.46%) compared to that of other indices, whereas the overall accuracy for mapping WSC was observed to be 84.61% and the kappa coefficient was κ = 0.76. Moreover, the rice yield model was established by correlating between the peak NDVI values and rice grain yield collected from ground census data, with R2 = 0.85. The finding also revealed that the highest estimated yield (8439.67 kg/ha) was recorded with 68% WCS in the study region. This study confirmed that the NDVI and NDTI algorithms are very effective and robust indicators. Also, it can be strongly concluded that multispectral Landsat satellite imagery is capable of measuring the WSC percentage and successively determines the impact of different WSC percentages on rice crop yield within fields or across large regions through remote sensing (RS) and geographical information system (GIS) techniques for the long-term planning of agriculture sustainability in rice-wheat cropping systems.