Spatiotemporal crop NDVI responses to climatic factors in mainland China

Climate change has caused a great impact on vegetation growth, production and distribution through variations of precipitation, temperature and sunshine. In this study, a categorization of zones for vegetation responses to climatic variability was conducted. Seasonal and annual crop responses to climate change in each region were analysed with multiple linear regression. The results show that the annual impact of climatic factors on crop growth was most significant in lower North China (R² = 0.48) and most insignificant in Northeast China (R² = 0.22). Temperature is the limiting climatic factor for crop growth annually in North China and Northeast China (zones 1–3), (p ≤ 0.05), while sunshine duration plays an important role for crop growth in zones which are more southern (zones 3 ~ 5). Precipitation significantly affects the annual crop growth in Inner Mongolia-Hebei-Shandong zone (zone 2) and Southeast zone (zone 5). Therefore, more attention should be paid to these zones. The spring temperature is the limiting climatic factor for crop growth in all the zones (p ≤ 0.05). Spring warming is helpful for crop growth in mainland China. Different agricultural and administrative measures should be taken in each zone to adapt to future climate change.     

Recent trends of ice phenology for eight large lakes using MODIS products in Northeast China

Optical remote sensing images with high temporal resolution can be used to monitor lake ice phenology, a periodic freezing and thawing cycle of ice resulting from seasonal and inter-annual climate variations. In the research reported here, we used MODIS satellite data to establish the time series of lake ice extent and extracted lake ice phenology dates and durations for eight large typical lakes in Northeast China for the hydrological years from 2003 to 2016. The MODIS-derived results were validated against ice records at hydrological stations. The mean absolute error for a freeze-up start (FUS), freeze-up end (FUE), break-up start (BUS) and break-up end (BUE) was 3.1, 4.8, 6.6 and 6.6 days, respectively. Our findings indicated that the investigated lakes were tending to freeze later and melt earlier and were frozen for a shortened period over time. FUS was experiencing a delay of 0.65 days per year and BUE was advancing by 0.19 days per year, implying a decrease of frozen duration (FD) of 0.84 days per year taking all eight lakes into consideration. The lake ice duration increased with latitude, and the lakes with a relatively smaller area had a higher yearly rate of change and were more variable compared with the larger ones. The relationship between lake ice phenology and other influencing factors was evaluated using correlation coefficients and partial least squares regression. The results showed that the freeze-up process was more dependent on the lake morphometry, while the break-up process was more dependent on climate changes, particularly on air temperature, which had the highest correlation coefficient (r = ?0.69, p < 0.01).     

Sensitivity of large-scale vegetation greenup and dormancy dates to climate change in the north−south transect of eastern China

Phenology event responses, based on vegetation types, are strong indicators of climate variability and the ability of the vegetation to adapt to future climate changes. However, the sensitivity of phenology events to climate change along either environmental or vegetation type gradients is rarely examined. Phenological curves of major vegetation types along the North?South Transect of Eastern China (NSTEC) have been developed using wavelet and smooth-spline methods based on the normalized difference vegetation index from 1982 to 2006. Spatial?temporal patterns, trends of greenup-onset dates, dormancy dates, and growing season lengths (GSLs) during the period of 1982?2006 are presented.

The greenup-onset dates were most significantly and negatively related to the temperature in cold and humid areas, but insignificantly and positively in semi-arid regions. However, dormancy date showed a positive correlation with temperature. In populations of the same vegetation type, distributed along thermal gradients of NSTEC, the phenology sensitivities to warming were different. Greenup sensitivities of cold temperate coniferous forest (CTCF) and temperate meadow steppe (TMS) increased significantly from??6.0 to 0 days °C^?1 (p < 0.001) and from about??2.0 to 2.0 days °C^?1 (p < 0.001), respectively. In contrast, temperate grass steppe (TGS) and temperate deciduous shrubland (TDS) showed a decreased trend of greenup sensitivity from 2.0 to??4.0 days °C^?1 (p < 0.001) and from 2.0 to??6.0 days °C^?1 (p < 0.001), respectively. For the dormancy date sensitivity, CTCF showed a decreasing trend from about 6.0 to 0 days °C^?1 (p < 0.001), and subtropical evergreen-broadleaved forest (SEBF) decreased from 5.0 to??5.0 days °C^?1 (p < 0.05).     

Remote sensing the orographic effects of dry-hot valley on vegetation distribution in the southeast Tibetan Plateau

ABSTRACT

Mountains in the southeast Tibetan Plateau (TP) often intercept and precipitate abundant monsoon-transported vapours, but some deep valleys of this region are likely subjected to heavy water stress possibly related to orographic effects. Understanding the orographic effects of these dry-hot valleys (DHV) on vegetation distribution is crucial to project local ecological response to global warming. In the study, we used multiple satellite observations with limited in-situ records to investigate the links between vegetation cover and geomorphology in the southeast TP. We designed two types of transects to distinguish altitudinal properties of heat and vegetation between the DHV and non-DHV areas with satellite-retrieved enhanced vegetation index and land surface temperature (LST). Our results showed that the DHVs are characterized by the seemingly ‘abnormal’ decreasing of vegetation density from intermediate elevation simultaneously towards both ridge and valley. The significant increase in LST lapse rate with valley depth (1.8 × 10^?3°C km^?1 m^?1, p < 0.01) suggested the positive role of local valley wind system in the DHV development. Satellite observations revealed that there are, respectively, about 530, 420, and 300 km of DHVs developed in the Nujiang, Lancangjiang, and upper Yangtze rivers, and the DHVs are mostly deeper than 1600 m. Current global warming may lead to the altitudinal expansion of DHV dry and hot effects on local ecosystems, which should be carefully accounted in local ecosystem conservation and management.     

Natural vegetation responses to warming climates in Qaidam Basin 1982–2003

In this paper, we quantified vegetation variations in the Qaidam Basin from 1982 to 2003 by using growing-season NDVI sequences, which were defined as the summation of monthly NDVI values from May to September, and were calculated pixel-by-pixel from a successive 8-km NDVI dataset. We adopt linear regressions to examine the trends in growing-season NDVI and the trends in climate (temperature, precipitation and sunshine duration) during this period in an attempt to depict their temporal and spatial variability. Our results indicate that climate in the Qaidam Basin has homogeneously warmed at a rate of about 0.6°C/decade during the study period, with significant trends in monthly mean temperatures in April–September. However, there were no statistically significant trends observed in precipitation and sunshine duration. We found positive growing-season NDVI trends in 31.6% of the vegetated lands in 1982–2003 and in 24.1% over the first half period, 1982–1992. In addition, few areas were shown to have negative trends during these periods. In 1993–2003, however, the percentage of land with a positive trend decreased to 13.1%, and the percentage of vegetated land with a negative trend increased to 10.2%. Growing-season NDVI trends show both temporal and spatial variability. Areas with negative trends are distributed mostly at lower elevations and near oasis boundaries, and areas with positive trends at higher elevations. Using correlation analyses we estimated the relationship between growing-season NDVI and the climatic factors with the consideration of duration and lagging effects. The results suggest that growing-season NDVI trends are more correlated to temperature increases in growing-season months when compared to variations in precipitation and sunshine duration; however increased precipitation amounts within May–August can also facilitate vegetation growth in some of this arid basin. However, we found no significant correlations between growing-season NDVI and temperature in the non-trend areas, which account for the majority of the vegetated land. We suggest that the variability in vegetation responses to the observed warming climates results from the differences in background thermal condition and moisture availability, which depend on elevation and other factors, such as hydrological conditions.     

Experimental investigation of psychophysiological stress-reactions induced by different system response times in human-computer interaction∗

In human-computer interaction, system response times are considered to have important effects on operator performance and stress response. To evaluate the effects of short (2s) and long (8s), as well as constant and variable, system response times, a laboratory study was conducted with 68 subjects in four independent groups working at a simulated computer workplace. Subjects had to perform a simple detection and correction task at a visual display terminal in six trials of 20min each, the first being a training trial with identical conditions for all subjects. Performance and physiological measures (heart rate, electrodermal activity, and blood pressure) were taken during the trials, and subjective measures of mood and physical state as well as physiological measures were done in the resting periods iiftcr the trials. In addition to a general adaptation effect over the trials, experimental effects were shown mainly for the duration factor: subjects under conditions of long, as compared to those under short, system response times showed a lower mean error rate (p < 0·01) in the performance measures without differences in work speed. In the physiological measures, lower levels of systolic blood pressure (p < 0·05) were seen under long system response times, but a greater number of skin conductance reactions (p < 0·05). Pain symptoms (headache, p < 0·05; eye pain, p < 0·05) were more pronounced under short system response times. The results are interpreted as differential effects of duration of system response times on variables indicating stress responses. In general, the results obtained in this study confirm the results of a previous pilot study. Further research will be necessary to evaluate the effects of system response time variability.     

Satellite-based analysis of regional evapotranspiration trends in a semi-arid area

A regional framework for a spatial and temporal distributed assessment of time series trends in the hydrological variable and its related ecological factors of an arid area was presented in this research. To achieve this, we tested the Surface Energy Balance System (SEBS) algorithm for estimating evapotranspiration (ET) in regional scale and the result was further validated by water budget. The ET assessment was applied for the Yinchuan and Weining (YW) Plains (China), the upstream areas of the Yellow River. Moreover, we analysed the recent trends (from year 2001 to 2014) in actual ET, normalized difference vegetation index (NDVI), farmland and wetland using a combination of remote sensing and ground observations. The results illustrated that the yearly ET of 78% areas has no change during the study period and the areas with decreasing ET are larger than the areas with increasing trend. The highest decreasing rate is observed in urban areas and the value is –20 mm year^–1, while the rate of increasing trend is especially higher in the wetlands reaching around 60 mm year^–1. This phenomenon can be explained by changes in NDVI, farmland and wetland. The distribution and magnitude of NDVI trends shows that the higher NDVI (NDVI > 0.4) area is occupied 56% and the farmland (NDVI > 0.55) covered about 35% of the YW Plains. The increasing trends of mean NDVI is mostly observed in farmland and shrub covers, while the decreasing NDVI areas are mainly wetlands and urban area. The emerging image showed that the greening trend of vegetation and variation of evapotranspiration in the YW Plains are related to land-cover changes and to the adjustment of crop pattern for agriculture. The increasing of wetland area is also believed as a cause related to evapotranspiration change. Such spatiotemporally distributed analysis in regional level is important for water management at this scale, which can be also applied to other similar areas.     

Vegetation phenology and its variations in the Tibetan Plateau,China

Understanding the vegetation phenology and its variations in the Tibetan Plateau is critical to the study of ecological responses to global climate change. In this study, several pre-processed methods or techniques were applied to filter the Global Inventory Modelling and Mapping Study’s Normalized Difference Vegetation Index (GIMMS NDVI) data from 1982 to 2006, and construct the daily NDVI series. Firstly, vegetation and non-vegetation were determined by NDVI quantity contour, and cloud-covered pixels were also eliminated by NDVI change characteristics in a year. Then, the NDVI series were filtered by three-standard deviation and Savitzky-Golay method. Finally, the Savitzky–Golay method was employed to fit and construct the daily NDVI series. These methods guarantee a more reliable subsequent calculation of subsequent vegetation phenology. The vegetation phenology parameters including the start of growth season (SOG), the end of growth season (EOG), the lengths of growth season (LOG) and the absolute increase in vegetation (AIV), defined as the difference between the maximum NDVI and the NDVI for SOG in a year, were derived from the daily NDVI series based on the maximum ratio threshold method and their variations were analysed. The results showed that the SOGs were gradually delayed from the southeast to the northwest of the Tibetan Plateau, but the distribution pattern of the EOGs was opposite to that of the SOGs. From 1982 to 2006, SOGs were advanced approximately 3–18 days and EOGs delayed around 0–24 days in the southeast, whereas AIVs decreased around 0–0.3. In the northwest, these phenology parameters followed inverse trends compared with those of the southeast. Over the 25-year period, LOG changes had no constructive or active effects on the vegetation absolute increase. These complex phenological shifts were mainly due to the spatial differences in the environmental changes. However, in some extent, they might be related to the vegetation itself, such as its fractional cover. These findings may help to understand the alpine vegetation responds to climate change in the Tibetan Plateau.     

Major trends in the land surface phenology (LSP) of Africa,controlling for land-cover change

ABSTRACT

Monitoring land surface phenology (LSP) trends is important in understanding how both climatic and non-climatic factors influence vegetation growth and dynamics. Controlling for land-cover changes in these analyses has been undertaken only rarely, especially in poorly studied regions like Africa. Using regression models and controlling for land-cover changes, this study estimated LSP trends for Africa from the enhanced vegetation index (EVI) derived from 500 m surface reflectance Moderate-Resolution Imaging Spectroradiometer (MOD09A1), for the period from 2001 to 2015. Overall end of season showed slightly more pixels with significant trends (12.9% of pixels) than start of season (11.56% of pixels) and length of season (LOS) (5.72% of pixels), leading generally to more ‘longer season’ LOS trends. Importantly, LSP trends that were not affected by land-cover changes were distinguished from those that were influenced by land-cover changes such as to map LSP changes that have occurred within stable land-cover classes and which might, therefore, be reasonably associated with climate changes through time. As expected, greater slope magnitudes were observed more frequently for pixels with land-cover changes compared to those without, indicating the importance of controlling for land cover. Consequently, we suggest that future analyses of LSP trends should control for land-cover changes such as to isolate LSP trends that are solely climate-driven and/or those influenced by other anthropogenic activities or a combination of both.     

Retrieving and broadcasting near-real-time biophysical parameters from MODIS and SEVIRI receiving stations at the global change unit of the University of Valencia

We present here the automatic processing chains implemented at the Global Change Unit of the University of Valencia. These allow for a near-real-time retrieval of various biophysical parameters from both Sun-synchronous TERRA/AQUA Moderate Resolution Imaging Spectroradiometer (MODIS) and geostationary Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI) sensors. Retrieved parameters, namely sea and land surface temperatures (SST and LST, respectively), normalized difference vegetation index (NDVI), and vegetation condition index (VCI), are similar for both sensors, and specific approaches have been developed and implemented for near-real-time parameter retrievals: <2 hours for MODIS and <5 min for MSG SEVIRI. Bidirectional reflectance distribution function (BRDF) correction is still required to be implemented in both processing chains, while more advanced parameters are already retrieved (hot spot detection and MSG SEVIRI phenology), in good agreement with independent ground observations. Validation of the retrieved products is underway and the above-mentioned products are available for downloading at http://ceosspain.lpi.uv.es.     

Autumn leaf phenology: discrepancies between in situ observations and satellite data at urban and rural sites

ABSTRACT

Autumn phenophases, such as leaf colouration (LC) and leaf fall (LF), have received considerably less attention than their spring counterparts (budburst and leaf unfolding) but are equally important determinants of the duration of the growing season and thus have a controlling in?uence on the carbon-uptake period. Here, we examined THE trends (1968–2016) in in situ observations of the timing of LC and LF from a suite of deciduous trees at three rural sites and one urban site in Ireland. Satellite-derived autumn phenological metrics including mid-senescence (MS) and end of senescence (ES) based on two-band enhanced vegetation index (EVI2) from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) from 1982 to 2016 at a spatial resolution of 5km² were also examined. The aim of this study was to assess the effectiveness of satellite remote sensing in capturing autumn phenology as determined by in situ observations . Analysis of in situ data (1968–2016) revealed the urban site to be significantly different from the rural sites as LC and LF occurred later in the season and the duration of the autumn season (LF-LC) became shorter over time. These trends may be partly driven by the presence of artificial light in the city. On average (1982–2016), there was a 6-day delay in the timing of MS compared to LC and a much larger difference (21 days) between ES and LF. This resulted in a 31-day autumn duration as defined by satellite data compared to 16 days from in situ observations. Furthermore, there was little overlap in timing between LC and MS, and LF and ES at the rural sites only. Discrepancies between in situ and satellite data may be attributed to the satellite data integrating a much broader vegetation signal across a heterogeneous landscape than in situ observations of individual trees. Therefore, at present, satellite-derived autumn phenology may be more successful in capturing in situ observations across large homogeneous landscapes of similar vegetation types (e.g. forested areas) than in heterogeneous landscapes (e.g. small mixed farms, urban areas, etc.) as is the case in Ireland where the in situ observations of trees may not be reflective of the overall vegetation. Matching the scale of satellite data with in situ observations remains a challenging task but may, at least in part, be overcome by increasing the extent of observations to include a wider range of species and in future as satellite data become available at higher spatial and temporal resolutions.     

A global NDVI and EVI reference data set for land-surface phenology using 13 years of daily SPOT-VEGETATION observations

Time series of vegetation indices (VIs) obtained by remote sensing are widely used to study phenology on regional and global scales. The aim of the study is to design a method and to produce a reference data set describing the seasonal and inter-annual variability of the land-surface phenology on a global scale. Specific constraints are inherent in the design of such a global reference data set: (1) the high diversity of vegetation types and the heterogeneous conditions of observation, (2) a near-daily resolution is needed to follow the rapid changes in phenology, (3) the time series used to depict the baseline vegetation cycle must be long enough to be representative of the current vegetation dynamic and encompass anomalies, and (4) a spatial resolution consistent with a land-cover-specific analysis should be privileged. This study focuses on the SPOT (Satellite Pour l’Observation de la Terre)-VEGETATION sensor and its 13-year time series of reflectance values. Five steps addressing the noise and the missing data in the reflectance time series were selected to process the daily multispectral reflectance observations. The final product provides, for every pixel, three profiles for 52 × 7-day periods: a mean, a median, and a standard deviation profile. The mean and median profiles represent the reference seasonal pattern for variation of the vegetation at a specific location whereas the standard deviation profile expresses the inter-annual variability of VIs. A quality flag at the pixel level demonstrated that the reference data set can be considered as a reliable representation of the vegetation phenology in most parts of the Earth.     

Temporal and spatial dynamics of phenology along the North–South Transect of Northeast Asia

Vegetation phenology is sensitive to climate change and, as such, is often regarded as an indicator of climate change. It is a common practice to extract vegetation phenological indicators based on satellite remote sensing data. In this study, we used the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Study (GIMMS) Third-Generation normalized difference vegetation index (NDVI_3G) to investigate temporal and spatial changes in phenology in Northeast Asia. Based on the maximum rate of change in the NDVI and dynamic threshold, we used the Asymmetric Gaussian model, Double Logistic method, and Savitzky-Golay filter to extract the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS), respectively, along the North–South Transect of Northeast Asia (NSTNEA) from 1982 to 2014. We then compared the differences in SOS, EOS, and LOS and considered their spatio-temporal dynamics and relationship with temperature. The results show that the Asymmetric Gaussian model has the highest stability among the three methods. Dynamic thresholds corresponding to the maximum change rate of NDVI were mainly between 0.5 and 0.6. From 1982 to 2014, the SOS in the NSTNEA region occurred approximately 0.19 days earlier each year; the trends in EOS and LOS were not significant. In general, temperature and latitude have a strong linear relationship, both of which significantly impact vegetation phenology in the NSTNEA region. In addition, elevation also significantly impacts on vegetation phenology in the NSTNEA region.     

Analysis of climate change impacts on lake ice phenology in Canada using the historical satellite data record

Variability and trends in lake ice dynamics (i.e. lake ice phenology) are related to climate conditions. Climate influences the timing of lake ice melt and freeze onset, ice duration, and lake thermal dynamics that feedback to the climate system initiating further change. Phenology records acquired in a consistent manner and over long time periods are required to better understand variability and change in climate conditions and how changes impact lake processes. In this study, we present a new technique for extracting lake ice phenology events from historical satellite records acquired by the series of Advanced Very High Resolution Radiometer (AVHRR) sensors. The technique was used to extend existing in-situ measurements for 36 Canadian lakes and to develop records for 6 lakes in Canada's far north. Comparison of phenology events obtained from the AVHRR record and in-situ measurements show strong agreement (20 lakes, 180 cases) suggesting, with high confidence especially in the case of break-up dates, the use of these data as a complement to ground observations. Trend analysis performed using the combined in-situ and AVHRR record ∼ 1950-2004 shows earlier break-up (average — 0.18 days/year) and later freeze-up (average 0.12 days/year) for the majority of lakes analyzed. Less confidence is given to freeze-up date results due to lower sun elevation during this period making extraction more difficult. Trends for the 20 year record in the far north showed earlier break-up (average 0.99 days/year) and later freeze-up (average 0.76 days/year). The established lake ice phenology database from the historical AVHRR image archive for the period from 1985 to 2004 will to a certain degree fill data gaps in the Canadian in-situ observation network. Furthermore, the presented extraction procedure is not sensor specific and will enable continual data update using all available satellite data provided from sensors such as NOAA/AVHRR, MetOp/AVHRR, MODIS, MERIS and SPOT/VGT.     

Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Leaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρ_red), near-infrared (ρ_NIR), shortwave infrared (ρ_SWIR1, ρ_SWIR2) reflectance bands (R² > 0.6), and all SVIs (R² > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R², low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R² = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.     

21世纪初中国农作物与自然植被物候时空差异

利用离散小波方法对2001~2012年MODIS EVI时序数据进行平滑,基于动态阈值法提取我国植被物候信息,探讨农作物和自然植被物候的时空变化特征。结果表明:(1)我国第一季农作物开始、峰值和结束日期主要以华北平原为中心随海拔的上升而推迟,而自然植被物候更早20d左右,且随海拔的上升先推迟后提前;(2)物候在时序上有显著变化的第一季区域,43.98%开始日期、52.83%峰值日期呈现提前趋势,多在开始晚、结束早的西南区及东北与内蒙古交界处,其余区域开始、峰值日期及81.80%结束日期呈推迟趋势,发生在开始早、结束晚的黄土高原及双季农作区;农作物物候推迟幅度小于自然植被。     

Quantifying characteristic growth dynamics in a semi-arid grassland ecosystem by predicting short-term NDVI phenology from daily rainfall: a simple four parameter coupled-reservoir model

Predicting impacts on phenology of the magnitude and seasonal timing of rainfall pulses in water-limited grassland ecosystems concerns ecologists, climate scientists, hydrologists, and a variety of stakeholders. This report describes a simple, effective procedure to emulate the seasonal response of grassland biomass, represented by the satellite-based normalized difference vegetation index (NDVI), to daily rainfall. The application is a straightforward adaptation of a staged linear reservoir that simulates the pulse-like entry of rainwater into the soil and its redistribution as soil moisture, the uptake of water by plant roots, short-term biomass development, followed by the subsequent transpiration of water through foliage. The algorithm precludes the need for detailed, site specific information on soil moisture dynamics, plant species, and the local hydroclimate, while providing a direct link between discrete rainfall events and consequential biomass responses throughout the growing season. We applied the algorithm using rainfall data from the Central Plains Experimental Range to predict vegetation growth dynamics in the semi-arid shortgrass steppe of North America. The mean annual rainfall is 342 mm, which is strongly bifurcated into a dominantly ‘wet’ season, where during the three wettest months (May, June and July) the mean monthly rainfall is approximately 55 mm month^?1; and a ‘dry’ season, where during the three driest months (December, January and February), the mean monthly rainfall is approximately 7 mm month^?1. NDVI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 16 day, 250 m × 250 m product were used as a proxy for grassland phenology for the period-of-record 2000–2013. Allowing for temporal changes in basic parameters of the response function over the growing season, the predicted response of the model tracks the observed NDVI metric with correlation coefficients exceeding 0.92. A two-stage series reservoir is preferred, whereby the characteristic time for transfer of a rainfall event to the peak response of NDVI decreases from 24 days (early growing season) to 12 days (late growing season), while the efficiency of a given volume of rainfall to produce a correspondingly similar amount of aboveground biomass decreases by a factor of 40% from April to October. Behaviours of the characteristic time of greenup and loss of rainfall efficiency with progression of the growing season are consistent with physiological traits of cool-season C₃ grasses versus warm-season C₄ grasses, and with prior research suggesting that early season production by C₃ grasses is more responsive to a given amount of precipitation than mid-summer growth of C₄ shortgrasses. Our model explains >90% of seasonal biomass dynamics. We ascribe a systematic underprediction of observed early season greenup following drought years to a lagged or ‘legacy’ effect, as soil inorganic nitrogen, accumulated during drought, becomes available for future plant uptake.     

Comparison of vegetation phenology in the western USA determined from reflected GPS microwave signals and NDVI

The normalized microwave reflection index (NMRI) is a measure of multipath scattering calculated daily from continuously operating GPS sites. GPS satellites transmit L-band microwave signals, and thus NMRI is sensitive to the amount of water in vegetation, not plant greenness or dry biomass. The sensing footprint is approximately 1000 m², although reflections from a distance of hundreds of metres are important at some sites. NMRI exhibits clear seasonal variations that are linked to the changes in vegetation water content that accompany plant growth and senescence. In this paper, NMRI and the normalized difference vegetation index (NDVI) are compared for the period 2008–2012. NMRI data are derived from 184 GPS sites in the western USA. NDVI data are from the 250 m, 16-day pixel containing each GPS station. Amplitude of the annual growth cycle and correlation between NMRI and NDVI are estimated, with and without lags. Phenology metrics are calculated from both indices (i.e. the start of the growing season, timing of peak growth, and season length).

NMRI and NDVI are correlated at most sites, but the degree of correlation varies regionally. Correlation is lowest in California and coastal regions (R = 0.25), where NDVI increases earlier in the spring than NMRI. It is highest for mountain and prairie sites (R = 0.66 and 0.73, respectively). Allowing for a lag between NMRI and NDVI greatly increases the correlation. The lag that yields the greatest correlation is nearly 30 days for the California sites (R = 0.71 with lag), but only 10 days for mountain and prairie sites (R = 0.78 and 0.85 with lag). There are clear differences in phenology metrics extracted from NMRI and NDVI that are consistent with the correlation-lag analysis. Using NMRI, there is a later start to the season, later peak day of the year, and shorter season length. The greatest differences are in California where NDVI start of the season is nearly 60 days earlier than that calculated from NMRI. These data suggest that green-up precedes increases in vegetation water content, with the duration of offset varying regionally. This study is the first to compare GPS-derived microwave reflectance data with NDVI at multiple sites, and highlights both opportunities and limitations offered by NMRI data.     

The potential role of very high-resolution imagery to characterise lake,wetland and stream systems across the Prairie Pothole Region,United States

Aquatic features critical to watershed hydrology range widely in size from narrow, shallow streams to large, deep lakes. In this study we evaluated wetland, lake, and river systems across the Prairie Pothole Region to explore where pan-sharpened high-resolution (PSHR) imagery, relative to Landsat imagery, could provide additional data on surface water distribution and movement, missed by Landsat. We used the monthly Global Surface Water (GSW) Landsat product as well as surface water derived from Landsat imagery using a matched filtering algorithm (MF Landsat) to help consider how including partially inundated Landsat pixels as water influenced our findings. The PSHR outputs (and MF Landsat) were able to identify ~60–90% more surface water interactions between waterbodies, relative to the GSW Landsat product. However, regardless of Landsat source, by documenting many smaller (<0.2 ha), inundated wetlands, the PSHR outputs modified our interpretation of wetland size distribution across the Prairie Pothole Region.