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Climate change is altering the timing of seasonal events for many taxa. There is limited understanding of how northward/southward songbird migration follows or is limited by the latitudinal progression of seasonal transitions. Consistent environmental conditions that migrating birds encounter across latitudes likely represent or correlate with important resources or limiting factors for migration. We tested whether migratory passage—observed via radar—consistently tracked land surface variables and phenophases across latitudes in the US Central Flyway in both spring and fall. The daily temperatures, precipitation and vegetation greenness occurring on 10%, 50% and 90% cumulative passage dates changed substantially with latitude, indicating that most migrants experienced rapidly changing conditions as they headed north or south. Temperature did not limit the progression of migration in either season. Peak spring migration in the southern US occurred nearly 40 days after the spring green wave, the northward progression of vegetation growth, but nearly caught up to green‐up at 48° N. Spring migration phenology may have evolved to prioritize earlier arrival for breeding. Across all latitudes, peak fall migration coincided with the same land surface phenophase, an interval of 26 days prior to dormancy onset. Migrants may rely on phenological events in vegetation during fall stopovers. Considering that (a) migratory passage tracked fall land surface phenology across latitudes at a continental scale, (b) previous studies at local scales have demonstrated the importance of fruit during fall migratory stopover and (c) fruiting phenology in North America is occurring later over time while fall migration is advancing, the potential for mismatch between fall fruiting and bird migration phenology urgently needs further investigation. [ABSTRACT FROM AUTHOR]

Across Eastern Africa, croplands cover 45 million ha. The regional economy is heavily dependent on small holder traditional rain-fed peasant agriculture (up to 90%), which is vulnerable to extreme weather events such as drought and floods that leads to food insecurity. Agricultural production in the region is moisture limited. Weather station data are scarce and access is limited, while optical satellite data are obscured by heavy clouds limiting their value to study cropland dynamics. Here, we characterized cropland dynamics in Eastern Africa for 2003-2015 using precipitation data from Tropical Rainfall Measuring Mission (TRMM) and a passive microwave dataset of land surface variables that blends data from the Advanced Microwave Scanning Radiometer (AMSR) on the Earth Observing System (AMSR-E) from 2002 to 2011 with data from AMSR2 from 2012 to 2015 with a Chinese microwave radiometer to fill the gap. These time series were analyzed in terms of either cumulative precipitable water vapor-days (CVDs) or cumulative actual evapotranspiration-days (CETaDs), rather than as days of the year. Time series of the land surface variables displayed unimodal seasonality at study sites in Ethiopia and South Sudan, in contrast to bimodality at sites in Tanzania. Interannual moisture variability was at its highest at the beginning of the growing season affecting planting times of crops, while it was lowest at the time of peak moisture. Actual evapotranspiration (ETa) from the simple surface energy balance (SSEB) model was sensitive to track both unimodal and bimodal rainfall patterns. ETa as a function of CETaD was better fitted by a quadratic model (r² > 0.8) than precipitable water vapor was by CVDs (r² > 0.6). Moisture time to peak (MTP) for the land surface variables showed strong, logical correspondence among variables (r² > 0.73). Land surface parameters responded to El Niño-Southern Oscillation and the Indian Ocean Dipole forcings. Area under the curve of the diel difference in vegetation optical depth showed correspondence to crop production and yield data collected by local offices, but not to the data reported at the national scale. A long-term seasonal Mann-Kendall rainfall trend showed a significant decrease for Ethiopia, while the decrement was not significant for Tanzania. While there is significant potential for passive microwave data to augment cropland status and food security monitoring efforts in the region, more research is needed before these data can be used in an operational environment. [ABSTRACT FROM AUTHOR]

Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and End of Season (EOS), leading to differing results. This discrepancy is partly due to spatial and temporal compositing of the VNIR satellite data to minimize data gaps resulting from cloud cover, atmospheric aerosols, and solar illumination constraints. Phenometrics derived from the downward Convex Quadratic model (CxQ) include Peak Height (PH) and Thermal Time to Peak (TTP), which are more consistent than SOS and EOS because they are minimally affected by snow and frost and other non-vegetation related issues. Here, we have determined PH using the vegetation optical depth (VOD) in three microwave frequencies (6.925, 10.65 and 18.7 GHz) and accumulated growing degree-days derived from AMSR-E (Advanced Microwave Scanning Radiometer on EOS) data at a spatial resolution of 25 km. We focus on 50 AMSR-E cropland pixels in the major grain production areas of Northern Eurasia (Ukraine, southwestern Russia, and northern Kazakhstan) for 2003-2010. We compared the land surface phenologies of AMSR-E VOD and MODIS NDVI data. VOD time series tracked cropland seasonal dynamics similar to that recorded by the NDVI. The coefficients of determination for the CxQ model fit of the NDVI data were high for all sites (0.78 < R2 < 0.99). The 10.65 GHz VOD (VOD1065GHz) achieved the best linear regression fit (R2 = 0.84) with lowest standard error (SEE = 0.128); it is therefore recommended for microwave VOD studies of cropland land surface phenology. Based on an Analysis of Covariance (ANCOVA) analysis, the slopes from the linear regression fit were not significantly different by microwave frequency, whereas the intercepts were significantly different, given the different magnitudes of the VODs. PHs for NDVI and VOD were highly correlated. Despite their strong correspondence, there was generally a lag of AMSR-E PH VOD10.65GHz by about two weeks compared to MODIS peak greenness. To evaluate the utility of the PH determination based on maximum value, we correlated the CxQ derived and maximum value determined PHs of NDVI and found that they were highly correlated with R2 of 0.87, but with a one-week bias. Considering the one-week bias between the two methods, we find that PH of VOD10.65GHz lags PH of NDVI by three weeks. We conclude, therefore, that maximum-value based PH of VOD can be a complementary phenometric for the CxQ model derived PH NDVI, especially in cloud and aerosol obscured regions of the world. [ABSTRACT FROM AUTHOR]

We demonstrate the synergistic use of surface air temperature retrieved from AMSR-E (Advanced Microwave Scanning Radiometer on Earth observing satellite) and two vegetation indices (VIs) from the shorter wavelengths of MODIS (MODerate resolution Imaging Spectroradiometer) to characterize cropland phenology in the major grain production areas of Northern Eurasia from 2003-2010. We selected 49 AMSR-E pixels across Ukraine, Russia, and Kazakhstan, based on MODIS land cover percentage data. AMSR-E air temperature growing degree-days (GDD) captures the weekly, monthly, and seasonal oscillations, and well correlated with station GDD. A convex quadratic (CxQ) model that linked thermal time measured as growing degree-days to accumulated growing degree-days (AGDD) was fitted to each pixel's time series yielding high coefficients of determination (0.88 ⩽ r2 ⩽ 0.98). Deviations of observed GDD from the CxQ model predicted GDD by site corresponded to peak VI for negative residuals (period of higher latent heat flux) and low VI at beginning and end of growing season for positive residuals (periods of higher sensible heat flux). Modeled thermal time to peak, i.e., AGDD at peak GDD, showed a strong inverse linear trend with respect to latitude with r2 of 0.92 for Russia and Kazakhstan and 0.81 for Ukraine. MODIS VIs tracked similar seasonal responses in time and space and were highly correlated across the growing season with r2 > 0.95. Sites at lower latitude (⩽49°N) that grow winter and spring grains showed either a bimodal growing season or a shorter unimodal winter growing season with substantial inter-annual variability, whereas sites at higher latitude (⩾56°N) where spring grains are cultivated exhibited shorter, unimodal growing seasons. Sites between these extremes exhibited longer unimodal growing seasons. At some sites there were shifts between unimodal and bimodal patterns over the study period. Regional heat waves that devastated grain production in 2007 in Ukraine and in 2010 in Russia and Kazakhstan appear clearly anomalous. Microwave based surface air temperature data holds great promise to extend to parts of the planet where the land surface is frequently obscured by clouds, smoke, or aerosols, and where routine meteorological observations are sparse or absent. [ABSTRACT FROM AUTHOR]

This study examines the effects of urban aerosols on a mesoscale convective system (MCS) in the central Great Plains with the Weather Research and Forecasting Model coupled with chemistry (WRF-Chem). Urban emissions from Kansas City, Missouri, were scaled by factors of 0.5, 1.0, and 2.0 to investigate the impact of urban aerosol load on MCS propagation and strength. The first half of the storm development is characterized by a stationary front to the north of Kansas City (phase I; 1800 UTC 26 May-0600 UTC 27 May), which develops into a squall line south of the urban area (phase II; 0600-1800 UTC 27 May). During phase I, doubling urban emissions shifts the precipitation accumulation, with enhancement downwind of the storm propagation and suppression upwind. During phase II, a squall line develops in the baseline and doubled emissions scenarios but not when emissions are halved. These changes in MCS propagation and strength are a function of cold pool strength, which is determined by microphysical processes and directly influenced by aerosol load. Overall, changes in urban emissions drive changes in cloud microphysics, which trigger large-scale changes in storm morphology and precipitation patterns. These results show that urban emissions can play an important role in mesoscale weather systems. [ABSTRACT FROM AUTHOR]

A primary impact of urbanization on the local climate is evident in the phenomenon recognized as the Urban Heat Island (UHI) effect. This urban thermal anomaly can increase the health risks of vulnerable populations to heat waves. The surface UHI results from emittance in the longer wavelengths of the thermal infrared; however, there are also urban anomalies that are detectable from radiance in the shorter wavelengths (3-5 micron) of the Middle Infrared (MIR). Radiance in the MIR can penetrate urban haze which frequently obscures urban areas by scattering visible and near infrared radiation. We analyzed seasonal and spatial variations in MIR for three Central European cities from 2003 through 2012 using Moderate Resolution Imaging Spectrometer (MODIS) band 23 (~4 micron) to evaluate whether MIR radiance could be used to characterize heat anomalies associated with urban areas. We examined the seasonality of MIR radiance over urban areas and nearby croplands and found that the urban MIR anomalies varied due to time of year: cropland MIR could be larger than urban MIR when there was more exposed soil at planting and harvest times. Further, we compared monthly mean MIR with the Normalized Difference Vegetation Index (NDVI) to analyze contrasts between urban and rural areas. We found that the seasonal dynamic range of the MIR could exceed that of the NDVI.We explored the linkage between meteorological data and MIR radiance and found a range of responses from strong to weak dependence of MIR radiance on maximum temperature and accumulated precipitation. Our results extend the understanding of the anomalous characteristics of urban areas within a rural matrix. [ABSTRACT FROM AUTHOR]

Indo-Pacific sea surface temperature dynamics play a prominent role in Asian summer monsoon variability. Two interactive climate modes of the Indo-Pacific-the El Niño/Southern Oscillation (ENSO) and the Indian Ocean dipole mode-modulate the amount of precipitation over India, in addition to precipitation over Africa, Indonesia, and Australia. However, this modulation is not spatially uniform. The precipitation in southern India is strongly forced by the Indian Ocean dipole mode and ENSO. In contrast, across northern India, encompassing the Ganges and Brahmaputra basins, the climate mode influence on precipitation is much less. Understanding the forcing of precipitation in these river basins is vital for food security and ecosystem services for over half a billion people. Using 28 years of remote sensing observations, we demonstrate that (i) the tropical west-east differential heating in the Indian Ocean influences the Ganges precipitation and (ii) the north-south differential heating in the Indian Ocean influences the Brahmaputra precipitation. The El Niño phase induces warming in the warm pool of the Indian Ocean and exerts more influence on Ganges precipitation than Brahmaputra precipitation. The analyses indicate that both the magnitude and position of the sea surface temperature anomalies in the Indian Ocean are important drivers for precipitation dynamics that can be effectively summarized using two new indices, one tuned for each basin. These new indices have the potential to aid forecasting of drought and flooding, to contextualize land cover and land use change, and to assess the regional impacts of climate change. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs) have been long studied using both ground-based observations of air temperature and remotely sensed thermal infrared (TIR) data. While ground-based observations lack spatial detail even in the occasional “dense” urban network, skin temperature retrievals using TIR data have lower temporal coverage due to revisit frequency, limited swath width, and cloud cover. Algorithms have recently been developed to retrieve near-surface air temperatures using microwave radiometer data, which enables characterization of UHIs in metropolitan areas, major conurbations, and global megacities at regional to continental scales using temporally denser time series than those that have been available from TIR sensors. Here we examine how UHIs appear across the entire Western Hemisphere using surface air temperatures derived from the Advanced Microwave Scanning Radiometers (AMSRs), AMSR-E onboard the National Aeronautics and Space Administration’s (NASA’s) Aqua and AMSR2 onboard the Japan Aerospace eXploration Agency’s Global Change Observation Mission-Water1 (JAXA’s GCOM-W1) satellites. We compare these data with station observations from the Global Historical Climate Network (GHCN) for 27 major cities across North America (in 83 urban-rural groupings) to demonstrate the capability of microwave data in a UHI study. Two measures of thermal time, accumulated diurnal and nocturnal degree-days, are calculated from the remotely sensed surface air temperature time series to characterize the urban-rural thermal differences over multiple growing seasons. Daytime urban thermal accumulations from the microwave data were sometimes lower than in adjacent rural areas. In contrast, station observations showed consistently higher day and night thermal accumulations in cities. UHIs are more pronounced at night, with 55% (AMSRs) and 93% (GHCN) of urban-rural groupings showing higher accumulated nocturnal degree-days in cities. While urban-rural thermal gradients may vary according to different datasets or locations, day-night differences in thermal time metrics were consistently lower (>90% of urban-rural groupings) in urban areas than in rural areas for both datasets. We propose that the normalized difference accumulated thermal time index (NDATTI) is a more robust metric for comparative UHI studies than simple temperature differences because it can be calculated from either station or remotely sensed data and it attenuates latitudinal effects. [ABSTRACT FROM AUTHOR]

Traditional studies of urban climate used air temperature observations from local urban/rural weather stations in order to analyze the general pattern of higher temperatures in urban areas compared with corresponding rural regions, also known as the Urban Heat Island (UHI) effect. More recently, satellite remote sensing datasets of land surface temperature have been exploited to monitor UHIs. While closely linked, air temperature and land surface temperature (LST) observations do not measure the same variables. Here we analyze land surface temperature vs. air temperature-based characterization and seasonality of the UHI and the surface UHI (SUHI) from 2003 to 2012 over the Upper Midwest region of the United States using LST from MODIS, and air temperature from the Daymet modeled gridded daily air temperature dataset, and compare both datasets to ground station data from first-order weather stations of the Global Historical Climatology Network (GHCN) located in eleven urban areas spanning our study region. We first convert the temperature data to metrics of nocturnal, diurnal, and daily thermal time and their annual accumulations to draw conclusions on nighttime vs. daytime and seasonal dynamics of the UHI. In general, the MODIS LST-derived results are able to capture urban-rural differences in daytime, nighttime, and daily thermal time while the Daymet air temperature-derived results show very little urban-rural differences in thermal time. Compared to the GHCN ground station air temperature-derived observations, MODIS LST-derived results are closer in terms of urban-rural differences in nighttime thermal time, while the results from Daymet are closer to the observations from GHCN during the daytime. We also found differences in the seasonal dynamics of UHIs measured by air temperature observations and SUHIs measured by LST observations. [ABSTRACT FROM AUTHOR]

To understand the land surface changes that Central Asia experienced between 2001 and 2013, we applied a non-parametric change analysis method to the standard vegetation indices (NDVI and EVI), as well as to the MODIS Tasseled Cap indices Brightness, Greenness and Wetness. In addition, we evaluated the MODIS nighttime and daytime land surface temperature products and the MODIS evapotranspiration product. We compared the change results by country, land cover type, and anthropogenic biome, and we also evaluated the results according to an index of human influence (HII). We found that EVI, NDVI and Tasseled Cap Greenness reveal very similar changes (r > 0.8), while there was a much lower correlation between the vegetation indices and results based on other portions of the electromagnetic spectrum. Thus, we found it informative to expand the analysis beyond the optical and near infrared portions of the electromagnetic spectrum, into the thermal regions. We found that the majority of the changes occurred in Kazakhstan and Uzbekistan, while Turkmenistan, Kyrgyzstan and Turkmenistan appeared more stable during this period. The observed changes were attributable to a combination of anthropogenic changes and weather effects. For example, changes in crop type south of the Aral Sea were revealed as increases in vegetation indices but declines in evapotranspiration, resulting from a shift from cotton to wheat. Across Kazakhstan large patches of negative vegetation changes, combined with increasing temperatures and declines in evapotranspiration were attributable to persistent droughts. Generally, we found that most browning of vegetation occurred in areas with lower human influence (except for areas with very high human influence) and most greening of the vegetation occurred in the areas with intermediate human influence. The use of multiple indicators of significant trends improved trend interpretation and attribution of proximate causes. [ABSTRACT FROM AUTHOR]

Summary Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the downscaled precipitation indicated that the uncertainty in the downscaled precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 downscaled precipitation was a better input for the regional hydrological impact studies. However, downscaled precipitation from multiple GCMs is suggested for comprehensive impact studies. [ABSTRACT FROM AUTHOR]

We present the Moment Distance (MD) method to advance spectral analysis in vegetation studies. It was developed to take advantage of the information latent in the shape of the reflectance curve that is not available from other spectral indices. Being mathematically simple but powerful, the approach does not require any curve transformation, such as smoothing or derivatives. Here, we show the formulation of the MD index (MDI) and demonstrate its potential for vegetation studies. We simulated leaf and canopy reflectance samples derived from the combination of the PROSPECT and SAIL models to understand the sensitivity of the new method to leaf and canopy parameters. We observed reasonable agreements between vegetation parameters and the MDI when using the 600 to 750 nm wavelength range, and we saw stronger agreements in the narrow red-edge region 720 to 730 nm. Results suggest that the MDI is more sensitive to the Chl content, especially at higher amounts (Chl > 40 μg/cm2) compared to other indices such as NDVI, EVI, and WDRVI. Finally, we found an indirect relationship of MDI against the changes of the magnitude of the reflectance around the red trough with differing values of LAI. [ABSTRACT FROM AUTHOR]

Remittances are an important source of cash flows for lower-middle income countries, but may also enhance vulnerability to shocks if these financial flows are concentrated among a few sources. We utilize descriptive statistics and panel econometric models estimated via generalized least squares (GLS) to investigate whether remittances are a source of vulnerability for the newly independent states (NIS) arising from the dissolution of the former Soviet Union, who receive a substantial proportion of remittance flows from the Russian Federation. Analytical results reveal remittance inflows from the Russian Federation to the NIS, are more significant than remittance inflows from the Russian Federation to all other countries. Model results also indicate remittances to the NIS are more susceptible to geopolitical and economic shocks affecting the Russian Federation than are remittance inflows to other countries. From a policy perspective, our results suggest policymakers should strive to eliminate barriers to migration between countries to reduce the likelihood of concentrated financial flows, since concentrated remittances sources represent potential exposure to economic and geopolitical shocks of sending countries. [Display omitted] • Investigate remittance network vulnerability when inflows are undiversified. • Focus analysis on newly independent states (NIS) from dissolution of Soviet Union. • Remittances do not enhance economic vitality of NIS relative to other countries. • NIS remittances from the Russian Federation more significant than other countries. • NIS remittances more susceptible to shocks to Russian Federation than other countries. [ABSTRACT FROM AUTHOR]

Abstract: Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically. This study compared environmental measurements from the NASA Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and in situ weather station data to examine their ability to predict the abundance of two important mosquito species (Aedes vexans and Culex tarsalis) in Sioux Falls, South Dakota, USA from 2005 to 2010. The AMSR-E land parameters included daily surface water inundation fraction, surface air temperature, soil moisture, and microwave vegetation opacity. The AMSR-E derived models had better fits and higher forecasting accuracy than models based on weather station data despite the relatively coarse (25-km) spatial resolution of the satellite data. In the AMSR-E models, air temperature and surface water fraction were the best predictors of Aedes vexans, whereas air temperature and vegetation opacity were the best predictors of Cx. tarsalis abundance. The models were used to extrapolate spatial, seasonal, and interannual patterns of climatic suitability for mosquitoes across eastern South Dakota. Our findings demonstrate that environmental metrics derived from satellite passive microwave radiometry are suitable for predicting mosquito population dynamics and can potentially improve the effectiveness of mosquito-borne disease early warning systems. [Copyright &y& Elsevier]

We introduce a new framework for analyzing spectra called Moment Distance that uses metrics derived from the shape of the curve described by fine resolution spectra. We locate potential pivot wavelength regions (PWR) useful for estimation of chlorophyll and carotenoids, and explore the spectral separability of maize (Zea mays L.) and soybean (Glycine max (L.) Merr.) at specific PWRs. We find the Moment Distance Index (MDI) to perform as well as or better than optimized band ratio models in terms of bias and RMSE. The 720-730 nm PWR for chlorophyll and 450-500 nm PWR for carotenoids are not only sensitive to pigment concentrations, but could distinguish maize spectra from soybean spectra. The separability of the spectra appears as the absence of crossings that define the maximal spectral shape difference. The larger the difference, the more the maize MDI can be distinguished from the soybean MDI. MDI yields significantly different linear models (p < 0.05) for retrieval of chlorophyll from maize and soybean. What we present here works at the leaf level. Next, we need to investigate MDI performance at canopy, field, and landscape scales, especially with imaging spectrometer data containing both pure and mixed spectra. [ABSTRACT FROM AUTHOR]

Abstract: How does observed spatial structure change as a function of spectral wavelength and resolution? The fine spectral resolution data of a Hyperion image acquired over agricultural research fields in eastern Nebraska, USA, were analyzed using variography to characterize spatial patterns within each wavelength-specific image. The geospatial analysis was repeated simulating Landsat TM/ETM+ bands from the Hyperion data. Significant changes in spatio-spectral heterogeneity were observed across the red edge. The total amount of spatial variability is closely correlated to the contrast in brightness of objects within the scene. The spatial dependence in an image is also influenced by differential brightness, but to a lesser extent. Increasing bandwidth may blur spatial structure that occurs at finer spectral resolution, resulting in the loss of spatial structure that may relate to significant features within the scene. [Copyright &y& Elsevier]

Abstract: Spatial structure in imagery depends on a complicated interaction between the observational regime and the types and arrangements of entities within the scene that the image portrays. Although block averaging of pixels has commonly been used to simulate coarser resolution imagery, relatively little attention has been focused on the effects of simple rescaling on spatial structure and the explanation and a possible solution to the problem. Yet, if there are significant differences in spatial variance between rescaled and observed images, it may affect the reliability of retrieved biogeophysical quantities. To investigate these issues, a nested series of high spatial resolution digital imagery was collected at a research site in eastern Nebraska in 2001. An airborne Kodak DCS420IR camera acquired imagery at three altitudes, yielding nominal spatial resolutions ranging from 0.187 m to 1 m. The red and near infrared (NIR) bands of the co-registered image series were normalized using pseudo-invariant features, and the normalized difference vegetation index (NDVI) was calculated. Plots of grain sorghum planted in orthogonal crop row orientations were extracted from the image series. The finest spatial resolution data were then rescaled by averaging blocks of pixels to produce a rescaled image series that closely matched the spatial resolution of the observed image series. Spatial structures of the observed and rescaled image series were characterized using semivariogram analysis. Results for NDVI and its component bands show, as expected, that decreasing spatial resolution leads to decreasing spatial variability and increasing spatial dependence. However, compared to the observed data, the rescaled images contain more persistent spatial structure that exhibits limited variation in both spatial dependence and spatial heterogeneity. Rescaling via simple block averaging fails to consider the effect of scene object shape and extent on spatial information. As the features portrayed by pixels are equally weighted regardless of the shape and extent of the underlying scene objects, the rescaled image retains more of the original spatial information than would occur through direct observation at a coarser sensor spatial resolution. In contrast, for the observed images, due to the effect of the modulation transfer function (MTF) of the imaging system, high frequency features like edges are blurred or lost as the pixel size increases, resulting in greater variation in spatial structure. Successive applications of a low-pass spatial convolution filter are shown to mimic a MTF. Accordingly, it is recommended that such a procedure be applied prior to rescaling by simple block averaging, if insufficient image metadata exist to replicate the net MTF of the imaging system, as might be expected in land cover change analysis studies using historical imagery. [Copyright &y& Elsevier]

Degradation in the highland pastures of the Kyrgyz Republic, a small country in Central Asia, has been reported in several studies relying on coarse spatial resolution imagery, primarily MODIS. We used the results of land surface phenology modeling at higher spatial resolution to characterize spatial and temporal patterns of phenometrics indicative of the seasonal peak in herbaceous vegetation. In particular, we explored whether proximity to villages was associated with substantial decreases in the seasonal peak values. We found that terrain features—elevation and aspect—modulated the strength of the influence of village proximity on the phenometrics. Moreover, using contrasting hotter/drier and cooler/wetter years, we discovered that the growing season weather can interact with aspect to attenuate the negative influences of dry conditions on seasonal peak values. As these multiple contingent and interactive factors that shape the land surface phenology of the highland pastures may be blurred and obscured in coarser spatial resolution imagery, we discuss some limitations with prior and recent studies of pasture degradation. [ABSTRACT FROM AUTHOR]

The International Geosphere–Biosphere Program has delineated five study areas that form a northern high-latitude network for the analyses of vegetation and carbon dynamics. We examined the magnitude and significance of changes in the land surface phenologies of ecoregions within these transects using the NASA Pathfinder Advanced Very High-Resolution Radiometer Land dataset. We applied the seasonal Mann–Kendall (SMK) trend test, a robust and nonparametric approach, to determine the significance of trends in the normalized difference vegetation index (NDVI) over the five transects. The SMK trend test provides an important alternative over the frequently used but unreliable trend analysis based on linear regression. In addition, we modeled the land surface phenology using quadratic or nonlinear spherical models to relate the NDVI data to accumulated growing degree-days (base 0°C). Nonlinear spherical models parsimoniously describe the green-up dynamics in taiga and tundra ecoregions. Models for each ecoregion within each transect were fitted separately for two time periods (1985–1988 and 1995–1999) and their parameter coefficient estimates were compared. In 10 of 24 ecoregions that comprise 72% of the land area in the transects, the date of the peak NDVI value was significantly earlier (range 2–18 days) in the second study period than in the first study period. This progression was more pronounced in North America than in Siberia (weighted average of 9.3 vs. 6.3 days earlier). Understanding of what constitutes significant change in land surface phenology amidst background variation is a critical component of global change science. A diversity of datasets, techniques, and study areas has led to a range of conclusions about boreal phenology. We discuss statistical pitfalls in standard analyses and offer a framework to conduct statistically reliable change assessments of land surface phenologies. [ABSTRACT FROM AUTHOR]

Kazakhstan is the second largest country to emerge from the collapse of the Soviet Union. Consequent to the abrupt institutional changes surrounding the disintegration of the Soviet Union in the early 1990s, Kazakhstan has reportedly undergone extensive land cover/land use change. Were the institutional changes sufficiently great to affect land surface phenology at spatial resolutions and extents relevant to mesoscale meteorological models? To explore this question, we used the NDVI time series (1985–1988 and 1995–1999) from the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset, which consists of 10 days maximum NDVI composites at a spatial resolution of 8 km. Daily minimum and maximum temperatures were extracted from the NCEP Reanalysis Project and 10 days composites of accumulated growing degree-days (AGDD) were produced. We selected for intensive study seven agricultural areas ranging from regions with rain-fed spring wheat cultivation in the north to regions of irrigated cotton and rice in the south. We applied three distinct but complementary statistical analyses: (1) nonparametric testing of sample distributions; (2) simple time series analysis to evaluate trends and seasonality; and (3) simple regression models describing NDVI as a quadratic function of AGDD.The irrigated areas displayed different temporal developments of NDVI between 1985–1988 and 1995–1999. As the temperature regime between the two periods was not significantly different, we conclude that observed differences in the temporal development of NDVI resulted from changes in agricultural practices.In the north, the temperature regime was also comparable for both periods. Based on extant socioeconomic studies and our model analyses, we conclude that the changes in the observed land surface phenology in the northern regions are caused by large increases in fallow land dominated by weedy species and by grasslands under reduced grazing pressure. Using multiple lines of evidence allowed us to build a case of whether differences in land surface phenology were mostly the result of anthropogenic influences or interannual climatic fluctuations. [Copyright &y& Elsevier]

The structural integrity of vertebral trabecular bone is determined by the continuity of its trabecular network and the size of the holes comprising its marrow space, both of which determine the apparent size of the marrow spaces in a transaxial CT image. A model-independent assessment of the trabeculation pattern was determined from the lacunarity of thresholded CT images. Using test images of lumbar vertebrae from human cadavers, acquired at different slice thicknesses, we determined that both median thresholding and local adaptive thresholding (using a 7×7 window) successfully segmented the grey-scale images. Lacunarity analysis indicated a multifractal nature to the images, and a range of marrow space sizes with significant structure around 14–18 mm2. Preliminary studies of in vivo images from a clinical CT scanner indicate that lacunarity analysis can follow the pattern of bone loss in osteoporosis by monitoring the homogeneity of the marrow spaces, which is related to the connectivity of the trabecular bone network and the marrow space sizes. Although the patient sample was small, derived parameters such as the maximum deviation of the lacunarity from a neutral (fractal) model, and the maximum derivative of this deviation, seem to be sufficiently sensitive to distinguish a range of bone conditions. Our results suggest that these parameters, used with bone mineral density values, may have diagnostic value in characterizing osteoporosis and predicting fracture risk. [Copyright &y& Elsevier]

Examines the relationship between pattern and process in ecology. Basic assumption of scientific investigation; How patterns observed in ecology are the result of underlying physical causes; Pattern in ecological theory; Ecological competition; Stochastic model; Patterns and processes; Significance for ecology.

• Explored the influence of five climate oscillation modes on land surface phenology (LSP). • Previous studies found influence of modes on precipitation and temperature in Central Asia. • At 30 m resolution, we found generally negligible influence of modes in highland pastures. • Snow cover seasonality and terrain factors more tightly linked to LSP. • Study demonstrates how local factors can override influence of teleconnections. Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degree-days based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearman's rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30 m resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia. [ABSTRACT FROM AUTHOR]

Land surface phenologies and seasonalities in the US Prairie Pothole Region (PPR) were characterized using land surface variables derived from the coarse spatial resolution (25 km) Advanced Microwave Scanning Radiometer (AMSR) blended data for 2003 to 2016 linked with the optically based USDA NASS Crop Data Layer (CDL) at a much finer spatial resolution. Two transects of AMSR pixels—one in east-central North Dakota and the other in eastern South Dakota—were selected for analysis. The AMSR data were grouped earlier (2003–2005, 2007) and later (2013–2016) to emphasize temporal change and to avoid data discontinuity in 2011–2012 and a major drought in 2006. The nonparametric Mann-Kendall trend test on the CDL data revealed that area in grasslands and wetlands strongly decreased in both transects, while corn and soybean coverage strongly increased. In crop-dominated sites, the AMSR vegetation optical depth (VOD) time series caught the early spring growth, ploughing, and crop growth and senescence. In contrast, the VOD time series at grassland dominated sites exhibited a lower peak but extended growth period. Crop-dominated sites had significantly higher amplitude VODs in both periods and transects. Based on the paired two-sample t-test, neither the peak VOD amplitude nor the peak VOD timing measured in accumulated growing degree-days was significantly different between temporal groups in the North Dakota transect. In contrast, in South Dakota, both the peak VOD amplitude and its timing were significantly different with shifts to later peak timing during the 2013–2016 period. In addition, in South Dakota but not North Dakota, there were significantly earlier shifts in the timing of peak growing degree-days and peak precipitation water vapor. Both spatial and temporal changes in AMSR land surface variables are linked to shifts in land cover along the South Dakota transect as revealed in the CDL data. More research is required to understand the dynamics evident in the passive microwave time series. [ABSTRACT FROM AUTHOR]

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area. [ABSTRACT FROM AUTHOR]

Land surface phenology (LSP) provides critical information for investigating vegetation growth and development, studying ecosystem biodiversity, modeling terrestrial carbon and surface energy budgets, detecting land cover and land use change, and monitoring climate change. Although operational 500 m LSP products have been produced from coarse resolution data observed from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), an LSP product is also needed at the Landsat scale (30 m) to enhance the environmental monitoring and modeling. However, temporal frequency of 30 m satellite data is always inadequate for reliable LSP detection, despite enrichment by the operational harmonized Landsat and Sentinel-2 (HLS) product. In this study, we propose a new algorithm of LSP detection for the generation of a 30 m LSP product using routinely produced HLS and VIIRS surface reflectance products. Specifically, the new algorithm compares a HLS EVI2 (two-band enhanced vegetation index) time series at a given 30 m pixel with the set of 500 m VIIRS EVI2 time series neighboring the HLS pixel and selects the most similar temporal shape of VIIRS time series even though the amplitude and/or phase between HLS and VIIRS EVI2 time series may be mismatched. The shape of the selected VIIRS EVI2 time series is then used to match to the given HLS EVI2 time series to generate a synthetic HLS-VIIRS time series. The HLS-VIIRS time series is subsequently processed using the hybrid piecewise logistic model to detect the phenological transition dates and to quantify the confidence of LSP detection. This new algorithm is evaluated by implementing 30 m LSP detection in eight HLS tiles in the northeastern (forests), central (croplands), and western (shrublands) United States. Evaluation finds that the new-algorithm-detected greenup onset (1) agrees well with the standard VIIRS LSP product without bias, (2) closely correlates to PhenoCam observations with a slope close to one, and (3) compares well with both PhenoCam and field species-specific observations with a mean absolute difference of 8 days and a difference less than 10 days in more than 70% of the validation samples. This implementation suggests that the new algorithm could be implemented for regional and global LSP product generation at a 30 m resolution. [ABSTRACT FROM AUTHOR]

Abstract Vegetation phenology contributes to, and is diagnostic of, seasonal variation in ecosystem processes and exerts important controls on land-atmosphere exchanges of carbon, water, and energy. Satellite remote sensing provides a valuable source of data for monitoring the phenology of terrestrial ecosystems and has been widely used to map geographic and interannual variation in land surface phenology (LSP) over large areas. The Visible Infrared Imaging Radiometer Suite (VIIRS) land surface phenology product provides global data sets characterizing the annual LSP of terrestrial ecosystems, and is designed to support long-term continuity of LSP measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). We used data from VIIRS and MODIS to evaluate the agreement and characterize the similarities and differences between LSP data from each instrument. Specifically, we compare data from the Collection 6 MODIS Land Cover Dynamics (LCD) product with data from the newly developed VIIRS LSP product over the most common land cover types in North America. To do this, we assessed the overall agreement between time series of vegetation indices from VIIRS and MODIS, evaluated the correspondence between retrieved phenometrics from each instrument, and analyzed sources of differences between phenometrics from the each product. As part of this analysis, we also compared phenometrics from MODIS and VIIRS with phenometrics derived from Landsat Analysis Ready Data and PhenoCam time series. Results show that two-band enhanced vegetation index (EVI2) values from VIIRS and MODIS are similar (R2 > 0.81; root mean square deviation < 0.062), but that VIIRS EVI2 time series show more high frequency variation than time series from MODIS. Further, even though the VIIRS and MODIS products are generated using different instruments and algorithms, phenometrics from each product are similar and show only minor differences within and across land cover types. Systematic differences between phenometrics from the two products were generally less than one week (absolute bias 4.8 ± 3.0 days), and RMSDs were less than two weeks for most phenometrics across different land cover classes (10.7 ± 4.3 days). Comparison of VIIRS and MODIS LSP data with corresponding metrics estimated from Landsat and PhenoCam data consistently showed high agreement among the data sets. Overall, results from this analysis indicate that the VIIRS LSP product provides excellent continuity with the MODIS record. However, studies attempting to create high-fidelity long-term LSP time series by merging these products should exploit the overlap period of MODIS and VIIRS to estimate land cover-specific corrections for modest systematic bias in the MODIS LCD product relative to the VIIRS LSP product. Highlights • We compare the VIIRS and MODIS Collection 6 land surface phenology products. • The VIIRS and MODIS products show only minor differences across land cover types. • Landsat and PhenoCam derived LSP data show high agreements with the two products. • The VIIRS product provides an excellent continuity with the MODIS record. [ABSTRACT FROM AUTHOR]

The article discusses a modelling study that was intended to explore the trajectories of carbon accumulation, both above and below ground, following land abandonment. This study was applied to approximately 171,000 square kilometres in the agricultural regions of Ukraine, Belarus, and the European region of the Russian Federation, due to the fact that the dissolution of the Soviet Union in 1991 instigated the most widespread episode of land use change in the twentieth century. The researchers linked models of crop production with models of ecosystem processes to calculate the amount of carbon sequestered as grasslands replace agricultural lands.

A consistent dataset delineating and characterizing changes in urban environments will be valuable for socioeconomic and environmental research and for sustainable urban development. Remotely sensed data have been long used to map urban extent and infrastructure at various spatial and spectral resolutions. Although many datasets and approaches have been tried, there is not yet a universal way to map urban extents across the world. Here we combined a microwave scatterometer (QuikSCAT) dataset at ~ 1 km posting with percent impervious surface area (%ISA) data from the National Land Cover Dataset (NLCD) that was generated from Landsat data, and ambient population data from the LandScan product to characterize and quantify growth in nine major urban areas in the US Great Plains from 2000 to 2009. Nonparametric Mann-Kendall trend tests on backscatter time series from urban areas show significant expanding trends in eight of nine urban areas with p -values ranging 0.032 to 0.001. The sole exception is Houston, which has a substantial non-urban backscatter at the northeastern edge of the urban core. Strong power law scaling relationships between ambient population and either urban area or backscatter power (r 2 of 0.96 in either model) with sub-linear exponents (β of 0.911 and 0.866, respectively) indicate urban areas become more compact with more vertical built-up structure than lateral expansion to accommodate the increased population. Increases in backscatter and %ISA datasets between 2001 and 2006 show agreement in both magnitude and direction for all urban areas except Minneapolis-St. Paul (MSP), likely due to the presence of many lakes and ponds throughout the MSP metropolitan area. We conclude discussing complexities in the backscatter data caused by large metal structures and rainfall. [ABSTRACT FROM AUTHOR]

Urban areas alter local atmospheric conditions by modifying surface albedo and consequently the surface radiation and energy balances, releasing waste heat from anthropogenic uses, and increasing atmospheric aerosols, all of which combine to increase temperatures in cities, especially overnight, compared with surrounding rural areas, resulting in a phenomenon called the “urban heat island” effect. Recent rapid urbanization of the planet has generated calls for remote sensing research related to the impacts of urban areas and urbanization on the natural environment. Spatially extensive, high spatial resolution data products are needed to capture phenological patterns in regions with heterogeneous land cover and external drivers such as cities, which are comprised of a mixture of land cover/land uses and experience microclimatic influences. Here we use the 30 m normalized difference vegetation index (NDVI) product from the Web-Enabled Landsat Data (WELD) project to analyze the impacts of urban areas and their surface heat islands on the seasonal development of the vegetated land surface along an urban–rural gradient for 19 cities located in the Upper Midwest of the United States. We fit NDVI observations from 2003–2012 as a quadratic function of thermal time as accumulated growing degree-days (AGDD) calculated from the Moderate-resolution Imaging Spectroradiometer (MODIS) 1 km land surface temperature product to model decadal land surface phenology metrics at 30 m spatial resolution. In general, duration of growing season (measured in AGDD) in green core areas is equivalent to duration of growing season in urban extent areas, but significantly longer than duration of growing season in areas outside of the urban extent. We found an exponential relationship in the difference of duration of growing season between urban and surrounding rural areas as a function of distance from urban core areas for perennial vegetation, with an average magnitude of 669 AGDD (base 0 °C) and the influence of urban areas extending greater than 11 km from urban core areas. At the regional scale, relative change in duration of growing season does not appear to be significantly related to total area of urban extent, population, or latitude. The distance and magnitude that urban areas exert influence on vegetation in and near cities is relatively uniform. [ABSTRACT FROM AUTHOR]

This letter explores key variables affecting radiance observed in the middle infrared (MIR) region (3−5 μm) over urban areas. To date, MIR has received little attention in urban remote sensing. Using Moderate Resolution Imaging Spectrometer (MODIS) band 23, which spans 4.02–4.08 μm, we examine (1) the seasonality of MIR, (2) the relationship between MIR radiance and percent impervious surface area (percentage ISA) and (3) sensor view zenith angle (VZA) effects on observed MIR radiance over cities. We find the effects of sensor VZA and percentage ISA to be two important factors influencing MIR radiance in urban areas, which allows settlements to emerge as islands of MIR brightness amidst MIR-dark vegetated landscapes. [ABSTRACT FROM AUTHOR]

Background: Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia. Methods: In this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series. Results: Malaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates. Conclusions: Malaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions. [ABSTRACT FROM AUTHOR]

The authors investigate the meteorology associated with two elongated swaths of crop damage produced by severe hailstorms that crossed North Dakota and South Dakota on 4 July and 20 July 2003. These hailswaths, which were observed in a Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) image time series, each persisted for more than a month and were associated with local temperature increases, presumably owing to the enhanced Bowen ratio over dry, crop-free ground. This paper documents the creation and evolution of the convective storms that produced the hailswaths, and then presents evidence that devegetated hailswaths may impact future convective weather. Idealized numerical simulations including hailswath-like surface anomalies, as well as analogies to the extant literature, suggest that hailswaths may play a part in initiating new convective clouds, especially when the low-level winds are parallel to their long axes. Analyses of conventional operational observations also show that the 4 July hailswath may have played a part in the initiation and intensification of a convective storm on 17 July and in the temporarily deviant motion of a convective storm that crossed it on 20 July. Because devegetated hailswaths are common to the central and northern plains during the summer months, and because they have heretofore received little study, a deeper understanding of hailswaths’ impacts upon moist convection may lead to improved short-term weather prediction. [ABSTRACT FROM AUTHOR]

While it has long been recognized that the anisotropic reflectance properties of a natural surface affect the intensity and spectral distribution of radiance received by a remote sensing instrument, the effects of canopy reflectance geometry on the observed spatial structure of canopy reflectance have not adequately been evaluated. In this paper, near-surface spectrometers were used as part of two experiments to evaluate the systematic variations in the sun-target-sensor geometry on semivariogram metrics (range, silI+nugget variance) summarizing the spatial structure observed in a tallgrass prairie canopy. In the first experiment, reflectance measurements and normalized difference vegetation index (NDVI) values were collected at five sensor viewing angles (-50°, -25°, 0°, 25°, and 50°) from six measurement grids representing three burn treatments and two slope/aspect situations. In the second experiment, data were collected at 2-h intervals, beginning at &ap;0800 LST from the same grids with the radiometer at nadir, allowing the spatial structure of reflectance and NDVI to be observed under naturally changing illumination. Results of the geostatistical analysis show that both the range and sill+nugget variance values change with viewing angle. These effects were consistent across all treatments and slope/aspect combinations. However, when viewed from nadir, the sill+nugget variance values of the canopy changed with solar illumination angle and the range values remained nearly constant. These relationships were also observed across all treatments and slope/aspect combinations. The results suggest that sill+nugget values for the same surface may not be directly comparable if not acquired under very similar view angle and illumination conditions. Range values are comparable if the nadir view is used, but not under off-nadir viewing conditions. [ABSTRACT FROM AUTHOR]

Abstract Aside from deforestation and selective logging, mature tropical rain forests appear to be in a state of near-term flux. At plot scales over a 20-year period, forest inventory data (representing areas < 750 ha) have shown stands to be accumulating above-ground biomass pantropically while displaying signs of increasing turnover. To assess whether or not such disequilibrium is manifest at broader spatial scales (i.e. coarser grain sizes and larger extents), we performed a change detection analysis of landscape texture (i.e. the organization of reflective properties), in satellite images of closed-canopy tropical rain forests considered to be anthropically undisturbed. Here we show that fractal properties of pixel spectral values depicting low and high levels of photosynthetic activity underwent significant shifts from the 1970s to the 1980s. Following expectations for aggrading forests, canopy texture became more random throughout the tropics. Although subject to periodic disturbance events such as natural exogenic perturbations and/or synchronous die-off, which should produce no consistent trend, these forested landscapes across the globe exhibited similar dynamics at fine temporal (decadal) intervals. Such biophysical changes (representing areas > 1000 000 ha) directly affect atmospheric boundary layer conditions and could have implications with respect to biodiversity and carbon cycling in these systems. [ABSTRACT FROM AUTHOR]

Rising temperature shifts plant phenology. Chinese cities, experiencing extensive expansion and intensive warming, spanning a wide latitudinal range, might provide ideal experimental opportunities for observing and predicting phenological responses to warming temperature. Using the urban–rural gradient approach, we explored urbanization imprint on land surface phenology across the entire urbanization intensity (UI) gradient ranging from 0% to 100% in 343 Chinese cities using the VIIRS Land Surface Phenology along with MODIS Land Surface Temperature (LST) products. We found prevalent advancing and delaying trends for the start of the growing season (SOS) and the end of the growing season (EOS) with increasing UI across 343 Chinese cities, respectively. Overall, the phenology shifted earlier by 8.6 ± 0.54 days for SOS, later by 1.3 ± 0.51 days for EOS, and lengthened by 9.9 ± 0.77 days for the growing season length (GSL) in urban core areas (UI above 50%) relative to their rural counterparts (UI lower than 1%). The temperature sensitivity of SOS and EOS was 10.5 ± 0.25 days earlier and 2.9 ± 0.16 days later per 1°C LST increase in spring and autumn, respectively. Moreover, the northern cities witnessed higher temperature sensitivity for SOS and EOS than the southern ones. Both spring and autumn temperature sensitivity across these 343 cities would likely decrease with future urban warming, suggesting any projections of future phenological responses to continued warming must be approached with caution. [ABSTRACT FROM AUTHOR]

Evidence-based responses to climate change by society require operational and sustained information including biophysical indicator systems that provide up-to-date measures of trends and patterns against historical baselines. Two key components linking anthropogenic climate change to impacts on socio-ecological systems are the periodic inter- and intra-annual variations in physical climate systems (seasonality) and in plant and animal life cycles (phenology). We describe a set of national indicators that reflect sub-seasonal to seasonal drivers and responses of terrestrial physical and biological systems to climate change and variability at the national scale. Proposed indicators and metrics include seasonality of surface climate conditions (e.g., frost and freeze dates and durations), seasonality of freeze/thaw in freshwater systems (e.g., timing of stream runoff and durations of lake/river ice), seasonality in ecosystem disturbances (e.g., wildfire season timing and duration), seasonality in vegetated land surfaces (e.g., green-up and brown-down of landscapes), and seasonality of organismal life-history stages (e.g., timings of bird migration). Recommended indicators have strong linkages to variable and changing climates, include abiotic and biotic responses and feedback mechanisms, and are sufficiently simple to facilitate communication to broad audiences and stakeholders interested in understanding and adapting to climate change. [ABSTRACT FROM AUTHOR]

Many studies have shown that high elevation environments are among very sensitive to climatic changes and where impacts are exacerbated. Across Central Asia, which is especially vulnerable to climate change due to aridity, the ability of global climate projections to capture the complex dynamics of mountainous environments is particularly limited. Over montane Central Asia, agropastoralism constitutes a major portion of the rural economy. Extensive herbaceous vegetation forms the basis of rural economies in Kyrgyzstan. Here we focus on snow cover seasonality and the effects of terrain on phenology in highland pastures using remote sensing data for 2001–2017. First, we describe the thermal regime of growing season using MODerate Resolution Imaging Spectrometer (MODIS) land surface temperature (LST) data, analyzing the modulation by elevation, slope, and aspect. We then characterized the phenology in highland pastures with metrics derived from modeling the land surface phenology using Landsat normalized difference vegetation index (NDVI) time series together with MODIS LST data. Using rank correlations, we then analyzed the influence of four metrics of snow cover seasonality calculated from MODIS snow cover composites—first date of snow, late date of snow, duration of snow season, and the number of snow-covered dates (SCD)—on two key metrics of land surface phenology in the subsequent growing season, specifically, peak height (PH; the maximum modeled NDVI) and thermal time to peak (TTP; the amount of growing degree-days accumulated during modeled green-up phase). We evaluated the role of terrain features in shaping the relationships between snow cover metrics and land surface phenology metrics using exact multinomial tests of equivalence. Key findings include (1) a positive relationship between SCD and PH occurred in over 1664 km2 at p < 0.01 and 5793 km2 at p < 0.05, which account for >8% of 68,881 km2 of the pasturelands analyzed in Kyrgyzstan; (2) more negative than positive correlations were found between snow cover onset and PH, and more positive correlations were observed between snowmelt timing and PH, indicating that a longer snow season can positively influence PH; (3) significant negative correlations between TTP and SCD appeared in 1840 km2 at p < 0.01 and 6208 km2 at p < 0.05, and a comparable but smaller area showed negative correlations between TTP and last date of snow (1538 km2 at p < 0.01 and 5188 km2 at p < 0.05), indicating that under changing climatic conditions toward earlier spring warming, decreased duration of snow cover may lead to lower pasture productivity, thereby threatening the sustainability of montane agropastoralism; and (4) terrain had a stronger influence on the timing of last date of snow cover than on the number of snow-covered dates, with slope being more important than aspect, and the strongest effect appearing from the interaction of aspect and steeper slopes. In this study, we characterized the snow-phenology interactions in highland pastures and revealed strong dependencies of pasture phenology on timing of snowmelt and the number of snow-covered dates. • Modeled LSP in pastures at 30 m using 17 years of Landsat and MODIS data • Prevalent trend of increasing peak NDVI in highland pastures 2001–2017 • Later snowmelt date and more snow covered dates increased peak NDVI • Slope was more important than aspect on linkages between snow cover & LSP. [ABSTRACT FROM AUTHOR]

The latest collection (C6) of MODIS data provides several algorithmic improvements and calibration adjustments that correct for sensor degradation, theoretically making the C6 MODIS products more accurate compared to previous collections. C6 adjustments also introduce several improvements in the vegetation index (VI) retrieval algorithms. With these improvements, we expect only minor differences between data from Terra and Aqua, but significantly different results between C5 and C6. In this paper, we investigate three different MODIS products to determine the extent that improvements made to C6 influence the overall trend results for time series between 2001 and 2017. We focus on these three products specifically, both to allow for a comparison of vegetation index products—NDVI and EVI from MOD13C1, and NDVI and EVI calculated based on surface reflectance from MCD43C4—and also to gain an understanding of the improvements on an entirely different product from the same sensor, namely Land Surface Temperature (LST) from MOD11C2. For the MCD43C4 dataset, we find that 17.9% and 16.4% of EVI and NDVI pixels, respectively, display trend discordance between C5 and C6. For the MOD13C1 vegetation indices, we found comparable rates of trend discordance between C5 and C6: 18.5% and 17.4% for the EVI and NDVI pixels, respectively. For both products the greatest changes between C5 and C6 are an overall increase in pixels exhibiting a significant greening trend and an overall decline in pixels exhibiting a significant browning trend. Moreover, the largest differences between C5 and C6 for the NDVI and EVI data appear in cropland areas and in regions with relatively little human influence. In the Land Surface Temperature product (MOD11C2), the discordance between C5 and C6 is much lower: only 3.2% of day and 5.0% of night LST trends exhibited discordance between C5 and C6. We analyze the complementary results of vegetation index and land surface temperature trends and demonstrate that combining the results from different products observed at different portions of the electromagnetic spectrum—but linked through the biogeophysical processes of surface energy balance—allows us to portray change with more confidence than when relying on vegetation index data alone. [ABSTRACT FROM AUTHOR]

Tropical cyclones are natural events that transform into natural disasters as they approach and reach land. In 2017 alone, tropical cyclones caused an estimated $215 billion in damage. While MODIS data are regularly used in the analysis of hurricanes and typhoons, damage studies typically focus on just a few events without providing a comprehensive overview and comparison across events. The MODIS record is now sufficiently long to enable standardization in time, allowing us to extend previously developed disturbance methodology and to remove dependency on land cover datasets. We apply this new approach to detect the impact of both droughts and hurricanes on the four largest Caribbean islands since 2001. We find that the percentage of disturbed land on the four islands varies from approximately 0–50% between 2001 and 2017, with the highest percentages coinciding with major droughts in Cuba, and Hurricane Maria in Puerto Rico. We demonstrate that (1) Hurricane Maria resulted in significant disturbance across 50% of Puerto Rico (4549 km2), and (2) gradual recovery started about 2.5 months after the hurricane hit. While our approach focuses on the identification of damage arising from hurricanes, it is also capable of identifying the damage from droughts. This approach ultimately enables a better understanding of the combined effects of these two natural hazards across island landscapes. Unlabelled Image • Disturbance index allows for standardized detection of droughts and hurricane damage. • Hurricane Maria caused significant disturbance across 50% of Puerto Rico (4549 km2). • Gradual recovery started about 2.5 months after hurricane Maria hit. [ABSTRACT FROM AUTHOR]

• Remittances implicated in land system maintenance and transformation. • Remittances and land change seldom a primary focus of research. • Four non-exclusive pathways identified for impacts of remittance expenditures on land systems. • Remittance expenditures associated with six types of land system changes. • Four avenues for future research identified to advance understanding of remittance-land dynamics. Remittances—funds sent by migrants to family and friends back home—are an important source of global monetary flows, and they have implications for the maintenance and transformation of land systems. A number of published reviews have synthesized work on a variety of aspects of remittances (e.g., rural livelihoods, disasters, and economic development). To our knowledge, there are no reviews of work investigating the linkages between remittances and land change, broadly understood. This knowledge gap is important to address because researchers have recognized that remittances flows are a mechanism that helps to explain how migration can affect land change. Thus, understanding the specific roles remittances play in land system changes should help to clarify the multiple processes associated with migration and their independent and interactive effects. To address the state of knowledge about the connection between remittances and land systems, this paper conducts a systematic review. Our review of 51 journal articles finds that the linkages uncovered were commonly subtle and/or indirect. Very few studies looked at the direct connections between receipt of remittances and quantitative changes in land. Most commonly, the relationship between remittances and land change was found to occur through pathways from labor migration to household income to agricultural development and productivity. We find four non-exclusive pathways through which households spend remittances with consequent changes to land systems: (1) agricultural crops and livestock, (2) agricultural labor and technologies, (3) land purchases, and (4) non-agricultural purchases and consumables. In the papers reviewed, these expenditures are linked to various land system change outcomes, including land use change, soil degradation, pasture degradation, afforestation/deforestation/degradation, agricultural intensification/extensification/diversification, and no impact. These findings suggest four avenues for future research. One avenue is the use of the theoretical lens of telecoupling to understand how remittances may produce wider-scale changes in land systems. A second avenue is further examination of the impacts of shocks and disturbances to remittance flows on land change both in migrant sending and in remittance receiving areas. A third avenue is scholarship that examines the extent that household uses of remittances have a "ripple effect" on land uses in nearby interlinked systems. A fourth avenue for future work is the use of spatially explicit modeling that leverages land cover and land use data based on imagery and other geospatial information. [ABSTRACT FROM AUTHOR]

Vegetation phenology is widely acknowledged to be a sensitive indicator of the response of ecosystems to climate change, and phenological shifts have been shown to exert substantial impacts on ecosystem function, biodiversity, and carbon budgets at multiple scales. Therefore, long-term records of the phenology of the vegetated land surface are critical in exploring the biological response to environmental change at regional to global scales. Land surface phenology (LSP) from satellite observations has been extensively used to monitor the dynamics of terrestrial ecosystems in the face of a changing climate. Here we introduce and describe the global land surface phenology (GLSP) product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) data at a gridded spatial resolution of 500 m. This new product will provide continuity for the Moderate Resolution Imaging Spectroradiometer (MODIS) GLSP product that has been produced on an operational basis since 2001. The VIIRS GLSP algorithm uses daily VIIRS Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR) data as the primary input to calculate the two-band enhanced vegetation index (EVI2) at each 500 m pixel. The temporal EVI2 trajectory is modeled using a hybrid piecewise logistic function to track the seasonal vegetation development, detect phenological transition dates, calculate the magnitude of vegetation greenness development, and characterize the confidence of phenology detections. The VIIRS GLSP algorithm has been implemented across the contiguous United States, and the resulting phenological metrics have been evaluated through comparisons with species-specific field phenological observations, Landsat phenology retrievals, and the MODIS phenology detections. The results demonstrate that the VIIRS GLSP metrics are of high quality and are in a good agreement with the other independent satellite and field observations. The results also indicate that the uncertainty in the VIIRS GLSP retrievals is primarily associated with missing high quality observations in VIIRS EVI2 time series. [ABSTRACT FROM AUTHOR]

Land surface phenology (LSP) has been widely retrieved from time series of various satellite instruments in order to monitor climate change and ecosystem dynamics. However, any evaluation of the quality of LSP data sets is quite challenging because the in situ observations on a limited number of individual trees, shrubs, or other plants are rarely representative of the landscape sampled in a single satellite pixel. Moreover, vegetation indices detecting biophysical features of vegetation seasonality are different from (but related to) the specific plant life history stages observed by humans at ground level. This study is the first comprehensive evaluation of the LSP product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) data using both MODIS LSP products and observations from the PhenoCam network across the Contiguous United States during 2013 and 2014. PhenoCam observes vegetation canopy over a landscape at very high frequency, providing nearly continuous canopy status and enabling the estimate of discrete phenophase using vegetation indices that are conceptually similar to satellite data. Six phenological dates (greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset) were retrieved separately from daily VIIRS NDVI (normalized difference vegetative index) and EVI2 (two-band enhanced vegetation index) time series. Similarly, the six phenological dates were also extracted from the 30-min time series of PhenoCam data using GCC (green chromatic coordinate) and VCI (vegetation contrast index) separately. Phenological dates derived from VIIRS NDVI and EVI2 and PhenoCam GCC and VCI were generally comparable for the vegetation greenup phase, but differed considerably for the senescence phase. Although all indices captured green leaf development effectively, performance discrepancies arose due to their ability to track the mixture of senescing leaf colors. PhenoCam GCC and VCI phenological observations were in better agreement with the phenological dates from VIIRS EVI2 than from VIIRS NDVI. Further, the VIIRS EVI2 phenological metrics were more similar to those from PhenoCam VCI than from PhenoCam GCC time series. Overall, the average absolute difference between the VIIRS EVI2 and PhenoCam VCI phenological dates was 7–11 days in the greenup phase and 10–13 days in the senescence phase. The difference was smaller in forests, followed by grasslands and croplands, and then savannas. Finally, the phenological dates derived from VIIRS EVI2 were compared with MODIS detections, which showed a good agreement with an average absolute difference less than a week except for the senescence onset. These results for the first time demonstrate the upper boundary of uncertainty in VIIRS LSP detections and the continuity to MODIS LSP product. [ABSTRACT FROM AUTHOR]

Five years of 0.01° latitude × 0.01° longitude multiradar multisensor grids of composite reflectivity and vertically integrated signals from the maximum expected size of hail (MESH) and vertically integrated liquid (VIL) were created to examine the role of city size on thunderstorm occurrence and strength around four cities: Dallas-Fort Worth, Texas; Minneapolis-St. Paul, Minnesota; Oklahoma City, Oklahoma; and Omaha, Nebraska. A storm-tracking algorithm identified thunderstorm areas every minute and connected them together to form tracks. These tracks defined the upwind and downwind regions around each city on a storm-bystorm basis and were analyzed in two ways: 1) by sampling the maximum value every 10 min and 2) by accumulating the spatial footprint over its lifetime. Beyond examining all events, a subset of events corresponding to favorable conditions for urban modification was explored. This urban favorable (UF) subset consisted of nonsupercells occurring in the late afternoon/evening in the meteorological summer on weak synoptically forced days. When examining all thunderstorm events, regions at variable ranges upwind of all four cities generally had higher areal mean values of reflectivity, MESH, and VIL relative to downwind areas. In the UF subset, the larger cities (Dallas-Fort Worth and Minneapolis-St. Paul) had a 24%-50% increase in the number of downwind thunderstorms, resulting in a higher areal mean reflectivity, MESH, and VIL in this region. The smaller cities (Oklahoma City and Omaha) did not show such a downwind enhancement in thunderstorm occurrence and strength for the radar variables examined. This pattern suggests that larger cities could increase thunderstorm occurrence and intensity downwind of the prevailing flow under unique environmental conditions. [ABSTRACT FROM AUTHOR]

Rangelands are among the most extensive anthropogenic landscapes on earth, supporting nearly 500 million people. Disagreements over the extent and severity of rangeland degradation affect pastoralist livelihoods, especially when impacts of drought and over-grazing are confounded. While vegetation indices (such as NDVI, or Normalized Difference Vegetation Index) derived from remotely sensed imagery are often used to monitor rangelands, their strategic integration with local ecological knowledge (LEK) is under-appreciated. Here, we explore these complementary approaches in Kyrgyzstan’s pasture-rich province of Naryn, where disagreements regarding pasture degradation could greatly benefit from additional information. We examine a time series of MODIS satellite imagery (2000–2015) to characterize browning trends in vegetation as well as to distinguish between climate- and grazing-induced trends. We also compare and contrast measured trends with LEK perceptions of pasture degradation. To do so, we first examine statistical trends in NDVI as well as in NDVI residuals after de-trending with meteorological data. Second, we use participatory mapping to identify areas local pasture managers believe are overgrazed, a particularly useful approach in lieu of reliable historical stocking rates for livestock in this region. Lastly, we compare the strengths and weaknesses of LEK and remote sensing for landscape monitoring. Browning trends were widespread as declining trends in NDVI (and NDVI residuals) covered 24% (and 9%) of the landscape, respectively. Local managers’ perceptions of pasture degradation better reflected trends seen in NDVI than in climate-controlled NDVI residuals, suggesting patterns in the latter are less apparent to managers. Our approach demonstrated great potential for the integration of two inexpensive and effective methods of rangeland monitoring well-suited to the country’s needs. Despite limitations due to terrain, our approach was most successful within the semi-arid steppe where pasture degradation is believed to be most severe. In many parts of the world, sources of long-term spatially extensive data are rare or even non-existent. Thus, paired LEK and remote sensing can contribute to comprehensive and informative assessments of land degradation, especially where contentious management issues intersect with sparse data availability. LEK is a valuable source of complementary information to remote sensing and should be integrated more routinely and formally into landscape monitoring. To aid this endeavor, we synthesize advice for linking LEK and remote sensing across diverse landscape situations. [ABSTRACT FROM AUTHOR]

Urban parks are considered as an effective, sustainable, and affordable heat mitigation strategy. At present, there is a lack of understanding of the carbon saving potential of urban parks in the context of urban warming. Here we provide a simple approach to estimate the potential carbon savings due to heat mitigation based on analysis of 1510 parks in 26 major urban areas of the years 2017–2021 within the Yangtze River Economic Belt (YREB), China's largest sustainability experiment. On average, an urban park with 26.9 ± 1.5 ha in the YREB could avoid 23.7 ± 1.6 t CO 2 (1.08 ± 0.03 t CO 2 /ha) in emissions due to heat mitigation per summer day. Considered altogether, the 1510 urban parks can offset 5.37% of the daily fossil fuel emissions in the YREB. To boost carbon saving efficiency, lush vegetation should be first considered and the coupled green and blue infrastructures are always advocated. The total parks carbon saving of cities gradually improved along the Yangtze River from west to east, among which Shanghai city (in the east of the YREB) is with the largest amount (4341 t CO 2). The strategies using exquisite landscape design for improving carbon saving may differ across different climate zones. These findings can inform decision-making for urban sustainable development and climate change mitigation, which may benefit China's movement toward carbon neutrality. [ABSTRACT FROM AUTHOR]

Half of Earth's land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts. [ABSTRACT FROM AUTHOR]