Land Surface Properties Research Articles

Evaluating Bi-temporal Dynamics and Trend of Urbanization-induced Land Cover Temperature in Shanghai, China

Abstract. In China today, massive land use/land cover change (LULCC) driven by urbanization has become a dominant phenomenon in the interactions between the human system and land surface. Drastic urbanization has changed land surface properties where a great deal of physical landscapes has been replaced by artificial buildings, and this evolution has brought about great potential risks and pressures physically and socially. In this study, taking the Shanghai metropolitan area (SMA) as the study area, we examine the spatiotemporal dynamics of temperature in urban land cover and explore the sustainable strategies for future development.Shanghai, one of the Chinese gigantic cities, had the residential population of 24.15 million in 2015. The phenomena of urban heat island (UHI) in SMA have become prominent by the urbanization-related anthropic activities and increasing population. Until the present, a substantial number of investigations has been conducted in the Shanghai's thermal environment based on remote sensing data. However, the comprehensive assessments of urban thermal environment are not enough in Shanghai. This kind of research is expected to provide a significant paradigm for the exploration of a sustainable development of Chinese megacities, which examines the mechanism of the interactions between urban thermal environment and human system and addresses its adverse effect on the urban environment.In this study, bi-temporal Landsat satellite data (Sep.2000 and Aug.2015) were used for the detection of LULCC and the estimation of land surface temperature (LST) in SMA. The LST was retrieved using the radiation transfer equation (RTE), and then normalized and graded in order to remove the influence of the seasonality and local climate characteristics. The spatiotemporal configuration and variation of urban thermal environment in SMA between 2000 and 2015 were synthetically monitored and evaluated through the analyses of the urban-rural gradient, gravity center change, spatial landscape pattern, and geographically weighted regression. Furthermore, the spatial determinants for the formation of UHI in SMA were identified in the analysis.The results showed that (1) in 2000, the LST in the downtown of SMA was higher than that in the outskirts and rural area; (2) the extent of UHI in SMA was gradually expanding from 2000 to 2015 along the direction of urban growth, however, the UHI intensity in the downtown area was relatively declining due to the explosive increase of impervious surfaces in the suburban and rural area; (3) the spatiotemporal evolution of SMA's urban thermal environment was highly consistent with the track of LULCC, and greatly influenced by the spatial pattern and variation of urban landscape; (4) impervious surfaces obviously facilitated and strengthened the UHI effect, inversely, greenbelt and water space contributed to the mitigation and alleviation of the UHI effect; and (5) population convergence, industrial growth, dense land utilization, and urban development policies also affected the SMA's urban thermal environment.

Application of Satellite Remote Sensing for Estimation of Dust Emission Probability in the Urmia Lake Basin in Iran

Saline playas in arid and semiarid regions of the world are significant sources of unconsolidated sediments susceptible to aeolian transport. Lake Urmia in Iran, one of the largest saltwater lakes on Earth, has waned to approximately 18% of its original size due to groundwater pumping and surface water diversions. This has led to ecosystem degradation, accelerated desertification and frequent dust storms, causing public health problems. In this paper we introduce a new framework for delineation of dust source zones and estimation of dust occurrence probability based on remotely sensed land surface properties (soil moisture and vegetation cover), soil texture, wind speed, and measured dust frequencies. Observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the Urmia Lake basin were utilized to determine the Normalized Difference Vegetation Index (NDVI) and to estimate surface soil moisture with a recently introduced optical trapezoid model. The soil textures extracted from the SoilGrids database and wind speeds obtained from local weather stations together with the estimated surface soil moisture were found to be highly correlated with dust emission probability. When the surface soil moisture is low, wind speed is the major determinant for dust occurrence. With increasing surface soil moisture, the dust occurrence probability decreases. Soil moisture effects are more pronounced at high wind speeds. While at low wind speeds the dust occurrence probabilities for the investigated soil textures loam, clay loam and sandy clay loam are similar, at higher wind speeds the sandy clay loam texture exhibits the highest susceptibility to dust generation.Core Ideas A remote sensing framework for delineation of dust source zones is introduced. Surface soil moisture and soil texture are correlated with dust emission frequency. Dust occurrence probability is highest for low moisture conditions and sandy soils.

Malay Archipelago Forest Loss to Cash Crops and Urban Expansion Contributes to Weaken the Asian Summer Monsoon: An Atmospheric Modeling Study

AbstractIn the Malay Archipelago (Indonesia and Malaysia), forest is lost on large scales to cash-crop plantation (oil palm, rubber, and acacia, including fallow lands) and urban expansion. Deforestation changes land surface properties and fluxes, thereby modifying wind and rainfall. Despite the expansive land-cover change over a climatically sensitive region of the tropics, the resulting impact on the Asian summer monsoon has not been studied. Here we study the atmospheric response caused by the island surface change due to deforestation into cash-crop plantations and urban expansion. Using a large ensemble of atmospheric model experiments with observed and idealized land-cover-change specifications, we show that the deforestation warms the Malay Archipelago, caused by an increase in soil warming due to decreased evapotranspirative cooling. The island warming agrees well with in situ and satellite observations; it causes moisture to converge from the surrounding seas into Sumatra and Malaya, and updrafts, rainfall, and cyclonic circulations to spread northwestward into southern India and the Arabian Sea, as well as a drying anticyclonic circulation over the Indo-Gangetic plains, Indochina, and the South China Sea, weakening the Asian summer monsoon. The modeled monsoon weakening agrees well with, and tends to enhance, the observed long-term trend, suggesting the potential for continued weakening with protracted cash-crop plantation and urban expansion.

Effects of land surface inhomogeneity on convection-permitting WRF simulations over central Europe

This sensitivity study investigates the impact of the spatial scales of land use, soil moisture and orography patterns on land–atmosphere exchange fluxes, domain-wide averages and meteorologic conditions in convection-permitting RCM simulations. We perform five WRF RCM simulations, each with the same 3 km resolution atmospheric setup but different combinations of coarsely resolved (12 km) land use and soil type, initial soil moisture and orography for the heat-wave summer 2003 over central Europe. Our results indicate that a coarser-resolved orography significantly alters the flow over and around mountain ridges such as the Alps and impact the large-scale flow pattern. The smoothed mountain ridges result in weaker Föhn effects and in enhanced locally generated convective precipitation patterns, peaking earlier in the afternoon. In comparison, the impact of a coarser-resolved land use is smaller and mainly related to changes in the overall spatial fraction of a land use, rather than to the loss of heterogeneity of the different land use types on the scale analyzed here. Albeit, even small changes in the initial soil moisture (both spatial averages and local differences) have a higher potential to affect the overall simulation results, although this might also depend on the land surface model. Overall, effects induced by the coarsely resolved land surface properties are small compared to the differences between simulations with 3 km and 12 km grid spacings of the atmosphere.

Sensitivity of isoprene emissions to drought over south-eastern Australia: Integrating models and satellite observations of soil moisture

South-east Australia, characterized by arid and semi-arid climate, has experienced large-scale rainfall reductions in recent decades. Larger temporal and spatial drought conditions are predicted in future. The temperate south east coastal zone is characterized by dense forests of Eucalyptus. Drought conditions have implications for the functioning of these indigenous ecosystems, and for emissions of reactive gases that are upwind of major metropolitan regions along the eastern coast. Here, we focus on the impact of drought on the emission of isoprene, a volatile organic compound emitted by a range of trees and shrubs. Previous model calculations grossly overestimate observations of the isoprene mixing ratio, potentially due to overestimated emission factors for native vegetation, but could also be due to drought-induced isoprene emission reductions. We develop the implementation of the Model of Emissions of Gases and Aerosols from Nature (MEGAN) within the CSIRO Chemical Transport Model to include a parameterization of drought using soil moisture. We test this parameterization using two approaches. First, we drive MEGAN using soil moisture fields from two Australian land surface models achieving reductions in isoprene emissions of 24–52% during summer. Second, we use a simple statistical approach to nudge model soil moisture towards satellite observations from the Soil Moisture and Ocean Salinity (SMOS) instrument. This work is the first application of SMOS towards isoprene emission modelling, providing a constraint on surface soil moisture which extends to global scales. Applying these soil moisture approaches, domain average isoprene emissions reduce by 38–58% in summer. Using these results, errors in basal emissions are likely in the region of 40%. Comparison of modelled soil moisture to observations using root density weighted averages across depths of 4 m showed minor differences of up to 0.04 m3 m−3. We find that the choice of land surface model used in the SMOS assimilations has a greater impact on isoprene emissions than adjusting either the nudging strength or the number of soil levels these satellite data may influence. However there are only small differences of 3% when using hourly or 24-hourly soil moisture input data to drive the emission calculations, suggesting that the lower temporal availability of satellite data does not reduce model quality. As the spatial resolution of satellite observations of atmospheric composition and land surface properties begin to approach the resolution of regulatory air quality models, we anticipate that these data will improve model predictive skills further.

Mapping Climatological Bare Soil Albedos over the Contiguous United States Using MODIS Data

Surface bare soil albedo is an important variable in climate modeling studies and satellite-based retrievals of land-surface properties. In this study, we used multiyear 500 m albedo products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive the bare soil albedo for seven spectral bands and three broadbands over the contiguous United States (CONUS). The soil line based on red and green spectral signatures derived from MODIS data was used as the basis to detect and extract bare soil albedo. A comparison against bare soil albedo derived from 30 m Landsat data has been made, showing that the MODIS bare soil albedo had a bias of 0.003 and a root-mean-square-error (RMSE) of 0.036. We found that the bare soil albedo was negatively correlated with soil moisture from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), with a relatively stable exponential relationship reflecting the darkening effect that moisture has on most soils. However, quantification of the relationship between bare soil albedo and soil moisture still needs to be improved through simultaneous and instantaneous measurements at a finer spatial resolution. Statistics of the multiyear climatological bare soil albedos calculated using soil types and the International Geosphere-Biosphere Programme (IGBP) land cover types suggest that: Land cover type is a better indicator for determining the magnitude of bare soil albedos for the vegetated areas, as the vegetation density is correlated with soil moisture; and soil type is a better indicator for determining the slope of soil lines over sparsely vegetated areas, as it contains information of the soil texture, roughness, and composition. The generated bare soil albedo can be applied to improve the parameterization of surface energy budget in climate and remote sensing models as well as the retrieval accuracy of some satellite products.

Multi-year surface radiative properties and vegetation parameters for hydrologic modeling in regions of complex terrain—Methodology and evaluation over the Integrated Precipitation and Hydrology Experiment 2014 domain

Study regionSoutheast US (SE US). Study focusThis study probes the propagation of errors in standard remote-sensing vegetation products caused by cloud contamination and the impact of time-variant radiative properties for correctly describing land-surface properties in hydrologic models. Spatiotemporally-varying quality-controlled vegetation attributes (i.e., leaf area index and fractional vegetation coverage) and surface radiative properties (i.e., longwave broadband emissivity and shortwave broadband albedo) were derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products over the SE US at 1 km × 1 km and hourly resolutions from 2007 to 2013. The data sets are publicly available. The impact of uncorrected standard vegetation products and static treatments of radiative properties was assessed systematically clearly illustrating improvements in simulated water and energy fluxes using the developed landscape attributes with a fully-distributed uncalibrated hydrologic model. New hydrological insights for the regionThrough simulations in the Southern Appalachian Mountains, we found that the spatiotemporal variability of radiative properties significantly influences the diurnal cycle of the surface energy budget with marked differences in sensible heat fluxes (up to 10–20%). Better performance of streamflow simulations achieved by using the improved vegetation attributes is tied to changes in rainfall interception and evapotranspiration, reflecting the importance of SE forests in the regional water cycle. The largest improvements in streamflow simulations result from larger corrections to MODIS products in the inner mountain region where cloudiness is persistent.

Deforestation effects on land surface energy coupling: a data-driven perspective

Deforestation dramatically alters land surface properties and functions through multiple biogeophysical and biogeochemical pathways. However, a quantitative identification of how deforestation affects local energy-water-vegetation coupling is still challenging. In this study we employed information theory and transfer entropy framework to identify the overall feedback pattern of land surface water-energy-vegetation coupling, using high frequency eddy covariance measurements at forested versus deforested sites. We found that deforestation strengthened the directional influence of atmospheric demand on land surface water flux, and more importantly, deforestation broke the coupling between vegetation activities and local precipitation, which led to a less efficient ecosystem to recycle and maintain water within this system.

Estimation of water provision service for monsoon catchments of South China: Applicability of the InVEST model

With recognizing the value of ecosystem services to society, the efforts to manage ecosystem services have increased. One such tool to help manage ecosystem services is the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, a new but powerful ecosystem service model. However, the InVEST model still requires testing in various geographic regions. This study assessed the performance of the InVEST water yield model in a typical East Asian monsoon catchment of South China. A sensitivity analysis shows that precipitation is the most sensitive factor affecting water yield compared with two other important variables: ET0 (reference evapotranspiration) and Z (climate seasonality factor). Especially for some southeast sub-watersheds, a 46% increase in precipitation from 2005 to 2015 resulted in an approximately 138% increase in water yield. The model performed well when the Z parameter was calibrated with available observed data (R2 = 0.92) or inferred from the globally available ω (R2 = 0.92) which is a key parameter that characterizes the land surface properties of catchments. This study will be helpful to other users who employ InVEST model and ecosystem services management in East Asian monsoon catchments.

Characterizing uncertainties in the ESA-CCI land cover map of the epoch 2010 and their impacts on MPI-ESM climate simulations

Limitations of mapping land surface properties and their conversion into climate model boundary conditions are major sources of uncertainty in climate simulations. In this paper, the range of the largest possible uncertainty in satellite-derived land cover (LC) map is estimated and its impact on climate simulations is quantified with the Earth System Model of the Max-Planck Institute for Meteorology utilizing prescribed sea surface temperature and sea ice. Two types of uncertainty in the LC map are addressed: (i) uncertainty due to classification algorithm of spectral reflectance into LC classes, and (ii) uncertainty due to conversion of LC classes into the climate model vegetation distribution. For forest cover, each of them is about the same order of magnitude as the uncertainty range in recent observations (∼± 700 Mha). Superposing two sources of uncertainty results in LC maps that feature the range of vegetation deviation that is about the same order of magnitude as the recent (since year 1700) forest loss due to agriculture (forest cover uncertainty range ∼± 1700 Mha). These uncertainties in vegetation distribution lead to noticeable variations in near-surface climate variables, local, regional, and global climate forcing. Temperature does not show significant uncertainty in global mean, but rather exhibits regional deviations with an opposite response to LC uncertainty that compensate each other in the global mean (e.g., albedo feedback controls temperature in boreal North America resulting in cooling (warming) with decrease (increase) of vegetation while evaporative cooling controls temperature in South America and sub-Saharan Africa resulting in cooling (warming) with increase (decrease) of vegetation). Large-scale circulation is also affected by the LC uncertainty, and consequently precipitation pattern as well. It is demonstrated that precipitation uncertainty in the monsoonal regions are about the same order of magnitude as in previous studies with idealized perturbations of vegetation. These findings indicate that the range of uncertainty in satellite-derived vegetation maps for climate models is about the same order of magnitude as the uncertainty in recent observations of forest cover or as the forest lost due to agriculture. Consequently, climate simulations have a similar range of uncertainty in variables representing near-surface climate as the observed climate change due to land use. Hence, more accurate methods are needed for mapping and converting LC properties into model vegetation in order to increase reliability of climate model simulations.

Influence of radiative forcing factors on ground–air temperature coupling during the last millennium: implications for borehole climatology

Abstract. Past climate variations may be uncovered via reconstruction methods that use proxy data as predictors. Among them, borehole reconstruction is a well-established technique to recover the long-term past surface air temperature (SAT) evolution. It is based on the assumption that SAT changes are strongly coupled to ground surface temperature (GST) changes and transferred to the subsurface by thermal conduction. We evaluate the SAT–GST coupling during the last millennium (LM) using simulations from the Community Earth System Model LM Ensemble (CESM-LME). The validity of such a premise is explored by analyzing the structure of the SAT–GST covariance during the LM and also by investigating the evolution of the long-term SAT–GST relationship. The multiple and single-forcing simulations in the CESM-LME are used to analyze the SAT–GST relationship within different regions and spatial scales and to derive the influence of the different forcing factors on producing feedback mechanisms that alter the energy balance at the surface. The results indicate that SAT–GST coupling is strong at global and above multi-decadal timescales in CESM-LME, although a relatively small variation in the long-term SAT–GST relationship is also represented. However, at a global scale such variation does not significantly impact the SAT–GST coupling, at local to regional scales this relationship experiences considerable long-term changes mostly after the end of the 19th century. Land use land cover changes are the main driver for locally and regionally decoupling SAT and GST, as they modify the land surface properties such as albedo, surface roughness and hydrology, which in turn modifies the energy fluxes at the surface. Snow cover feedbacks due to the influence of other external forcing are also important for corrupting the long-term SAT–GST coupling. Our findings suggest that such local and regional SAT–GST decoupling processes may represent a source of bias for SAT reconstructions from borehole measurement, since the thermal signature imprinted in the subsurface over the affected regions is not fully representative of the long-term SAT variations.

Retrieval of land surface properties from an annual time series of Landsat TOA radiances during a drought episode using coupled radiative transfer models

The accurate retrieval of land surface vegetation properties under varying environmental conditions from time series of moderately high spatial resolution satellite observations is challenging. By coupling various Radiative Transfer (RT) models one can describe the soil, vegetation and atmosphere contributions in a “bottom-up” approach and, thereby, simulate top-of-atmosphere (TOA) spectral radiance data comparable to satellite-observed TOA radiances. This makes it possible to retrieve vegetation properties directly from TOA radiances rather than from atmospherically corrected top-of-canopy (TOC) reflectance data. The advantages of this approach are that a separate atmospheric correction of the satellite images is not necessary, and that the anisotropic surface reflection can also be taken into account effectively. In this study, we coupled various RT models, including the brightness – shape – moisture (BSM) reflectance model of the soil, the optical radiative transfer (RTMo) model of vegetation and the ‘MODerate resolution atmospheric TRANsmission’ (MODTRAN) model of the atmosphere, to simulate an annual time series of Landsat satellite TOA radiances observed during a drought episode in California Mediterranean grasslands in 2004. The inversion of this coupled system through an optimization technique against Landsat TOA radiances resulted in direct retrieval of vegetation properties. We accommodated the surface anisotropic reflection in our coupled modeling and also defined a novel anisotropy index to quantitatively express the importance of this phenomenon in satellite image analysis for the first time. Our study showed that the coupled use of RT models was able to accurately reproduce the time series of observed TOA radiances collected under varying soil moisture contents during an extended drought episode. The proposed coupling approach is useful for successful retrieval of vegetation properties from time series of satellite TOA radiance data to produce maps of land surface properties and to monitor vegetation properties variations in an operational straightforward way. The approach can also be easily adapted for conducting multi-sensor time series studies, creating a much denser temporal sampling than would be possible for separate single sensors.

The Effects of Tropical Vegetation on Rainfall

Vegetation modifies land-surface properties, mediating the exchange of energy, moisture, trace gases, and aerosols between the land and the atmosphere. These exchanges influence the atmosphere on local, regional, and global scales. Through altering surface properties, vegetation change can impact on weather and climate. We review current understanding of the processes through which tropical land-cover change (LCC) affects rainfall. Tropical deforestation leads to reduced evapotranspiration, increasing surface temperatures by 1–3 K and causing boundary layer circulations, which in turn increase rainfall over some regions and reduce it elsewhere. On larger scales, deforestation leads to reductions in moisture recycling, reducing regional rainfall by up to 40%. Impacts of future tropical LCC on rainfall are uncertain but could be of similar magnitude to those caused by climate change. Climate and sustainable development policies need to account for the impacts of tropical LCC on local and regional rainfall.

Fire Activity and Fuel Consumption Dynamics in Sub-Saharan Africa

African landscape fires are widespread, recurrent and temporally dynamic. They burn large areas of the continent, modifying land surface properties and significantly affect the atmosphere. Satellite Earth Observation (EO) data play a pivotal role in capturing the spatial and temporal variability of African biomass burning, and provide the key data required to develop fire emissions inventories. Active fire observations of fire radiative power (FRP, MW) have been shown to be linearly related to rates of biomass combustion (kg s−1). The Meteosat FRP-PIXEL product, delivered in near real-time by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF), maps FRP at 3 km resolution and 15-min intervals and these data extend back to 2004. Here we use this information to assess spatio-temporal variations in fire activity across sub-Saharan Africa, and identify an overall trend of decreasing annual fire activity and fuel consumption, agreeing with the widely-used Global Fire Emissions Database (GFEDv4) based on burned area measures. We provide the first comprehensive assessment of relationships between per-fire FRE-derived fuel consumption (Tg dry matter, DM) and temporally integrated Moderate Resolution Imaging Spectroradiometer (MODIS) net photosynthesis (PSN) (Tg, which can be converted into pre-fire fuel load estimates). We find very strong linear relationships over southern hemisphere Africa (mean r = 0.96) that are partly biome dependent, though the FRE-derived fuel consumptions are far lower than those derived from the accumulated PSN, with mean fuel consumptions per unit area calculated as 0.14 kg DM m−2. In the northern hemisphere, FRE-derived fuel consumption is also far lower and characterized by a weaker linear relationship (mean r = 0.76). Differences in the parameterization of the biome look up table (BLUT) used by the MOD17 product over Northern Africa may be responsible but further research is required to reconcile these differences. The strong relationship between fire FRE and pre-fire fuel load in southern hemisphere Africa is encouraging and highlights the value of geostationary FRP retrievals in providing a metric that relates very well to fuel consumption and fire emission variations. The fact that the estimated fuel consumed is only a small fraction of the fuel available suggests underestimation of FRE by Spinning Enhanced Visible and Infrared Imager (SEVIRI) and/or that the FRE-to-fuel consumption conversion factor of 0.37 MJ kg−1 needs to be adjusted for application to SEVIRI. Future geostationary imaging sensors, such as on the forthcoming Meteosat Third Generation (MTG), will reduce the impact of this underestimation through its ability to detect even smaller and shorter-lived fires than can the current second generation Meteosat.

Use of Radiocarbon Ages to Narrow Groundwater Recharge Estimates in the Southeastern Mojave Desert, USA

Estimating groundwater recharge in arid or semiarid regions can be a difficult and complex task, since it is dependent on a highly variable set of spatial and temporal hydrologic parameters and processes that are dependent on the local climate, the land surface properties, and subsurface characteristics. As a result, traditional methods for estimating the recharge can result in a wide range of derived values. This is evident in the southeastern Mojave Desert, where calculated recharge estimates by previous investigators that range over an order of magnitude (from ~2500 to ~37,000 acre feet per year) are reported. To narrow down this large span of recharge estimates to narrower and more plausible values, this study evaluates the previous recharge estimates in this region, to examine the sources of variability in the reported results and to constrain the recharge estimates based on the hydrologic conditions and the radiocarbon age-dating of spring flows—even without knowledge of the precise subsurface hydrology. The groundwater age and perennial flow characteristics of springs in this study could not be derived from waters sourced solely from local recharge. Therefore, the springs in this study require a significant groundwater contribution to their overall discharge. A previously described conceptual site model in the region established that Bonanza Spring is similarly hydrologically connected to the regional basin-fill aquifer, based on geologic and geochemical/isotopic analyses, and this conceptual site model for where perennial spring water is sourced should readily be extended to these other perennial springs in this region.

A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon

This study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation.

Aerosol retrieval algorithm based on adaptive land–atmospheric decoupling for polarized remote sensing over land surfaces

Land–atmospheric decoupling is important for the retrieval accuracy in atmospheric remote sensing. In this study, we developed an algorithm that adaptively decouples the land surface and atmosphere reflected solar radiation and retrieves the aerosol properties (adaptive land–atmospheric decoupling algorithm, ALAD). The ALAD algorithm allows precise global aerosol optical depth (AOD) retrievals. The four sub-procedures in ALAD comprise estimating a first guess for surface properties, aerosol property retrieval, atmospheric correction, and assessing the results. The ALAD algorithm was applied to PARASOL (Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) and the retrieved AODs were compared with the AOD products from the Moderate resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET). ALAD and MODIS retrieved AOD values were with good consistency in terms of their spatial distribution, regardless of the region or the global distribution. The correlation coefficient between the global ALAD and AERONET AOD was 0.875, and about 60.7% of the AODs retrieved by ALAD were within the expected error range of ±(15% AOD + 0.05). The results obtained using ALAD agreed well with the AERONET values. We also investigated the aerosol and land surface properties that might introduce errors into the AOD values retrieved by ALAD. It was found that the value of AOD had the greatest effect on the accuracy of the retrieval results. The errors introduced by the Angstrom exponent and Normalized Difference Vegetation Index varied slightly but they made no significant contributions.

Global land change from 1982 to 2016.

Land change is a cause and consequence of global environmental change1,2. Changes in land use and land cover considerably alter the Earth's energy balance and biogeochemical cycles, which contributes to climate change and-in turn-affects land surface properties and the provision of ecosystem services1-4. However, quantification of global land change is lacking. Here we analyse 35years' worth of satellite data and provide a comprehensive record of global land-change dynamics during the period 1982-2016. We show that-contrary to the prevailing view that forest area has declined globally5-tree cover has increased by 2.24millionkm2 (+7.1% relative to the 1982 level). This overall net gain is the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16millionkm2 (-3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover. The mapped land changes and the driver attributions reflect a human-dominated Earth system. The dataset we developed may be used to improve the modelling of land-use changes, biogeochemical cycles and vegetation-climate interactions to advance our understanding of global environmental change1-4,6.

Changes of surface energy partitioning caused by plastic mulch in a cotton field

Abstract Widely used in croplands, plastic mulch can significantly change land surface properties and energy partitioning. However, the magnitude of these modifications caused by plastic mulch (and its variations) on leaf area index remain largely unclear. Field experiments were, therefore, conducted to analyse the differences in energy partitioning between plastic mulch and non-plastic mulch conditions in cotton fields in arid Tarim Basin. Each component net radiation, surface soil heat flux, sensible heat and latent heat was either measured or estimated at different growth stages of the cotton crop. Results showed that the effects of plastic mulch on field energy partitioning was most evident when leaf area index was less than 1.0. During this period, net radiation decreased mainly due to the increase of surface reflectance. Surface soil heat flux and sensible heat were also increased due to the increase of surface temperature. Finally, latent heat decreased after plastic mulch application. As over 20% of net radiation was allocated to the soil surface under plastic mulch at the seedling stage, this suggests that surface soil heat flux should not be ignored for evaluating surface energy balance at the seedling stage under plastic mulch conditions.

TAMSAT-ALERT v1: a new framework for agricultural decision support

Abstract. Early warning of weather-related hazards enables farmers, policy makers and aid agencies to mitigate their exposure to risk. We present a new operational framework, Tropical Applications of Meteorology using SATellite data and ground based measurements-AgricuLtural EaRly warning sysTem (TAMSAT-ALERT), which aims to provide early warning for meteorological risk to agriculture. TAMSAT-ALERT combines information on land-surface properties, seasonal forecasts and historical weather to quantitatively assess the likelihood of adverse weather-related outcomes, such as low yield. This article describes the modular TAMSAT-ALERT framework and demonstrates its application to risk assessment for low maize yield in northern Ghana (Tamale). The modular design of TAMSAT-ALERT enables it to accommodate any impact or land-surface model driven with meteorological data. The implementation described here uses the well-established General Large Area Model (GLAM) for annual crops to provide probabilistic assessments of the meteorological hazard for maize yield in northern Ghana (Tamale) throughout the growing season. The results show that climatic risk to yield is poorly constrained in the beginning of the season, but as the season progresses, the uncertainty is rapidly reduced. Based on the assessment for the period 2002–2011, we show that TAMSAT-ALERT can estimate the meteorological risk on maize yield 6 to 8 weeks in advance of harvest. The TAMSAT-ALERT methodology implicitly weights forecast and observational inputs according to their relevance to the metric being assessed. A secondary application of TAMSAT-ALERT is thus an evaluation of the usefulness of meteorological forecast products for impact assessment. Here, we show that in northern Ghana (Tamale), the tercile seasonal forecasts of seasonal cumulative rainfall and mean temperature, which are routinely issued to farmers, are of limited value because regional and seasonal temperature and rainfall are poorly correlated with yield. This finding speaks to the pressing need for meteorological forecast products that are tailored for individual user applications.