Fractional Vegetation Research Articles

Simulation of the mid-Pliocene Warm Period using HadGEM3: experimental design and results from model–model and model–data comparison

Abstract. Here we present the experimental design and results from a new mid-Pliocene simulation using the latest version of the UK's physical climate model, HadGEM3-GC31-LL, conducted under the auspices of CMIP6/PMIP4/PlioMIP2. Although two other palaeoclimate simulations have been recently run using this model, they both focused on more recent periods within the Quaternary, and therefore this is the first time this version of the UK model has been run this far back in time. The mid-Pliocene Warm Period, ∼3 Ma, is of particular interest because it represents a time period when the Earth was in equilibrium with CO2 concentrations roughly equivalent to those of today, providing a possible analogue for current and future climate change. The implementation of the Pliocene boundary conditions is firstly described in detail, based on the PRISM4 dataset, including CO2, ozone, orography, ice mask, lakes, vegetation fractions and vegetation functional types. These were incrementally added into the model, to change from a pre-industrial setup to a Pliocene setup. The results of the simulation are then presented, which are firstly compared with the model's pre-industrial simulation, secondly with previous versions of the same model and with available proxy data, and thirdly with all other models included in PlioMIP2. Firstly, the comparison with the pre-industrial simulation suggests that the Pliocene simulation is consistent with current understanding and existing work, showing warmer and wetter conditions, and with the greatest warming occurring over high-latitude and polar regions. The global mean surface air temperature anomaly at the end of the Pliocene simulation is 5.1 ∘C, which is the second highest of all models included in PlioMIP2 and is consistent with the fact that HadGEM3-GC31-LL has one of the highest Effective Climate Sensitivities of all CMIP6 models. Secondly, the comparison with previous generation models and with proxy data suggests a clear increase in global sea surface temperatures as the model has undergone development. Up to a certain level of warming, this results in a better agreement with available proxy data, and the “sweet spot” appears to be the previous CMIP5 generation of the model, HadGEM2-AO. The most recent simulation presented here, however, appears to show poorer agreement with the proxy data compared with HadGEM2 and may be overly sensitive to the Pliocene boundary conditions, resulting in a climate that is too warm. Thirdly, the comparison with other models from PlioMIP2 further supports this conclusion, with HadGEM3-GC31-LL being one of the warmest and wettest models in all of PlioMIP2, and if all the models are ordered according to agreement with proxy data, HadGEM3-GC31-LL ranks approximately halfway among them. A caveat to these results is the relatively short run length of the simulation, meaning the model is not in full equilibrium. Given the computational cost of the model it was not possible to run it for a longer period; a Gregory plot analysis indicates that had it been allowed to come to full equilibrium, the final global mean surface temperature could have been approximately 1.5 ∘C higher.

Remote-Sensing-Combined Haplotype Analysis Using Multi-Parental Advanced Generation Inter-Cross Lines Reveals Phenology QTLs for Canopy Height in Rice.

High-throughput phenotyping systems with unmanned aerial vehicles (UAVs) enable observation of crop lines in the field. In this study, we show the ability of time-course monitoring of canopy height (CH) to identify quantitative trait loci (QTLs) and to characterise their pleiotropic effect on various traits. We generated a digital surface model from low-altitude UAV-captured colour digital images and investigated CH data of rice multi-parental advanced generation inter-cross (MAGIC) lines from tillering and heading to maturation. Genome-wide association studies (GWASs) using the CH data and haplotype information of the MAGIC lines revealed 11 QTLs for CH. Each QTL showed haplotype effects on different features of CH such as stage-specificity and constancy. Haplotype analysis revealed relationships at the QTL level between CH and, vegetation fraction and leaf colour [derived from UAV red–green–blue (RGB) data], and CH and yield-related traits. Noticeably, haplotypes with canopy lowering effects at qCH1-4, qCH2, and qCH10-2 increased the ratio of panicle weight to leaf and stem weight, suggesting biomass allocation to grain yield or others through growth regulation of CH. Allele mining using gene information with eight founders of the MAGIC lines revealed the possibility that qCH1-4 contains multiple alleles of semi-dwarf 1 (sd1), the IR-8 allele of which significantly contributed to the “green revolution” in rice. This use of remote-sensing-derived phenotyping data into genetics using the MAGIC lines gives insight into how rice plants grow, develop, and produce grains in phenology and provides information on effective haplotypes for breeding with ideal plant architecture and grain yield.

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.

Modeling sensitivities of BVOCs to different versions of MEGAN emission schemes in WRF-Chem (v3.6) and its impacts over eastern China

Abstract. Biogenic volatile organic compounds (BVOCs) simulated by current air quality and climate models still have large uncertainties, which can influence atmospheric chemistry and secondary pollutant formation. These modeling sensitivities are primarily due to two sources. One originates from different treatments in the physical and chemical processes associated with the emission rates of BVOCs. The other is errors in the specification of vegetation types and their distribution over a specific region. In this study, the version of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) updated by the University of Science and Technology of China (USTC version of WRF-Chem) from the public WRF-Chem(v3.6) is used. The modeling results over eastern China with different versions (v1.0, v2.0, v3.0) of the Model of Emissions of Gases and Aerosols from Nature (MEGAN) in WRF-Chem are examined or documented. Sensitivity experiments with these three versions of MEGAN and two vegetation datasets are conducted to investigate the difference of three MEGAN versions in modeling BVOCs and its dependence on the vegetation distributions. The experiments are also conducted for spring (April) and summer (July) to examine the seasonality of the modeling results. The results indicate that MEGAN v3.0 simulates the largest amount of biogenic isoprene emissions over eastern China. The different performance among MEGAN versions is primarily due to their different treatments of applying emission factors and vegetation types. In particular, the results highlight the importance of considering the sub-grid vegetation fraction in estimating BVOC emissions over eastern China, which has a large area of urbanization. Among all activity factors, the temperature-dependent factor dominates the seasonal change of activity factor in all three versions of MEGAN, while the different response to the leaf area index (LAI) change determines the difference among the three versions in seasonal variation of BVOC emissions. The simulated surface ozone concentration due to BVOCs can be significantly different (ranging from 1 to more than 10 ppbv in some regions) among the experiments with three versions of MEGAN, which is mainly due to their impacts on surface VOCs and NOx concentrations. Theoretically MEGAN v3.0 that is coupled with the land surface scheme and considers the sub-grid vegetation effect should overcome previous versions of MEGAN in WRF-Chem. However, considering uncertainties of retrievals and anthropogenic emissions over eastern China, it is still difficult to apply satellite retrievals of formaldehyde and/or limited sparse in situ observations to constrain the uncertain parameters or functions in BVOC emission schemes and their impacts on photochemistry and ozone production. More accurate vegetation distribution and measurements of biogenic emission fluxes and species concentrations are still needed to better evaluate and optimize models.

Urban Heat Island and Its Interaction with Heatwaves: A Review of Studies on Mesoscale

With rapid urbanization, population growth and anthropogenic activities, an increasing number of major cities across the globe are facing severe urban heat islands (UHI). UHI can cause complex impacts on the urban environment and human health, and it may bring more severe effects under heatwave (HW) conditions. In this paper, a holistic review is conducted to articulate the findings of the synergies between UHI and HW and corresponding mitigation measures proposed by the research community. It is worth pointing out that most studies show that urban areas are more vulnerable than rural areas during HWs, but the opposite is also observed in some studies. Changes in urban energy budget and major drivers are discussed and compared to explain such discrepancies. Recent studies also indicate that increasing albedo, vegetation fraction and irrigation can lower the urban temperature during HWs. Research gaps in this topic necessitate more studies concerning vulnerable cities in developing countries. Moreover, multidisciplinary studies considering factors such as UHI, HW, human comfort, pollution dispersion and the efficacy of mitigation measures should be conducted to provide more accurate and explicit guidance to urban planners and policymakers.

GeoClimate: a Geospatial processing toolbox for environmental and climate studies

Human activities induce changes on land use and land cover. These changes are most significant in urban areas where topographic features (e.g., building, road) affect the density of impervious surface areas and introduce a range of urban morphological patterns. Those characteristics impact the energy balance and modify the climate locally (e.g., inducing the so-called Urban Heat Island phenomenon). Therefore, there is a need for georeferenced morphological indicators as well as urban classifications (such as Local Climate Zones) that can be directly used for planning or as inputs of climate models. GeoClimate is dedicated to this purpose: it converts raw geographical data (OpenStreetMap and French BDTopo) into indicators useful for climate applications (sky view factor, vegetation fraction, etc.) However, its application is not limited to the climate field. The indicators calculated in GeoClimate can also be used for other diagnostic or planning purposes: studying the territory fragmentation, the influence of the urban fabric on pollution (noise or air chemical transport), the energy consumption, etc. GeoClimate is available as free and open source geospatial software.

Simulating Airflow Around Flexible Vegetative Windbreaks

AbstractVegetative windbreaks play an important role in reducing soil erosion and sand storms, which are widely applied in arid/semiarid regions. An optimal design of vegetative windbreaks is essential in aeolian engineering practice. In this study, three‐dimensional airflows around multiple arrangements of vegetative windbreaks were simulated using a modified Reynolds‐Average Navier‐Stokes (RANS)‐based computational fluid dynamics (CFD) model, based on which, optimal designs of vegetative windbreaks were discussed. We proposed a correction factor in the source terms of the momentum and turbulent transport equations representing the effects due to the vibration of the vegetative windbreaks, which was further calibrated using both simulated and wind tunnel experimental results. Moreover, a quantitative expression of the correction factors associated with structural parameters of vegetative windbreaks was established and verified. For optimal designs to reduce wind erosion, a suite of numerical experiments was conducted to find the optimal height, leaf area density, and vegetation fraction of the vegetative windbreaks under different free‐stream wind speeds. The results identified that under low free‐stream wind speed (6.4 m/s), the optimal parameters are 9.0 cm, 0.45 cm2/cm3, and 3.92%. Under moderate free‐stream wind speed (8.0 m/s), those optimal parameters are 12.4 cm, 0.47 cm2/cm3, and 3.92%. Under relatively high free‐stream wind speed (10.8 m/s), the optimal parameters are 12.4 cm, 0.47 cm2/cm3, and 8.00%. The results of the current study will aid in optimizing vegetative windbreak designs and preventing wind erosion in aeolian engineering.

Assessing Drought Vegetation Dynamics in Semiarid Grass- and Shrubland Using MESMA

Drought intensity and duration are expected to increase over the coming century in the semiarid western United States due to anthropogenic climate change. Historic data indicate that megadroughts in this region have resulted in widespread ecosystem transitions. Landscape-scale monitoring with remote sensing can help land managers to track these changes. However, special considerations are required: traditional vegetation indices such as NDVI often underestimate vegetation cover in semiarid systems due to short and multimodal green pulses, extremely variable rainfall, and high soil fractions. Multi-endmember spectral mixture analysis (MESMA) may be more suitable, as it accounts for both green and non-photosynthetic soil fractions. To determine the suitability of MESMA for assessing drought vegetation dynamics in the western US, we test multiple endmember selection and model parameters for optimizing the classification of fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S) in semiarid grass- and shrubland in central New Mexico. Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge over six field sessions from May–September 2019. Landsat Thematic Mapper imagery from 2009 (two years pre-drought), and Landsat Operational Land Imager imagery from 2014 (final year of drought), and 2019 (five years post-drought) was unmixed. The best fit model had high levels of agreement with reference plots for all three classes, with R2 values of 0.85 (NPV), 0.67 (GV), and 0.74 (S) respectively. Reductions in NPV and increases in GV and S were observed on the landscape after the drought event, that persisted five years after a return to normal rainfall. Results indicate that MESMA can be successfully applied for monitoring changes in relative vegetation fractions in semiarid grass and shrubland systems in New Mexico.

Global Change in Terrestrial Ecosystem Detected by Fusion of Microwave and Optical Satellite Observations

The detection of global land change via satellite observation is a major challenge in improving the understanding of global environmental change. In this study, we develop a new vegetation index which can be used as a proxy for the fractions of tree canopy and short vegetation, based on the simple linear regression between microwave vegetation optical depth (VOD) and optical leaf area index (LAI). Although we use no high-resolution reference data, the newly developed vegetation index successfully detects global land change which has been reported by previous estimations based on high-resolution reference data. We find that the relationship between VOD and LAI is non-stationary and the temporal change in the VOD-LAI relationship is an important signal for detecting global change in the terrestrial ecosystem.

Estimating aboveground net primary productivity of reforested trees in an urban landscape using biophysical variables and remotely sensed data

Recently, urban reforestation programs have emerged as potential carbon sinks and climate mitigates in urban landscapes. Thus, spatially explicit information on net primary productivity (NPP) of reforested trees in urban environments is central to understanding the value of reforestation initiatives in the global carbon budget and climate regulation potential. To date, numerous studies have mainly focused on natural and commercial forests NPP at a regional scale based on coarse spatial resolution remotely sensed data. Generally, local scale NPP studies based on fine spatial resolution data are limited. Therefore, this study sought to estimate aboveground NPP of an urban reforested landscape using biophysical and Sentinel-2 Multispectral Imager data derived variables. Using the MOD17 model, results showed that mean NPP ranged between 6.24 Mg C ha−1 with high coefficient of determination (R2: 0.92) and low RMSE (0.82 Mg ha−1) across all reforested trees within the study area. Results also showed a considerable variation in NPP among the reforested trees, with deciduous Acacia and Dalbergia obovate species showing the highest NPP (7.62 Mg C ha−1 and 7.58 Mg C ha-1, respectively), while the evergreen Syzygium cordatum and shrub Artemisia afra had the lowest NPP (4.54 Mg C ha−1 and 5.26 Mg C ha−1). Furthermore, the multiple linear regression analysis showed that vegetation specific biophysical variables (i.e. leaf area index, Normalized Difference Vegetation Index and Fraction of Photosynthetically Active Radiation) significantly improved the estimation of reforested aboveground NPP at a fine-scale resolution. These findings demonstrate the effectiveness of biophysical and remotely sensed variables in determining NPP (as carbon sequestration surrogate) at fine-scaled reforested urban landscape. Furthermore, the utility of species biometric measurements and MOD17 model offers unprecedented opportunity for improved local scale reforestation assessment and monitoring schedules.

Effects of the Harvest Stage of Maize Hybrids on the Chemical Composition of Plant Fractions: An Analysis of the Different Types of Silage

The chemical composition of plant components of three maize hybrids harvested at the beginning of six reproductive stages of maturity was compared. The hybrids evaluated included Maximus VIP3, Defender VIP and Feroz VIP, which were evaluated at each of following stages: R1 (grain formation), R2 (milky grain), R3 (pasty grain), R4 (floury grain), R5 (hard grain) and R6 (ripe grain). The advancement in maturation was linearly related to the crude protein (CP) content of the stem, whole plant, and leaves, and there was a difference among the hybrids. Between R4 and R5 stages, Maximus and Defender presented the highest CP contents for husk (6.58 and 5.42% for Maximus; 5.54 and 5.17% for Defender). The neutral detergent fiber (NDF) of the leaves showed a quadratic relationship with the advancement of maturation but did not differ among the hybrids. For all the hybrids, the NDF content in the husk and cobs increased linearly during the reproductive stages (>77 and 78%, respectively, for the three hybrids in R6). Defender had the lowest NDF content of the cob in R3. The acid detergent lignin contents did not differ among stages in the stems, and showed a linear decrease throughout the whole plant, though the contents did not differ among the hybrids. Due to the differences observed, recommendations for harvest based on the maturity stage for each hybrid should be taken into consideration. There seems to be no important distinction among hybrids for harvesting and use of straw. Despite the reduction in grain yield, an early harvest for earlage or snaplage can provide lower lignin content in husk and cob, as well as higher protein content in the husk, favoring the nutritional value of the vegetative fraction (husk and/or cob).

Spatiotemporal patterns and driving forces of remotely sensed urban agglomeration heat islands in South China

Rapid urbanization and increasing population have widely caused the urban heat island effect. Due to the decreasing distance between cities, there is an urgent need to reevaluate regional heat island intensity (RHII) in an urban agglomeration scale by considering all cities together instead of from conventional single city perspective. Using cropland land surface temperature as the reference temperature, we assessed the diurnal and seasonal RHII variations in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) urban agglomeration in South China. The boosted regression trees (BRT) method was then used to analyze the relative influence and marginal effect of possible drivers to disentangle their underlying driving mechanisms. Results showed that the daytime RHII spatial patterns averaged over the period 2003–2017 illustrated higher intensity and greater heterogeneity than their nighttime counterparts, especially for the stronger RHII in the central GBA around the estuary area. Seasonal dynamics of daytime RHII displayed a generally descending trend from summer to winter, but the opposite for night. BRT analyses indicated that at both annual and seasonal scales, vegetation fraction and background temperature had a dominant influence on RHII in daytime and nighttime, respectively. RHII variations were also considerably attributed to other drivers for different seasons. For daytime RHII, the other influential drivers included anthropogenic heat emissions and precipitation in summer, anthropogenic heat emissions and terrain in the transition season, and temperature and albedo in winter. For nighttime RHII, anthropogenic heat emissions for all seasons, vegetation activities for summer and the transition season, and precipitation for winter also had important contributions. The marginal effects detected the different nonlinear responses of diurnal and seasonal RHII to potential drivers, suggesting contrasting driving mechanisms. Results of this study highlight more targeted and informed strategies for RHII mitigation in the GBA and provide helpful insights into RHI evaluation in other urban agglomerations.

Mapping the risks of the spread of peste des petits ruminants in the Republic of Kazakhstan

Peste des petits ruminants (PPR) is a viral transboundary disease seen in small ruminants, that causes significant damage to agriculture. This disease has not been previously registered in the Republic of Kazakhstan (RK). This paper presents an assessment of the susceptibility of the RK's territory to the spread of the disease in the event of its importation from infected countries. The negative binomial regression model that was trained on the PPR outbreaks in China, was used to rank municipal districts in the RK in terms of PPR spread risk. The outbreak count per administrative district was used as a risk indicator, while a number of socio-economic, landscape, and climatic factors were considered as explanatory variables. Summary road length, altitude, the density of small ruminants, the maximum green vegetation fraction, cattle density, and the Engel coefficient were the most significant factors. The model demonstrated a good performance in training data (R2 =0.69), and was transferred to the RK, suggesting a significantly lower susceptibility of this country to the spread of PPR. Hot spot analysis identified three clusters of districts at the highest risk, located in the western, eastern, and southern parts of Kazakhstan. As part of the study, a countrywide survey was conducted to collect data on the distribution of livestock populations, which resulted in the compilation of a complete geo-database of small ruminant holdings in the RK. The research results may be used to formulate a national strategy for preventing the importation and spread of PPR in Kazakhstan through targeted monitoring in high-risk areas.

Using Google Earth Engine to Assess Temporal and Spatial Changes in River Geomorphology and Riparian Vegetation

AbstractWe developed a new approach using a cloud‐based remote sensing and geospatial analysis platform, Google Earth Engine, to quantify temporal changes in river channel location and adjacent riparian vegetation extent and fraction. Our new method uses publicly available 1 m aerial images and eliminates manual processing need by incorporating an automatic image classification algorithm. Classification of riparian vegetation is enhanced by increasing the temporal resolution to monthly and by mapping vegetation at high spatial resolution. We illustrate the application of our method by characterizing temporal and spatial trends in riparian vegetation and river channel position for the mainstem of the Genesee River, New York, from 2006 to 2015. Annual change in riparian vegetation extent along the Genesee River ranged from a loss of 17% to a gain of 13%, and 64 km (28%) of the river demonstrated channel migration across consecutive aerial images. Our method also successfully distinguished differences between sections of the river above and below a dam. The enhanced capacity to map vegetation monthly allowed us to identify that seasonal active vegetation fractions along the Genesee River peaked at 75% in the summer and remained lower than 25% in winter. Our method allows stakeholders and managers to process remotely sensed imagery and investigate trends in river channel and riparian vegetation dynamics over time, while reducing the costs of data processing and storage.

Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters

Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

Crop Area Mapping in Southern and Central Malawi With Google Earth Engine

Agriculture in sub-Saharan Africa consists primarily of smallholder farms of rainfed crops. Historically, satellite data were too coarse to account for the heterogeneity in these landscapes. Sentinel-2 data have improved spectral resolution and much higher spatial resolution (10 m) than previously available satellites with global coverage, such as Landsat or MODIS, making mapping smallholder farms possible. Spectral mixture analysis was used to convert the Sentinel-2 signal into fractions of green vegetation, non-photosynthetic vegetation, soil, and shade endmembers. Very high spatial resolution imagery in Google Earth Pro was used to identify locations of crop and natural vegetation classes, with over 20,000 reference points interpreted. The high temporal resolution of Sentinel-2 (5 days repeat) allows for classification of landcover based on the phenological signal, with natural areas having smoothly varying amounts of photosynthetic vegetation annually, while cropped areas show more abrupt changes, and also the presence of bare soil due to agricultural activity at some point during the year. We summarized the endmember values using monthly medians, extracted values for the reference data points, randomly split them into training and test data sets, and input the training data into the random forests algorithm in Google Earth Engine to map crop area. We divided southern and central Malawi into tiles, and found crop/no crop classification accuracies on the test data for each tile to be between 87 and 93%. The 10 m map of crop area was aggregated to the district level and showed an R2 of 0.74 with ground-based statistics from the Malawi government and 0.79 with a remotely sensed product developed by the USGS.

Accounting for land model error in numerical weather prediction ensemble systems: toward ensemble-based coupled land/atmosphere data assimilation

AbstractThe ensembles used in the NOAA National Centers for Environmental Prediction (NCEP) global data assimilation and numerical weather prediction (NWP) system are under-dispersed at and near the land surface, preventing their use in ensemble-based land data assimilation. Comparison to offline (land-only) data assimilation ensemble systems suggests that while the relevant atmospheric fields are under-dispersed in NCEP’s system, this alone cannot explain the under-dispersed land component, and an additional scheme is required to explicitly account for land model error. This study then investigates several schemes for perturbing the soil (moisture and temperature) states in NCEP’s system, qualitatively comparing the induced ensemble spread to independent estimates of the forecast error standard deviation in soil moisture, soil temperature, 2m temperature, and 2m humidity. Directly adding perturbations to the soil states, as is commonly done in offline systems, generated unrealistic spatial patterns in the soil moisture ensemble spread. Application of a Stochastically Perturbed Physics Tendencies scheme to the soil states is inherently limited in the amount of soil moisture spread that it can induce. Perturbing the land model parameters, in this case vegetation fraction, generated a realistic distribution in the ensemble spread, while also inducing perturbations in the land (soil states) and atmosphere (2m states) that are consistent with errors in the land/atmosphere fluxes. The parameter perturbation method is then recommended for NCEP’s ensemble system, and it is currently being refined within the development of an ensemble-based coupled land/atmosphere data assimilation for NCEP’s NWP system.

Influence of Near Real-Time Green Vegetation Fraction Data on Numerical Weather Prediction by WRF over North China

The green vegetation fraction (GVF) can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction (NWP) models. However, the multiyear averaged monthly GVF climatology—the most commonly used representation of the vegetation state in models—cannot capture the real-time vegetation state well. In this study, a near real-time (NRT) GVF dataset generated from an 8-day composite of the normalized difference vegetation index is compared with the 10-yr averaged monthly GVF provided by the WRF model. The annual variability of the GVF over North China is examined in detail. Many differences between the two GVF datasets are found over dryland, grassland, and cropland/grassland mosaic areas. Two experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and humidity. The results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to October. Evaluation against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the afternoon. Our study demonstrates that NRT GVF can provide a more realistic representation of the vegetation state, which in turn helps to improve short-range forecasts in arid and semiarid regions of North China. Moreover, our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.

Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART (Discrete Anisotropic Radiative Transfer) to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in a complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Therefore, we recommend future research on forest biodiversity using physical modeling of remote-sensing data to account for LOP variability within crowns and species. Our simulation framework could contribute to better understanding of performances of species discrimination and the relationship between spectral variations and taxonomic and functional dimensions of biodiversity. This work contributes to the improved integration of physical modeling tools for applications, focusing on remotely sensed monitoring of biodiversity in complex ecosystems, for current sensors, and for the preparation of future multispectral and hyperspectral satellite missions.