Satellite-derived Leaf Area Index Research Articles

Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM

ABSTRACT Ecosystem process modeling is vital for a broad range of applications. It requires a key metric to characterize vegetation canopies: the leaf area index (LAI). Many land surface models utilize satellite-derived LAI for modeling ecosystem processes. The use of satellite-derived LAI constrains the models for predicting ecosystem processes when observational data are unavailable. Enriching prognostic models for predicting the LAI is favorable to land surface studies for forecasting vegetation-related processes. An attention-enhanced long and short memory (AELSTM) model was developed to predict the vegetation LAI time series based on climatic data. The AELSTM outperformed comparative machine learning models when assessed using satellite and flux tower data. The LAI predicted by AELSTM are temporally and spatially consistent with satellite-derived LAI. The R2 values attained 0.86–0.93 across biomes. AELSTM can be coupled with the land surface models to predict gross primary productivity. Our proposed model was demonstrated to be effective in predicting the LAI time series under different Shared Socioeconomic Pathways in CMIP6. Our study demonstrated that deep learning approaches are capable of modeling and characterizing the spatial and temporal patterns of key vegetation variables such as LAI. Moreover, the study has provided references for vegetation processes in land surface studies.

Insights into Canopy Escape Ratio from Canopy Structures: Correlations Uncovered through Sentinel-2 and Field Observation

This study explores the quantitative relationship between canopy structure and the canopy escape ratio (fesc), measured as the ratio of near-infrared reflectance of vegetation (NIRv) to the fraction of absorbed photosynthetically active radiation (fAPAR). We analyzed the correlation between fesc and key indicators of canopy structure—specifically, leaf area index (LAI) and clumping index (CI)—utilizing both Sentinel-2 satellite data and in situ observations. Our analysis revealed a moderate correlation between fesc and LAI, evidenced by an R2 value of 0.37 for satellite-derived LAI, which contrasts with the lower correlation (R2 of 0.15) observed with field-measured LAI. Conversely, the relationship between fesc and CI proved to be significantly weaker (R2 < 0.1), indicating minimal interaction between foliage distribution and light escape at the canopy level. This disparity in correlation strength was further evidenced in time series analysis, which showed little phenological variation in fesc compared to LAI. Our findings elucidate the complexities of estimating fesc based on the NIRv to fAPAR ratio and underscore the need for advanced methodologies in future research to enhance the accuracy of canopy escape models.

Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins

Information on water availability in basins can be crucial for making decisions for effective water resource management in basins. As the operation of hydrometric stations in Korea is mainly focused on flood season and large rivers, most basins have lack or no observed data. Consequently, this complicates water resource planning and management. Remote sensing data is emerging as a powerful alternative to hydrological information in ungauged basins. This study investigated the applicability of Satellite-Remote Sensed Data (SRSD) as a source for model calibration in Prediction in Ungauged Basins (PUB) through modeling. Remote sensed leaf area index (LAI), actual evapotranspiration, and soil moisture data were used. Each SRSD was used alone to calibrate a hydrologic model to predict the daily streamflow for 28 basins in Korea. A vegetation module was added to the existing hydrologic model to use LAI. Among the SRSDs tested, the model calibrated with LAI had the most robust performance, predicting streamflow with acceptable accuracy compared to the traditional calibration based on streamflow. In particular, since the model account for vegetation actively interacting with evapotranspiration and soil moisture in the season of low flow, the LAI-calibrated model showed an advantage in improving the flow prediction performance. Although further research is required to utilize evapotranspiration and soil moisture data, the overall results of the LAI-based calibration were promising for predicting streamflow in ungauged basins where observations are scarce or absent, given that the satellite-derived LAI data were used alone without any preprocessing such as a bias correction. However, the prediction performance of the LAI-calibrated model was found to have a statistically significant relationship with local conditions. Therefore, by evaluating and improving the potential of SRSD in different region and climatic conditions, it is expected that the application of the SRSD-only calibration method can be extended to various ungauged basins.

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R2 = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R2 = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R2 = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future.

Through eagle eyes—the potential of satellite-derived LAI time series to estimate masting events and tree-ring width of European beech

Masting is a well-known phenomenon in forest science in which trees recurrently produce exceptional high seed amounts throughout their lifespan. Occurring simultaneously across wide regions, masting may impact carbon sequestration and carbon cycling of forest ecosystems. To identify masting-induced changes in tree canopies, remote sensing methods may offer a suitable tool, though so far only few studies have used RS methods to investigate the relationship between masting and tree-ring width (TRW). Therefore, we employed a regional network of 19 European beech sites along a precipitation gradient in north-eastern Germany, providing time series of TRW (1961–2017) and categorical masting data (1992–2020). Combined with leaf area index (LAI) from the medium-resolution satellite sensors SPOT-VGT/PROBA-V and MODIS, we assessed the relationship between all three parameters by correlation analysis. Furthermore, we identified common climatic drivers and spatial masting patterns along gradients of water availability. We found negative relationships for both TRW and LAI with masting and a positive correlation for TRW with LAI for SPOT-VGT/PROBA-V data. Contrary, MODIS LAI provided no significant relationships. Water availability did not impact masting occurrence or intensity. The climate correlations showed that masting and TRW are commonly driven by summer climate in June and July, while we found no correlations between climate and LAI. We conclude that medium-resolved SPOT-VGT/PROBA-V LAI is suitable to observe masting but not to estimate TRW. Nonetheless, the results should be thoroughly interpreted in the context of uncertainties of RS measurements and complex interactions of different environmental drivers.

Uncertainty propagation in a global biogeochemical model driven by leaf area data

Satellite-observed leaf area index (LAI) is often used to depict vegetation canopy structure and photosynthesis processes in terrestrial biogeochemical models. However, it remains unclear how the uncertainty of LAI among different satellite products propagates to the modeling of carbon (C), nitrogen (N), and phosphorus (P) cycles. Here, we separately drive a global biogeochemical model by three satellite-derived LAI products (i.e., GIMMS LAI3g, GLASS, and GLOBMAP) from 1982 to 2011. Using a traceability analysis, we explored the propagation of LAI-driven uncertainty to modeled C, N, and P storage among different biomes. The results showed that the data uncertainty of LAI was more considerable in the tropics than in non-tropical regions, whereas the modeling uncertainty of C, N, and P stocks showed a contrasting biogeographic pattern. The spread of simulated C, N, and P storage derived by different LAI datasets resulted from assimilation rates of elements in shrubland and C3 grassland but from the element residence time (τ) in deciduous needle leaf forest and tundra regions. Moreover, the assimilation rates of elements are the main contributing factor, with 67.6, 93.2, and 93% of vegetated grids for the modeled uncertainty of C, N, and P storage among the three simulations. We further traced the variations in τ to baseline residence times of different elements and the environmental scalars. These findings indicate that the data uncertainty of plant leaf traits can propagate to ecosystem processes in global biogeochemical models, especially in non-tropical forests.

Is stunting in children under five associated with the state of vegetation in the Democratic Republic of the Congo? Secondary analysis of Demographic Health Survey data and the satellite-derived leaf area index

BackgroundThe prevalence of stunting in the Democratic Republic of the Congo (DRC) is one of the highest globally. However, only a few studies have attempted to measure the association between stunting and vegetation, which is an important food source. The leaf area index (LAI) is an excellent measure for the vegetation state. ObjectiveThis paper intended to measure the association between the LAI and stunting among children under five years of age in the DRC. Its aim was to better understand the boundary conditions of stunting and explore potential links to climate and environmental change. MethodsThis paper adopts a secondary data analysis approach. We used data on 5241 children from the DRC Demographic Health Survey (DHS) 2013–2014, which was collected from a nationally representative cross-sectional survey. We used the satellite-derived LAI as a measure for the state of vegetation and created a 10-km buffer to extract each DHS cluster centroid’s corresponding mean leaf-area value. We used a generalised mixed-effect logistic regression to measure the association between LAI and stunting, adjusting the model for mother’s education, occupation and birth interval, as well as child’s age and national wealth quintile. A height-for-age Z-score (HAZ) was calculated and classified according to WHO guidelines. ResultsChildren in communities surrounded by high LAI values have lower odds of being stunted (OR [odds ratio] = 0.63; 95% CI [confidence interval] = 0.47–0.86) than those exposed to low LAI values. The association still holds when the exposure is analysed as a continuous variable (OR = 0.84; 95% CI = 0.74–0.95).When stratified in rural and urban areas, a significant association was only observed in rural areas (OR = 0.6; 95% CI = 0.39–0.81), but not in urban areas (OR = 0.9; 95% CI = 0.5−0.5). Furthermore, the study showed that these associations were robust to LAI buffer variations under 25 km. ConclusionsGood vegetation conditions have a protective effect against stunting in children under five years of age. Further advanced study designs are needed to confirm these findings.

Improved modeling of canopy transpiration for temperate forests by incorporating a LAI-based dynamic parametrization scheme of stomatal slope

The ecosystem-level conductance-photosynthesis models, which represent a linearly coupled relationship between canopy stomatal conductance (Gs) and CO2 assimilation, have been increasingly used for modeling transpiration (Tc). As a key parameter in these models, the slope parameter (G1) has been observed to vary considerably over the seasons in the field, but is often parametrized with a biome-specific temporally constant G1, resulting in large potential uncertainty. Here we hypothesized that G1 varies with leaf area index (LAI) phenology and soil water content (SWC) seasonality, and accurate characterization of G1 seasonality offers an avenue to improve Tc modelling. To test these hypotheses, we first investigated the seasonality of Eddy flux-derived G1 and then explored its relationship with satellite-derived LAI and field-observed SWC seasonality at 12 temperate forest FLUXNET sites across the Northern Hemisphere. Last, we cross-compared the two schemes of model parameterization of G1 for modeling Tc: (1) a constant G1 (FIX) and (2) a dynamic G1 parameterized using the selected variables (DYN). Our results show G1 displays considerable seasonal variations across all sites, with a minimum value in mid-summer. Further variance partitioning analysis demonstrates that the seasonal variations in G1 show direct linkages with LAI phenology rather than SWC seasonality likely associated with leaf aging and ontogeny development. Last, we found relative to the FIX model, the DYN model (using LAI for G1 parameterization) significantly reduced the model uncertainty in terms of RMSE by 24.6 ± 11.8% and 32.0 ± 8.7%, respectively for Gs and Tc at a daily scale. These results collectively improve our understanding of the dynamic pattern and proximate controls of G1 seasonality, and highlight the effectiveness of using remote sensing-derived LAI phenology for improved characterization of G1 seasonality that ultimately contributes to the improved process model simulations of the seasonal dynamics of Gs and Tc across temperate forest landscapes.

Estimating Biophysical Parameters of Native Grasslands Using Spectral Data Derived from Close Range Hyperspectral and Satellite Data

Estimating biophysical parameters of native grassland enables management changes that affect ecological processes and economic benefits. Although multiple hyperspectral studies were focused on native grasslands, just a few compare data at different scales and among ecoregions. In this study, we compared data collected at different spectral and spatial scales and among Canadian Prairie ecoregions. Field observations indicate that the Fescue Ecoregion grasslands has specific dominant species, while the Moist-Mixed and Mixed Ecoregions share similar dominant species, which is important in determining parameters such as leaf area index (LAI) and canopy height. Hyperspectral measurements showed a specific signature for the Fescue Ecoregion, due to denser canopies, while the Moist-Mixed and Mixed Ecoregions showed similar spectral characteristics to each other. The correlation between biophysical parameters and spectral indices reveals the importance of LAI, since it was significantly correlated with all spectral indices analyzed. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and the Plant Senescence Reflectance Index (PSRI) showed significant correlations with biophysical parameters. The comparison results indicated the PSRI being overestimated at all sites (satellite data) and NDVI underestimated at all sites. Finally, the satellite-derived LAI showed a significant positive relationship with the field-measured LAI.

Evaluation of satellite Leaf Area Index in California vineyards for improving water use estimation

Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management.

The sensitivities of ozone and PM2.5 concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM2.5 concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM2.5 concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from −56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM2.5 concentrations over inland areas ranging from 0.27 ppbV to −7.17 ppbV and 0.89 μg/m3 to −2.65 μg/m3, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to −2.86 ppbV and 3.41 μg/m3 to −7.47 μg/m3. The decreases in inland ozone and PM2.5 concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM2.5 formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM2.5 concentrations due to reduced reactions of PM2.5 precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM2.5 concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM2.5 inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Combining Crop Modeling with Remote Sensing Data Using a Particle Filtering Technique to Produce Real-Time Forecasts of Winter Wheat Yields under Uncertain Boundary Conditions

Within-season crop yield forecasting at national and regional levels is crucial to ensure food security. Yet, forecasting is a challenge because of incomplete knowledge about the heterogeneity of factors determining crop growth, above all management and cultivars. This motivates us to propose a method for early forecasting of winter wheat yields in low-information systems regarding crop management and cultivars, and uncertain weather condition. The study was performed in two contrasting regions in southwest Germany, Kraichgau and Swabian Jura. We used in-season green leaf area index (LAI) as a proxy for end-of-season grain yield. We applied PILOTE, a simple and computationally inexpensive semi-empirical radiative transfer model to produce yield forecasts and assimilated LAI data measured in-situ and sensed by satellites (Landsat and Sentinel-2). To assimilate the LAI data into the PILOTE model, we used the particle filtering method. Both weather and sowing data were treated as random variables, acknowledging principal sources of uncertainties to yield forecasting. As such, we used the stochastic weather generator MarkSim® GCM to produce an ensemble of uncertain meteorological boundary conditions until the end of the season. Sowing dates were assumed normally distributed. To evaluate the performance of the data assimilation scheme, we set up the PILOTE model without data assimilation, treating weather data and sowing dates as random variables (baseline Monte Carlo simulation). Data assimilation increased the accuracy and precision of LAI simulation. Increasing the number of assimilation times decreased the mean absolute error (MAE) of LAI prediction from satellite data by ~1 to 0.2 m2/m2. Yield prediction was improved by data assimilation as compared to the baseline Monte Carlo simulation in both regions. Yield prediction by assimilating satellite-derived LAI showed similar statistics as assimilating the LAI data measured in-situ. The error in yield prediction by assimilating satellite-derived LAI was 7% in Kraichgau and 4% in Swabian Jura, whereas the yield prediction error by Monte Carlo simulation was 10 percent in both regions. Overall, we conclude that assimilating even noisy LAI data before anthesis substantially improves forecasting of winter wheat grain yield by reducing prediction errors caused by uncertainties in weather data, incomplete knowledge about management, and model calibration uncertainty.

On the distribution and productivity of mountain grasslands in the Gran Paradiso National Park, NW Italy: A remote sensing approach

Mountain grazing lands are key constituents of the natural, economical and cultural heritage, but at the same time sensitive to climate and land use change, hence requiring urgent adaptation and management strategies. These must be based on a better understanding of the distribution of mountain pastoral resources across space and time.In this study we model the distribution and the productivity of grassland surfaces in a topographically complex protected area (Gran Paradiso National Park, 710 km2) in north-western Italian Alps. The objective of our work was threefold: a) modelling the distribution of mountain grasslands across the entire park at a 20-meters spatial resolution, b) classify pastoral surfaces according to productivity classes, and c) according to thirteen pastoral categories. We used a random forest approach to combine a massive terrain vegetation survey as ground truth, with remote-sensing-derived, climatic and topographic layers as predictors.Grassland presence/absence was classified with high accuracy (up to 88%) and, compared to the standard Copernicus European Grassland Product, revealed the presence of extensive high altitude grassland areas potentially available for wild herbivores. Grassland productivity was modelled with remarkably high accuracy both according to three broad productivity classes (90% accuracy) and to a more detailed classification into thirteen pastoral categories (83% accuracy). Productivity estimates agree well with satellite-derived leaf area index maps and with area-averaged NDVI seasonal patterns.We conclude that combining tailored field campaigns and high-resolution remote sensing allows for robust prediction of grassland distribution and productivity even in complex terrains. This information can contribute to improve the management of pastoral resources and promote effective adaptation strategies.

Detection of Multidecadal Changes in Vegetation Dynamics and Association with Intra-Annual Climate Variability in the Columbia River Basin

Remotely-sensed Leaf Area Index (LAI) is a useful metric for assessing changes in vegetation cover and greeness over time and space. Satellite-derived LAI measurements can be used to assess these intra- and inter-annual vegetation dynamics and how they correlate with changing regional and local climate conditions. The detection of such changes at local and regional levels is challenged by the underlying continuity and extensive missing values of high-resolution spatio-temporal vegetation data. Here, the feasibility of functional data analysis methods was evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation in LAI is conducted in the Columbia River Watershed, as detected by NOAA Advanced Very High-Resolution Radiometer (AVHRR) satellite imaging. The inter- and intra-annual correlation of LAI with temperature and precipitation were then investigated using data from the European Centre for Medium-Range Weather Forecasts global atmospheric re-analysis (ERA-Interim) in the period 1996–2017. A functional cluster analysis model was implemented to identify regions in the Columbia River Watershed that exhibit similar long-term greening trends. Across this region, a multidecadal trend toward earlier and higher annual LAI peaks was detected, and strong correlations were found between earlier and higher LAI peaks and warmer temperatures in late winter and early spring. Although strongly correlated to LAI, maximum temperature and precipitation do not demonstrate a similar strong multidecadal trend over the studied time period. The modeling approach is proficient for analyzing tens or hundreds of thousands of sampled sites without parallel processing or high-performance computing (HPC).

A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets

ABSTRACT Accurately quantifying the aboveground biomass (AGB) of forests is crucial for understanding global change-related issues such as the carbon cycle and climate change. Many studies have estimated AGB from multiple remotely sensed datasets using various algorithms, but substantial uncertainties remain in AGB predictions. In this study, we aim to explore whether diverse algorithms stacked together are able to improve the accuracy of AGB estimates. To build the stacking framework, five base learners were first selected from a series of algorithms, including multivariate adaptive regression splines (MARS), support vector regression (SVR), multilayer perceptron (MLP) model, random forests (RF), extremely randomized trees (ERT), stochastic gradient boosting (SGB), gradient-boosted regression tree (GBRT) algorithm, and categorical boosting (CatBoost), based on diversity and accuracy metrics. Ridge and RF were utilized as the meta learner to combine the outputs of base learners. In addition, six important features were selected according to the feature importance values provided by the CatBoost, ERT, GBRT, SGB, MARS and RF algorithms as inputs of the meta learner in the stacking process. We then used stacking models with 3–5 selected base learners and ridge or RF to estimate AGB. The AGB data compiled from plot-level forest AGB, high-resolution AGB data derived from field and lidar data and the corresponding predictor variables extracted from the satellite-derived leaf area index, net primary production, forest canopy height, tree cover data, and Global Multiresolution Terrain Elevation Data 2010, as well as climate data, were randomly split into groups of 80% for training the model and 20% for model evaluation. The evaluation results showed that stacking generally outweighed the optimal base learner and provided improved AGB estimations, mainly by decreasing the bias. All stacking models had relative improvement (RI) values in bias of at least 22.12%, even reaching more than 90% under some scenarios, except for deciduous broadleaf forests, where an optimal algorithm could provide low biased estimations. In contrast, the improvements of stacking in R2 and RMSE were not significant. The stacking of MARS, MLP, and SVR provided improved results compared with the optimal base learner, and the average RI in R2 was 3.54% when we used all data without separating forest types. Finally, the optimal stacking model was used to generate global forest AGB maps.

Effects of Dynamic Vegetation on Global Climate Simulation Using the NCEP GFS and SSiB4/TRIFFID

Two global experiments were carried out to investigate the effects of dynamic vegetation processes on numerical climate simulations from 1948 to 2008. The NCEP Global Forecast System (GFS) was coupled with a biophysical model, the Simplified Simple Biosphere Model (SSiB) version 2 (GFS/SSiB2), and it was also coupled with a biophysical and dynamic vegetation model, SSiB version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) (GFS/SSiB4/TRIFFID). The effects of dynamic vegetation processes on the simulation of precipitation, near-surface temperature, and the surface energy budget were identified on monthly and annual scales by assessing the GFS/SSiB4/TRIFFID and GFS/SSiB2 results against the satellite-derived leaf area index (LAI) and albedo and the observed land surface temperature and precipitation. The results show that compared with the GFS/SSiB2 model, the temporal correlation coefficients between the globally averaged monthly simulated LAI and the Global Inventory Monitoring and Modeling System (GIMMS)/Global Land Surface Satellite (GLASS) LAI in the GFS/SSiB4/TRIFFID simulation increased from 0.31/0.29 (SSiB2) to 0.47/0.46 (SSiB4). The correlation coefficients between the simulated and observed monthly mean near-surface air temperature increased from 0.50 (Africa), 0.35 (Southeast Asia), and 0.39 (South America) to 0.56, 0.41, and 0.44, respectively. The correlation coefficients between the simulated and observed monthly mean precipitation increased from 0.19 (Africa), 0.22 (South Asia), and 0.22 (East Asia) to 0.25, 0.27, and 0.28, respectively. The greatest improvement occurred over arid and semiarid areas. The spatiotemporal variability and changes in vegetation and ground surface albedo modeled by the GFS with a dynamic vegetation model were more consistent with the observations. The dynamic vegetation processes contributed to the surface energy and water balance and in turn, improved the annual variations in the simulated regional temperature and precipitation. The dynamic vegetation processes had the greatest influence on the spatiotemporal changes in the latent heat flux. This study shows that dynamic vegetation processes in earth system models significantly improve simulations of the climate mean status.

Comparison of Three Different Satellite-Based Approaches for Aboveground Biomass Estimation

Effective estimation of crop yield on a regional scale in a short time with low cost would only be possible utilizing remote sensing. Several approaches have been proposed based on remotely sensed data for estimating Aboveground Biomass (AGB). In this study, three satellite-based approaches, including Radiation Use Efficiency (RUE). The soil water atmosphere plant (SWAP) model, and FAO33, were evaluated in the irrigated wheat and barley fields in the Qazvin irrigation network, Iran. To this end, the leaf area index (LAI) and relative evapotranspiration (ETrel) were extracted from the Landsat data and incorporated into the methods. A comparative analysis was undertaken to evaluate the performance of the satellite-based approaches using percent absolute error (PAE). By updating SWAP with satellite-derived LAI and surface incoming solar radiation, the PAE decreased significantly. Results of different Spectral Indices (SIs) in the RUE method showed that NDVI performed best with PAE of 1.52 percent and RMSE of 664.6 kg ha−1. Also, the RUE method with RMSE of 664.6 kg ha−1 had 4.7 and 23.8 lower PAE compared to the SWAP (RMSE = 2221.4 kg ha−1) and FAO33 (RMSE = 4394.2 kg ha−1), respectively. However, this was not the only criteria for a well-performed method, because earlier AGB forecast was only feasible by making use of SWAP since the satellite-derived parameters were only incorporated into the model about one month before the harvest.

Improving Estimation of Seasonal Evapotranspiration in Australian Tropical Savannas using a Flexible Drought Index

Savannas, occupying a fifth of the global land surface, are characterized by the coexistence of trees and grasses. Accurate estimation of savanna evapotranspiration (ET) is vital for understanding the regional and global water balance and its feedback to climate. However, the overlapping phenology and different water-use patterns of trees and grasses constitute a major challenge for modeling efforts. To estimate savanna ET, we used a three-source ET model, partitioning ET among soil, trees, and grasses. To represent legacy effects of precipitation on ecosystem water use, the Normalized Ecosystem Drought Index (NEDI, i.e. a function of precipitation and potential evapotranspiration) was included to limit canopy conductances in the model and also in two other classic two-layer models (Shuttleworth-Wallace model and Penman-Monteith-Leuning model). The results of our model and the other models were tested and compared using tower-based eddy covariance flux data collected at six sites (including four savanna sites, one pasture site, and one grassland site) along a precipitation gradient in northern Australia, together with satellite-derived leaf area index, which was partitioned to represent the canopy dynamics of trees and grasses. Inclusion of NEDI significantly reduced seasonal biases in ET estimation results for all models compared with observations at savanna sites (fitted slopes were closer to unity by 0.08–0.10, R2 increased by 0.03–0.04, and RMSE decreased by 0.07–0.09 mm d−1). The three-source model provides insights into simulation of water fluxes over vegetated areas of complex composition. Our work makes a contribution to savanna research by determining a flexible indicator defining the seasonal water availability limitation on savanna ET. The inclusion of NEDI in ET models could guide future research on modeling ecosystem water and carbon fluxes in response to seasonal droughts.

Using Remotely Sensed Information to Improve Vegetation Parameterization in a Semi-Distributed Hydrological Model (SMART) for Upland Catchments in Australia

Appropriate representation of the vegetation dynamics is crucial in hydrological modelling. To improve an existing limited vegetation parameterization in a semi-distributed hydrologic model, called the Soil Moisture and Runoff simulation Toolkit (SMART), this study proposed a simple method to incorporate daily leaf area index (LAI) dynamics into the model using mean monthly LAI climatology and mean rainfall. The LAI-rainfall sensitivity is governed by a parameter that is optimized by maximizing the Pearson correlation coefficient (R) between the estimated and satellite-derived LAI time series. As a result, the LAI-rainfall sensitivity is smallest for forest, shrub, and woodland regions across Australia, and increases for grasslands and croplands. The impact of the proposed method on catchment-scale simulations of soil moisture (SM), evapotranspiration (ET) and discharge (Q) in SMART was examined across six eco-hydrologically contrasted upland catchments in Australia. Results showed that the proposed method produces almost identical results compared to simulations by the satellite-derived LAI time series. In addition, the simulation results were considerably improved in nutrient/light limited catchments compared to the cases with the default vegetation parameterization. The results showed promise, with possibilities of extension to other hydrologic models that need similar specifications for inbuilt vegetation dynamics.

Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales.

Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a 3 × 3 grid (or 36 km × 36 km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29-1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.