Moderate Resolution Imaging Spectroradiometer Leaf Area Index Research Articles

Remote Prediction of Oilseed Rape Yield via Gaofen-1 Images and a Crop Model

The fast and accurate prediction of crop yield at the regional scale is of great significance to food policies or trade. In this study, a new model is developed to predict the yield of oilseed rape from high-resolution remote sensing images. In order to derive this model, the ground experiment and remote sensing data analysis are carried out successively. In the ground experiment, the leaf area index (LAI) of four growing stages are measured, and a regression model is established to predict yield from ground LAI. In the remote sensing analysis, a new model is built to predict ground LAI from Gaofen-1 images where the simple ratio vegetation index at the bolting stage and the VARIgreen vegetation index at the flowering stage are used. The WOFOSTWOrld FOod STudy (WOFOST) crop model is used to generate time-series ground LAI from discontinuous ground LAI, which is calibrated coarsely with the MODerate resolution imaging spectroradiometer LAI product and finely with the ground-measured data. By combining the two conclusive formulas, an estimation model is built from Gaofen-1 images to the yield of oilseed rape. The effectiveness of the proposed model is verified in Wuxue City, Hubei Province from 2014 to 2019, with the pyramid bottleneck residual network to extract oilseed rape planting areas, the proposed model to estimate yields, and the China statistical yearbooks for comparison. The validation shows that the prediction error of the proposed algorithm is less than 5.5%, which highlights the feasibility of our method for accurate prediction of the oilseed rape yield in a large area.

Leaf area index retrieval with ICESat-2 photon counting LiDAR

This is the first study to inverse leaf area index (LAI) with photon counting LiDAR (i.e., Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS). A new framework composed of three major steps was applied: noise removal algorithm, photon classification algorithm, and the LAI estimation model. To evaluate the feasibility and accuracy of ICESat-2 derived LAI, the MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product at the spatial resolution of 500-m over two sites (Amazon rainforest and Daxing’an mountains forests) were selected for comparison purpose. The results show satisfactory agreements (Amazon: R=0.693, RMSE=2.545; Daxing’an: R=0.626, RMSE=1.893; Two sites together: R=0.667, RMSE=2.433) between MODIS LAI and ICESat-2 derived LAI. Moreover, we also selected the LAI derived from Sentinel-2 to further validate the ICESat-2 derived LAI, and got better results than MODIS LAI (Amazon: R=0.752, RMSE=2.329; Daxing’an: R=0.704, RMSE=1.724). Our study found that: (1) The new applied framework can effectively inverse LAI with ICESat-2. (2) The ICESat-2 derived LAI are reliable. (3) ICESat-2 exists many advantages over MODIS in terms of LAI estimation, for example, it not only can alleviate the issue of saturation of MODIS when LAI is high, but also can mitigate the problem of poor inversion effect of MODIS where the vegetation types are diversity.

Assessing land surface phenology of the savanna ecosystem in Southeast Asia using Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index from 2002 to 2020

The sustainability of the global savanna ecosystem is currently under threat from climate and anthropological change. Despite the immense threats, the existence of the savanna ecosystem is undervalued and understudied. This study examined the dynamics of the savanna ecosystem in the southern part of Southeast Asia (SEA) using MODIS leaf area index (LAI) data (MOD15A3H) with 500-m spatial resolution and 4-day data, from 2002 to 2020. The annual phenological metrics comprising the start of season (SOS), end of season (EOS), length of season (LOS), rate of greening, rate of browning, and the maximum peak values were derived from the daily interpolated data using the spline function. Additional oscillation and trend analysis using empirical ensemble decomposition methods (EEMD) was conducted to derive the nonlinear trend dynamics of the savanna ecosystem in East Nusa Tenggara (ENT). We found that the SOS at the Savanna ecosystem in SEA is 253.76 ± 2.1 days, the EOS is 161.12 ± 4.0 days, and the average LOS is 170.68 ± 6.5 days. The rainfall variabilities can explain around 35% of the variability in the LAI of the savanna ecosystem. Our EEMD analysis captured the decreasing LAI trend, showing a net change between 2002 and 2015 from 1.08 LAI units (scale of 10−2) to − 0.17 LAI units (scale of 10−2) from 2015 onwards. The result indicated a declining trend of LAI values of savanna ecosystem in ENT, thus requiring further monitoring to ensure the sustainability of this ecosystem.

Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models

Ecosystem models have been widely used for obtaining gross primary productivity (GPP) estimations at multiple scales. Leaf area index (LAI) is a critical variable in these models for describing the vegetation canopy structure and predicting vegetation-atmosphere interactions. However, the uncertainties in LAI datasets and the effects of their representation on simulated GPP remain unclear, especially over complex terrain. Here, five most popular datasets, namely the Long-term Global Mapping (GLOBMAP) LAI, Global LAnd Surface Satellite (GLASS) LAI, Geoland2 version 1 (GEOV1) LAI, Global Inventory Monitoring and Modeling System (GIMMS) LAI, and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI, were selected to examine the influences of LAI representation on GPP estimations at 95 eddy covariance (EC) sites. The GPP estimations from the Boreal Ecosystem Productivity Simulator (BEPS) model and the Eddy Covariance Light Use Efficiency (EC-LUE) model were evaluated against EC GPP to assess the performances of LAI datasets. Results showed that MODIS LAI had stronger linear correlations with GLASS and GEOV1 than GIMMS and GLOMAP at the study sites. The GPP estimations from GLASS LAI had a better agreement with EC GPP than those from other four LAI datasets at forest sites, while the GPP estimations from GEOVI LAI matched best with EC GPP at grass sites. Additionally, the GPP estimations from GLASS and GEOVI LAI presented better performances than the other three LAI datasets at crop sites. Besides, the results also showed that complex terrain had larger discrepancies of LAI and GPP estimations, and flat terrain presented better performances of LAI datasets in GPP estimations. Moreover, the simulated GPP from BEPS was more sensitive to LAI than those from EC - LUE, suggesting that LAI datasets can also lead to different uncertainties in GPP estimations from different model structures. Our study highlights that the satellite-derived LAI datasets can cause uncertainties in GPP estimations through ecosystem models.

Quantifying grazing patterns using a new growth function based on MODIS Leaf Area Index

Monitoring grazing activities on grassland is crucial for ensuring sustainable grassland development and for protecting it from grazing-led degradation. The Leaf Area Index (LAI), which measures leaf coverage over a surface area, is commonly used as a proxy for grassland condition. However, current studies focus on the year-round or seasonal aggregated LAI change rather than the change that can be attributed explicitly to grazing, which is the important indicator for quantifying grassland grazing. This paper presents a new exponential growth function under grazing with an estimation algorithm, the purpose of which is to extract grazing-led LAI changes for every 8 days' satellite observations. All the analyses are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H products. An improved MODIS LAI and an expected LAI are produced separately, considering both current and previous grazing-led LAI changes. The differences between expected LAI and improved LAI are then converted to the equivalent carbon mass of grazed material. This grazed carbon mass is then aggregated within the growing season, and compared with the expected carbon mass consumed by livestock (calculated from statistics yearbooks). In addition, Net Primary Productivity (NPP) is produced using the improved LAI, simulated by a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM). This is compared with the NPP produced by LUE-VPM based on original MODIS LAI, MODIS NPP products (MOD17A2H) and grassland monitoring stations' in situ measured data. Results show that the NPP calculated from the improved LAI is statistically the same as in situ converted NPP with a p-value equalling 0.998 (the RMSE between the two is 97.77 gC/m2). Conversely, the p-value between converted in situ measured carbon mass and the MODIS NPP product is 0.011 (the RMSE between the two is 133.98 gC/m2), indicating they are statistically different. The results detailed in this paper provide precise and almost real-time grassland grazing monitoring information for policy makers managing grassland.

Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016

The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) algorithm has been successfully implemented for Visible Infrared Imager Radiometer Suite (VIIRS) observations by optimizing a small set of configurable parameters in Look-Up-Tables (LUTs). Our preliminary evaluation showed reasonable agreement between VIIRS and MODIS LAI/FPAR retrievals. However, there is a need for a more comprehensive investigation to assure continuity of multi-sensor global LAI/FPAR time series, as the preliminary evaluation was spatiotemporally limited. In this study, we use a multi-year (2012–2016) global LAI/FPAR product generated from VIIRS and MODIS to evaluate for spatiotemporal consistency. We also quantify uncertainty of the product by utilizing available ground measurements. For both consistency and uncertainty evaluation, we account for variations in biome type and temporal resolution. Our results indicate that the LAI/FPAR retrievals from VIIRS and MODIS are consistent at different spatial (i.e., global and site) and temporal (i.e., 8-day, seasonal and annual) scales. The estimate of mean discrepancy (−0.006 ± 0.013 for LAI and −0.002 ± 0.002 for FPAR) meets the stability requirement for long-term LAI/FPAR Earth System Data Records (ESDRs) from multi-sensors as suggested by the Global Climate Observing System (GCOS). It is noteworthy that the rate of retrievals from the radiative transfer-based main algorithm is also comparable between two sensors. However, a relatively larger discrepancy over tropical forests was observed due to reflectance saturation and an unexpected interannual variation of main algorithm success was noticed due to instability in input surface reflectances. The uncertainties/relative uncertainties of VIIRS and MODIS LAI (FPAR) products assessed through comparisons to ground measurements are estimated to be 0.60/42.2% (0.10/24.4%) and 0.55/39.3% (0.11/26%), respectively. Note that the validated LAI were only distributed in low domains (~2.5), resulting in large relative uncertainty. Therefore, more ground measurements are needed to achieve a more comprehensive evaluation result of product uncertainty. The results presented here generally imbue confidence in the consistency between VIIRS and MODIS LAI/FPAR products and the feasibility of generating long-term multi-sensor LAI/FPAR ESDRs time series.

Spatially and temporally continuous LAI datasets based on the mixed pixel decomposition method

The leaf area index (LAI) is a key biophysical parameter that determines the state of plant growth. A global LAI has been routinely produced by the Moderate Resolution Imaging Spectro-radiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, the MODIS and AVHRR LAI products cannot be synchronized with the same spatial and temporal resolution. The LAI features are not discernible when a global LAI product is implemented at the regional scale because it has low resolution and different land cover types. To obtain high spatial and temporal resolution of LAI products, an empirical model based on the pixel scale was developed. The approach to generate a long (multi-decade) time series of a 1-km spatial resolution LAI normally integrates both AVHRR and MODIS datasets for different land cover types. In this paper, a regression-based model for generating a vegetation LAI was developed using the AVHRR Global Inventory Modelling and Mapping Studies Normalized Difference Vegetation Index (NDVI), MODIS LAI and land cover as input data; the model was evaluated by using relevant data from the same period data from 2000 to 2006. The results of this method show a good consistency in LAI values retrieved from the AVHRR NDVI and MODIS LAI. This simple method has no specific-limited data requirements and can provide improved spatial and temporal resolution in a region without ground data.

Improved meteorology from an updated WRF/CMAQ modeling system with MODIS vegetation and albedo

AbstractRealistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) that employs the Pleim‐Xiu land surface model (PX LSM). Recently, PX LSM WRF/CMAQ has been updated in vegetation, soil, and boundary layer processes resulting in improved 2 m temperature (T) and mixing ratio (Q), 10 m wind speed, and surface ozone simulations across the domain compared to the previous version for a period around August 2006. Yearlong meteorology simulations with the updated system demonstrate that MODIS input helps reduce bias of the 2 m Q estimation during the growing season from April to September. Improvements follow the green‐up in the southeast from April and move toward the west and north through August. From October to March, MODIS input does not have much influence on the system because vegetation is not as active. The greatest effects of MODIS input include more accurate phenology, better representation of leaf area index (LAI) for various forest ecosystems and agricultural areas, and realistically sparse vegetation coverage in the western drylands. Despite the improved meteorology, MODIS input causes higher bias for the surface O3 simulation in April, August, and October in areas where MODIS LAI is much less than the base LAI. Thus, improvements may be needed in the CMAQ dry deposition model for low LAI areas where deposition on the soil surface becomes important.

Method for improvement of MODIS leaf area index products based on pixel-to-pixel correlations

A new method, based on pixel-to-pixel correlations, was proposed to replace unreliable Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) time series (termed the “pixel-to-pixel correction (PPC) method”). MODIS LAI products improved by the PPC method were compared to field-measured LAI data. Results show that the PPC method could reduce the error associated with MODIS LAI products in relation to the original MODIS LAI product, and provide a level of accuracy that meets the most reliable quality control level criterion (quality control = 0). The PPC method performed well at correcting unrealistically high and unreliable LAI values, and performed slightly better than the “upper envelope” smoothing method. The corrected data were also more continuous and smoother than the raw MODIS LAI products. Thus, the PPC method can successfully reduce the uncertainty and improve the accuracy of raw MODIS LAI products.

Sensitivity of the Weather Research and Forecast/Community Multiscale Air Quality modeling system to MODIS LAI, FPAR, and albedo

AbstractThis study aims to improve land surface processes in a retrospective meteorology and air quality modeling system through the use of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation and albedo products for more realistic vegetation and surface representation. MODIS leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and albedo are incorporated into the Pleim‐Xiu land surface model (PX LSM) used in a combined meteorology and air quality modeling system. The current PX LSM intentionally exaggerates vegetation coverage and LAI in western dry lands so that its soil moisture nudging scheme is more effective in simulating surface temperature and mixing ratio. Reduced vegetation coverage from the PX LSM with MODIS input results in hotter and dryer daytime conditions with reduced ozone dry deposition velocities in much of western North America. Evaluations of the new system indicate greater error and bias in temperature, but reduced error and bias in moisture with the MODIS vegetation input. Hotter daytime temperatures and reduced dry deposition result in greater ozone concentrations in the western arid regions even with deeper boundary layer depths. MODIS albedo has much less impact on the meteorology simulations than MODIS LAI and FPAR. The MODIS vegetation and albedo input does not have much influence in the east where differences in vegetation and albedo parameters are less extreme. Evaluation results showing increased temperature errors with more accurate representation of vegetation suggests that improvements are needed in the model surface physics, particularly the soil processes in the PX LSM.

Integrating two layers of soil moisture parameters into the MOD16 algorithm to improve evapotranspiration estimations

To integrate soil moisture into the algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS) global evapotranspiration (ET) project (MOD16), two improvements were implemented: two layers of relative soil moisture parameters were combined with a surface resistance model; and the complementary relationship was replaced with the Penman-Monteith (P-M) method to estimate the dry soil surface evaporation. In the vegetation surface resistance model, a multiplier Rsm1 was added, and the influence of the relative soil moisture in the root zone was accounted for. In the soil surface resistance model, an empirical exponential relationship was used. To calculate the relative soil moisture parameters, soil hydraulic parameters, such as field capacity (Fc), wilting point (Wp), and saturation point (Sp), were estimated according to the soil texture information; these parameters were used as critical values to estimate the relative soil moisture. Both the MOD16 method and improved method were validated using ET flux data collected at nine flux-tower sites in the USA from 2000 to 2009. The mean absolute BIAS and the root mean square error (RMSE) decreased from 0.36 to 0.30 mm day–1 and from 1.14 to 0.97 mm day–1, respectively, after integrating the soil moisture parameters. Meanwhile, the mean correlation coefficient (R) for the nine sites increased from 0.54 to 0.70. Therefore, the improved method performed better than the MOD16 method. Furthermore, the uncertainties associated with the MODIS leaf area index (LAI) products, flux-tower measurements, soil texture, soil moisture, and model parameters were analysed. The outlook for future modifications was also discussed.

Comparing the Dry Season In-Situ Leaf Area Index (LAI) Derived from High-Resolution RapidEye Imagery with MODIS LAI in a Namibian Savanna

The Leaf Area Index (LAI) is one of the most frequently applied measures to characterize vegetation and its dynamics and functions with remote sensing. Satellite missions, such as NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) operationally produce global datasets of LAI. Due to their role as an input to large-scale modeling activities, evaluation and verification of such datasets are of high importance. In this context, savannas appear to be underrepresented with regards to their heterogeneous appearance (e.g., tree/grass-ratio, seasonality). Here, we aim to examine the LAI in a heterogeneous savanna ecosystem located in Namibia’s Owamboland during the dry season. Ground measurements of LAI are used to derive a high-resolution LAI model with RapidEye satellite data. This model is related to the corresponding MODIS LAI/FPAR (Fraction of Absorbed Photosynthetically Active Radiation) scene (MOD15A2) in order to evaluate its performance at the intended annual minimum during the dry season. Based on a field survey we first assessed vegetation patterns from species composition and elevation for 109 sites. Secondly, we measured in situ LAI to quantitatively estimate the available vegetation (mean = 0.28). Green LAI samples were then empirically modeled (LAImodel) with high resolution RapidEye imagery derived Difference Vegetation Index (DVI) using a linear regression (R2 = 0.71). As indicated by several measures of model performance, the comparison with MOD15A2 revealed moderate consistency mostly due to overestimation by the aggregated LAImodel. Model constraints aside, this study may point to important issues for MOD15A2 in savannas concerning the underlying MODIS Land Cover product (MCD12Q1) and a potential adjustment by means of the MODIS Burned Area product (MCD45A1).

MODIS NBAR Time Series Modeling With Two Statistical Methods and Application to Leaf Area Index Recursive Estimation

The inconsistent data quality of remote sensing observation, which is largely a result of atmospheric conditions, presents problems in the application of these data. Pixel reflectance in remote sensing observation varies with the type of land cover and the observation time. For land cover types that cycle yearly, such as vegetation, the variations in surface reflectance usually have dynamic periodic characteristics. In this study, we modeled the temporal feature of Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-adjusted reflectance (NBAR) time series data of typical forest and cropland areas using two statistical methods: season-trend and seasonal autoregressive (AR) integrated moving average (SARIMA). The fitting values of these models were used in the recursive estimation of leaf area index (LAI) time series based on a nonlinear AR exogenous (NARX) neural network. This suppressed interferences from observational data noise and missing values. The results from 6 years (2008–2013) of MODIS NBAR modeling indicate that the two statistical methods are effective to model the NBAR time series of the vegetation surface; the season-trend model can extract both seasonal and trend components of long time series, and the SARIMA model has a good fitting capacity for general time series data. The NARX neural network performs well with the improved NBAR time series input, and the estimated LAI time series is more continuous than the MODIS LAI. Comparison with field data reveals the reliability of the estimated LAI values.

Evaluation and intercomparison of MODIS and GEOV1 global leaf area index products over four sites in North China.

This study investigated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) and GEOLAND2 Version 1 (GEOV1) Leaf Area Index (LAI) products using ground measurements and LAI reference maps over four sites in North China for 2011–2013. The Terra + Aqua MODIS and Terra MODIS LAI retrieved by the main algorithm and GEOV1 LAI within the valid range were evaluated and intercompared using LAI reference maps to assess their uncertainty and seasonal variability The results showed that GEOV1 LAI is the most similar product with the LAI reference maps (R2 = 0.78 and RMSE = 0.59). The MODIS products performed well for biomes with low LAI values, but considerable uncertainty arose when the LAI was larger than 3. Terra + Aqua MODIS (R2 = 0.72 and RMSE = 0.68) was slightly more accurate than Terra MODIS (R2 = 0.57 and RMSE = 0.90) for producing slightly more successful observations. Both MODIS and GEOV1 products effectively followed the seasonal trajectory of the reference maps, and GEOV1 exhibited a smoother seasonal trajectory than MODIS. MODIS anomalies mainly occurred during summer and likely occurred because of surface reflectance uncertainty, shorter temporal resolutions and inconsistency between simulated and MODIS surface reflectances. This study suggests that further improvements of the MODIS LAI products should focus on finer algorithm inputs and improved seasonal variation modeling of MODIS observations. Future field work considering finer biome maps and better generation of LAI reference maps is still needed.

Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003–2013

Summary Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and functioning have been attributed to cloud and aerosol contamination of shortwave reflectance composites, particularly over the rainforested regions of the Amazon basin which are subject to prolonged periods of cloud cover and episodes of intense biomass burning. This study compares timeseries of satellite-derived maps of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and precipitation from the Tropical Rainfall Mapping Mission (TRMM) with a diagnostic Evaporative Stress Index (ESI) retrieved using thermal infrared remote sensing over South America for the period 2003–2013. This period includes several severe droughts and floods that occurred both over the Amazon and over unforested savanna and agricultural areas in Brazil. Cross-correlations between absolute values and standardized anomalies in monthly LAI and precipitation composites as well as the actual-to-reference evapotranspiration (ET) ratio used in the ESI were computed for representative forested and agricultural regions. The correlation analyses reveal strong apparent anticorrelation between MODIS LAI and TRMM precipitation anomalies over the Amazon, but better coupling over regions vegetated with shorter grass and crop canopies. The ESI was more consistently correlated with precipitation patterns over both landcover types. Temporal comparisons between ESI and TRMM anomalies suggest longer moisture buffering timescales in the deeper rooted rainforest systems. Diagnostic thermal-based retrievals of ET and ET anomalies, such as used in the ESI, provide independent information on the impacts of extreme hydrologic events on vegetation health in comparison with VI and precipitation-based drought indicators, and used in concert may provide a more reliable evaluation of natural and managed ecosystem response to variable climate regimes.

Spatial and seasonal variations of CO2 flux and photosynthetic and respiratory parameters of larch forests in East Asia

Larch (Larix spp.) forests are predominantly distributed across high latitudes of Eurasia. They potentially have a strong influence on the terrestrial carbon and energy cycles, because of their vast area and the large carbon stocks in their peat soils in the permafrost. In this study, we elucidated intersite variation of ecosystem photosynthetic and respiratory parameters of eight larch forests in East Asia using the CarboEastAsia carbon flux and micrometeorology dataset. These parameters were determined using the empirical relationship between the carbon fluxes (photosynthesis and respiration) and micrometeorological variables (light and temperature). In addition, we examined leaf area index (LAI) determined by Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to explain the intersite variation. Linear or exponential relationships with annual mean temperature or seasonal maximum LAI at the study sites were found for the annual carbon fluxes (gross primary production [GPP] and total ecosystem respiration [RE]) as well as for four of the five seasonal maximum values of determined photosynthetic and respiratory parameters (maximum GPP at light saturation, initial slope of the light-response curve, daytime respiration, and RE at the reference temperature of 10°C). Phenological indices, such as start day of the growing season, growing season length and growing season degree days explained much of the intersite variation of GPP and RE of the studied larch forests; however, the relationship between MODIS LAI and photosynthetic or respiratory parameters implies that the intersite variation in GPP and RE was caused not only by the temperature variation (abiotic factor), but also by the variation in the photosynthetic and respiration activity by vegetation (biotic factor) through the change in leaf (or whole vegetation) biomass. Our analysis shows that MODIS LAI serves as a good index to explain the variation of the ecosystem photosynthetic and respiratory characteristics of East Asian larch forests.

Upscaling of leaf area index in Terai forest plantations using fine- and moderate-resolution satellite data

Ecophysiological variables, such as leaf area index (LAI), play a key role in the functioning of ecosystem processes and are thus a useful determinant of primary production, evapotranspiration, and biogeochemical cycling. In the present study, upscaling of LAI was carried out by applying a transfer function from field LAI measurements to fine-resolution LISS III images and subsequently to coarse resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data using a photosynthetically active radiation (PAR)/LAI ceptometer (AccuPAR model LP-80). Field data were collected from differently aged forest plantation types in the Terai Central Forest Division of Nainital district in Uttarakhand, India. The upscaling was done by establishing an empirical exponential relationship between normalized difference vegetation index (NDVI) and LAI. Results reveal a significant relationship (p < 0.01) between NDVI and LAI for each of the studied plantation types, i.e. teak, poplar, eucalyptus, and mixed plantation. The LAI was mapped at 23.5 m resolution by applying a plantation-specific LAI versus NDVI relationship derived from IRS Linear Imaging Self-Scanning Sensor images. The LAI maps were upscaled by using a simple linear averaging within nonoverlapping windows to match with Terra–MODIS 250 m resolution NDVI images. The upscaled time series of LAI was compared with representative field-measured LAI measurements and also with MODIS LAI at a resolution of 1000 m. Upscaled LAI was found to be significantly related to the field-measured LAI with a value of 0.69 for coefficient of determination and with a root mean square error of 0.77. On the other hand, upscaled LAI has less agreement with the MODIS LAI product (R2 = 0.5 and root mean square error = 0.70).

Assessment of SMOS soil moisture retrieval parameters using tau–omega algorithms for soil moisture deficit estimation

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.

Data-based mechanistic modelling and validation for leaf area index estimation using multi-angular remote-sensing observation time series

Spatially and temporally complete leaf area index (LAI) time series are required for crop growth monitoring, forest biomass estimation, and land surface process simulation studies. Global LAI products currently available include the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. However, data quality still needs to be improved, especially with respect to temporal continuity. In this research, a new approach has been developed to estimate LAI time series using the data-based mechanistic (DBM) modelling procedure. Both the nadir viewing reflectance and anisotropic index (ANIX) time series derived from the MODIS product are used in LAI_DBM modelling and estimation, where the ANIX values are used as an auxiliary variable to represent the bidirectional reflectance anisotropy of the vegetation canopy. Both the MOD09GA multi-angular remote-sensing observations and the MOD15A2 LAI products are used in the LAI time series modelling and retrieval procedure. Ground measurements at typical vegetation sites are used to validate the estimated LAI. The preliminary results show that: (1) the new LAI_DBM approach using nadir viewing reflectance observation and ANIX time series can be used to improve the continuity of estimated LAI time series. The disturbance noise introduced by using the MOD09A1 directional reflectance observations directly can thus be reduced. (2) An ANIX time series can represent the vegetation canopy bidirectional reflectance anisotropy information and its dynamic changes. It works well in the retrieval procedure for improving LAI time series estimation. (3) The preliminary retrieval results demonstrate that the estimated LAIs can achieve better time series continuity than the original MODIS LAI product.