MODIS Leaf Area Index Product Research Articles

Enhancing Leaf Area Index Estimation with MODIS BRDF Data by Optimizing Directional Observations and Integrating PROSAIL and Ross–Li Models

The Leaf Area Index (LAI) is a crucial vegetation parameter for climate and ecological models. Reflectance anisotropy contains valuable supplementary information for the retrieval of properties of an observed target surface. Previous studies have utilized multi-angular reflectance data and physically based Bidirectional Reflectance Distribution Function (BRDF) models with detailed vegetation structure descriptions for LAI estimation. However, the optimal selection of viewing angles for improved inversion results has received limited attention. By optimizing directional observations and integrating the PROSAIL and Ross–Li models, this study aims to enhance LAI estimation from MODIS BRDF data. A dataset of 20,000 vegetation parameter combinations was utilized to identify the directions in which the PROSAIL model exhibits higher sensitivity to LAI changes and better consistency with the Ross–Li BRDF models. The results reveal significant variations in the sensitivity of the PROSAIL model to LAI changes and its consistency with the Ross–Li model over the viewing hemisphere. In the red band, directions with high sensitivity to LAI changes and strong model consistency are mainly found at smaller solar and viewing zenith angles. In the near-infrared band, these directions are distributed at positions with larger solar and viewing zenith angles. Validation using field measurements and LAI maps demonstrates that the proposed method achieves comparable accuracy to an algorithm utilizing 397 viewing angles by utilizing reflectance data from only 30 directions. Moreover, there is a significant improvement in computational efficiency. The accuracy of LAI estimation obtained from simulated multi-angle data is relatively high for LAI values below 3.5 when compared with the MODIS LAI product from two tiles. Additionally, there is also a slight improvement in the results when the LAI exceeds 4.5. Overall, our results highlight the potential of utilizing multi-angular reflectance in specific directions for vegetation parameter inversion, showcasing the promise of this method for large-scale LAI estimation.

Improving the MODIS leaf area index product for a cropland with the nonlinear autoregressive neural network with eXogenous input model

The leaf area index (LAI) is a crucial descriptive parameter of the dynamic change of ground vegetation. The widely used MODIS LAI product, however, does not satisfy the requirements of regional eco-environment modeling. There is an urgent need to improve the product’s overall accuracy. Under this circumstance, this study proposed an improvement scheme based on the nonlinear autoregressive neural network with eXogenous input (NARXNN) model and the high-quality time series LAI inversion result. Case studies were implemented for two seasons a year croplands in Wuzhi, Xinzheng, and Xiangcheng in Henan province. This research acquired 46 periods of the NARXNN model-improved LAI, which went through rigid in situ LAI validation. The in situ measured LAI by LAI-2000 was used to validate the accuracy of NARXNN-enhanced LAI data. The R2 values of the improved LAI of the three research areas are 0.54, 0.41, and 0.51, while the RMSE decreased by 0.07, 0.1, and 0.03, and the bias also decreased to a certain extent. Direct validation using the in situ measured LAI demonstrates that the NARXNN model-enhanced LAI data were more accurate and had a lower bias than MCD15A2H. A comparison of the time series change indicates that the NARXNN-enhanced LAI shows a smoother bimodal change trend and is more conformed to the actual cropland growth than the original MODIS product. The results indicated that the NARXNN neural network further increased the accuracy of the MODIS product and has a particular practical value in future research.

Comprehensive evaluation of global CI, FVC, and LAI products and their relationships using high-resolution reference data

In recent decades, several global clumping index (CI), fractional vegetation cover (FVC), and leaf area index (LAI) products have been generated using a range of optical satellite sensors. It is essential for the application community to understand the accuracy and uncertainty of these products. However, current validation studies have limited spatial coverage and temporal continuity, and the relationships between different products are not clearly understood. This study aims to validate the most recent global CAS-CI, GEOV2 FVC, and MODIS LAI products using the Ground-Based Observations for Validation (GBOV) and DIRECT 2.1 datasets. The relationships between these products, the leaf projection function in the nadir direction (G(0)), and the vertical structural characteristics of the forest were also analyzed. The GEOV2 FVC and MODIS LAI show good performance, whereas CAS-CI shows a slight underestimation (bias = −0.10) and strong seasonal variations. FVC and LAI are strongly correlated at different spatial scales [field, high-resolution (30 m and upscaled 3 km), and moderate-resolution (3 km)], particularly for forests [mean Pearson's correlation coefficients (r) = 0.90]. The CI-FVC/LAI relationship is more consistent across spatial scales for grass and shrubs than for forests. The derived G(0) overestimates the reference value (bias = 0.28) mainly due to inconsistencies between the CI, FVC, and LAI products. The overstory CI is very close to the overall forest CI, indicating that the understory may be neglected when estimating the overall CI. The overstory G(0) is similar to the overall G(0) for most forest types, except for the evergreen needleleaf forest. The overall CI-FVC/LAI relationship is mainly influenced by the overstory, while the FVC-LAI relationship is affected by both the overstory and understory. Overall, the CI-FVC/LAI relationship should be further evaluated and applied cautiously across different scales. The forest vertical structural characteristics revealed in this study are important for field measurement, remote sensing estimation, and modeling studies.

LAI-NOS: An automatic network observation system for leaf area index based on hemispherical photography

The leaf area index (LAI) is an important indicator reflecting the growth status of vegetation and is widely used in agriculture, ecology, climate change, and other fields. The shortcomings of the currently available methods for manually measuring LAI include labor-intensive, low sampling frequency, and asynchronous data collection. Focusing on these issues, a LAI sensor based on hemispherical photogrammetry and an automatic network observation system (LAI-NOS) for LAI were developed, which consists of four parts: LAI sensor, sensor node, sink node, and online data management system. The LAI sensor measures LAI values based on hemispherical photography. The sensor node is responsible for controlling the sensor and obtaining the data measured by the LAI sensor. The sink node is responsible for local networking and communication with the remote server. Data storage, data management, data display, and sampling frequency are managed by the online data management system. Comparative studies with LAI-2200C and satellite products were also conducted in this study. The comparative study with LAI-2200C showed that the LAI measurements of different vegetation types from both sources were highly significantly correlated whether based on Pearson regression or Passing & Bablok regression. A preliminary study comparing LAI-NOS measurements with Sentinel-2 inversion LAI and MODIS LAI products (MOD15A2H) showed (1) all LAI-NOS nodes measurements agreed very well with Sentinel-2 inversion LAI in the experimental period (average R2=0.94, RMSE=0.41); (2) the possible overestimate of Sentinel-2 inversion LAI was found in the middle stage of wheat (jointing-anthesis); (3) MOD15A2H and LAI-NOS measurements showed similar crop growth trends in long-term observations.

A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation

As a key variable to characterize the process of crop growth, the aboveground biomass (AGB) plays an important role in crop management and production. Process-based models and remote sensing are two important scientific methods for crop AGB estimation. In this study, we combined observations from agricultural meteorological stations and county-level yield statistics to calibrate a process-based crop growth model for winter wheat. After that, we assimilated a reprocessed temporal-spatial filtered MODIS Leaf Area Index product into the model to derive the 1 km daily AGB dataset of the main winter wheat producing areas in China from 2007 to 2015. The validation using ground measurements also suggests the derived AGB dataset agrees well with the filed observations, i.e., the R2 is above 0.9, and the root mean square error (RMSE) reaches 1,377 kg·ha−1. Compared to county-level statistics during 2007–2015, the ranges of R2, RMSE, and mean absolute percentage error (MAPE) are 0.73~0.89, 953~1,503 kg·ha−1, and 8%~12%, respectively. We believe our dataset can be helpful for relevant studies on regional agricultural production management and yield estimation.

Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data

Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation.

Improved retrieval of land surface biophysical variables from time series of Sentinel-3 OLCI TOA spectral observations by considering the temporal autocorrelation of surface and atmospheric properties

Estimation of essential vegetation properties from remote sensing is crucial for a quantitative understanding of the Earth system. Ill-posedness of the model inversion problem leads to multiple interpretations of one satellite observation, and using prior information is a promising way to reduce the ill-posedness and increase the accuracy of land surface products. Tobler's first law of geography states that “everything is related to everything else, but near things are more related than distant things”. Likewise, it is expected that the state of an object at a single moment is related to the state at every other moment, but temporally near attributes are more related than distant ones. This temporal autocorrelation is a vital source of prior information and can be used to improve the retrieval accuracy. In this study, we develop a retrieval framework that makes use of the temporal autocorrelation and dependence of land surface and atmospheric properties. We apply this retrieval algorithm to Sentinel-3 Ocean and Land Colour Instrument (OLCI) satellite data to derive land surface biophysical variables with a focus on leaf area index (LAI) from top-of-atmosphere (TOA) radiance observations. The results from both a synthetic dataset and a real satellite dataset show that the use of the temporal continuity as a priori information improves the accuracy of the estimation of land surface properties, such as leaf chlorophyll content and LAI. Compared with the MODIS LAI products, much less unrealistic short-term fluctuations are found in the LAI retrievals from OLCI with the time-series retrieval approach across different land cover types including cropland, forest and savannah. Field measurements of LAI at two forest sites quantitatively confirm that the estimated LAI from OLCI is reasonably accurate with R2 > 0.65 and RMSE < 1.00 m2m−2. Overall, the time series retrieval results in more robust and smoother time series than standard retrievals of LAI from individual scenes, more stable retrievals than the MODIS LAI product, and values of LAI that match better with reported measurements in the field. The present retrieval framework can make better use of time series of spectral observations and potentially of multi-sensor observations.

Estimation of leaf area index using PROSAIL based LUT inversion, MLRA-GPR and empirical models: Case study of tropical deciduous forest plantation, North India

Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.

Impact of leaf area index from various sources on estimating gross primary production in temperate forests using the JULES land surface model

The effects of various types of leaf area index (LAI) on simulations of stand scale gross primary production (GPP) in evergreen needleleaf (TCK) and deciduous broadleaf (TBK) temperate forests were analyzed using the Joint UK Land Environment Simulator (JULES) land surface model (LSM). LAI was observed during the period from 2015 to 2017 with an LAI-2200 plant canopy analyzer. Other LAIs included the enhanced vegetation index (EVI) of two satellites with different temporal and spatial resolutions (Landsat and MODIS), the LAI product of MODIS, and the JULES phenology model. These LAIs were compared with in situ observations of LAI, and their effects on GPP simulation were compared with simulated GPP using in situ observed LAI (GPPobs). Our results show that the JULES phenology model does not adequately express the LAI of either TCK or TBK forests. This misrepresentation was improved through simple modifications. LAI data from a remote-sensing EVI corresponded relatively well with the observations, whereas the MODIS LAI product, with a relatively low spatial resolution (500 m), could not discern the complex heterogeneity of a temperate forest and provided a 31% higher maximum value. Compared with GPPobs, the difference in annual GPP was smaller than in LAI, ranging from −3.48% to 6.0% in TCK and −6.52% to 10.9% in TBK. The differences in seasonal GPP estimates were up to 14.6% in spring for TCK and −17.4% in autumn for TBK. Our study identified limitations of the JULES leaf phenology model, and suggested modifications to reduce the differences in GPP estimation for temperate forests at the stand scale. In addition, we suggested using sufficiently high spatial resolution data, especially for LAI, as inadequately represented forest cover types resulted in seasonal GPP discrepancies.

An Approach for Estimating Monthly Curve Number Based on Remotely-Sensed MODIS Leaf Area Index Products

Curve number (CN) is a principal factor which is widely used in hydrology, specifically in the rainfall-runoff modelling. Its value varies based on soil moisture condition; soil hydrologic group, land use, and vegetation cover type. Remote sensing technology provides a tool to investigate spatiotemporal variations of land-cover. This may lead to generation of some continuous spatiotemporal CN datasets required for many hydrological applications. The purpose of this research is to develop an approach for estimating a monthly-distributed curve number via MODIS leaf area index (LAI) products. For calculating monthly CN, we investigated the relationship between monthly LAI, rainfall and CN under different computational scenarios of arithmetic mean, median, and geometric mean for estimating monthly LAI and rainfall. For this purpose, rainfall data for the period 2002–2016 were collected. Further, LAI data were obtained from MODIS for the same period. The performance of the modelled CN (correlation coefficients and Nash-Sutcliffe coefficient) was compared against obtained values of monthly rainfall and runoff in the SCS-CN method (observation-based CN). The findings of this study suggested that CN values obtained from the arithmetic mean of LAI and the geometric mean of rainfall yielded the best scenario (R2 = 0.916 and NS = 0.903 for calibration set; R2 = 0.926 and NS = 0.892 for validation set). Therefore, we suggest a simple appropriate method to generate monthly spatially distributed CN for hydrological applications.

Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat Imagery and Terrestrial LiDAR Data

Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents a LAI retrieval method for corn canopies using PROSAIL model with leaf angle distribution functions referred from terrestrial laser scanning points at four phenological stages during the growing season. Specifically, four inferred maximum-probability leaf angles were used in the Campbell ellipsoid leaf angle distribution function of PROSAIL. A Lookup table (LUT) is generated by running the PROSAIL model with inferred leaf angles, and the cost function is minimized to retrieve LAI. The results show that the leaf angle distribution functions are different for the corn plants at different phenological growing stages, and the incorporation of derived specific corn leaf angle distribution functions distribute the improvement of LAI retrieval using the PROSAIL model. This validation is done using in-situ LAI measurements and MODIS LAI in Baoding City, Hebei Province, China, and compared with the LAI retrieved using default leaf angle distribution function at the same time. The root-mean-square error (RMSE) between the retrieved LAI on 4 September 2014, using the modified PROSAIL model and the in-situ measured LAI was 0.31 m2/m2, with a strong and significant correlation (R2 = 0.82, residual range = 0 to 0.6 m2/m2, p &lt; 0.001). Comparatively, the accuracy of LAI retrieved results using default leaf angle distribution is lower, the RMSE of which is 0.56 with R2 = 0.76 and residual range = 0 to 1.0 m2/m2, p &lt; 0.001. This validation reveals that the introduction of inferred leaf angle distributions from TLS data points can improve the LAI retrieval accuracy using the PROSAIL model. Moreover, the comparations of LAI retrieval results on 10 July, 26 July, 19 August and 4 September with default and inferred corn leaf angle distribution functions are all compared with MODIS LAI products in the whole study area. This validation reveals that improvement exists in a wide spatial range and temporal range. All the comparisons demonstrate the potential of the modified PROSAIL model for retrieving corn canopy LAI from Landsat imagery by inferring leaf orientation from terrestrial laser scanning data.

Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm

The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of &gt;0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest.

Evaluation of four long time-series global leaf area index products

As an important vegetation biophysical variable, leaf area index (LAI) is a critical input parameter in many climate and ecological models. There exist four long time-series global LAI products since the 1980s, namely GLASS AVHRR, NCEI AVHRR, GIMMS3g and GLOBMAP. Currently, no inter-comparison studies exist to evaluate these LAI products and understand their differences for effective applications. In this study, the four long time-series global LAI products were inter-compared to evaluate their temporal and spatial discrepancies. These LAI products were also compared with MODIS LAI product and LAI values derived from high-resolution reference maps at VAlidation of Land European Remote sensing Instruments (VALERI) sites. The results show that the GLASS AVHRR and GLOBMAP LAI products are spatially complete, but the NCEI AVHRR and GIMMS3g LAI products contain many missing pixels, especially in rainforest regions. These LAI products reasonably represent the global vegetation characteristics and their seasonal variability. A relatively large discrepancy among these LAI products was observed in tropical forest regions, where the GLASS AVHRR and NCEI AVHRR LAI values achieved good agreement with the MODIS LAI values, but were between 0.5 and 1.0 LAI units lower than the GLOBMAP LAI values and higher than the GIMMS3g LAI values (more than 0.5 LAI units). Over the last three decades, the GLASS AVHRR, NCEI AVHRR, GIMMS3g LAI products show increasing trends for all biome types except evergreen needleleaf forests and deciduous needleleaf forests, but the GLOBMAP LAI product shows positive trends only for the grasses/cereal crops and shrubs. A comparison of these LAI products against the LAI values derived from high-resolution reference maps demonstrated that the GLASS AVHRR LAI values provided the better performance (RMSE=0.9014 and Bias=−0.1885) than the NCEI AVHRR LAI values (RMSE=1.0459 and Bias=−0.5695), the GIMMS3g LAI values (RMSE=1.0971 and Bias=−0.3904) and the GLOBMAP LAI values (RMSE=1.6145 and Bias=−0.9414).

Modeling and Predicting of MODIS Leaf Area Index Time Series Based on a Hybrid SARIMA and BP Neural Network Method

The modeling and predicting of vegetation Leaf area index (LAI) is an important component of land surface model and assimilation of remote sensing data. The MODIS LAI product (i.e. MOD15A2) is one of the most widely used LAI data sources. However, the time series of MODIS LAI contains some data of low quality. For example, because of the influence of the cloud, aerosol, etc., the MODIS LAI presents the characteristics of the discontinuous in time and space. In fact, the time series of MODIS LAI include both linear and nonlinear components, which cannot be accurately modeled and predicted by either linear method or nonlinear method alone. In this paper, the original LAI time series data were first smoothed with Savitzky-Golay (SG) filtration and linear interpolation; SARIMA, BP neural network and a hybrid method of SARIMA-BP neural network were then used for modeling and predicting MODIS LAI time series. The SARIMA-BP neural network combined both SARIMA and BP neural network, which could model the linear and the nonlinear component of MODIS LAI time series respectively. That is, the final result of SARIMA-BP neural network was the sum of results of the two methods. Experiments showed that the time series of MODIS LAI that were smoothed with the SG filtration and linear interpolation were more smooth than original time series, with a determination coefficient up to 0.981, closer to 1 than that of SARIMA (0.941) and BP neural network (0.884); the correlation coefficient between SARIMA-BP neural network and the observation is 0.991, higher than that of between SARIMA (0.971) or BP neural network (0.942) SARIMA and the observation. Thus, it can be concluded that, the proposed SARIMA-BP neural network method can better adapt to the LAI time series, and it outperforms the SARIMA and BP neural network methods.

Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data

The spatially and temporally continuous leaf area index (LAI) mapping is very crucial for many agricultural applications, such as crop yield estimation and growth status monitoring. Data assimilation technology provides an innovational way to improve spatio-temporally continuous crop LAI estimation through integration of remotely sensed observations and crop growth models. In this study, a very fast simulated annealing (VFSA)-based variational assimilation scheme was proposed to integrate the crop growth model (CERES-Maize), MODIS reflectance product (MODO9A1) and a two-layer canopy reflectance model (ACRM) to estimate time-series crop LAI at regional scale. Simultaneously, a new sensitivity analysis method (called histogram comparison) was developed to identify sensitive parameters of CERES-Maize and ACRM models. The proposed scheme was applied for continuous crop LAI estimation during the maize growing season in the dominating spring maize planting area of Jilin province, China. Results showed that R-2 values between LAI estimations from the proposed assimilation scheme (referred to as assimilated LAI) and fine resolution LAI reference maps were 0.24 and 0.63, with RMSE values of 0.21 and 0.54 for Julian day 176, 2010, and Julian day 196, 2010, respectively. The assimilated results were closer to LAI reference maps than the MODIS LAI product and ACRM-based inversion results (referred to as ACRM LAI). Moreover, by introducing the prior information of LAI dynamics depicted by a crop growth model, the assimilated LAI showed better temporal consistency than the MODIS LAI product, LAI profiles simulated by CERES-Maize model (referred to as CERES-Maize LAI), and ACRM LAI. It was found that the accuracies of LAI estimations could be enhanced by assimilating satellite observations into a crop simulation model in the VFSA framework at a regional scale. (C) 2016 Elsevier B.V. All rights reserved.

Retrieval of Grassland Live Fuel Moisture Content by Parameterizing Radiative Transfer Model With Interval Estimated LAI

Live fuel moisture content (LFMC) is an important factor in wildfire risk assessment, but the estimation of LFMC is affected by the leaf area index (LAI) if it is retrieved using a radiative transfer model. To reduce the influence of LAI in the LFMC retrieval process, we used the interval estimated LAI to parameterize the PROSAIL ( ${\mathbf{PROSPECT}} + {\mathbf {SAIL}}$ ) radiative transfer model. The LAI values were first estimated based on the MODIS LAI product (MOD15A2) using a simple downscaling technique, and then were constrained using an interval estimation method at the confidence levels of 80%, 90%, and 95%. To evaluate the reliability of this approach, another two LAI constraint strategies were applied, weakly constrained LAI over a wide range and strongly constrained LAI with fixed values. A look up table (LUT) was set up for the parameterized PROSAIL model. Furthermore, the LFMC can be retrieved based on the Landsat TM and 8 products. The results showed that the retrieved LFMC using the interval estimated LAI at the 95% confidence level resulted in the best accuracy level ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.78}}$ , ${\mathbf{RMSE}} = {\mathbf{35.80}}\% $ ), which was slightly better than the LFMC obtained when using the interval estimated LAI at confidence levels of 90% ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.75}},\;{\mathbf{RMSE}} = {\mathbf{39.38}}\% $ ) and 80% ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.70}},\;{\mathbf{RMSE}} = {\mathbf{41.21}}\% $ ). The accuracy levels of the retrieved LFMC using weakly constrained LAI ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.64}},\;{\mathbf{RMSE}} = {\mathbf{41.68}}\% $ ) and strongly constrained LAI ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.68}},\;{\mathbf{RMSE}} = {\mathbf{45.53}}\% $ ) were poor. Thus, this study demonstrated that LFMC retrieval could be improved by using the interval estimated LAI.

Aboveground biomass production of a semi-arid southern African savanna: towards a new model

Following a field campaign to determine the species composition, canopy cover, aboveground annual production and leaf area index (LAI) of the semi-arid savanna of north-western Namibia, we present a production model that can be used by graziers to determine the livestock carrying capacity. The model predicts the annual aboveground net primary production (ANPP) from regression equations of canopy cover by annual production fraction for plant functional classes. We tested the output of the model against another fully independent net primary production (NPP) model, namely the MODIS NPP product. The mean MODIS NPP for the 29 sites was 343 ± 22 kg dry matter (DM) ha−1 y−1 as opposed to 285 ± 142 kg DM ha−1 y−1 for the fAP model that used the regression method (p < 0.01). As a proof of concept, these landscape-scale ANPP values are used to calculate a recommended livestock carrying capacity for the Ehirovipuka Communal Conservancy, a 1 980 km2 communal area with both wildlife and livestock populations. In addition, we also provide details of a field method for predicting landscape-scale LAI from line transect data. This approach can be used to ground reference the LAI values generated from the MODIS LAI product.

A 3D approach to reconstruct continuous optical images using lidar and MODIS

Monitoring forest health and biomass for changes over time in the global environment requires the provision of continuous satellite images. However, optical images of land surfaces are generally contaminated when clouds are present or rain occurs. To estimate the actual reflectance of land surfaces masked by clouds and potential rain, 3D simulations by the RAPID radiative transfer model were proposed and conducted on a forest farm dominated by birch and larch in Genhe City, DaXing’AnLing Mountain in Inner Mongolia, China. The canopy height model (CHM) from lidar data were used to extract individual tree structures (location, height, crown width). Field measurements related tree height to diameter of breast height (DBH), lowest branch height and leaf area index (LAI). Series of Landsat images were used to classify tree species and land cover. MODIS LAI products were used to estimate the LAI of individual trees. Combining all these input variables to drive RAPID, high-resolution optical remote sensing images were simulated and validated with available satellite images. Evaluations on spatial texture, spectral values and directional reflectance were conducted to show comparable results. The study provides a proof-of-concept approach to link lidar and MODIS data in the parameterization of RAPID models for high temporal and spatial resolutions of image reconstruction in forest dominated areas.

An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities

A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R2, RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.

Improving LAI Mapping by Integrating MODIS and CYCLOPES LAI Products Using Optimal Interpolation

Leaf area index (LAI) is an important land surface biophysical variable used to characterize vegetation amount and activity. Current satellite LAI products, however, do not satisfy the requirements of the modeling community due to their large uncertainties and frequent missing values. There is an urgent need for advanced methods to integrate multiple LAI products to improve the product's accuracy and integrity for various applications. To meet this need, this study proposes a method based on Optimal Interpolation (OI) to integrate MODIS true LAI and CYCLOPES effective LAI retrievals. Multiple years' LAI means and variances (LAI climatology) are pre-calculated and used as the baseline for data integration. The locally adjusted cubic-spline capping algorithm is used to smooth the climatology data. An LAI normalization scheme, based on a linear measurement error model, is developed to account for the systematic difference between the two products and to generate the LAI predictions. This integration process removes the unrealistically large temporal variations of the original LAI products. The data gaps are filled with information from adjacent pixels and the prior knowledge acquired from multiyear climatology. A spatially and temporally continuous true LAI data set is generated. High resolution reference maps of true LAI are collected to validate the two true LAI data sets: MODIS LAI product and the integrated LAI values. The validation results suggest that the integrated results agree better with the LAI reference maps than the MODIS LAI product, showing higher R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , smaller bias and root mean squared error.