Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

Functional Plant Biology ◽

10.1071/fp08045 ◽

2008 ◽

Vol 35 (10) ◽

pp. 1070 ◽

Cited By ~ 46

Author(s):

Sigfredo Fuentes ◽

Anthony R. Palmer ◽

Daniel Taylor ◽

Melanie Zeppel ◽

Rhys Whitley ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Gap Analysis ◽

Digital Camera ◽

Field Measurements ◽

Accurate Estimation ◽

Image Capture ◽

Area Index ◽

Modis Lai ◽

Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

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Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Remote Sensing ◽

10.3390/rs12132148 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2148 ◽

Cited By ~ 1

Author(s):

Adnan Rajib ◽

I Luk Kim ◽

Heather E. Golden ◽

Charles R. Lane ◽

Sujay V. Kumar ◽

...

Keyword(s):

Water Quality ◽

Soil Moisture ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Dynamics ◽

Watershed Modeling ◽

Remotely Sensed ◽

Area Index ◽

Daily Streamflow ◽

Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

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Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

Remote Sensing ◽

10.3390/rs11212517 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2517 ◽

Cited By ~ 2

Author(s):

Huaan Jin ◽

Weixing Xu ◽

Ainong Li ◽

Xinyao Xie ◽

Zhengjian Zhang ◽

...

Keyword(s):

Time Series ◽

Leaf Area Index ◽

Leaf Area ◽

Landsat 8 ◽

Area Index ◽

Spatio Temporal ◽

Fine Resolution ◽

Modis Lai ◽

Temporal Profiles ◽

Reflectance Data

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.

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Field‐scale Leaf Area Index estimation using IRS‐1D LISS‐III data

International Journal of Remote Sensing ◽

10.1080/01431160500262620 ◽

2006 ◽

Vol 27 (4) ◽

pp. 637-644 ◽

Cited By ~ 9

Author(s):

Sasmita Chaurasia ◽

Bimal K. Bhattacharya ◽

Vinay K. Dadhwal ◽

Jai Singh Parihar

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Field Scale ◽

Area Index ◽

Liss Iii ◽

Index Estimation

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Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

Remote Sensing ◽

10.3390/rs14010148 ◽

2021 ◽

Vol 14 (1) ◽

pp. 148

Author(s):

Yang Chen ◽

Lixia Ma ◽

Dongsheng Yu ◽

Kaiyue Feng ◽

Xin Wang ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Complex Terrain ◽

Absolute Error ◽

Sensor Data ◽

Area Index ◽

Subtropical Forests ◽

Optical Images ◽

The Status ◽

Elevation Model

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

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Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia

10.32920/14669124 ◽

2021 ◽

Author(s):

Yonghua Qu ◽

Ahmed Shaker ◽

Carlos Alberto Silva ◽

Carine Klauberg ◽

Ekena Rangel Pinagé

Keyword(s):

Tropical Forest ◽

Leaf Area Index ◽

Leaf Area ◽

Tropical Forests ◽

Airborne Lidar ◽

Lidar Data ◽

Area Index ◽

Dense Forest ◽

Modis Lai ◽

Airborne Lidar Data

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.

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