Inversion Of Leaf Area Index Research Articles

Improved maize leaf area index inversion combining plant height corrected resampling size and random forest model using UAV images at fine scale

ContextAccurate monitoring of leaf area index (LAI) is conducive to timely and targeted management measures. Unmanned aerial vehicle (UAV) remote sensing provides an important way for non-destructive monitoring of crop leaf area index. ObjectiveIn this study, visible light (RGB) and multispectral remote sensing data from the UAV and ground-measured LAI data from the Plant Canopy Analyzer LAI-2200 C were used to conduct inversion of maize LAI on a fine scale. MethodsTo address the problem of spatial scale mismatch between the spatial resolution of UAV images and the ground-measured LAI, the scale difference between UAV image data and ground-measured data was reduced by removing the outermost ring data measured by the LAI-2200 C instrument, calculating the spatial resolution of the UAV images after resampling based on the height of the plant, and the resampling method based on the circle. Finally, through the above method to resample the UAV images, we extract the vegetation index and canopy height features as the input variables of the random forest model to build the maize LAI inversion model in vegetative stages and reproductive stages respectively, which is referred to as the Vis_H+RF method. Results and conclusionsThe Vis_H+RF method of Tongliao experimental station has an R2 of 0.96 in the vegetative stages and a R2 of 0.61 in the reproductive stages, both of which perform well and have certain migration capabilities. SignificanceThe LAI inversion model constructed based on the method in this study is basically consistent with the actual situation and can provide data support for maize growth monitoring.

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

Assessment of ecosystem quality changes based on optimizing key indicators in Nanwenghe National Nature Reserve, Heilongjiang, China.

Net primary productivity (NPP) is an indicator to reflect the production capacity of terrestrial ecosystems, as well as a key indicator for ecological quality. NPP at large scale is difficult to be measured. At present, most of the assessment of ecosystem quality uses NPP products with low resolution, which cannot capture the detailed characteristics of the ecosystem and is not conducive to the assessment of ecosystem quality at small-scale. The establishment of a rapid and efficient assessment method for small-scale ecosystem quality will greatly promote the protection and restoration of ecosystems in China. We focused on the calculation method of ecosystem quality assessment and NPP, and optimized the calculation process of the NPP, and obtained NPP by remote sensing without ground observation data. We established a regression model for remote sensing inversion of leaf area index, and estimated the vegetation coverage by using dimidiate pixel model, developed a set of methods for rapid assessment of ecosystem quality by remote sensing. Taking Nanwenghe National Nature Reserve as an example, we evaluated the change of ecosystem quality from 2001 to 2022. The results showed that from 2001 to 2022, the ecosystem qua-lity of the reserve was good as a whole, and that the area with good and excellent quality accounted for more than 85% in 2022. High vegetation coverage was the backbone of the sustainable good ecosystem quality of the reserve. From 2001 to 2022, ecosystem quality of the reserve showed a trend of first decreasing and then increasing, with the lowest point of ecosystem quality in 2013. This method had achieved good results in the evaluation of ecosystem quality in Nanwenghe National Nature Reserve. The index optimization method proposed in this study could facilitate the rapid and accurate assessment of ecosystem quality in small-scale regions, and thus provides technical reference for the precise improvement of ecosystem quality.

Multiscale Inversion of Leaf Area Index in Citrus Tree by Merging UAV LiDAR with Multispectral Remote Sensing Data

The LAI (leaf area index) is an important parameter describing the canopy structure of citrus trees and characterizing plant photosynthesis, as well as providing an important basis for selecting parameters for orchard plant protection operations. By fusing LiDAR data with multispectral data, it can make up for the lack of rich spatial features of multispectral data, thus obtaining higher LAI inversion accuracy. This study proposed a multiscale LAI inversion method for citrus orchard based on the fusion of point cloud data and multispectral data. By comparing various machine learning algorithms, the mapping relationship between the characteristic parameters in multispectral data and point cloud data and citrus LAI was established, and we established the inversion model based on this, by removing redundant features through redundancy analysis. The experiment results showed that the BP neural network performs the best at both the community scale and the individual scale. After removing redundant features, the R2, RMSE, and MAE of the BP neural network at the community scale and individual scale were 0.896, 0.112, 0.086, and 0.794, 0.408, 0.328, respectively. By adding the three-dimensional gap fraction feature to the two-dimensional vegetation index features, the R2 at community scale and individual scale increased by 4.43% and 7.29%, respectively. The conclusion of this study suggests that the fusion of point cloud and multispectral data exhibits superior accuracy in multiscale citrus LAI inversion compared to relying solely on a single data source. This study proposes a fast and efficient multiscale LAI inversion method for citrus, which provides a new idea for the orchard precise management and the precision of plant protection operation.

Leaf area index estimation from the time-series SAR data using the AIEM-MWCM model

ABSTRACTThe leaf area index (LAI) inversion accuracy of the water cloud model (WCM) based on the SAR images is low. The main reason is that the WCM model assumes two independent contributions from plant and soil surfaces, without taking into account their interactions. Based on this, this study proposes an efficient LAI estimation method that combines the advanced integral equation model (AIEM) and modified water cloud model (MWCM). Images collected by GaoFen-3 synthetic aperture radar (GF-3 SAR) in the Xiangfu area in the east of Kaifeng City, Henan Province, are processed with a modified SAR-BM3D method and used as test data of the proposed method. The proposed AIEM-MWCM method that combines the AIEM and MWCM models is employed to perform the remote sensing inversion of winter wheat LAI throughout the growth cycle. The results show that the fitting accuracy of winter wheat LAI in the five growth stages achieved by the proposed AIEM-MWCM method inversion is better than that of the Dubois-MWCM model. The R2 value of the proposed method is higher than 0.8, and its root mean squared error (RMSE) is lower than 0.3.

Synergetic inversion of leaf area index and leaf chlorophyll content using multi-spectral remote sensing data

ABSTRACT Individual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We selected subtropical forest plantations, where canopy reflectance data were collected using a DJI Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) every month during 2021–2022. Monthly in-situ observations of LAI and Clumping Index (CI) were also made in 23 broadleaf tree plots of dimension 12 m × 12 m. Vegetation Indices (VI) were calculated with the mean reflectance of all pixels at 0.06 m resolution within each sampling plot, and only those VIs with highest sensitivities to LAI or LCC were selected and correlated to LAI and LCC. An empirical model in the form of VI = f(LAI, LCC) was constructed for synergetic inversion of LAI and LCC. For the purpose of comparison, two models VI = f(LAI) and VI = f(LCC) were also constructed and used for the inversions of LAI and LCC, separately. The synergetic inversion model yields R 2 = 0.60 and RMSE = 2.80 cm2/cm2 for LAI and R 2 = 0.45 and RMSE = 32.71 μg/cm2 for LCC, whereas the separate inversion models result in R 2 = 0.59 and RMSE = 2.82 cm2/cm2 for LAI and R 2 = 0.35 and RMSE = 35.86 μg/cm2 for LCC. Moreover, we found that the inclusion of VIs containing a red edge band in the synergetic inversion can effectively improve the inversion accuracy. The proposed synergetic inversion method based on multiple VIs would be an effective way to separate the mutual interference between LAI and LCC and improve the accuracy of LCC inversion from remote sensing data.

Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing

To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R2 of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R2 increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health.

Inversion of Leaf Area Index in Citrus Trees Based on Multi-Modal Data Fusion from UAV Platform

The leaf area index (LAI) is an important growth indicator used to assess the health status and growth of citrus trees. Although LAI estimation based on unmanned aerial vehicle (UAV) platforms has been widely used for field crops, mainly focusing on food crops, less research has been reported on the application to fruit trees, especially citrus trees. In addition, most studies have used single-modal data for modeling, but some studies have shown that multi-modal data can be effective in improving experimental results. This study utilizes data collected from a UAV platform, including RGB images and point cloud data, to construct single-modal regression models named VoVNet (using RGB data) and PCNet (using point cloud data), as well as a multi-modal regression model called VPNet (using both RGB data and point cloud data). The LAI of citrus trees was estimated using deep neural networks, and the results of two experimental hyperparameters (loss function and learning rate) were compared under different parameters. The results of the study showed that VoVNet had Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared (R2) of 0.129, 0.028, and 0.647, respectively. In comparison, PCNet decreased by 0.051 and 0.014 to 0.078 and 0.014 for MAE and MSE, respectively, while R2 increased by 0.168 to 0.815. VPNet decreased by 0% and 42.9% relative to PCNet in terms of MAE and MSE to 0.078 and 0.008, respectively, while R2 increased by 5.6% to 0.861. In addition, the use of loss function L1 gave better results than L2, while a lower learning rate gave better results. It is concluded that the fusion of RGB data and point cloud data collected by the UAV platform for LAI estimation is capable of monitoring citrus trees’ growth process, which can help farmers to track the growth condition of citrus trees and improve the efficiency and quality of orchard management.

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R2=0.67–0.89, RMSE=13.63–23.71 mg g–1, MAE=10.75–17.59 mg g–1) performed better than the direct inversion models (R2=0.61–0.80, RMSE=18.01–25.12 mg g–1, MAE=12.96–18.88 mg g–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R2=0.89, RMSE=13.63 mg g–1, MAE=10.75 mg g–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Inversion of the hybrid machine learning model to estimate leaf area index of winter wheat from GaoFen-6 WFV imagery

The leaf area index (LAI) is an important agroecological physiological parameter affecting vegetation growth and is an essential indicator for estimating crop growth. To apply the hybrid machine learning model to the remote sensing inversion of winter wheat LAI at three growth stages based on the wide field of view (WFV) of the GaoFen-6 (GF-6 WFV) images, the Xiangfu District in the east of Kaifeng City, Henan Province, was selected as the testing region. The LAI values, which were simulated using the CERES-wheat model, were an input for the PROSAIL model to simulate spectral reflectance. Different machine learning regression algorithms (MLRA) were trained with simulated spectra reflectance by PROSAIL and subsequently applied to the GF-6 WFV reflectance spectra. The random forest regression (RFR) model achieved reliable LAI estimates at the three growth stages. CERES-wheat and PROSAIL preserved the most informative spectra reflectance for LAI estimation so that each RFR could achieve satisfactory estimation results. The hybrid machine learning model could accurately reverse the growth state of winter wheat in three growth stages.

Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

ABSTRACT The leaf area index (LAI) is an important agroecological physiological parameter affecting vegetation growth. To apply the genetic algorithms neural network model (GANNM) to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar (GF-3 SAR) images and GaoFen-1 Wide Field of View (GF-1 WFV) images, the Xiangfu District in the east of Kaifeng City, Henan Province, was selected as the testing region. Winter wheat LAI data from five growth stages were combined, and optical and microwave polarization decomposition vegetation index models were used. The backscattering coefficient was extracted by modified water cloud model (MWCM), and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI. The root mean square error (RMSE) and determination coefficient (R 2) were used as evaluation indicators of the model. The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model; the R 2 was higher than 0.8, and RMSE was lower than 0.3, indicating that the model could accurately invert the growth status of winter wheat in five growth stages .

Inversion of leaf area index of winter wheat based on GF-1/2 image

Leaf area index (LAI) is an important parameter for monitoring crop growth, and an important input parameter for crop yield prediction model, hydrological and climatic models. LAI can be used in field crop growth monitoring and verification of remote sensing products. Therefore, accurate, rapid and large-scale estimation of LAI is not only conducive to better monitoring crops, but also conducive to its application in modeling, crop management and precision agriculture. Remote sensing technique has become a promising method to detect and monitor the crop LAI due to its many advantages. In this paper, Banjiequan Village, Wumachang Township, Qitai County, Xinjiang, China was selected as the study area. In order to rapidly and extensively retrieve LAI of winter wheat using domestic remote sensing images, 17 common vegetation indices were extracted from the GF-1/2 images, which was synchronized with field sampling, and observation at intervals of 20 m in the east-west direction and 30 m in the north-south direction in a 130 m × 420 m block. A total of 78 sampling points were taken from a small area of 4 rows × 50 cm. Sampling width was measured by tape ruler and coordinates of sampling points were given by GPS. Based on the vegetation indices extracted from GF-1/2 image and LAI data measured at erecting stage, jointing stage and flowering stage, we established univariate (linear, exponential, power, quadratic polynomials) and multivariate (partial least squares regression, PLSR) empirical models for inversion of winter wheat LAI, and validated them. The correlation coefficients of LAI with MSR (modified simple ratio), GNDVI (green normalized difference vegetation index), EVI (enhanced vegetation index) extracted from the erecting, jointing and flowering stages of GF-1 were the maximum, which were 0.708, 0.671, and 0.743, respectively, indicating that the correlation between these vegetation indices and LAI of winter wheat was significant. The univariate model R 2 based on MSRGF-1, NDVIGF-2 (normalized difference vegetation index), GNDVIGF-1 at jointing stage and EVIGF-1 at flowering stage were all greater than 0.7. Compared with different image data at the same growth stage, the quadratic polynomial model based on NDVIGF-2 and PLSR model based on NDVIGF-2, MSRGF-2, and SAVIGF-2 (soil-adjusted vegetation index) were more precise than those based on GF-1, with R 2 of 0.768 and 0.809 respectively. Compared with the models with the same data (GF-1) at different growth stages, the quadratic polynomial model based on GNDVIGF-1 at jointing stage and the PLSR model based on EVIGF-1, GSRGF-1 (green simple ratio) and NDVIGF-1 at flowering stage had the maximum value of R 2, which was 0.783. The RMSE of the PLSR model was smaller than that of the quadratic polynomial model, indicating the stability of the multivariate model was better than univariate model. Analyzing the LAI distribution maps inverted from different growth stages, it was found that the LAI inversion values basically coincided with the measured LAI values. The above results show that the domestic high-resolution remote sensing image has certain application value in crop physiological parameter inversion, and provides some references for related researches in the future.

Model inversion of BCVL based on DWD of MISR image

This paper puts forward a novel algorithm for the inversion of biophysical components of vegetation leaves (BCVL) by means of Remote Sensing (RS) image based on multi-scale two-dimension Discrete Wavelet Decomposition (DWD). With the aid of the MISR (multi-angle imaging spectro-radiometer) image, the multi-scale BCVL inversion is implemented with the algorithm on each scale of wavelet decomposition based on DWD of MISR image. The retrieved results of the rough-scale wavelet decomposition are regarded as a priori knowledge of the fine-scale inversion, and then the retrieved results of the rough-scale are also considered as the initial values of the fine-scale inversion. In this way, wavelet decomposition of MISR image is gradually refined, and the retrieved result is likewise gradually refined until the final retrieved result of BCVL is achieved in the end. The algorithm provides a novel solution scheme for the model inversion of BCVL with the aid of DWD of MISR image. Furthermore, this article also discusses the arithmetic principle of the model inversion of BCVL by employing the radiative transfer model of vegetation leaves (RTMV). On this basis, this manuscript builds the inversion model of BCVL based on RTMV. The proposed algorithm is applied specially to the model inversion of BCVL by using MISR image in this experiment, and satisfactory results are finally achieved. Taking the model inversion of LAI (leaf area index) as an example, the retrieved result of a higher precision is achieved in the experiment. Where, correlation coefficient between measured LAI and retrieved LAI is R=0.84, and root-mean-square error is RMSE=0.26. Experiment demonstrates that the proposed algorithm is very effectual and reliable in the model inversion of BCVL. The proposed algorithm opens up a novel algorithmic pathway for the model inversion of BCVL by means of DWD of MISR image.

Feasibility of using mobile phone to estimate forest Leaf Area Index: a case study in Yunnan Pine

ABSTRACTRemotely-sensed Leaf Area Index (LAI) is vital to describe the vegetation canopy and assess plant growth condition and healthy status. However, sufficient instant ground LAI samples are pre-required for calibration or validation, which is generally difficult to collect. We proposed a method, LAI-Mobile, to use mobile phones with low-cost fisheye lens to take fisheye photos and to invert LAIs, which may be popularized for ordinary people to generate big volume of LAI sample data. The feasibility of LAI-Mobile was tested by comparing with LAI−2200 and GF-1 satellite data (GF = high resolution) in a pest-invaded Yunnan pine forest area in Yunnan province of China. Results show significant correlation between LAI-2200 and LAI-Mobile data for forest plots with coefficient of determination (R2) = 0.706 and Root Mean Square Error (RSME) = 0.241, and GF-1 satellite images (R2 = 0.659 and RMSE = 0.268). The linear regression shows a good agreement between the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and the inverted GF-1 LAI, with R2 = 0.649, RMSE = 0.795. Despite larger uncertainty for single fisheye image than LAI-2200, LAI-mobile can provide fast and convenient method to collect large amount of LAI, which will support remote sensing inversion of LAI at large scale.

Inversion of leaf area index in Heihe Oasis based on CASI data

Inversion of leaf area index in Heihe Oasis based on CASI data

The Model Inversion of Leaf Area Index of Vegetation by Means of Electromagenetic Wave Radiative Transfer Model

This paper puts forward a novel approach for model inversion of leaf area index (LAI) of vegetation based on the integrated arithmetic of data assimilation and genetic-particle swarm algorithm (DAGS). The article expounds the design principle of electromagenetic wave radiative transfer model (ERTM) for vegetation canopies. On this basis, this study constructs the inversion model of LAI based on DAGS. Furthermore, this experiment realizes the model inversion of LAI with the aid of Remote Sensing (RS) multi-spectral data and biophysical component data of vegetation canopies, which are provided by the multispectral RS observation data set (MOD15A2). The bullet points of the text are summarized as follows. (1) The contribution proposes DAGS for LAI inversion. (2) The article discusses ERTM model for electromagenetic wave radiative transfer mechanism of vegetation canopies. (3) This text achieves LAI inversion with the help of RS multi-spectral data and biophysical component data of vegetation canopies supplied by MOD15A2. The experimental results demonstrate the validity and reliability of the model inversion of LAIby making use of DAGS. The proposed algorithm exploits a novel algorithmic pathway for the model inversion of LAI by means of RS multi-spectral data and biophysical component data of vegetation canopies.

Model Inversion of BBPV Based on DWD of MISR RS Image

This letter puts forward a novel algorithm for the inversion of biophysical and biochemical parameter of vegetation leaves (BBPV) by means of the multiscale two-dimension discrete wavelet decomposition (DWD) of remote sensing (RS) image. With the aid of a multiangle imaging spectroradiometer (MISR) RS image, the multiscale BBPV inversion is implemented with the algorithm on each scale of wavelet decomposition based on DWD of MISR RS image. The retrieved results of the rough-scale wavelet decomposition are regarded as a priori knowledge of the fine-scale inversion, and then, the retrieved results of the rough-scale wavelet decomposition are also regarded as the initial values of the fine-scale inversion. In this way, the wavelet decomposition of the MISR RS image is gradually refined, and the retrieved result is likewise gradually refined until optimal BBPV is achieved in the end. Furthermore, this letter also probes the algorithm principle of the model inversion of BBPV, by employing the radiative transfer model of vegetation leaves (RTMV). Taking the model inversion of leaf area index as an example, a satisfactory retrieval result of higher precision is finally obtained. The experiment demonstrates that the proposed algorithm is very effectual and reliable in the model inversion of BBPV. The proposed algorithm opens up a novel algorithmic pathway for the model inversion of BBPV by means of DWD of MISR RS image.

Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index

This study proposes a new method for inverting radiative transfer models to retrieve canopy biophysical parameters using remote sensing imagery. The inversion procedure is improved with respect to standard inversion, and achieves simultaneous inversion of leaf area index (LAI), soil reflectance (ρsoil), chlorophyll content (Ca+b) and average leaf angle (ALA). In this approach, LAI is used to constrain modelling conditions during the inversion process, providing information about the phenological state of each plot under study. Due to the small area of the vegetation plots used for the inversion procedure and in order to avoid redundant information and improve computation efficiency, existing plot segmentation was used. All retrieved biophysical parameters, except LAI, were assumed to be invariant within each plot. The proposed methodology, based on the combination of PROSPECT and SAILH models, was tested over 16 cereal fields and 51 plots, on two dates, which were chosen to ensure crop assessment at different phenological stages. Plots were selected to provide a wide range of LAI between 0 and 6. Field measurements of LAI, ALA and Ca+b were conducted and used as ground truth for validation of the proposed model-inversion methodology. The approach was applied to very high spatial resolution remote sensing data from the QuickBird 2 satellite. The inversion procedure was successfully applied to the imagery and retrieved LAI with R 2 = 0.83 and RMSE = 0.63 when compared to LAI2000 ground measurements. Separate inversions for barley and wheat yielded R 2 = 0.89 (RMSE = 0.64) and R 2 = 0.56 (RMSE = 0.61), respectively.

A Quantitative Method for Grassland LAI Inversion Based on CHRIS/PROBA Data

Based on the CHRIS/PROBA hyperspectral remote sensing data,the authors retrieved the leaf area index(LAI) by A two-layer Canopy Reflectance Model(ACRM).The process consists of three main steps: Firstly,the high-spectral data was preprocessed and statistically analyzed.Secondly,sensitivity of the model to observing directions was analyzed.And finally,the best combinations of bands and parameters for the study area were chosen.The process was used to study the LAI of typical grass plots of Xilin River basin in Inner Mongolia.The results show that the application of CHRIS/PROBA data to the inversion of sparse grassland LAI is practical,and the multi-angle information of CHRIS/PROBA data has the potential advantages in decreasing the extent of LAI underestimation.

Inversion of leaf area index based on small-footprint waveform airborne LIDAR

In order to study the detection of vertical structure information of vegetation, a simulation of LIDAR return waveform based on the single scattering of crown reflectance model and the Gaussian character of incidence and return waveform was proposed. The corresponding inversed method was established to calculate the crop leaf area density and leaf area index. The sensitivity analysis illustrated G function had a larger impact on inversed results than soil and leaf reflectivity. Finally, the data of Water (Watershed Airborne Telemetry Experimental Research) was used to validate this method. The results showed that the inversed leaf area density was consistent with the field measurement data, and the relative error of the inversed LAI was 12.5%. Therefore, this method can provide a new way to detect the vegetation structural parameters.