Retrieval Of Vegetation Parameters Research Articles

Influence of different soil reflectance schemes on the retrieval of vegetation LAI and FVC from PROSAIL in agriculture region

The PROSAIL model is widely used to retrieve vegetation parameters from remote sensing data in agriculture regions. Soil reflectance is a key input to the PROSAIL model, but its influence on vegetation parameters retrieval accuracy of crops is rarely discussed. Therefore, this study investigated the influence of different soil reflectance schemes on leaf area index (LAI) and fractional vegetation cover (FVC) retrieval accuracy based on the PROSAIL model in an agriculture region. Firstly, three sources of soil reflectance including ICRAF-ISRIC soil spectral library (SR_SSL), general spectral vectors model (SR_GSV), and ASD spectroradiometer measurement (SR_ASD) and two reflectance extraction schemes were used to generate input soil reflectance for the PROSAIL model to simulate canopy reflectance. Then, the LAI and FVC retrieval models were developed using the random forest algorithm and validated using field survey data. Determination coefficient (R2), Root Mean Square Error (RMSE) and normalized RMSE (NRMSE) were used to evaluate the accuracy of LAI and FVC retrieval. Under the direct extraction scheme, the LAI retrieval based on SR_ASD (R2 = 0.78, RMSE = 0.613, NRMSE = 0.269) achieved better performance than SR_GSV (R2 = 0.73, RMSE = 0.671, NRMSE = 0.294) and SR_SSL (R2 = 0.71, RMSE = 0.762, NRMSE = 0.334), whereas the performances of FVC retrieval were comparable for SR_ASD (R2 = 0.91, RMSE = 0.084, NRMSE = 0.136), SR_GSV (R2 = 0.90, RMSE = 0.086, NRMSE = 0.139) and SR_SSL (R2 = 0.89, RMSE = 0.091, NRMSE = 0.147). The influence of soil reflectance on LAI retrieval is larger than that of FVC in this study. Furthermore, soil reflectance was more important for canopies characterized by low LAI and FVC. In addition, the combination of SR_SSL and multiplication coefficients scheme could be conveniently used for large areas vegetation parameters retrieval. While SR_GSV and SR_ASD with direct extraction scheme was suitable for small areas with field survey data and more homogeneous region.

A Normalized Spectral Angle Index for Estimating the Probability of Viewing Sunlit Leaves From Satellite Data

The probability of viewing sunlit leaves (PT) is a crucial variable influencing observed canopy spectra. Proper determination of PT is necessary for the quantitative retrieval of vegetation parameters using remote sensing. This paper describes a spectral index for estimating PT from satellite-observed canopy spectra. For this purpose, we propose a normalized spectral angle index (NSAI) at near-infrared (NIR) wavelengths, based on the spectral shapes of leaf and soil background. The performance of NSAI in estimating PT was evaluated using one ground-based high-resolution imaging dataset, one synthetic satellite dataset, and one satellite-ground synchronous observation dataset. The results demonstrate that NSAI is more suitable for estimating PT from satellite data than five commonly used spectral indices, including Enhanced Vegetation Index (EVI), Normalized Difference Spectral Index (NDSI), Normalized Difference Vegetation Index (NDVI), Simple Ratio Index (SR), and Photochemical Reflectance Index (PRI). NSAI exhibits a significant linear correlation with PT. The empirical model for estimating PT based on NSAI has the best transferability from simulated to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> satellite data. For the fine spectral-spatial resolution (Hyperion) data, the normalized Root Mean Square Error (nRMSE) and adjusted R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of estimated PT were 14.9% and 0.744, respectively. For MODIS images, PT was estimated with satisfactory accuracy, with an nRMSE of 18.71% and an adjusted R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.670. NSAI is potentially applicable to satellite images for direct estimation of PT to improve the inversion accuracy of vegetation parameters.

Fast and Accurate Simulation of Canopy Reflectance under Wavelength-Dependent Optical Properties Using a Semi-Empirical 3D Radiative Transfer Model

Generating canopy-reflectance datasets using radiative transfer models under various leaf and soil optical property combinations is important for remote sensing retrieval of vegetation parameters. One-dimensional radiative transfer models have been frequently used. However, three-dimensional (3D) models usually require detailed 3D information that is difficult to obtain and long model execution time, limiting their use in remote sensing applications. This study aims to address these limitations for practical use of 3D models, proposing a semi-empirical speed-up method for canopy-reflectance simulation based on a LargE-Scale remote sensing data and image Simulation model (LESS), called Semi-LESS. The speed-up method is coupled with 3D LESS to describe the dependency of canopy reflectance on the wavelength, leaf, soil, and branch optical properties for a scene with fixed 3D structures and observation/illumination configurations, allowing fast generating accurate reflectance images under various wavelength-dependent optical parameters. The precomputed dataset stores simulated multispectral coefficient images under few predefined soil, branch, and leaf optical properties for each RAdiation transfer Model Intercomparison-V scene, which can then be used alone to compute reflectance images on the fly without the participation of LESS. Semi-LESS has been validated with full 3D radiative-transfer-simulated images, showing very high accuracy (root mean square error &lt; 0.0003). The generation of images using Semi-LESS is much more efficient than full LESS simulations with an acceleration of more than 320 times. This study is a step further to promote 3D radiative transfer models in practical remote sensing applications such as vegetation parameter inversions.

Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content

The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval.

Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images

The canopy bidirectional reflectance distribution function (BRDF) plays a pivotal role in estimating the biophysical parameters of plants, whereas soil background anisotropy creates challenges for their retrieval. Soil optical properties affect canopy anisotropic characteristics, especially in open-canopy areas. However, the remote sensing of background anisotropy is challenging due to the difficulties of information extraction in complex forest ecosystems and varying illumination conditions. This study develops an efficient photogrammetric technique to extract the background soil bidirectional reflectance factor (BRF) from unmanned aerial vehicle (UAV)-based multiangular images and to verify the need for accurate soil anisotropy information in canopy radiative transfer modeling. Soil BRF profiles were measured over three open-canopy sample plots from multiangular remotely sensed multispectral images collected with a hexacopter. As validation, reference soil BRF profiles were synchronously acquired by a ground-based multiangular imaging system.A high level of consistency between the ground- and UAV-measured soil BRF was observed with an RMSE of less than 0.012. Uncertainty analysis of the measured soil BRF showed that multiple scattering between sunlit soil in large sunflecks and foliage elements contributed less than 5%. Both results demonstrated that soil anisotropy can be accurately extracted from UAV multiangular measurements. To explicitly demonstrate that the use of soil anisotropy can reduce uncertainties in canopy radiative transfer simulations, we simulated the canopy BRF with Lambertian soil and with anisotropic soil using a three-dimensional (3D) radiative transfer model under different soil moisture content (SMC) levels, canopy cover (CC) levels and solar zenith angles (SZAs) with simulated realistic forest scenes. We found that less CC, lower SZAs and less SMC lead to a more significant influence of soil anisotropy on canopy reflectance; e.g., the reflectance bias reaches up to 0.3 in the hotspot direction. This illustrates that neglecting soil anisotropy can cause considerable errors in the modeling of the canopy BRF of open forests (i.e., CC levels of less than 0.5). The proposed technique facilitates the characterization of anisotropic forest background soil, which is important for advancing canopy radiative transfer modeling and validation and for the retrieval of vegetation parameters.

Mapping properties of vegetation in a tidal salt marsh from multi-spectral satellite imagery using the SCOPE model

ABSTRACT Quantitative remote sensing of vegetation biophysical and biochemical parameters through the inversion of physical models is of great significance for monitoring vegetative growth, ecosystem function, and the global carbon and nitrogen cycles. However, mapping and comparing vegetation parameters in a tidal saltmarsh based on multi-spectral satellite imagery has been rarely documented. In this study, a coupled model (Soil-Canopy-Observation of Photochemistry and Energy fluxes, SCOPE) was used to quantitatively invert the spatial patterns of key parameters such as the leaf area index (LAI), leaf chlorophyll content (C ab), and fraction of absorbed photosynthetically active radiation (fPAR) of salt marsh vegetation from Land Remote-Sensing Satellite 8 (Landsat-8), Sentinel-2A, and RapidEye multispectral satellite imagery. The results show that RapidEye outperformed Landsat-8 and Sentinel-2A in the retrieval of LAI and C ab, with higher coefficients of determination (R 2 = 0.539 and 0.488, respectively) and lower normalized root mean square errors (NRMSE = 0.32 and 0.28, respectively). Without the red-edge band, the R 2 between the modelled and observed LAI and C ab for RapidEye decreased by 27.83% and 5.33%, respectively, while the NRMSE increased by 3.13% and 46.43%, respectively, highlighting the important role of the red-edge band in vegetation parameter retrieval. Tidal flooding resulted in poor retrieval results from Sentinel-2A. However, the accuracy of C ab inversion was significantly improved by removing the sampling sites near tidal trenches or in low-lying areas, indicating that tidal inundation exerted a notable effect on the retrieval of saltmarsh vegetation parameters when using multi-spectral images. In addition, the spatial pattern of the simulated fPAR from RapidEye is consistent with that of the gross primary productivity. This study provides an alternative method for estimating ecosystem functions in a tidal saltmarsh wetland, as well as for large-scale and high-precision monitoring of wetland restoration that is beneficial for the ecological protection of coastal zones.

Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data

With the free and full access to images from Sentinel-2 satellite, the interest to use this data for quantitative retrieval of vegetation parameters is ever-increasing. LAI and chlorophyll are two key variables which are desired for studying productivity, nutrient and stress status of vegetation. Studies carried out on croplands using simulated Sentinel-2 MSI and parametric approach have identified vegetation indices (VIs) with high sensitivity to LAI and chlorophyll. To test how Sentinel-2 red-edge based VIs perform for retrieval of LAI and Chlorophyll of tropical mixed forest canopies, this study has been performed. The field measurements of LAI and chlorophyll content were recorded in a total of 28 ESUs (Elementary Sampling Units) in Bhakra range in the Tarai Central Forest Division, Uttarakhand (India). The in-situ measurements were statistically correlated with Sentinel-2VIs and strength of correlation was validated using Predicted Residual Error Sum of Squares (PRESS) statistic. Field LAI corrected for foliage clumpiness effect improved correlation of VIs with LAI. Among all VIs tested, Normalized Difference Index (NDI) offered highest positive correlation (R2 = 0.79, p < 0.05) with LAI while Red-Edge Chlorophyll Index (RECI) (R2 = 0.83, RMSE = 0.24 g/m2, p < 0.05) and Simple Ratio (SR) 740/705 (R2 = 0.79, RMSE = 0.27 g/m2, p < 0.05) were the most closely related to chlorophyll content. VIs with red-edge and NIR combinations offered best results.

Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth

An important application of airborne- and satellite-based hyperspectral imaging is the mapping of the spatial distribution of vegetation biophysical and biochemical parameters in an environment. Statistical models, such as Gaussian processes, have been very successful for modeling vegetation parameters from captured spectra, however their performance is highly dependent on the amount of available ground truth. This is a problem because it is generally expensive to obtain ground truth information due to difficulties and costs associated with sample collection and analysis. In this paper, we present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data owing to their resilience to spectral variabilities. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. We experimentally demonstrate the efficacy of the proposed methods against existing approaches on three real-world hyperspectral datasets and one synthetic dataset.

Improved Three-Component Decomposition Technique for Forest Parameters Estimation from PolInSAR Image

Polarimetric SAR interferometry (PolInSAR) is an efficient remote sensing technique that allows to extract forest heights by means of model-based inversion. Recently, there have been plenty of researches on the retrieval of vegetation parameters by single frequency single baseline PolInSAR, such as the ESPRIT method and three-stage inversion method. However, these methods have several shortcomings which tend to underestimate the forest height due to attenuations of the electromagnetic waves in the ground medium. In order to overcome these shortcomings, an improved three-component decomposition technique using PolInSAR image is proposed in this paper. By means of coherence set and a Newton-Raphson method, the proposed method improves the accuracy of forest height estimation. The proposed algorithm performance is evaluated with simulated data from PolSARProSim software and L-band PolInSAR image pair of Tien-Shan test site which is acquired by the SIR-C/X-SAR system.

TREE CANOPY HEIGHT ESTIMATION USING MULTI BASELINE RVOG INVERSION TECHNIQUE

Abstract. Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technique utilizes the characteristics of both SAR polarimetry and Interferometry. PolInSAR technique is proved to be very useful for vegetation parameters retrieval. Estimation of the tree canopy height parameter is very important for the estimation of the Above Ground Biomass (AGB). The baseline separation between different PolInSAR datasets has a very important role in the tree canopy height estimation due to the sensitivity of the baseline to the tree height and the forest structure. So for accurately estimating the tree canopy height of a forest with varying tree heights and species several pairs of PolInSAR datasets with different baselines separations are required. Multi-baseline Random Volume over Ground (RVoG) inversion technique is the most successful method for tree height inversion. UAVSAR, the Quad-Pol L-band airborne SAR of JPL/NASA acquired PolInSAR datasets over the Gabon forest as a part of the AfriSAR campaign. Nine PolInSAR SLC datasets of this campaign acquired over the Mondah Forest site of Gabon forest is used for this study. Tree canopy height map produced from this datasets shows that the tree height is varying at this site and has a maximum height of 50 m. The results obtained are validated using the field data collected by JPL/NASA during March 2016. The comparison of the results with the field data showed that both are in good agreement with an average deviation of 3.75 m.

A shadow- eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains

ABSTRACTThe effect of terrain shadow, including the self and cast shadows, is one of the main obstacles for accurate retrieval of vegetation parameters by remote sensing in rugged terrains. A shadow- eliminated vegetation index (SEVI) was developed, which was computed from only red and near-infrared top-of-atmosphere reflectance without other heterogeneous data and topographic correction. After introduction of the conceptual model and feature analysis of conventional wavebands, the SEVI was constructed by ratio vegetation index (RVI), shadow vegetation index (SVI) and adjustment factor (f (Δ)). Then three methods were used to validate the SEVI accuracy in elimination of terrain shadow effects, including relative error analysis, correlation analysis between the cosine of solar incidence angle (cosi) and vegetation indices, and comparison analysis between SEVI and conventional vegetation indices with topographic correction. The validation results based on 532 samples showed that the SEVI relative errors for self and cast shadows were 4.32% and 1.51% respectively. The coefficient of determination between cosi and SEVI was only 0.032 and the coefficient of variation (std/mean) for SEVI was 12.59%. The results indicate that the proposed SEVI effectively eliminated the effect of terrain shadows and achieved similar or better results than conventional vegetation indices with topographic correction.

Image Enhancement through Denoising and Retrieval of Vegetation Parameters from Landsat8

This paper proposed the enhancement of Landsat8 imagery through an Un-decimated Dual-Tree Complex Wavelet Transform (UDT-CWT) based denoising method and modified homographic filter for edge preservation. This work has been extended by estimating several vegetation parameters like Normalized Difference of Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Modified Soil Adjusted Vegetation Index (MASVI), and Soil & Atmospherically Resistant Vegetation Index (SARVI). Once the estimation of these parameters was done, the effect of noise was verified. Wavelet decomposes the image into frequency subbands and de-noises each subband separately. These subbands help to increase the resolution. The general problem of the homomorphic filter is that it doesn’t enhance the Low-frequency components which also play a key role in estimating Vegetation Indices (VI).So it was modified to enhance the high-frequency components as well as low-frequency details. Monitoring of vegetation parameters using remote sensing is one of the prominent ways in the estimation of crop yield, Land Use Land Cover (LULC), Water resource management, Drought management, etc. The high-resolution image is more preferable than moderate resolution image to retrieve VI. Image denoising and enhancing the spatial resolution helps to retrieve the parameters well and accurate. The proposed algorithm was working on the images of Landsat8.

Simulating the Effects of the Airborne Lidar Scanning Angle, Flying Altitude, and Pulse Density for Forest Foliage Profile Retrieval

Foliage profile is a key biophysical parameter for forests. Airborne Light Detection and Ranging is an effective tool for vegetation parameter retrieval. Data acquisition conditions influence the estimation of biophysical parameters. To acquire accurate foliage profiles at the lowest cost, we used simulations to explore the effects of data acquisition conditions on forest foliage profile retrieval. First, a 3-D forest scene and the airborne small-footprint full-waveform LiDAR data were simulated by the DART model. Second, the foliage profile was estimated from LiDAR data based on a Geometric Optical and Radiative Transfer model. Lastly, the effects of the airborne LiDAR scanning angle, flying altitude, and pulse density on foliage profile retrieval were explored. The results indicated that the scanning angle was an important factor in the foliage profile retrieval, and the optimal scanning angle was 20°. The optimal scanning angle was independent of flying altitude and pulse density, and combinations of multiple scanning angles could improve the accuracy of the foliage profile estimation. The flying altitude and pulse density had little influence on foliage profile retrieval at plot level and could be ignored. In general, our study provides reliable information for selecting the optimal instrument operational parameters to acquire more accurate foliage profiles and minimize data acquisition costs.

Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy

Non-photosynthetic components within the canopy (e.g., dry leaves and stem) contribute little to photosynthesis and therefore, remote sensing of gross primary production (GPP) could be improved by the removal of these components. A scaled enhanced vegetation index (EVI), which is usually regarded as a linear function of EVI, was found to have the strongest relationship with chlorophyll level fraction of absorbed photosynthetically active radiation (FPARchl) and can help improve GPP estimation in croplands compared to canopy level FPAR (FPARcanopy). However, the application of the FPARchl theory to other plant functional types (PFTs) and the underlying reasons remain largely unknown. In this study, based on standard MODIS algorithm we comprehensively assessed the performances of FPARcanopy, scaled EVI (FPARchl1), normalized difference vegetation index (NDVI), scaled NDVI (FPARchl2) and EVI as proxies of FPAR for estimating GPP at four forest and six non-forest sites (e.g., grasslands, croplands and wetlands) from ChinaFLUX, representing a wide range of ecosystems with different canopy structures and eco-climatic zones. Our results showed that the scaled EVI (FPARchl1) as FPAR effectively improved the accuracy of estimated GPP for the entire PFTs. FPARchl1 substantially improved forest GPP estimations with higher coefficient of determination (R2), lower root mean square error (RMSE) and lower bias. In comparison, for non-forest PFTs, the improvement in R2 between estimated GPP based on FPARchl1 (GPPchl1) and flux tower GPP was less evident than those between flux GPP and GPP estimations from FPARcanopy (GPPcanopy), FPARchl2, NDVI and EVI. The temperature and water attenuation scalars played important roles in reducing the difference of various GPP and indirectly reducing the impact of different FPARs on GPP in non-forest PFTs. Even so, FPARchl1 is an ecologically more meaningful parameter since FPARchl1 and flux tower GPP dropped to zero more synchronously in both forest and non-forest sites. In particular, we found that the improvement of GPPchl1 relative to GPPcanopy was positively correlated with the maximum leaf area index (LAI), implying the importance of site characteristic in regulating the strength of the improvement. This is encouraging for remote sensing of GPP for which vegetation parameter retrieval has often been found to be less successful at high LAI due to saturations in reflective and scattering domains. Our results demonstrate the significance of accurate and ecologically meaningful FPAR parameterization for improving our current capability in GPP modeling.

INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

Abstract. Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS

Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. &amp;lt;br&amp;gt;&amp;lt;br&amp;gt; This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. &amp;lt;br&amp;gt;&amp;lt;br&amp;gt; The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.

Leaf Area Index Inversion of Winter Wheat Using Modified Water-Cloud Model

The inversion of vegetation parameters using microwave remote sensing is usually affected by the heterogeneous distribution of vegetation, sparse vegetation cover, and bare soil, which leads to unsatisfactory results in parameter estimation of agricultural applications. In this letter, in order to solve the problem of surface vegetation parameter retrieval by using microwave remote sensing, a modified water-cloud model (WCM) was developed to retrieve leaf area index (LAI) by adding vegetation coverage and direct effect of bare soil on the total backscatter coefficients, which fully took into account the distribution of vegetation cover. The modified model was validated between the simulated backscatter coefficients and measurements based on ground observations and RADARSAT-2 data in China. Then, a look-up table algorithm was applied to calculate the value of vegetation water content and retrieve LAI according to a linear relationship between vegetation water content and LAI. Results indicated that the modified model was more sensitive to vegetation condition and the estimation accuracy was higher than that of the original WCM. $R^{2}$ and rmse were 85.0% and 0.918 dB in HH polarization, and 73.9% and 1.475 dB in VV polarization, respectively. Meanwhile, the modified model could separate the scattering influences produced by the vegetation cover and bare soil components on the backscatter coefficients effectively. The accuracy of LAI retrieval was significantly high with $R^{2}$ and rmse of 84.1% and 0.233 m2/m2, respectively. This method will provide support for estimating LAI of winter wheat by using radar data in a wide range.

A Bayesian Network-Based Method to Alleviate the Ill-Posed Inverse Problem: A Case Study on Leaf Area Index and Canopy Water Content Retrieval

Retrieval of vegetation parameters from remotely sensed data using a radiative transfer model is generally hampered by the ill-posed inverse problem, which dramatically decreases the precision level of retrieved parameters. The purpose of this study was to use a Bayesian network-based method to allow the alleviation of the ill-posed inverse problem. This was achieved by introducing the correlations between the model free parameters into their prior joint probability distribution (PJPD), allowing the reduction of the probabilities of unrealistic combinations. Three sampling strategies intended to design three types of PJPDs that considered different correlations (represented by a correlation matrix) were presented. They were multivariate uniform distribution composed by independent free parameters, multivariate uniform distribution based on a simple correlation matrix, and multivariate Gaussian distribution based on a complicated correlation matrix, respectively. A case study of the presented method to retrieve leaf area index (LAI) and canopy water content (CWC) using the PROSAIL_5B (PROSPECT-5 + 4SAIL) model from Landsat 8 products was implemented. Results indicate that the presented method greatly improves the precision level of target parameters, with the coefficient of determination $R^2$ of 0.69, 0.77, and 0.82 and root-mean-square error (RMSE) of 0.55, 0.51, and 0.44 $\mbox{m}^{2}\cdot\mbox{m}^{-2}$ for LAI and $R^{2}=0.68, 0.78, \mbox{and} 0.84$ and $\mbox{RMSE}=230, 198, \mbox{and} 166 \mbox{g}\cdot\mbox{m}^{-2}$ for CWC, respectively. Hence, the ill-posed inverse problem can be alleviated by the presented method, which can be widely applied for vegetation parameters retrieval.

基于数码相片Gamma校正的水稻叶面积指数估算

PDF HTML阅读 XML下载 导出引用 引用提醒 基于数码相片Gamma校正的水稻叶面积指数估算 DOI: 10.5846/stxb201211231660 作者: 作者单位: 南京信息工程大学, 气象灾害省部共建教育部重点实验室,南京信息工程大学 遥感学院,南京信息工程大学 遥感学院,南京晓庄学院 生物化工与环境工程学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展计划（2010CB950701）；国家自然科学基金（41071281）；江苏省高校优势学科建设工程资助项目 Estimation of paddy rice leaf area index based on photo gamma correction Author: Affiliation: Key Laboratory of Meteorlogical Disaster of Ministry of Education, Nanjing University of Information Science Technology,Nanjing University of Information Science Technology,Nanjing University of Information Science Technology,School of Bio-Chemical and Environmental Engineering,Nanjing Xiaozhuang University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:随着数码相机的日益普及，利用数码相机进行作物叶面积指数（LAI）测量不断得以应用。由于数码相机成像时会对入射光辐射强度进行Gamma编码变换，输出的相片DN（Digital Number）值与入射光辐射强度呈非线性关系，会造成在确定相片中植被叶片与背景的分割阈值时出现误差，并最终导致LAI估算存在较大不确定性。以水稻为研究对象，获取不同生长期水稻冠层相片并结合同步LAI 2000测量的LAI数据，基于相片Gamma校正原理，对水稻不同生长期冠层相片进行Gamma校正，在此基础上利用冠层孔隙率方法，估算不同生长期水稻LAI。结果表明，经过Gamma校正相片估算的水稻LAI总体精度有显著提高，相片估算的IMAGE LAI与LAI-2000测量值比较的决定系数达到0.71（P < 0.05）。在整个观测期内，两种方法观测的LAI值在时间变化趋势上表现一致，但在不同生长期内存在差别，在水稻分蘖期和拔节期相片估算的IMAGE LAI要高于LAI-2000测量值，孕穗期到抽穗期期间IMAGE LAI低于LAI-2000测量值，乳熟期到成熟期IMAGE LAI又高于LAI-2000的观测结果。 Abstract:The automatic compensation effect in a digital camera can cause a bias of the digital number (DN) of the acquired images. This study carried out DN correction using gamma transformation theory combined with a look-up table method. The DN of the acquired image generally bears a nonlinear relationship with the intensity of incident light, which is an inherent characteristic in common commercial digital cameras. The gamma method is a nonlinear operation used to code and decode luminance or tri-stimulus values in video or still image systems. It has been widely used in photography and video productions, but is rarely used in image processing and applications to vegetation parameter determination. The gamma transformation is mainly used to compensate for the properties of human vision by maximizing the use of the bits or bandwidth relative to the perception of light and color. If images are not gamma encoded, they allocate too many bits or too much bandwidth to the darker areas that humans cannot differentiate and too few bits to the brighter areas that humans are sensitive to, and would thus require more bits to maintain the same visual quality. Although the visual quality of RAW image is generally not very appropriate in commercial use, the RAW image is precisely required in the retrieval of vegetation parameters to obtain a better fit with field measurements. Apart from the gamma correction, for the various annual paddy rice conditions we used the vertical gap fraction to obtain the leaf area index (LAI), which we named image LAI, and compared with the field LAI from LAI-2000. The results indicated that the correlation coefficients after the correction procedure were remarkably similar with an R2 of 0.71 (P < 0.05), better than for the uncorrected retrieved figures. In the tilling and jointing stages, there was a close approximation between the vertical gap fraction derived LAI and field LAI, but in the booting and heading stages the differences become obvious, reaching a maximum of 0.38. However, in the milk-ripened and maturation stages, the reverse occurred; the field LAI was higher than the vertical gap fraction derived-LAI mainly because the foliage of the rice paddy went yellow. In general, there was a good fit between the retrieved LAI and field LAI, but the estimation accuracy varied depending on the phenology of paddy rice. A better fit between image LAI and LAI 2000 was observed in the tilling and jointing stages, when a larger bias between image LAI and field LAI was observed in the booting and heading stages, while an overestimation in image LAI was observed in the milk-ripened and maturation stages. The commercial digital camera can carry out easy and low cost research given its characteristic properties (ease of use, less costly, and lack of atmospheric effects). These features enable anyone to carry out good estimation work related to vegetation. These factors and many related articles make the digital camera a useful alternative method for assessing vegetation parameters such as vegetation fraction, leaf color, plant type and leaf area index. 参考文献 相似文献 引证文献

Neural network method to solve inverse problems for canopy radiative transfer models

Vegetation parameter retrieval is considered as the inverse of modeling canopy radiative transfer. To solve this problem, a new computationally efficient method based on mixture density networks (MDNs) is proposed to estimate the errors of retrieved parameters for each given set of reflectances. The properties of neural networks of traditional architecture and MDNs are considered. The method is tested using a simple model and the PROSPECT leaf radiative transfer model and is validated against real data.