由光谱指数估算小麦截取光合有效辐射的数量

引用小麦叶面指数及其截取光合有效辐射数量之间的理论公式和经验公式，由实测的小麦叶面指数计算出它截取光合有效辐射的数童，再分别求出小麦的各种光谱指数和它截取光合有效辐射数量间的线性回归方程。得出的结论是，利用光谱指数PVI，并使用线性方程PAR=78.6PVI+55.2来估算小麦截取光合有效辐射数童是最可靠的。     

基于环境星HSI影像的草地叶面积指数反演

叶面积指数(Leaf Area Index,LAI)是定量研究陆地生态系统物质和能量交换的一个重要结构参数,具有重要的研究意义。针对HJ\|1A卫星HSI数据,利用野外实测LAI值,探讨利用HJ1A星HSI数据反演叶面积指数的可行性。选用比值植被指数(RVI)、归一化植被指数(NDVI)及改良型土壤调整植被指数(MSAVI)3种植被指数,与实测叶面积指数进行回归分析,构建回归模型。研究结果表明,基于影像提取的RVI、NDVI和MSAVI 3种植被指数均与叶面积指数有较好的定量关系。其中,MSAVI的拟合结果最优,其回归确定性系数为0.622。验证模型的确定性系数为0.547,均方根误差RMSE为0.202,说明实测和模拟LAI值之间具有较好的变化一致性。最后基于HJ1A星HSI影像和MSAVI的估测模型生成研究区叶面积指数空间分布图。     

杨树林叶面积稳定期的叶面积指数遥感估算方法

针对用归一化差值植被指数(Normalized Difference Vegetation Index,NDVI)估算植被叶面积指数(Leaf Area Index,LAI)不仅需要大量地面LAI观测及其数据统计,且在植被NDVI饱和时难以估算LAI等问题,提出了一种基于数据挖掘技术的LAI遥感估算方法。该方法借助数据挖掘技术从有限的数据中挖掘和发现有用的信息,排除人为干扰,提高模型构建效率和精度。文中以安徽滁州地区杨树林为研究对象,获取研究区杨树林展叶期和花果期的HJ-CDD遥感影像,利用LAI-2000同步测量杨树林LAI;借助数据挖掘技术并基于杨树林展叶期和花果期估算的LAI值,通过筛选优化构建了杨树林生长过程中叶面积稳定期的LAI估算模型,并结合叶面积稳定期实测的LAI值验证表明该模型用于杨树林叶面积稳定期LAI估算的可靠性,为植被NDVI饱和时的LAI遥感估算提供了一种有效的思路和方法。     

基于无人机高光谱数据的玉米叶面积指数估算

无人机高光谱遥感是低成本、高精度获取精细尺度农作物生物物理参数和生物化学参数的新型手段,以此快速反演叶面积指数(Leaf Area Index,LAI)对作物长势评价、产量预测具有重要意义。以山东禹城市玉米为研究对象,利用PROSAIL辐射传输模型模拟玉米冠层反射率获取LAI特征响应波段结合相关性定量分析获取对LAI变化最为敏感的波段,并以此计算6种植被指数(Vegetation Index,VI),利用6种回归模型分别对单一特征波段和VI进行反演建模,以实测LAI评定模型精度。研究表明,光谱反射率中516、636、702、760和867 nm等波段对LAI变化最为敏感,以此建立的单一特征波段反演模型预测LAI精度R~2为0.44～0.58;RMSE为0.16～0.18,其中636 nm建立的模型(LAI=21.86exp(-29.47R636))相比其他反演模型预测精度较高(R~2=0.58,RMSE=0.16);6种植被指数与LAI高度相关,相关性系数R2为0.85～0.86,以此建立的反演模型相比单一特征波段反演模型精度有所提高,R~2为0.66～0.72,RMSE为0.12～0.14;其中mNDVI构建的LAI估算模型(LAI=exp(2.76～1.77/mNDVI))精度最高(R~2=0.72,RMSE=0.13)。无人机高光谱遥感是快速、无损监测农作物生长信息的有效手段,为指导精细化尺度作物管理提供依据。     

基于无人机高光谱数据的玉米叶面积指数估算

无人机高光谱遥感是低成本、高精度获取精细尺度农作物生物物理参数和生物化学参数的新型手段，以此快速反演叶面积指数（Leaf Area Index, LAI）对作物长势评价、产量预测具有重要意义。以山东禹城市玉米为研究对象，利用PROSAIL辐射传输模型模拟玉米冠层反射率获取LAI特征响应波段结合相关性定量分析获取对LAI变化最为敏感的波段，并以此计算6种植被指数（Vegetation Index,VI），利用6种回归模型分别对单一特征波段和VI进行反演建模，以实测LAI评定模型精度。研究表明，光谱反射率中516、636、702、760和867 nm等波段对LAI变化最为敏感，以此建立的单一特征波段反演模型预测LAI精度R2为0.44~0.58；RMSE为0.16~0.18，其中636 nm建立的模型(LAI=21.86exp(-29.47R₆₃₆))相比其他反演模型预测精度较高（R2=0.58，RMSE=0.16）；6种植被指数与LAI高度相关，相关性系数R ²为0.85~0.86，以此建立的反演模型相比单一特征波段反演模型精度有所提高，R2为0.66~0.72，RMSE为0.12~0.14；其中mNDVI构建的LAI估算模型（LAI=exp(2.76~1.77/mNDVI))精度最高（R2=0.72，RMSE=0.13）。无人机高光谱遥感是快速、无损监测农作物生长信息的有效手段，为指导精细化尺度作物管理提供依据。     

基于TM遥感数据的西藏林芝地区叶面积指数反演

叶面积指数(LAI)是分析冠层结构最常用的参数之一,它控制着植被的生物、物理过程,如光合、呼吸、蒸腾、碳循环和降水截获等。但是通过野外实测获取大面积的LAI比较困难,通过对西藏林芝地区的TM遥感数据进行处理获取各种植被指数,然后分别与实测LAI建立相应的回归关系,并对不同的回归模型进行分析找出相关性较好、误差较低的回归模型,最后利用该模型对林芝地区的叶面积指数进行制图。通过植被指数与实测LAI进行回归分析建立LAI估算模型,其决定系数最高为R²=0.653,具有较好的相关性。研究结果表明:TM遥感数据可以实现林芝区域LAI估算,能为生态环境研究提供数据支持。     

基于无人机高光谱数据的玉米叶面积指数估算

无人机高光谱遥感是低成本、高精度获取精细尺度农作物生物物理参数和生物化学参数的新型手段,以此快速反演叶面积指数（Leaf Area Index, LAI）对作物长势评价、产量预测具有重要意义。以山东禹城市玉米为研究对象,利用PROSAIL辐射传输模型模拟玉米冠层反射率获取LAI特征响应波段结合相关性定量分析获取对LAI变化最为敏感的波段,并以此计算6种植被指数（Vegetation Index,VI）,利用6种回归模型分别对单一特征波段和VI进行反演建模,以实测LAI评定模型精度。研究表明,光谱反射率中516、636、702、760和867 nm等波段对LAI变化最为敏感,以此建立的单一特征波段反演模型预测LAI精度R2为0.44~0.58;RMSE为0.16~0.18,其中636 nm建立的模型(LAI=21.86exp(-29.47R₆₃₆))相比其他反演模型预测精度较高（R2=0.58,RMSE=0.16）;6种植被指数与LAI高度相关,相关性系数R ²为0.85~0.86,以此建立的反演模型相比单一特征波段反演模型精度有所提高,R2为0.66~0.72,RMSE为0.12~0.14;其中mNDVI构建的LAI估算模型（LAI=exp(2.76~1.77/mNDVI))精度最高（R2=0.72,RMSE=0.13）。无人机高光谱遥感是快速、无损监测农作物生长信息的有效手段,为指导精细化尺度作物管理提供依据。     

光合有效辐射传感器及其调理电路设计

当前光合有效辐射测量主要依赖进口产品,探头及信号转换设备价格昂贵,校对成本高;国内相关研究较多,但缺少商用及稳定准确的产品。通过分析光电池的工作特性,给出了光合有效辐射传感器的结构设计并详细介绍其输出微弱信号的调理电路设计。该传感器结构简单、安装方便,调理电路在低功耗的前提下实现微弱电流-电压信号的转换和放大、高频噪声滤波等功能。测试结果表明:开发的光合有效辐射传感器及其微弱信号调理电路稳定性良好,经调理的输出电压与光合有效辐射之间线性相关性达0.9879,且能够适应户外动态环境。     

基于植被指数的若尔盖高原湿地光合有效辐射吸收比例估算研究

植被光合有效辐射吸收比例（FPAR）是湿地生态系统碳收支和气候变化的关键参量,直接反映湿地植被生长发育状况。基于植被指数的经验统计方法简单高效,被广泛运用于草原、森林及作物等植被FPAR的模拟,却较少用于湿地,缺乏不同植被指数对湿地FPAR估算适应性的系统研究。研究对比了14种常见的植被指数,选出最优植被指数用于反演若尔盖高原湿地生长季FPAR。结果表明：常见的植被指数中,MSAVI指数动态考虑了土壤信息,能较好地适应湿地植被FPAR的估算,误差和R²均优于其他植被指数。若尔盖高原湿地生长季FPAR取值在0.22—0.80之间,整体分布较为均匀,泥炭湿地、湿草甸及沼泽湿地平均FPAR分别为0.46、0.63和0.58;生长季期间若尔盖高原不同类型湿地FPAR随时间呈现先增加后降低趋势。     

小麦时面积指数、覆盖率之间关系及遥感数值估算

前言叶面积指数LAI是确定作物长势与预报作物产量的一个重要农学参数。由于样本数目的庞大及不规则性,使其实地测量非常困难,且往往具有一定的破坏性,不能大面积推广。为此,根据植被的光谱特性,人们利用光谱数据,结合实测结果,对一些农学参数如叶面积指数LAI、覆盖率C_V,进行拟合,得到满意结果。Asrar、Wiegand等人的工作表明红外波段与红波段的比值RVI,归一化差ND和垂直植被指数PVI与叶面积指数LAI有着良好的相关性,并且与植被覆盖率C_V相关性也很好。     

Spectral components analysis Rationale,and results for three crops

Abstract

The spectral components analysis identities, LAI/VI × APAR/LAI = APAR/VI and LAI/VI × YIELD/LAI = YIELD/VI, where VI denotes any one of several spectral vegetation indices available, LAI is the leaf area index, APAR is the absorbed photosynthetically active radiation and YIELD is the saleable plant part (grain, fibre or root), express the information conveyed by canopies about their development, response to stresses and yield capability. The rationale includes the concepts that vegetation indices adequately measure the amount of photosynthetic-ally active tissue in plant canopies and that high yields cannot be achieved unless growing conditions permit canopies to develop that effectively intercept the available light during reproduction. For wheat, cotton and maize the coefficients of determination (r ²) usually exceeded 0.90 for exponential, power or linear expressions relating the numerator (dependent variable, y) to the denominator (x) variable of each term in the first equation. Results show that APAR can be reliably estimated from VI, and that the relation is nearly linear. The equations help to quantify remote assessments of crop productivity, to unify field-observed interrelations among LAI, APAR and YIELD and to validate remotely observable LAI and APAR inputs for plant process crop growth and yield models, or for growth analysis.     

Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Much work has been reported in the literature on LAI estimation in boreal forests using remotely sensed imagery. However, few if any explicit LAI retrieval studies on bamboo forests in Asian subtropical monsoon-climate regions based on remote sensing technology have been performed. Our goal is to carry out a comparative study on the LAI estimation methods of bamboo forest in Fujian province, China, based on IRS P6 LISS 3 imagery. Both the traditional empirical–statistical approach and the newly proposed normalized distance (ND) method were employed in this study, and a total of 18 modelling parameters were regressed against ground-based LAI measurements. The results show that simple ratio (SR) is the best predictor for LAI estimation in this study area, with the highest R ² (coefficient of determination) value of 0.68; modified simple ratio (MSR) and normalized difference vegetation index (NDVI) ranked second and third, respectively. The good performance of these three vegetation indices (VIs) can be explained by the ratioing principle. The overall good modelling performance of the ND method in our study area also indicates it is a promising method.     

Using multiple radiometric correction images to estimate leaf area index

Ecological applications of remote-sensing techniques are generally limited to images after atmospheric correction, though other radiometric correction data are potentially valuable. In this article, six spectral vegetation indices (VIs) were derived from a SPOT 5 image at four radiometric correction levels: digital number (DN), at-sensor radiance (SR), top of atmosphere reflectance (TOA) and post-atmospheric correction reflectance (PAC). These VIs include the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), slope ratio of radiation curve (K), general radiance level (L), visible-infrared radiation balance (B) and band radiance variation (V). They were then related to the leaf area index (LAI), acquired from in situ measurement in Hetian town, Fujian Province, China. The VI–LAI correlation coefficients varied greatly across vegetation types, VIs as well as image radiometric correction levels, and were not surely increased by image radiometric corrections. Among all 330 VI–LAI models established, the R ² of multi-variable models were generally higher than those of the single-variable ones. The independent variables of the best VI–LAI models contained all VIs from all radiometric correction levels, showing the potentials of multi-radiometric correction images in LAI estimating. The results indicated that the use of VIs from multiple radiometric correction images can better exploit the capabilities of remote-sensing information, thus improving the accuracy of LAI estimating.     

Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Leaf area index (LAI) is a key vegetation biophysical parameter and is extensively used in modelling of phenology, primary production, light interception, evapotranspiration, carbon, and nitrogen dynamics. In the present study, we attempt to spatially characterize LAI for natural forests of Western Ghats India, using ground based and Landsat-8 Operational Land Imager (OLI) sensor satellite data. For this, 41 ground-based LAI measurements were carried out across a gradient of tropical forest types, viz. dry, moist, and evergreen forests using LAI-2200 plant canopy analyser, during the month of March 2015. Initially, measured LAI values were regressed with 15 spectral variables, including nine spectral vegetation indices (SVIs) and six Landsat-8 surface reflectance (ρ) variables using univariate correlation analysis. Results showed that the red (ρ_red), near-infrared (ρ_NIR), shortwave infrared (ρ_SWIR1, ρ_SWIR2) reflectance bands (R² > 0.6), and all SVIs (R² > 0.7) except simple ratio (SR) have the highest and second highest coefficient of determination with ground-measured LAI. In the second step, to select significant (high R², low root mean square error (RMSE), and p-level < 0.05) SVIs to determine the best representative model, stepwise multiple linear regression (SMLR) was implemented. The results indicate that the SMLR model predicted LAI with better coefficient of determination (R² = 0.83, RMSE = 0.78) using normalized difference vegetation index, enhanced vegetation index, and soil-adjusted vegetation index variables compared to the univariate approach. The predicted SMLR model was used to estimate a spatial map of LAI. It is desirable to evaluate the stability and potentiality of regional LAI models in natural forest ecosystems against the operationally accepted Moderate Resolution Imaging Spectroradiometer (MODIS) global LAI product. To do this, the Landsat-8 pixel-based LAI map was resampled to 1 km resolution and compared with the MODIS derived LAI map. Results suggested that Landsat-8 OLI-based VIs provide significant LAI maps at moderate resolution (30 m) as well as coarse resolution (1 km) for regional climate models.     

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R ² > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R ² > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R ²?=?0.78, accuracy?=?73%, root mean square error (RMSE)?=?0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy?=?79%, RMSE?=?0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.     

Retrieval of leaf chlorophyll content in field crops using narrow-band indices: effects of leaf area index and leaf mean tilt angle

Reliable estimation of leaf chlorophyll-a and -b content (chl-a + b) at canopy scales is essential for monitoring vegetation productivity, physiological stress, and nutrient availability. To achieve this, narrow-band vegetation indices (VIs) derived from imaging spectroscopy data are commonly used. However, VIs are affected by canopy structures other than chl-a + b, such as leaf area index (LAI) and leaf mean tilt angle (MTA). In this study, we evaluated the performance of 58 VIs reported in the literature to be chl-a + b-sensitive against a unique measured set of species-specific leaf angles for six crop species in southern Finland. We created a large simulated canopy reflectance database (100,000 canopy configurations) using the physically based PROSAIL (coupling of PROSPECT and SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer models) model. The performance of model-simulated indices was compared against airborne AISA Eagle II imaging spectroradiometer data and field-measured chl-a + b, LAI, and MTA values. In general, LAI had a positive effect on the strength of the correlation between chl-a + b and VIs while MTA had a negative effect in both measured and simulated data. Three indices (REIP (red edge inflection point), TCARI (transformed chlorophyll absorption ratio index)/OSAVI (optimized soil-adjusted vegetation index), and CTR6 (Carter indices)) showed strong correlations with chl-a + b and similar performance in model-simulated and measured data set. However, only two (TCARI/OSAVI and CTR6) were independent from LAI and MTA. We consider these two indices robust proxies of crop leaf chl-a + b.     

Correlation between potato yield and MODIS‐derived vegetation indices

Monitoring of crop growth and forecasting its yield well before harvest is very important for crop and food management. Remote sensing images are capable of identifying crop health, as well as predicting its yield. Vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) calculated from remotely sensed data have been widely used to monitor crop growth and to predict crop yield. This study used 8 day TERRA MODIS reflectance data of 500 m resolution for the years 2005 to 2006 to estimate the yield of potato in the Munshiganj area of Bangladesh. The satellite data has been validated using ground truth data from fields of 50 farmers. Regression models are developed between VIs and field level potato yield for six administrative units of Munshiganj District. The yield prediction equations have high coefficients of correlation (R ²) and are 0.84, 0.72 and 0.80 for the NDVI, LAI and fPAR, respectively. These equations were validated by using data from 2006 to 2007 seasons and found that an average error of estimation is about 15% for the study region. It can be concluded that VIs derived from remote sensing can be an effective tool for early estimation of potato yield.     

Leaf area index for biomes of the Eastern Arc Mountains: Landsat and SPOT observations along precipitation and altitude gradients

Understanding of mechanisms underlying carbon flux dynamics in the Eastern Arc Mountains and their catchment areas is lacking, due to data shortage (e.g. biome specific canopy structure) and spatial heterogeneity of tropical ecosystems. This study focuses on documenting leaf area index (LAI) for the main biomes in the Eastern Arc Mountains and their surroundings. In situ optical instruments, i.e. hemispherical photography and a SunScan device, were used to acquire ground LAI measurements. Spectral vegetation indices (VIs) extracted from Landsat Enhanced Thematic Mapper (ETM +) and Système Probatoire d'Observation de la Terre (SPOT) reflectance data were used, along with mean annual precipitation (MAP), as explanatory variables of LAI variation. The results indicate that LAI significantly increases with increasing MAP for woody biomes. Implementing long-term MAP as a second predictor variable into the VI–LAI models significantly improved LAI predictions by up to 10% using the normalised difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI 2) and 2-band enhanced vegetation index (EVI 2). Varying forest disturbances and agricultural management practises may have contributed to observed discrepancies of LAI with MAP across biomes. The importance of altitudinal gradients is yet to be explained fully with more study required. However, LAI appears to be higher in low-altitude forests compared to forests at higher altitudes. Our results indicate that SPOT and Landsat-derived VIs, in combination with long-term MAP, may be a suitable tool to develop landscape maps of LAI in Eastern Africa. This study also presents the in situ LAI measurements for further validation of global products for areas that are currently under-represented in Earth Observation (EO) global validation networks.     

Assessing spectral indices to estimate the fraction of photosynthetically active radiation absorbed by the vegetation canopy

ABSTRACT

The fraction of absorbed photosynthetically active radiation (FPAR) by the vegetation canopy (FPAR_canopy) is an important parameter for vegetation productivity estimation using remote-sensing data. FPAR_canopy is widely estimated using many different spectral vegetation indices (VIs), especially the simple ratio vegetation index (SR) and normalized difference vegetation index (NDVI). However, there have been few studies into which VIs are most suitable for this estimation or into their sensitivities to the leaf area index and the observation geometry of remote-sensing data, which are very important for the accurate estimation of FPAR_canopy based on the plant growth stage and satellite imagery. In this study, nine main VIs calculated from field-measured spectra were evaluated and it was found that the SR and NDVI underestimated and overestimated FPAR_canopy, respectively. It was also found that the enhanced vegetation index produced lesser errors and a higher agreement than other broadband VIs used to estimate FPAR_canopy. Among all the selected VIs, the photochemical reflectance index (PRI) turned out to have the lowest root mean square error of 0.17. The SR produced the highest errors (about 0.37) and lowest index of agreement (about 0.50) compared to the measured values of FPAR_canopy. Except for carotenoid reflectance index (CRI), FPAR_canopy estimated by VIs are evidently sensitive to the leaf area index (LAI), especially for FPAR_canopy (SR), which are also most sensitive to solar zenith angles (SZA). SR, CRI, PRI, and EVI have remarked variations with view zenith angles. Our study shows that FPAR_canopy can be simply and accurately estimated using the most suitable VIs – i.e. EVI and PRI – with broadband and hyperspectral remote-sensing data, respectively, and that the nadir reflectance or nadir bidirectional reflectance distribution function adjusted reflectance should be used to calculate these VIs.