基于相对光谱变量的无人机遥感水稻估产及产量制图

及时准确地监测农作物产量信息对国家和区域的粮食生产、贸易及粮食安全预警具有重要意义。当前卫星遥感估产由于高时空分辨率难以同时满足、波段数量少等原因限制估产精度进一步提高,无人机成像高光谱技术以其高时空分辨率、丰富的波段数量和图谱结合的遥感影像等优势被广泛地应用到现代智慧农业与精准农业,使高精度的农作物估产成为了可能。常规无人机估产方法使用的不同时期植被指数在获取时具有不同的光照条件、大气条件和背景,这些外界条件的差异将会引起不同时期植被指数的误差,进而影响估产精度。针对该问题,提出"相对光谱变量"和"相对产量"的概念开展多时期相对变量水稻遥感估产。首先将高光谱成像仪获取的波段进行一对一的组合建立相对归一化光谱指数RNDSI集,并确定水稻不同生育期的最优RNDSI及其构成波段;然后建立不同生育期组合的水稻估产最优模型并做相应的验证。结果显示:使用分蘖期RNDSI[784,635]、拔节期RNDSI[807,744]、孕穗期RNDSI[784,712]和抽穗期RNDSI[816,736]组成的多元线性回归模型是多生育期估产的最优模型,R^2和RMSE分别为0.74和248.97 kg/hm^2,并对此结果进行验证,估产平均相对误差绝对值达到了4.31%,结果表明相对植被指数和相对产量的水稻遥感估产方法可较好地应用于像素级的水稻遥感估产。基于该模型绘制了水稻的田间产量分布图,可更加直观地表现不同区域的产量并进行精准地田间管理。     

Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops

An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R₇₅₀/R₇₁₀ and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies.     

联合无人机激光雷达和高光谱数据反演玉米叶面积指数

叶面积指数（Leaf Area Index,LAI）是作物长势监测及产量估算的重要指标,准确高效的LAI反演对农田经济的宏观管理具有重要作用。研究探索了联合无人机激光雷达（Light Detection and Ranging,LiDAR）和高光谱数据反演玉米叶面积指数的潜力,并分析了LiDAR数据不同采样尺寸、高度阈值、点密度对LAI反演精度的影响同时确定三者的最优值。该研究分别从重采样的LiDAR数据和高光谱影像中提取了LiDAR变量和植被指数,然后基于偏最小二乘回归（Partial Least Square Regression,PLSR）和随机森林（Random Forest,RF）回归两种算法分别利用LiDAR变量、植被指数、联合LiDAR变量和植被指数构建预测模型,并确定反演玉米LAI的最优预测模型。结果表明：反演玉米LAI的最优采样尺寸、高度阈值、点密度分别为5.5 m、0.55 m、18 points/m2,研究发现最高的点密度（420 points/m2）并没有产生最优的玉米LAI反演精度,因此单独依靠增加点密度的方法提高LAI的反演精度并不可靠。基于LiDAR变量获...     

Non-photosynthetic vegetation biomass estimation in semiarid Canadian mixed grasslands using ground hyperspectral data,Landsat 8 OLI,and Sentinel-2 images

ABSTRACT

Research on quantifying non-photosynthetic vegetation (NPV) with optical remote-sensing approaches has been focusing on optically distinguishing NPV from green vegetation and bare soil. With a very similar spectral response curve to NPV, dry moss is a significant component in semiarid mixed grasslands and plays a large role in NPV estimation. However, limited attention has been paid to this role. We investigated the potential of optical remote sensing to distinguish NPV biomass in semiarid grasslands characterized by NPV, biological soil crust dominated by moss and lichen, and bare soil. First, hyperspectral spectral indices were examined to determine the most useful spectral wavelength regions for NPV biomass estimation. Second, multispectral red-edge indices and shortwave infrared (SWIR) indices were simulated based on Landsat 8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument band reflectance, respectively, to determine the most suitable multispectral indices for NPV estimation. The potential multispectral indices were then applied to Landsat 8 OLI images and Sentinel-2A images acquired in early, middle, peak, and early senescence growing seasons to investigate the potential of satellite images for quantifying NPV biomass. Our results indicated that hyperspectral red-edge indices, modified simple ratio, modified red-edge normalized difference vegetation index (mNDVI₇₀₅), and normalized difference vegetation index (NDVI₇₀₅) are better than the SWIR hyperspectral indices, including cellulose absorption index for quantifying NPV biomass. The simulated multispectral red-edge spectral indices (NDVI_red-edge and mNDVI_red-edge) demonstrate good and comparable performance on quantifying NPV biomass with SWIR multispectral indices (normalized difference index [NDI5 and NDI7] and soil-adjusted corn residue index). Nevertheless, the multispectral indices derived from Landsat 8 OLI and Sentinel-2 images have limited potential for NPV biomass estimation.     

Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images

This paper demonstrates that Radarsat ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield in large areas in tropical and sub-tropical regions. Experiments were carried out to show the effectiveness of Radarsat ScanSAR data for rice yield estimation in the whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice. The study indicates that the whole procedure can become a low-cost and convenient operational system for large-scale rice yield estimation which is difficult for conventional methods.     

The relationships between electrical conductivity of soil and reflectance of canopy,grain, and leaf of rice in northeastern Thailand

Soil salinity is a global environmental problem and the most widespread land degradation problem that reduces crop yields and agricultural productivity. The characteristic of soil salinity is conventionally measured by the electric conductivity (EC) of soil while remote-sensing techniques have been extensively applied to detect the presence of salts indirectly through the vegetation using crop spectral reflectance. This study aims primarily to investigate whether salt stress the rice can be detected by field reflectance or not, and second, to search the significant bands of vegetation indices that can indicate the relationships between the EC of soil and field hyperspectral reflectance of canopy, grain, and leaf of rice, using the normalized difference spectral index (NDSI). Field investigations on various paddy fields in northeastern Thailand were carried out in late November 2010 during the ripening season just before harvest in an attempt to realize the applications of the field hyperspectral technique for monitoring the spread of saline soils and estimation of the effects of soil salinity on rice plants. Jasmine rice and glutinous rice were two different rice species selected for this study. Rice plant investigations were conducted by collecting data on crop length, panicle length, canopy openness, leaf area index, and digital photographs of plant conditions from each site. The statistical analysis revealed that the changes in soil EC were significantly sensitive to the ripening stages of both jasmine rice and glutinous rice planted on different levels of soil salinity. Among reflectance measurements, canopy reflectance was highly correlated with soil EC. However, the estimated accuracies of the relationship between soil EC and reflectance of glutinous rice were relatively lower than those of jasmine rice.     

Correction of the Soil Adjusted Factors of Vegetation Indices for Monitoring Rice Leaf Index Zrea

The vegetation indices that take the soil adjustment factor into consideration can reduce the influence of soil background conditions and have been widely used in monitoring all kinds of vegetation.However,the rice has been planted in the soil covered by a certain thickness of layer of water,which is different with other various soil backgrounds.Therefore,in this paper,through two years of rice plot experiments,we obtained the rice canopy spectral data and the corresponding leaf area index (LAI) data,and then calculated a series of vegetation indices (EVI,SAVI,WDVI) by using different soil adjustment factors changing within a certain range.We compared the abilities of these vegetation indices for rice LAI estimation,and then determine the optimum soil adjustment factors of vegetation indices to adjust the background of rice.In the study,we found that the best soil adjustment factor L for EVI,L of SAVI,a of WDVI are 0.25,0.10 and 1.25 respectively,and we further compared the LAI estimation results of the best soil adjustment factor with those of the conventional soil adjustment factor.For the model taking EVI as an independent variable,the RMSE of LAI estimation using the best soil adjustment factor is 6.82 % lower than that using the conventional soil adjustment factor;In SAVI model,the RMSE using the best soil adjustment factor is 10.23% lower than that using the conventional soil adjustment factor .These results indicate that the corrected vegetation indices considering the background of rice can improve the accuracy of rice leaf area index using remotely sensed data.     

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。     

Evaluation of hyperspectral indices for LAI estimation and discrimination of potato crop under different irrigation treatments

In this study, various hyperspectral indices were evaluated for estimation of leaf area index (LAI) and crop discrimination under different irrigation treatments. The study was conducted for potato crop using the spectral reflectance values measured by a hand‐held spectro‐radiometer. Three categories of hyperspectral indices, such as ratio/difference indices, multivariate indices and derivative based indices were computed. It was found that, among various band combinations for NDVI (normalized difference vegetation index) and SAVI (soil adjusted vegetation index), the band combination of the 780～680, produced highest correlation coefficient with LAI. Among all the forms of LAI and VI empirical relationships, the power and exponential equations had highest R ² and F values. Analysis of variance showed that, hyperspectral indices were found to be more efficient than the LAI to detect the differences among crops under different irrigation treatments. The discriminant analysis produced a set of five most optimum bands to discriminate the crops under three irrigation treatments.     

Estimation of Grassland Aboveground Biomass Using Landsat 8 OLI Satellite Image in the Northern Hillside of Tianshan Mountain

The Landsat 8 OLI remote sensing data was used to obtain six kinds of commonly vegetation indices including NDVI,RVI,DVI,EVI,GNDVI and SAVI.Meanwhile,combining with the measured data of grassland in the research area,the research area was divided into two kinds of shady and sunny slope according to the slope.Then the biomass remote sensing estimation models of shady and sunny slope in Ziniquan Ranch were created by Statistical analysis method and biomass space inversion and verification was implemented.The results of correlation analysis showed that the selected vegetation indices were significantly correlated with pasture biomass and there was a significant difference between the correlation of the classified data and the non classified data by slope,in which NDVI was the highest and EVI was the lowest.The optimal inversion model of Ziniquan Ranch biomass was based on the two order polynomial model of SAVI with the accuracy 80%.By using this model reversion,the grassland average yield of Ziniquan Ranch in 2015 was 113 g/m²,which equaled to dry grass yield 41.85 g/m².The research shows that the slope direction is an important factor affecting the distribution of biomass.Using remote sensing data and ground measured biomass data and combining with the characteristics of the topography of shady and sunny slope of the research area,the biomass estimation model has higher accuracy,which could provide scientific basis for the reasonable estimation of grassland biomass and management of grassland grazing in the pastoral area.     

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta,Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.     

Algorithmic mapping of feedforward neural networks onto multiplebus systems

This paper addresses the problem of mapping a feedforward ANN onto a multiple bus system, MBS, with p processors and b buses so as to minimize the total execution time. We present an algorithm which assigns the nodes of a given computational layer (c-layer) to processors such that the computation lower bound [N^l/p]t_p^l and the communication lower bound [N^l/b]t_c are achieved simultaneously, where N^l is the number of nodes in the mapped c-layer l and t_p^l and t_c are the computation and communication times, respectively, associated with a node in the layer. When computation and communication are not overlapped, we show that the optimal number of processors needed is either 1 or p, depending on the ratio t_p^l/t_c . When computation and communication are overlapped, we show that the optimal number of processors needed is either 1 or ([t_p ^l/t_c])b. We show that there is a unique arrangement of interfaces such that the total number of interfaces is minimum and the optimal time is reached. Finally, we compare the relative merits of the MBS simulating ANNs over the recently introduced checkerboarding scheme     

A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index

Remote sensing estimation of leaf chlorophyll content is of importance to crop nutrition diagnosis and yield assessment, yet the feasibility and stability of such estimation has not been assessed thoroughly for mixed pixels. This study analyses the influence of spectral mixing on leaf chlorophyll content estimation using canopy spectra simulated by the PROSAIL model and the spectral linear mixture concept. It is observed that the accuracy of leaf chlorophyll content estimation would be degraded for mixed pixels using the well-accepted approach of the combination of transformed chlorophyll absorption index (TCARI) and optimized soil-adjusted vegetation index (OSAVI). A two-step method was thus developed for winter wheat chlorophyll content estimation by taking into consideration the fractional vegetation cover using a look-up-table approach. The two methods were validated using ground spectra, airborne hyperspectral data and leaf chlorophyll content measured the same time over experimental winter wheat fields. Using the two-step method, the leaf chlorophyll content of the open canopy was estimated from the airborne hyperspectral imagery with a root mean square error of 5.18 μg cm^?2, which is an improvement of about 8.9% relative to the accuracy obtained using the TCARI/OSAVI ratio directly. This implies that the method proposed in this study has great potential for hyperspectral applications in agricultural management, particularly for applications before crop canopy closure.     

基于SPOT5遥感影像丰宁县植被地上生物量估测研究

利用SPOT5遥感影像数据和同期获得的野外调查样地数据,基于按植被类型分类估测的方法,研究了河北省丰宁满族自治县植被地上生物量的遥感估测技术。研究结果显示,SPOT5影像的4个波段反射率和中红外植被指数(VI3)结合建立的多元回归模型,可用于森林生物量的遥感估测,估测的R2值达0.540,说明中红外波段信息提高森林生物量的估测精度有一定作用;通过分析样地生物量与多种植被指数的相关性发现,基于比值植被指数(RVI)的指数回归模型是灌丛生物量估测的最佳模型,估测的R2值达0.711,基于归一化植被指数(NDVI)的简单线性回归模型为估测草地生物量的最佳模型,R2值达0.790。利用2008年的全覆盖SPOT5影像,获得了丰宁县2008年植被地上生物量分布图,除农田植被外,全县地上生物总量为3.706×107 t,单位面积生物量平均为51.223t/hm2,其中,森林植被总生物量为3.578×107 t,灌丛植被总生物量为1.048×106 t,草地植被总生物量为2.277×105 t。     

苹果叶片氮含量高光谱反演方法对比

快速、无损、及时地准确估算苹果叶片氮含量是保证苹果产量和质量的基础,利用高光谱技术对苹果叶片氮含量进行遥感反演可为合理施肥提供理论依据.利用2012年和2013年山东省肥城市潮泉镇下寨村不同生育期的苹果叶片氮含量和相应的叶片光谱数据进行分析和建模.首先分析了叶片氮含量与原始光谱、一阶微分及三边光谱指数之间的相关性,筛选...     

Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data

Temperate East Asia (TEA) is characterized by diverse land cover types, including forest and agricultural lands, one of the world's largest temperate grasslands, and extensive desert and barren landscapes. In this paper, we explored the potential of SPOT-4 VEGETATION (VGT) data for the classification of land cover types in TEA. An unsupervised classification was performed using multi-temporal (March–November 2000) VGT-derived spectral indices (Land Surface Water Index [LSWI] and Enhanced Vegetation Index [EVI]) to generate a land cover map of TEA (called VGT-TEA). Land cover classes from VGT-TEA were aggregated to broad, general class types, and then compared and validated with classifications derived from fine-resolution (Landsat) data. VGT-TEA produced reasonable results when compared to the Landsat products. Analysis of the seasonal dynamics of LSWI and EVI allows for the identification of distinct growth patterns between different vegetation types. We suggest that LSWI seasonal curves can be used to define the growing season for temperate deciduous vegetation, including grassland types. Seasonal curves of EVI tend to have a slightly greater dynamic range than LSWI during the peak growing season and can be useful in discriminating between vegetation types. By using these two complementary spectral indices, VGT data can be used to produce timely and detailed land cover and phenology maps with limited ancillary data needed.     

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world.     

Comparison of different vegetation indices for the remote assessment of green leaf area index of crops

Many algorithms have been developed for the remote estimation of biophysical characteristics of vegetation, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. However, the most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances, their applicability is specific to species, vegetation types or local conditions. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the green leaf area index (Green LAI) of two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. Among the indices tested, the chlorophyll Indices (the CI_Green, the CI_Red-edge and the MERIS Terrestrial Chlorophyll Index, MTCI) exhibited strong and significant linear relationships with Green LAI, and thus were sensitive across the entire range of Green LAI evaluated (i.e., 0.0 to more than 6.0 m²/m²). However, the CI_Red-edge was the only index insensitive to crop type and produced the most accurate estimations of Green LAI in both crops (RMSE = 0.577 m²/m²). These results were obtained using data acquired with close range sensors (i.e., field spectroradiometers mounted 6 m above the canopy) and an aircraft-mounted hyperspectral imaging spectroradiometer (AISA). As the CI_Red-edge also exhibited low sensitivity to soil background effects, it constitutes a simple, yet robust tool for the remote and synoptic estimation of Green LAI. Algorithms based on this index may not require re-parameterization when applied to crops with different canopy architectures and leaf structures, but further studies are required for assessing its applicability in other vegetation types (e.g., forests, grasslands).     

Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data

Biophysical parameters such as leaf area index (LAI) are key variables for vegetation monitoring and particularly important for modelling energy and matter fluxes in the biosphere. Therefore LAI has been derived from remote sensing data operationally based on data with a somewhat coarse spatial resolution. This study aims at deriving high-spatial resolution (6.5 m) multi-temporal LAI for grasslands based on RapidEye data by statistical regressions between vegetation indices (VIs) and field samplings. However, the suitability of those data for grassland LAI derivation has not been tested to date. Thus, the potential of RapidEye data in general and its red edge band in particular are investigated, as well as the robustness of the established relationships for different points in time.

LAI was measured repeatedly over summer 2011 at about 30 different meadows in the Bavarian alpine upland using the LAI-2000 and correlated with VI values. The best relationships resulted from using the ratio vegetation index and red edge indices (NDVI_rededge, rededge ratio index 1, and relative length) in non-linear models. Thus the indices based on the red edge channel improved regression modelling. The associated transfer functions achieved R² values ranging from 0.57 to 0.85. The temporal transferability of those transfer functions to other dates was shown to be limited, with the root mean square errors (RMSEs) of several scenes exceeding one. However, when the LAI ranges are similar, a reliable transfer is possible: for example, the transfer of the regression function based on early autumn measurements showed RMSEs of only 0.77–0.95 for the other scenes except for the high-density stage in July, when the LAI reaches unprecedented maximal values. Also, the combination of multi-temporal training data shows no saturation of the selected indices and enables a satisfactory LAI mapping of different dates (RMSE = 0.59 – 1.02).