针叶林冠层热点及多角度无人机遥感观测及特征分析

多角度遥感观测是研究植被冠层BRDF(Bidirectional Reflectance Distribution Function)特性的重要手段,但目前对森林冠层进行连续间隔采样的多角度遥感观测及数据较少,热点方向的观测尤为缺乏.本研究基于无人机多角度高光谱成像系统,在主平面上对针叶林冠层以等角度连续间隔采样进行多...     

不同传感器的模拟植被指数对水稻叶面积指数的估测精度和敏感性分析

本研究利用水稻冠层高光谱数据,模拟NOAA-AVHRR,Terra-MODIS和Landsat-TM的可见光波段反射率数据,计算各传感器的多种植被指数(NDVI,RVI,EVI,GNDVI,GRVI和Red-edge RVI),比较植被指数模型对水稻LAI的估测精度,分析不同植被指数对LAI变化的敏感性.相对于红波段植被指数,红边比值植被指数(Red-edge RVI)和绿波段指数GRVI与LAI有更好的线性相关关系,而GNDVI和LAI呈现更好的对数相关关系.MODIS的Red-edge RVI指数不仅模型拟合的精度最高,还有独立数据验证的估测精度也最高,而且它的验证精度较拟合精度下降幅度最小;其次是绿波段构建的GNDVI和GRVI植被指数的估测精度,再次是NDVI和EVI的估测精度,而RVI的估测精度最差.敏感性分析发现,13个植被指数对水稻LAI的估测能力都随着LAI的增加而下降,但归一化类植被指数和比值类植被指数对LAI变化反应的差异明显,归一化类植被指数在LAI较低时(LAI<1.5)对LAI变化的反应开始非常敏感,但迅速下降,而比值类植被指数在LAI较低时,明显小于归一化类植被指数,之后随着LAI的增大(LAI>1.5)比值类植被指数对LAI的变化敏感性,则明显高于归一化类植被指数.Red-edge RVI和绿波段指数GRVI和LAI不仅表现了很好的线性相关关系,而且在LAI大于2.9左右保持较高的敏感性.     

PROSAIL模型模拟下的植被指数土壤调节能力比较与适用环境分析

为了消除土壤背景信息对植被指数的影响,近几十年发展了土壤调节植被指数系列(SAVI family)。在不同环境条件下,不同指数抗土壤影响的能力不同。在总结了以消除土壤影响为目的的植被指数建立过程的基础上,利用PROSAIL辐射传输模型模拟的两组数据集,比较分析了NDVI、SAVI、TSAVI、MSAVI、OSAVI和GESAVI在不同叶面积指数(LAI)对应不同土壤背景的情况下抗土壤干扰、表达植被信息的能力,指出了不同植被指数应用的最适环境条件。结合植被指数—信噪比图,将这6种植被指数分成3类:在中低LAI值下,若植被覆盖度均匀,OSAVI和TSAVI有较强的消除土壤影响、表达植被信息的能力;当区域LAI分布不均、植被类型混杂时,MSAVI在表达植被信息时具有较好的稳定性。根据每类植被指数的特征,利用MODIS-VI和MODIS-LAI产品初步验证了上述结论的有效性。     

Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture

The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI–reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ≤ RMSE_cv ≤ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.     

Leaf Area Index Estimation Using Time-Series MODIS Data in Different Types of Vegetation

The aim of this study is to estimate leaf area index (LAI) in different type of plants using vegetation indices (VIs) and neural network algorithms retrieved from MODIS data. Four VI were calculated, and neural networks were built up based on MODIS surface reflectance products. Among the tested VIs, normalized difference vegetation index (NDVI) and chlorophyll index (CI) appeared to be the best candidate indices in estimating LAI across sites with different vegetation types. The models having the highest accuracy were CI for grassland and deciduous broad leaf forest with determination coefficients (R-square above 0.70, and NDVI for crop R-square?=?0.78). Neural network showed better results than VI methods except in grassland sites. The added VI information showed no significant improvement of model accuracy for the neural networks in most sites.     

双冠层反射率模型分析各向异性平整指数(AFX)对植被参数的敏感性

二向反射分布函数包含地表反射的方向性特征信息。研究二向反射分布函数BRDF(Bidirectional Reflectance Distribution Function)形状对植被结构参数的敏感性,有助于理解植被的二向性反射规律,进而反演植被参数。本文耦合双冠层反射率模型和核驱动的罗斯厚核-李氏稀疏互易核模型,利用EFAST全局敏感性分析方法,以各向异性平整指数为BRDF形状变化的衡量指标,研究了不同天空光比例(SKYL)下,各向异性平整指数AFX对植被参数敏感度的变化,以及SKYL=0.1时AFX的敏感性。结果表明:(1)在红波段,上、下层叶面积指数、上层叶绿素含量,以及上层叶倾角分布是AFX的敏感参数,在近红外波段,上、下层叶面积指数LAI是AFX的敏感参数。(2)冠层尺度上的参数敏感度总体大于叶片尺度。(3)晴天时(SKYL=0.1),红波段主敏感度较大的参数分别是上层LAI、上层叶倾角分布和下层叶片结构参数,近红外波段主敏感度较大的参数主要是上、下层LAI。     

Estimation of forest canopy structural parameters using kernel-driven bi-directional reflectance model based multi-angular vegetation indices

Near-surface bi-directional reflectance and high-spatial resolution true-color imagery of several forested canopies were acquired using an unmanned helicopter. The observed reflectance from multiple view-zenith angles were simulated with a kernel-driven bidirectional reflectance model, and the BRDF parameters were retrieved. Based on the retrieved BRDF parameters, kernel-derived multi-angular vegetation indices (KMVIs) were computed. The potential of KMVI for prediction of canopy structural parameters such as canopy fraction and canopy volume was assessed. The performance of each KMVI was tested by comparison to field measured canopy fraction and canopy volume. For the prediction of canopy fraction, the KMVI that included the nadir-based NDVI performed better than other KMVI emphasizing the importance of nadir observation for remote estimation of the canopy fraction. The Nadir BRDF-adjusted NDVI was found to be superior for the prediction of canopy fraction, which could explain 77% variation of the canopy fraction. However, none of the existing KMVI predicted the canopy volume better than Nadir BRDF-adjusted NDVI and Nadir-view NDVI. The Canopy structural index (CSI) was proposed with the combination of normalized difference between dark-spot near infrared reflectance and hot-spot red reflectance. The CSI could establish an improved relationship with the canopy volume over Nadir BRDF-adjusted NDVI and Nadir-view NDVI, explaining 72% variation in canopy volume. In addition, MODIS based KMVI were evaluated for the prediction of canopy fraction and canopy volume. MODIS based KMVI also showed similar results to the helicopter based KMVI. The promising results shown by the CSI suggest that it could be an appropriate candidate for remote estimation of three-dimensional canopy structure.     

Estimating the Leaf Area Index,height and biomass of maize using HJ-1 and RADARSAT-2

New optical and microwave integrated vegetation indices (VIs) were designed based on observations from both field experiments and satellite (HJ-1 and RADARSAT-2) data. It was found that these VIs perform better in estimating the structure parameters of maize, such as Leaf Area Index (LAI), height and biomass, than the original ones. This investigation focused on the difference of interaction between the multispectral reflectance and microwave backscattering signatures with the maize growth variables. Because the maize was near the heading stage with large vegetation coverage in the experiment, the reflectance of the near-infrared band of HJ-1 was much less sensitive to the structure variables than that of the visible-light band. Thus, the optical VIs formulated using those bands were saturated to estimate the structure parameters. With respect to the RADARSAT-2 data, there was a relatively strong relationship between the HV cross-polarization and the volume scattering of the maize, which was mostly determined by the crown structure. The modified VIs were designed using both the VIs of HJ-1 and the HV cross-polarization of RADARSAT-2 to overcome the saturation limitation. The validation showed that this integrated method of determining VIs is a good alternative to that using only the optical or microwave observation.     

光谱指数用于叶绿素含量提取的评价及一种改进的农作物冠层叶绿素含量提取模型

对目前提出的光谱指数用以提取叶片叶绿素含量的适应性进行了分析和评价。通过分析,解释了为什么研究者得出这些指数与他们的观测样本叶绿素含量有显著的相关的结论以及为什么某个研究者提出的某个指数和叶绿素含量间的关系用于其他样本时会失效。此外,改进了一个农作物冠层叶绿素含量的提取模型,通过独立实测数据验证,效果较好,认为是可以用于其他地区农作物叶绿素含量提取的模型。     

多光谱多角度遥感数据综合反演叶面积指数方法研究

叶面积指数是陆地生态系统的一个十分重要的结构参数。用遥感数据求取叶面积指数可以利用光谱的信息,比如通过植被指数来拟合一个经验关系,但很多植被指数明显受土壤背景的影响,对于有明显行结构的农作物,土壤的影响很难消除,植被指数的方法误差较大。多角度遥感包含了大量的地面目标的立体结构信息,具备求解植被特征参数的潜力,但通常多角度遥感反演对光谱信息的利用不足。与以往的反演方法相区别,该文利用行播作物二向反射模型,将多角度与多光谱数据结合进行行播作物LAI反演实验,并对反演算法进行了详细的敏感性分析实验,结果表明采用多角度、多光谱遥感数据相结合的方法可以有效反演行播作物的叶面积指数。     

冬小麦叶面积指数的高光谱估算模型研究

本文以山东禹城为研究区,利用地面实测光谱数据,探讨不同植被指数和红边参数建立高光谱模型反演冬小麦叶面积指数的精度。通过逐波段分析计算了4种植被指数(NDVI、RVI、SAVI、EVI),结合同步观测LAI数据,确定反演叶面积指数的最优波段;计算了5种常用的高光谱植被指数MCARI、MCARI2、OSAVI、MTVI2、MSAVI2,同时利用4种常用方法计算红边位置和红谷,与实测LAI进行回归分析,比较植被指数和红边参数模型对冬小麦LAI的估测精度。结果表明各因子与LAI均具有较高的相关性,整个研究区归一化植被指数具有最高的反演精度,确定了估算冬小麦LAI的最优模型,并使用独立的LAI观测数据对模型进行了验证。     

Investigating the impact of the temporal resolution of MODIS data on measured phenology in the prairie grasslands

ABSTRACT

The temporal resolution of vegetation indices (VIs) determines the details of seasonal variation in vegetation dynamics observed by remote sensing, but little has been known about how the temporal resolution of VIs affects the retrieval of land surface phenology (LSP) of grasslands. This study evaluated the impact of temporal resolution of MODIS NDVI, EVI, and per-pixel green chromatic coordinate (GCCpp) on the quality and accuracy of the estimated LSP metrics of prairie grasslands. The near-surface PheonoCam phenology data for grasslands centered over Lethbridge PhenoCam grassland site were used as the validation datasets due to the lack of in situ observations for grasslands in the Prairie Ecozone. MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data from 2001 to 2017 were used to compute the time series of daily reference and to simulate 2–32 day MODIS VIs. The daily reference and simulated multi-day time series were fitted with the double logistic model, and the LSP metrics were then retrieved from the modeled daily time series separately. Comparison within satellite-based estimates showed no significant difference in the phenological metrics derived from daily reference and multi-day VIs resampled at a time step less than 18 days. Moreover, a significant decline in the ability of multi-day VIs to predict detailed temporal dynamics of daily reference VIs was revealed as the temporal resolution increased. Besides, there were a variety of trends for the onset of phenological transitions as the temporal resolution of VIs changed from 1 to 32 days. Comparison with PhenoCam phenology data presented small and insignificant differences in the mean bias error (MBE) and the mean absolute error (MAE) of grassland phenological metrics derived from daily, 8-, 10-, 14-, and 16-day MODIS VIs. Overall, this study suggested that the MODIS VIs resampled at a time step less than 18 days are favorable for the detection of grassland phenological transitions and detailed seasonal dynamics in the Prairie Ecozone.     

Inversion of PROSAIL Model for Retrieval of Plant Biophysical Parameters

The current development of satellite technology particularly in the sensors like POLDER and MISR, has emphasized more on directional reflectance measurements (i.e. spectral reflectance of the target measured from different view zenith and azimuth angles) of the earth surface features mainly the vegetation for retrieval of biophysical parameters at regional scale using radiative transfer models. This approach being physical process based and uses directional reflectance measurement has been found to better and more reliable compared to the conventional statistical approach used till date and takes care of anisotropic nature (i.e. reflectance from the target is different if measured from different view angles) of the target. Keeping this in view a field experiment was conducted in mustard crop to evaluate the radiative transfer model for biophysical parameter retrieval through its inversion with the objectives set as (i) to relate canopy biophysical parameters and geometry to its bidirectional reflectance, (ii) to evaluate a canopy reflectance model to best represent the radiative transfer within the canopy for its inversion and (iii) to retrieve crop biophysical parameters through inversion of the model. Two varieties of the mustard crop (Brassica juncea L) were grown with two nitrogen treatments. The bidirectional reflectance data obtained at 5 nm interval for a range of 400–1100 nm were integrated to IRS LISS–II sensor’s four band values using Newton Cotes Integration technique. Biophysical parameters like leaf area index, leaf chlorophyll content, leaf length, plant height and average leaf inclination angle, biomass etc were estimated synchronizing with the bi-directional reflectance measurements. Radiative transfer model PROSAIL model was validated and its inversion was done to retrieve LAI and ALA. Look Up Table (LUT) of Bidirectional reflectance distribution function (BRDF) was prepared simulating through PROSAIL model varying only LAI (0.2 interval from 1.2 to 5.4 ) and ALA (5° interval from 40° to 55°) parameters and inversion was done using a merit function and numerical optimization technique given by Press et al. (1986). The derived LAI and ALA values from inversion were well matched with observed one with RMSE 0.521 and 5.57, respectively.     

Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice

The common spectra wavebands and vegetation indices (VI) were identified for indicating leaf nitrogen accumulation (LNA), and the quantitative relationships of LNA to canopy reflectance spectra were determined in both wheat (Triticum aestivum L.) and rice (Oryza sativa L.). The 810 and 870 nm are two common spectral wavebands indicating LNA in both wheat and rice. Among all ratio vegetation indices (RVI), difference vegetation indices (DVI) and normalized difference vegetation indices (NDVI) of 16 wavebands from the MSR16 radiometer, RVI (870, 660) and RVI (810, 660) were most highly correlated to LNA in both wheat and rice. In addition, the relations between VIs and LNA gave better results than relations between single wavebands and LNA in both wheat and rice. Thus LNA in both wheat and rice could be indicated with common VIs, but separate regression equations are better for LNA monitoring.     

山地叶面积指数反演理论、方法与研究进展

叶面积指数LAI（Leaf Area Index）是表征叶片疏密程度和冠层结构特征的重要植被参数,在气候变化、作物生长模型以及碳、水循环研究中发挥着重要作用。遥感是获取区域及全球尺度LAI的一个重要手段,当前LAI产品主要基于遥感数据反演得到,但是多数LAI产品算法并未考虑地形特征的影响,导致山地LAI遥感反演精度不确定性大。提高山地LAI遥感反演精度亟需考虑地形因子对冠层反射率的影响,其中山地冠层反射率模型和遥感数据地形校正是提升山地LAI遥感反演精度的关键。本文围绕山地LAI遥感反演理论与方法,综合分析了国内外山地冠层反射率模型和地形校正模型的研究进展,总结了目前山地LAI遥感反演存在的问题,并讨论了未来研究的发展趋势。     

Combining UAV-based plant height from crop surface models,visible, and near infrared vegetation indices for biomass monitoring in barley

In this study we combined selected vegetation indices (VIs) and plant height information to estimate biomass in a summer barley experiment. The VIs were calculated from ground-based hyperspectral data and unmanned aerial vehicle (UAV)-based red green blue (RGB) imaging. In addition, the plant height information was obtained from UAV-based multi-temporal crop surface models (CSMs). The test site is a summer barley experiment comprising 18 cultivars and two nitrogen treatments located in Western Germany. We calculated five VIs from hyperspectral data. The normalised ratio index (NRI)-based index GnyLi (Gnyp et al., 2014) showed the highest correlation (R² = 0.83) with dry biomass. In addition, we calculated three visible band VIs: the green red vegetation index (GRVI), the modified GRVI (MGRVI) and the red green blue VI (RGBVI), where the MGRVI and the RGBVI are newly developed VI. We found that the visible band VIs have potential for biomass prediction prior to heading stage. A robust estimate for biomass was obtained from the plant height models (R² = 0.80–0.82). In a cross validation test, we compared plant height, selected VIs and their combination with plant height information. Combining VIs and plant height information by using multiple linear regression or multiple non-linear regression models performed better than the VIs alone. The visible band GRVI and the newly developed RGBVI are promising but need further investigation. However, the relationship between plant height and biomass produced the most robust results. In summary, the results indicate that plant height is competitive with VIs for biomass estimation in summer barley. Moreover, visible band VIs might be a useful addition to biomass estimation. The main limitation is that the visible band VIs work for early growing stages only.     

Spectral anisotropy of subtropical deciduous forest using MISR and MODIS data acquired under large seasonal variation in solar zenith angle

Recent studies in Amazonian tropical evergreen forests using the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) have highlighted the importance of considering the view-illumination geometry in satellite data analysis. However, contrary to the observed for evergreen forests, bidirectional effects have not been evaluated in Brazilian subtropical deciduous forests. In this study, we used MISR data to characterize the reflectance and vegetation index anisotropies in subtropical deciduous forest from south Brazil under large seasonal solar zenith angle (SZA) variation and decreasing leaf area index (LAI) from the summer to winter. MODIS data were used to observe seasonal changes in the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Topographic effects on their determination were inspected by dividing data from the summer to winter and projecting results over a digital elevation model (DEM). By using the PROSAIL, we investigated the relative contribution of LAI and SZA to vegetation indices (VI) of deciduous forest. We also simulated and compared the MISR NDVI and EVI response of subtropical deciduous and tropical evergreen forests as a function of the large seasonal SZA amplitude of 33°. Results showed that the MODIS-MISR NDVI and EVI presented higher values in the summer and lower ones in the winter with decreasing LAI and increasing SZA or greater amounts of canopy shadows viewed by the sensors. In the winter, NDVI reduced local topographic effects due to the red-near infrared (NIR) band normalization. However, the contrary was observed for the three-band EVI that enhanced local variations in shaded and sunlit surfaces due to its strong dependence on the NIR band response. The reflectance anisotropy of the MISR bands increased from the summer to winter and was stronger in the backscattering direction at large view zenith angles (VZA). EVI was much more anisotropic than NDVI and the anisotropy increased from the summer to winter. It also increased from the forward scatter to the backscattering direction with the predominance of sunlit canopy components viewed by MISR, especially at large VZA. Modeling PROSAIL results confirmed the stronger anisotropy of EVI than NDVI for the subtropical deciduous and tropical evergreen forests. PROSAIL showed that LAI and SZA are coupled factors to decrease seasonally the VIs of deciduous forest with the first one having greater importance than the latter. However, PROSAIL seasonal variations in VIs were much smaller than those observed with MODIS data probably because the effects of shadows in heterogeneous canopy structures or/and cast by emergent trees and from local topography were not modeled.     

Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction

Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S. Midwest: gap-filled SIF from Orbiting Carbon Observatory 2 (OCO-2), new SIF retrievals from the TROPOspheric Monitoring Instrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2). The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to build yield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that high-resolution SIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yield prediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, using currently available high-resolution SIF products did not guarantee consistently better yield prediction performances than using other satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remote sensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (five-fold-cross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to better yield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yield prediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products could be beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future.     

Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices

The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R² = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.     

Exploring the use of vegetation indices to sense canopy nitrogen to phosphorous ratio in grasses

Reduced availability of plant nutrients such as nitrogen (N) and phosphorous (P) has detrimental effects on plant growth. Plant N:P ratio, calculated as the quotient of N and P concentrations, is an ecological indicator of relative N and P limitation. Remote sensing has already been widely used to detect plant traits in foliage, particularly canopy N and P concentrations and could be used to detect canopy N:P faster and at lower cost than traditional destructive methods. Despite the potential opportunity of applying remote sensing techniques to detect canopy N:P, studies investigating canopy N:P remote detection are scarce. In this study, we examined if vegetation indices developed for canopy N or P detection can also be used for canopy N:P detection. Using in situ spectrometry, we measured the reflectance of a common grass species, Yorkshire fog (Holcus lanatus L.), grown under different nutrient ratios and levels. We calculated 60 VIs found in literature and compared them to optimized VIs developed specifically for this study. The VIs were calculated using both the original narrow band spectra and the spectra resampled to the band properties of six satellite sensors (MSI – Sentinel 2, OLCI – Sentinel 3, MODIS – Terra/Aqua, OLI – Landsat 8, WorldView 4 and RapidEye) to investigate the influence of bandwidths and band positions. The results showed that canopy N:P was significantly related to both existing VIs (r² = 0.16 - 0.48) and optimized VIs (r² = 0.59 – 0.72) with correlations similar to what was observed for canopy N or canopy P. Existing VIs calculated with MSI and OLI sensors bands showed higher correlation with canopy N:P compared to the other sensors while the correlation with optimized VIs was not affected by the differences in sensors’ bands. This study might lead to future practical applications using in situ reflectance measurements to sense canopy N:P in grasslands.