粮食安全是国家安全的重要基础。现代粮食作物生产正朝着精确种植、高效管理、智能决策和定量实施的方向发展。氮素、叶绿素等营养参数与作物叶片光合能力紧密相关，对作物籽粒产量和干物质生产有重要调节作用。遥感技术的快速发展为作物营养状况实时监测提供了有效的途径。虽然基于作物反射光谱的叶绿素和氮素监测已取得了一系列研究成果，但在实际应用中仍然有一些问题有待解决。
在叶片尺度反射光谱测量时，一部分光线进入叶片内部再以漫散射光反射出来（称为漫反射），另一部分未进入叶片内部的光线在叶片表面发生反射（称为镜面反射）。镜面反射不包含生化参数信息，会干扰叶绿素含量（Leaf chlorophyll content, LCC）和氮含量（Lea nitrogen content, LNC）的光谱估算。不同于叶片反射光谱，近地面冠层反射光谱中不仅包含基于单位叶片面积的LCC信息，还受叶面积指数（Leaf area index，LAI）和土壤背景的影响，用于估算LCC时需要消除LAI和土壤背景的影响。另外，采用卫星影像在区域尺度估算LCC时，需要对影像数据进行大气校正得到冠层顶部（Top of canopy，TOC）反射率。然而大气校正过程复杂，所用气象数据的不确定性也会影响TOC反射率估算精度，从而影响了LCC估算效果。在叶片氮含量方面，目前基于可见近红外光谱区间的氮含量估算取得了一系列研究成果，主要依靠叶绿素和氮含量间的高度相关性。反射光谱能准确反映基于叶片面积的叶绿素信息，而过渡到基于质量的叶片氮含量（Mass-based leaf nitrogen content，LNCM）时，则需要考虑干物质含量信息。此外，农业领域常用的是叶片全氮含量，而叶绿素仅能反映与光合作用相关的光合氮信息，并不能直接反映非光合氮信息，使用传统方法估算全氮含量时会存在一定的不确定性。
鉴于此，本文首先从三个尺度（叶片尺度、冠层尺度和区域尺度）对叶绿素含量高光谱估算展开研究，最后将冠层尺度叶绿素估算方法移植到氮含量估算，本文主要研究内容及结果如下：
（1）解析了叶片DHRF和BRF光谱差异的内在机理，消除了镜面反射对经验模型方法和物理模型方法估算LCC的影响。
基于二向反射分布函数解析了积分球测得DHRF光谱和叶片夹测得BRF光谱的漫反射和镜面反射成分，引入镜面反射因子构建了DHRF光谱和BRF光谱的机理联系。叶片BRF光谱中不可忽略的镜面反射效应是叶片DHRF和BRF光谱差异主要原因，限制了基于DHRF光谱的经验模型和物理模型方法在BRF光谱中的应用。本文评价了四种不同数学组合或者计算形式的光谱特征（比值指数、调整比值指数、双差分指数和红边位置）对镜面反射的敏感性，并结合大量实测数据构建了基于DHRF光谱和BRF光谱估算LCC统一经验模型。双差分指数（比如Macc01）和红边位置（比如REPPF）对镜面反射不敏感，可建立DHRF和BRF光谱估算LCC统一模型。此外，耦合PROSPECT和连续小波变换（Continuous wavelet transform，CWT）的物理模型反演新方法PROCWT也可明显消除BRF光谱中镜面反射对PROSPECT标准模型反演的影响，降低了模型估算误差。
（2）提出了对叶面积指数不敏感的新型叶绿素指数LICI，并基于冠层反射率和大气层顶反射率模拟数据，分别构建了高精度LCC~LICI估算模型。
基于冠层辐射传输模型模拟了大量冠层结构场景冠层反射光谱数据，系统解析了已有植被指数对LCC与叶面积指数（Leaf area index，LAI）的敏感性，创建了双指数差值数学组合形式的对LAI不敏感叶绿素指数LAI Insensitive Chlorophyll Index（LICI = R735/R720 - (R573-R680)/(R573+R680)）。基于RowSAIL-PD冠层反射率模拟数据构建的LCC~LICI半经验模型在地面实测验证数据中表现较好。为了避免卫星影像大气校正的复杂性和不确定性，本文基于SAIL和MODTRAN大气层顶反射率模拟数据构建了LCC~LICITOA半经验模型，并结合TROPOMI大气层的反射率估算了全球叶片叶绿素。这些研究结果对于作物叶片叶绿素含量的高通量智能化监测和全球空间制图具有重要的应用价值。
（3）提出了优化作物生长前期冠层光谱观测时间的策略，有效减少光照土壤背景对LCC光谱估算的影响。
针对行播作物生育前期土壤背景对冠层光谱的干扰问题，提出了一种光谱观测时间优化策略，不局限于目前普遍采用的正午观测。基于SAIL和RowSAIL模拟的反射率数据厘清了土壤背景和热点效应对均匀冠层和未封行作物冠层反射率日变化模式的贡献。对于未封行作物来说，光照土壤比例变化是导致冠层反射率日变化的主要因素。对光照土壤比例的纬度变化、季节变化和日变化进行模拟，对于南—北朝向行播作物，在正午时间段（10:00 h - 14:00 h），光照土壤比例达到最大，而在非正午时间段，光照土壤比例逐渐减少，直至为0。因此，对于南—北朝向的未封行作物来说，采用非正午观测可以减少土壤背景对冠层光谱的干扰。将LCC~LICI半经验模型应用于非正午观测光谱的LCC估算误差RMSE为5.01 µg/cm2，显著低于采用传统光谱指数（如MTCI）和正午光谱观测策略估算误差（RMSE为11.50 µg/cm2）。

（4）厘清了基于可见近红外光谱区间的叶片氮含量估算机理，提出了基于氮分配理论的叶片氮含量间接估算模型。
基于叶片全氮由光合氮（与叶绿素相关）和非光合氮（与干物质相关）两部分组成的假设，厘清了可见近红外光谱区间的基于质量叶片氮含量（Mass-based leaf nitrogen content，LNCM）估算机理，构建了基于氮分配理论的LNCM估算模型；以多年稻麦叶片叶绿素、干物质和氮含量的综合叶片数据集为基础，采用线性模型拟合标定LNCM估算模型参数（模型R2为0.82）；结合田间冠层实测数据评价了该模型在冠层尺度估算稻麦LNCM的精度，并与传统基于叶绿素相关指数方法进行了比较。水稻干物质含量显著高于小麦干物质含量，不考虑干物质在叶片氮含量估算中的作用，传统仅基于叶绿素相关指数的氮含量监测模型在稻麦间没有统一的模型，比如CI800,710与叶片氮含量关系模型R2在小麦和水稻全生育期仅为0.27。基于氮分配理论的LNCM估算模型同时适用于小麦和水稻全生育期，稻麦全生育期的LNCM估算R2为0.63，明显优于传统方法。
以上研究揭示了多尺度叶片叶绿素和氮含量光谱监测机理，并构建了高精度的叶绿素和氮含量估算模型，建立了多尺度一体化的稻麦作物叶绿素和氮素营养状况实时监测技术，对氮肥精确管理有着重要的学术意义和应用价值。

Food security is an important guarantee for promoting harmonious social and economic development and building a well-off society in an all-round way. Modern food crop production is developing in the direction of precise planting, efficient management, intelligent decision-making and quantitative implementation, forming an integrated precision crop cultivation technology with the features of real-time monitoring, intelligent diagnosis, and quantitative regulation. Nutrient parameters such as nitrogen and chlorophyll are closely related to the photosynthetic capacity of crop leaves, and have an important regulating effect on crop grain yield and dry matter production. The rapid development of modern remote sensing technology provides an effective way for real-time monitoring of crop nutritional status. Although chlorophyll and nitrogen monitoring based on crop reflectance spectra have made a series of research results, there are still some problems to be solved in practical applications, such as the effects of specular reflection at the leaf scale, leaf area index (LAI) and soil background at the canopy scale, and the atmospheric correction uncertainty at the satellite scale on leaf chlorophyll content (LCC) estimation as well as the unclear mechanism of mass-based leaf nitrogen concentration (LNCM) estimation in the visible and near infrared spectral region. In view of this, this thesis first conducts research on hyperspectral estimation of LCC at three scales (leaf scale, canopy scale and regional scale), and finally transitions to LNCM estimation. The main research contents and results of this thesis are as follows:
(1) To analyze the internal mechanism of the difference between leaf DHRF and BRF spectra, and mitigate the influence of the specular reflection on the estimation of LCC by the empirical model method and the physical model method.
Based on the bidirectional reflection distribution function, the diffuse reflection and specular reflection components from the directional hemispheric reflection factor (DHRF) spectrum measured by the integrating sphere and the bidirectional reflection factor (BRF) spectrum measured by the leaf clip are analyzed. Mechanism connection between DHRF and BRF spectra was established by introducing the specular reflection factor. The non-negligible specular reflection in the BRF spectra is the main reason for the difference between the DHRF and BRF spectra, limiting the application of the empirical model and physical model methods based on the DHRF spectra to the BRF spectra. We evaluated the sensitivity of four types of spectral features with different mathematical combinations or calculation forms (ratio index, adjusted ratio index, double difference index and red edge position) to the specular reflection, and established a unified empirical model for LCC estimation across DHRF and BRF spectra based on a large amount of measured data. As a result, the double difference index (such as Macc01) and the red edge position (such as REPPF) are not sensitive to the specular reflection and hence can be used to establish the unified models for LCC estimation across DHRF and BRF spectra. In addition, PROCWT, a new method of physical model inversion coupled with PROSPECT and continuous wavelet transform (CWT), can also significantly eliminate the influence of specular reflection in the BRF spectra on the inversion of LCC compared to the PROSPECT standard inversion model, reducing model inversion errors. After eliminating the effect of specular reflection, both the empirical model method and the physical model method show good consistency in LCC estimation when applied to imaging hyperspectral data.
(2) To propose LAI-insensitive chlorophyll index (LICI), and establish semi-empirical models of LCC~LICI based on simulated reflectance at the top of canopy and at the top of atmosphere (TOA).
Based on the canopy radiation transfer model, canopy reflectance of a large number of canopy structural scenarios were simulated to analyze the sensitivity of the existing vegetation indices to LCC and LAI, and then propose LAI-Insensitive Chlorophyll Index (LICI = R735/R720-(R573-R680)/(R573+R680)). The first part of LICI (R735/R720) is positively correlated with both LAI and LCC, while the second part (R573-R680)/(R573+R680) is positively correlated with LAI and negatively correlated with LCC. The subtraction of the two parts weakened the correlation between LICI and LAI correlation, and enhanced the correlation between LICI and LCC. The LCC~LICI semi-empirical model constructed based on the RowSAIL-PD performed well in the independent ground validation data. In addition, in order to avoid the complexity and uncertainty of atmospheric correction of satellite images, this thesis constructs the LCC~LICITOA semi-empirical model based on the TOA reflectance simulated with coupled SAIL and MODTRAN. This model was successfully used to estimate global LCC based on the TROPOMI TOA reflectance. These results have important application value for high-throughput intelligent monitoring of LCC in crops and global mapping of LCC.
(3) To propose a strategy of optimizing the canopy spectra observation time in the early growth stage of crop, which can effectively reduce the influence of sunlit soil background on the LCC estimation.
To mitigate the effect of soil background on canopy spectrum during the early growth stage of row-planted crops, a strategy of optimizing the canopy spectra observation time is proposed, which is different from the traditional spectral observation during noon time. The SAIL and RowSAIL simulations were used to clarify the contribution of soil background and hotspot effects to the diurnal variation pattern of uniform canopies and open row-crop canopies. For open row crops, the change in the proportion of sunlit soil is the main factor leading to the daily variation in canopy reflectance. By simulating the latitude variation, seasonal variation, and diurnal variation of sunlit soil fraction, the sunlit soil fraction was highest during the noon period (10:00 h-14:00 h) for north-south orientation crop, while for off-noon period the sunlit soil fraction gradually decreased until it reached to zero. Therefore, for open crops in the north-south orientation, off-noon spectral observations can reduce the effect of the soil background on the canopy spectra. The estimation error (RMSE) of LCC was 5.01 µg/cm2 by applying the LCC~LICI semi-empirical model to the off-noon observed spectra, which was significantly lower than that of using traditional spectral index (such as MTCI) and noon observed spectra (RMSE is 11.50 µg/cm2).
(4) To clarify the estimation mechanism of LNCM in the visible and near infrared spectral region, and to propose an indirect estimation model of LNCM based on nitrogen distribution theory.
Based on the hypothesis that leaf total nitrogen consisted of two parts: photosynthetic nitrogen (related to chlorophyll) and non-photosynthetic nitrogen (related to dry matter), the estimation mechanism of LNCM in the visible and near infrared spectral region was clarified, and the LNCM estimation model based on nitrogen distribution theory was proposed. Using the comprehensive dataset including LNCM, LCC, and LMA of rice and wheat leaves for many years, the leaf-scale LNCM model was parameterized through a linear model fitting (R2 is 0.82). Its performance in canopy-scale LNCM estimation was evaluated using measurements in wheat and rice fields and was compared with the traditional method based on the chlorophyll related index. LMA in rice was significantly higher than that of wheat. If the role of LMA in the estimation of LNCM was not considered, the traditional LNCM monitoring model based on the chlorophyll related index (such as CI800,710) did not have a unified model between rice and wheat. The relationship between LNCM and CI800,710 was only 0.27 for the whole growth period of wheat and rice. On the contrary, the our LNCM estimation model based on LCC and LMA can be applied to the whole growth period of wheat and rice, with R2 of 0.63.
The above results provide theoretical and technical support for real-time and high-precision monitoring of chlorophyll and nitrogen nutrition status of rice and wheat crops by hyperspectral remote sensing, and provide an important reference basis for the formulation and implementation of crop cultivation plans.

Alexander, B., Patrick, C., Fred, H. (2015). An accelerated line-by-line option for MODTRAN combining on-the-fly generation of line center absorption within 0.1 cm-1 bins and pre-computed line tails. Proc.SPIE. http://doi.org/10.1117/12.2177444

Alexander, B., Patrick, C., Rosemary, K., Timothy, P., Frederick, H., Jeannette van den, B. (2014). MODTRAN6: a major upgrade of the MODTRAN radiative transfer code. Proc.SPIE. http://doi.org/10.1117 /12.2050433

Ali, A.M., Darvishzadeh, R., Skidmore, A.K., Duren, I.v., Heiden, U., Heurich, M. (2016). Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest. International Journal of Applied Earth Observation and Geoinformation, 45: 66-76. http://doi.org/10.1016/j.jag.2015.11.004

Anderson, J.M. (1986). Photoregulation of the composition, function, and structure of thylakoid membranes. Annual Review of Plant Biology, 37: 93-136. http://doi.org/10.1146/annurev.pp.37.060186.000521

Andrieu, B., Baret, F., Jacquemoud, S., Malthus, T., Steven, M. (1997). Evaluation of an improved version of SAIL model for simulating bidirectional reflectance of sugar beet canopies. Remote Sensing of Environment, 60: 247-257. http://doi.org/10.1016/s0034-4257(96)00126-5

Asrar, G., Kanemasu, E.T., Yoshida, M. (1985). Estimates of leaf area index from spectral reflectance of wheat under different cultural practices and solar angle. Remote Sensing of Environment, 17: 1-11. http://doi.org/10.1016/0034-4257(85)90108-7

Baret, F., Fourty, T. (1997). Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements. Agronomie, 17: 455-464.

Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., Smets, B. (2013). GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137: 299-309. http://doi.org/10.1016/j.rse.2012.12.027

Barry, K.M., Newnham, G.J., Stone, C. (2009). Estimation of chlorophyll content in Eucalyptus globulus foliage with the leaf reflectance model PROSPECT. Agricultural and Forest Meteorology, 149: 1209-1213. http://doi.org/10.1016/j.agrformet.2009.01.005

Bayat, B., van der Tol, C., Verhoef, W. (2020). Retrieval of land surface properties from an annual time series of Landsat TOA radiances during a drought episode using coupled radiative transfer models. Remote Sensing of Environment, 238: 110917. http://doi.org/10.1016/j.rse.2018.09.030

Berger, K., Verrelst, J., Féret, J.-B., Hank, T., Wocher, M., Mauser, W., Camps-Valls, G. (2020a). Retrieval of aboveground crop nitrogen content with a hybrid machine learning method. International Journal of Applied Earth Observation and Geoinformation, 92: 102174. http://doi.org/10.1016/j.jag.2020.102174

Berger, K., Verrelst, J., Féret, J.-B., Wang, Z., Wocher, M., Strathmann, M., et al. (2020b). Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions. Remote Sensing of Environment, 242: 111758. http://doi.org/10.1016/j.rse.2020.111758

Blackburn, G.A., Ferwerda, J.G. (2008). Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sensing of Environment, 112: 1614-1632. http://doi.org/10.1016 /j.rse.2007.08.005

Bousquet, L., Lachérade, S., Jacquemoud, S., Moya, I. (2005). Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sensing of Environment, 98: 201-211. http://doi.org/10.1016/j.rse.2005.07.005

Brede, B., Suomalainen, J., Bartholomeus, H., Herold, M. (2015). Influence of solar zenith angle on the enhanced vegetation index of a Guyanese rainforest. Remote Sensing Letters, 6: 972-981. http://doi.org/10.1080/2150704X.2015.1089362

Bullock, D., Anderson, D. (1998). Evaluation of the Minolta SPAD‐502 chlorophyll meter for nitrogen management in corn. Journal of Plant Nutrition, 21: 741-755.

Butz, A., Galli, A., Hasekamp, O., Landgraf, J., Tol, P., Aben, I. (2012). TROPOMI aboard Sentinel-5 Precursor: Prospective performance of CH4 retrievals for aerosol and cirrus loaded atmospheres. Remote Sensing of Environment, 120: 267-276. http://doi.org/10.1016/j.rse.2011.05.030

Camacho, F., Cernicharo, J., Lacaze, R., Baret, F., Weiss, M. (2013). GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sensing of Environment, 137: 310-329. http://doi.org/10.1016/j.rse.2013.02.030

Cersosimo, A., Serio, C., Masiello, G. (2020). TROPOMI NO2 tropospheric column data: regridding to 1 km grid-resolution and assessment of their consistency with in situ surface observations. Remote Sensing, 12: 2212. http://doi.org/10.3390/rs12142212

Chen, H., Huang, W., Li, W., Niu, Z., Zhang, L., Xing, S. (2018). Estimation of LAI in winter wheat from multi-angular hyperspectral VNIR data: Effects of view angles and plant architecture. Remote Sensing, 10: 1630. http://doi.org/10.3390/rs10101630

Cheng, T., Li, D., Zheng, H., Yao, X., Tian, Y., Zhu, Y., Cao, W. (Year). Towards decomposing the effects of foliar nitrogen content and canopy structure on rice canopy spectral variability through multi-scale spectral analysis. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). http://doi.org/10.1109/IGARSS.2016.7729907

Cheng, T., Ria?o, D., Ustin, S.L. (2014a). Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sensing of Environment, 143: 39-53. http://doi.org/10.1016/j.rse.2013.11.018

Cheng, T., Rivard, B., Sánchez-Azofeifa, A. (2011). Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sensing of Environment, 115: 659-670. http://doi.org/10.1016/j.rse.2010.11.001

Cheng, T., Rivard, B., Sanchez-Azofeifa, A.G., Féret, J.-B., Jacquemoud, S., Ustin, S.L. (2012). Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. Journal of Plant Physiology, 169: 1134-1142. http://doi.org/10.1016 /j.jplph.2012.04.006

Cheng, T., Rivard, B., Sánchez-Azofeifa, A.G., Féret, J.-B., Jacquemoud, S., Ustin, S.L. (2014b). Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 28-38. http://doi.org/10.1016/ j.isprsjprs.2013.10.009

Cheng, T., Rivard, B., Sanchez-Azofeifa, G.A., Feng, J., Calvo-Polanco, M. (2010). Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 114: 899-910. http://doi.org/10.1016/j.rse.2009.12.005

Cheng, T., Zhu, Y., Li, D., Yao, X., Zhou, K. (2018). Hyperspectral remote sensing of leaf nitrogen concentration in cereal crops. In P.S. Thenkabail,J.G. Lyon,A. Huete (Eds.), Hyperspectral Indices and Image Classifications for Agriculture and Vegetation. Boca Raton: CRC Press. http://doi.org/10.1201/ 9781315159331-6

Cho, M.A., Skidmore, A.K. (2006). A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment, 101: 181-193. http://doi.org/10.1016/ j.rse.2005.12.011

Croft, H., Arabian, J., Chen, J.M., Shang, J., Liu, J. (2019). Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery. Precision Agriculture: . http://doi.org/10.1007/s11119-019-09698-y

Croft, H., Chen, J.M. (2018). Leaf Pigment Content. In S. Liang (Ed.), Comprehensive Remote Sensing (pp. 117-142). Oxford: Elsevier. http://doi.org/10.1016/B978-0-12-409548-9.10547-0

Croft, H., Chen, J.M., Luo, X., Bartlett, P., Chen, B., Staebler, R.M. (2017). Leaf chlorophyll content as a proxy for leaf photosynthetic capacity. Global Change Biology, 23: 3513-3524. http://doi.org/10.1111/ gcb.13599

Croft, H., Chen, J.M., Wang, R., Mo, G., Luo, S., Luo, X., et al. (2020). The global distribution of leaf chlorophyll content. Remote Sensing of Environment, 236: 111479. http://doi.org/10.1016/ j.rse.2019.111479

Croft, H., Chen, J.M., Zhang, Y. (2014). The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecological Complexity, 17: 119-130. http://doi.org/10.1016/j.ecocom.2013.11.005

Curran, P.J. (1989). Remote sensing of foliar chemistry. Remote Sensing of Environment, 30: 271-278. http://doi.org/10.1016/0034-4257(89)90069-2

Curran, P.J., Dungan, J.L., Peterson, D.L. (2001). Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76: 349-359. http://doi.org/10.1016/S0034-4257(01)00182-1

Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C. (2008). Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of Environment, 112: 2592-2604. http://doi.org/10.1016/j.rse.2007.12.003

Dash, J., Curran, P.J. (2004). The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 25: 5403-5413. http://doi.org/10.1080/0143116042000274015

Datt, B. (1999a). A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves. Journal of Plant Physiology, 154: 30-36. http://doi.org/10.1016/ S0176-1617(99)80314-9

Datt, B. (1999b). Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. International Journal of Remote Sensing, 20: 2741-2759. http://doi.org/10.1080/014311699211778

Daughtry, C.S.T., Walthall, C.L., Kim, M.S., de Colstoun, E.B., McMurtrey, J.E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74: 229-239. http://doi.org/10.1016/s0034-4257(00)00113-9

de Souza, E.G., Scharf, P.C., Sudduth, K.A. (2010). Sun position and cloud effects on reflectance and vegetation indices of corn. Agronomy Journal, 102: 734-744. http://doi.org/10.2134/agronj2009.0206

Demotes-Mainard, S., Jeuffroy, M.-H. (2004). Effects of nitrogen and radiation on dry matter and nitrogen accumulation in the spike of winter wheat. Field Crops Research, 87: 221-233. http://doi.org/10.1016/j.fcr.2003.11.014

Dreccer, M., Van Oijen, M., Schapendonk, A., Pot, C., Rabbinge, R. (2000). Dynamics of vertical leaf nitrogen distribution in a vegetative wheat canopy. Impact on canopy photosynthesis. Annals of Botany, 86: 821-831.

Duffie, J.A., Beckman, W.A. (2013). Solar Engineering of Thermal Processes. John Wiley & Sons. http://doi.org/10.1002/9781118671603

Estévez, J., Vicent, J., Rivera-Caicedo, J.P., Morcillo-Pallarés, P., Vuolo, F., Sabater, N., et al. (2020). Gaussian processes retrieval of LAI from Sentinel-2 top-of-atmosphere radiance data. ISPRS Journal of Photogrammetry and Remote Sensing, 167: 289-304. http://doi.org/10.1016/j.isprsjprs.2020.07.004

Evans, J.R. (1989). Photosynthesis and nitrogen relationships in leaves of C? plants. Oecologia, 78: 9-19. http://doi.org/10.1007/BF00377192

Fang, H., Liang, S. (2003). Retrieving leaf area index with a neural network method: simulation and validation. IEEE Transactions on Geoscience and Remote Sensing, 41: 2052-2062. http://doi.org/10.1109/TGRS.2003.813493

Farquhar, G.D., Caemmerer, S.V., Berry, J.A. (1980). A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta, 149: 78-90. http://doi.org/10.1007/BF00386231

Faurtyot, T., Baret, F. (1997). Vegetation water and dry matter contents estimated from top-of-the-atmosphere reflectance data: A simulation study. Remote Sensing of Environment, 61: 34-45. http://doi.org/10.1016/S0034-4257(96)00238-6

Feng, W., Zhang, H.-Y., Zhang, Y.-S., Qi, S.-L., Heng, Y.-R., Guo, B.-B., et al. (2016). Remote detection of canopy leaf nitrogen concentration in winter wheat by using water resistance vegetation indices from in-situ hyperspectral data. Field Crops Research, 198: 238-246. http://doi.org/10.1016/j.fcr.2016.08.023

Fensholt, R., Sandholt, I., Stisen, S. (2006). Evaluating MODIS, MERIS, and VEGETATION vegetation indices using in situ measurements in a semiarid environment. IEEE Transactions on Geoscience and Remote Sensing, 44: 1774-1786. http://doi.org/10.1109/TGRS.2006.875940

Féret, J.-B., Asner, G.P. (2011). Spectroscopic classification of tropical forest species using radiative transfer modeling. Remote Sensing of Environment, 115: 2415-2422. http://doi.org/10.1016/j.rse.2011.05.004

Féret, J.-B., Fran?ois, C., Asner, G.P., Gitelson, A.A., Martin, R.E., Bidel, L.P., et al. (2008). PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112: 3030-3043. http://doi.org/10.1016/j.rse.2008.02.012

Féret, J.-B., Fran?ois, C., Gitelson, A., Asner, G.P., Barry, K.M., Panigada, C., et al. (2011). Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment, 115: 2742-2750. http://doi.org/10.1016/j.rse.2011.06.016

Féret, J.-B., Gitelson, A.A., Noble, S.D., Jacquemoud, S. (2017). PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle. Remote Sensing of Environment, 193: 204-215. http://doi.org/10.1016/j.rse.2017.03.004

Féret, J.B., le Maire, G., Jay, S., Berveiller, D., Bendoula, R., Hmimina, G., et al. (2019). Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning. Remote Sensing of Environment: 110959. http://doi.org/10.1016/j.rse.2018.11.002

Friedl, M., Sulla-Menashe, D. (2015). MCD12C1 MODIS/Terra+Aqua land cover type yearly L3 global 0.05deg CMG V006. In: Distributed by NASA EOSDIS Land Processes DAAC

Galv?o, L.S., Ponzoni, F.J., Epiphanio, J.C.N., Rudorff, B.F.T., Formaggio, A.R. (2004). Sun and view angle effects on NDVI determination of land cover types in the Brazilian Amazon region with hyperspectral data. International Journal of Remote Sensing, 25: 1861-1879. http://doi.org/10.1080/ 01431160310001598908

Gao, B.C., Goetz, A.F.H. (1994). Extraction of dry leaf spectral features from reflectance spectra of green vegetation. Remote Sensing of Environment, 47: 369-374. http://doi.org/10.1016/ 0034- 4257 (94) 90104-X

Gitelson, A., Solovchenko, A. (2017a). Generic algorithms for estimating foliar pigment content. Geophysical Research Letters, 44: 9293-9298. http://doi.org/10.1002/2017GL074799

Gitelson, A., Solovchenko, A. (2017b). Non-invasive quantification of foliar pigments: Possibilities and limitations of reflectance- and absorbance-based approaches. Journal of Photochemistry and Photobiology B: Biology. http://doi.org/10.1016/j.jphotobiol.2017.11.023

Gitelson, A., Vi?a, A., Solovchenko, A., Arkebauer, T., Inoue, Y. (2019). Derivation of canopy light absorption coefficient from reflectance spectra. Remote Sensing of Environment, 231: 111276. http://doi.org/10.1016/j.rse.2019.111276

Gitelson, A.A., Chivkunova, O.B., Merzlyak, M.N. (2009). Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. American Journal of Botany, 96: 1861-1868. http://doi.org/10.3732/ajb.0800395

Gitelson, A.A., Gritz, Y., Merzlyak, M.N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160: 271-282. http://doi.org/10.1078/0176-1617-00887

Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58: 289-298. http://doi.org/10.1016/s0034-4257(96)00072-7

Gitelson, A.A., Keydan, G.P., Merzlyak, M.N. (2006). Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33. http://doi.org/10.1029/2006gl026457

Gitelson, A.A., Merzlyak, M.N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18: 2691-2697. http://doi.org/10.1080/014311697217558

Gitelson, A.A., Merzlyak, M.N., Chivkunova, O.B. (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74: 38-45. http://doi.org/10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2

Gitelson, A.A., Vi?a, A., Ciganda, V., Rundquist, D.C., Arkebauer, T.J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: L08403. http://doi.org/ 10.1029/2005gl022688

Gitelson, A.A., Zur, Y., Chivkunova, O.B., Merzlyak, M.N. (2002). Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75: 272-281. http://doi.org/10.1562/0031-8655(2002)075<0272:accipl>2.0.co;2

Gnyp, M.L., Panitzki, M., Reusch, S. (2015). Proximal nitrogen sensing by off-nadir and nadir measurements in winter wheat canopy. Precision agriculture'15 (pp. 43 - 50): Wageningen Academic Publishers. http://doi.org/10.3920/978-90-8686-814-8_4

Griffin, D., Zhao, X., McLinden, C.A., Boersma, F., Bourassa, A., Dammers, E., et al. (2019). High-resolution mapping of nitrogen dioxide with TROPOMI: First results and validation over the canadian oil sands. Geophysical Research Letters, 46: 1049-1060. http://doi.org/10.1029/2018GL081095

Gross, M.F., Hardisky, M.A., Klemas, V. (1988). Effects of solar angle on reflectance from wetland vegetation. Remote Sensing of Environment, 26: 195-212. http://doi.org/10.1016/0034-4257(88)90077-6

Guanter, L., Aben, I., Tol, P., Krijger, J.M., Hollstein, A., K?hler, P., et al. (2015a). Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence. Atmospheric Measurement Techniques, 8: 1337-1352. http://doi.org/10.5194/amt-8-1337-2015

Guanter, L., Kaufmann, H., Segl, K., Foerster, S., Rogass, C., Chabrillat, S., et al. (2015b). The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sensing, 7: 8830-8857.

Guo, B.B., Zhu, Y.J., Feng, W., He, L., Wu, Y.P., Zhou, Y., et al. (2018). Remotely estimating aerial N uptake in winter wheat using red-ddge area index from multi-angular hyperspectral data. Frontiers in Plant Science, 9: 675. http://doi.org/10.3389/fpls.2018.00675

Guyot, G., Baret, F., Jacquemoud, S. (1992). Imaging spectroscopy for vegetation studies. Imaging Spectroscopy : Fundamentals and Prospective Applications, 2: 145-165.

Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81: 416-426. http://doi.org/10.1016/s0034-4257(02)00018-4

Haboudane, D., Tremblay, N., Miller, J.R., Vigneault, P. (2008). Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 46: 423-437. http://doi.org/10.1109/tgrs.2007.904836

He, J., Zhang, X., Guo, W., Pan, Y., Yao, X., Cheng, T., et al. (2020). Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data. Frontiers in Plant Science, 10. http://doi.org/10.3389/fpls.2019.01802

He, L., Song, X., Feng, W., Guo, B.-B., Zhang, Y.-S., Wang, Y.-H., et al. (2016). Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data. Remote Sensing of Environment, 174: 122-133. http://doi.org/10.1016/j.rse.2015.12.007

Hirose, T., Werger, M. (1987). Maximizing daily canopy photosynthesis with respect to the leaf nitrogen allocation pattern in the canopy. Oecologia, 72: 520-526. http://doi.org/10.1007/BF00378977

Houborg, R., McCabe, M., Cescatti, A., Gao, F., Schull, M., Gitelson, A. (2015). Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC). Remote Sensing of Environment, 159: 203-221. http://doi.org/10.1016/j.rse.2014.12.008

Hsia, J.J., Richmond, J.C. (1976). Bidirectional reflectometry part I. A high resolution laser bi-directional reflectometer with results on several optical coatings. Journal of Reseach of the National Bureau of Standards-A. Physics and Chemistry A, 80: 189-205. http://doi.org/10.6028/jres.080A.021

Hu, H., Landgraf, J., Detmers, R., Borsdorff, T., Aan de Brugh, J., Aben, I., et al. (2018). Toward global mapping of methane with TROPOMI: First results and intersatellite comparison to GOSAT. Geophysical Research Letters, 45: 3682-3689. http://doi.org/10.1002/2018GL077259

Huang, W., Wang, Z., Huang, L., Lamb, D.W., Ma, Z., Zhang, J., et al. (2011). Estimation of vertical distribution of chlorophyll concentration by bi-directional canopy reflectance spectra in winter wheat. Precision Agriculture, 12: 165-178.

Huete, A.R. (1987). Soil and Sun angle interactions on partial canopy spectra. International Journal of Remote Sensing, 8: 1307-1317. http://doi.org/10.1080/01431168708954776

Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25: 295-309. http://doi.org/10.1016/0034-4257(88)90106-X

Hunt, E.R., Doraiswamy, P.C., McMurtrey, J.E., Daughtry, C.S.T., Perry, E.M., Akhmedov, B. (2013). A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21: 103-112. http://doi.org/10.1016/j.jag.2012.07.020

Inoue, Y., Guerif, M., Baret, F., Skidmore, A., Gitelson, A., Schlerf, M., et al. (2016). Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation. Plant, Cell and Environment, 39: 2609-2623. http://doi.org/10.1111/pce.12815

Ishihara, M., Inoue, Y., Ono, K., Shimizu, M., Matsuura, S. (2015). The impact of sunlight conditions on the consistency of vegetation indices in croplands-effective usage of vegetation indices from continuous ground-based spectral measurements. Remote Sensing, 7: 14079-14098. http://doi.org/10.3390/ rs71014079

Jackson, R.D., Pinter, P.J., Idso, S.B., Reginato, R.J. (1979). Wheat spectral reflectance: interactions between crop configuration, sun elevation, and azimuth angle. Applied Optics, 18: 3730-3732. http://doi.org/10.1364/AO.18.003730

Jacquemoud, S. (1993). Inversion of the PROSPECT + SAIL canopy reflectance model from AVIRIS equivalent spectra: theoretical study. Remote Sensing of Environment, 44: 281-292. http://doi.org/10.1016/ 0034-4257(93)90022-p

Jacquemoud, S., Baret, F. (1990). PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34: 75-91. http://doi.org/10.1016/0034-4257(90)90100-z

Jacquemoud, S., Ustin, S.L., Verdebout, J., Schmuck, G., Andreoli, G., Hosgood, B. (1996). Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sensing of Environment, 56: 194-202. http://doi.org/10.1016/0034-4257(95)00238-3

Jay, S., Bendoula, R., Hadoux, X., Féret, J.-B., Gorretta, N. (2016). A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy. Remote Sensing of Environment, 177: 220-236. http://doi.org/10.1016/j.rse.2016.02.029

Jay, S., Maupas, F., Bendoula, R., Gorretta, N. (2017). Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping. Field Crops Research, 210: 33-46. http://doi.org/10.1016/j.fcr.2017.05.005

Jia, M., Zhu, J., Ma, C., Alonso, L., Li, D., Cheng, T., et al. (2018). Difference and potential of the upward and downward sun-induced chlorophyll fluorescence on detecting leaf nitrogen concentration in wheat. Remote Sensing, 10: 1315. http://doi.org/10.3390/rs10081315

Jiao, Q., Zhang, B., Liu, J., Liu, L. (2014). A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index. International Journal of Remote Sensing, 35: 7363-7375. http://doi.org/10.1080/2150704X.2014.968681

Ju, C.-H., Tian, Y.-C., Yao, X., Cao, W.-X., Zhu, Y., Hannaway, D. (2010). Estimating leaf chlorophyll content using red edge parameters. Pedosphere, 20: 633-644. http://doi.org/10.1016/S1002-0160(10)60053-7

Kattenborn, T., Schiefer, F., Zarco-Tejada, P., Schmidtlein, S. (2019). Advantages of retrieving pigment content [μg/cm2] versus concentration [%] from canopy reflectance. Remote Sensing of Environment, 230: 111195. http://doi.org/10.1016/j.rse.2019.05.014

Kimes, D.S., Smith, J.A., Ranson, K.J. (1979). Interpreting vegetation reflectance measurements as a function of solar zenith angle.

Knyazikhin, Y., Schull, M.A., Stenberg, P., Mottus, M., Rautiainen, M., Yang, Y., et al. (2013). Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences of the United States of America, 110: E185-E192. http://doi.org/10.1073/pnas.1210196109

K?hler, P., Frankenberg, C., Magney, T.S., Guanter, L., Joiner, J., Landgraf, J. (2018). Global retrievals of Solar‐Induced Chlorophyll Fluorescence with TROPOMI: First results and intersensor comparison to OCO‐2. Geophysical Research Letters, 45: 10456-10463. http://doi.org/10.5194/bg-12-4067-2015

Kokaly, R.F. (2001). Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment, 75: 153-161. http://doi.org/10.1016/s0034-4257(00)00163-2

Kokaly, R.F., Clark, R.N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67: 267-287. http://doi.org/10.1016/s0034-4257(98)00084-4

Kollenkark, J., Vanderbilt, V., Daughtry, C., Bauer, M. (1982). Influence of solar illumination angle on soybean canopy reflectance. Applied Optics, 21: 1179-1184. http://doi.org/10.1364/AO.21.001179

Kong, W., Huang, W., Casa, R., Zhou, X., Ye, H., Dong, Y. (2017). Off-nadir hyperspectral sensing for estimation of vertical profile of leaf chlorophyll content within wheat canopies. Sensors (Basel), 17: 2711. http://doi.org/10.3390/s17122711

Laurent, V.C.E., Schaepman, M.E., Verhoef, W., Weyermann, J., Chávez, R.O. (2014). Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image. Remote Sensing of Environment, 140: 318-329. http://doi.org/10.1016/j.rse.2013.09.005

Laurent, V.C.E., Verhoef, W., Clevers, J.G.P.W., Schaepman, M.E. (2011a). Estimating forest variables from top-of-atmosphere radiance satellite measurements using coupled radiative transfer models. Remote Sensing of Environment, 115: 1043-1052. http://doi.org/10.1016/j.rse.2010.12.009

Laurent, V.C.E., Verhoef, W., Clevers, J.G.P.W., Schaepman, M.E. (2011b). Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study. Remote Sensing of Environment, 115: 2603-2612. http://doi.org/10.1016/j.rse.2011.05.016

Laurent, V.C.E., Verhoef, W., Damm, A., Schaepman, M.E., Clevers, J.G.P.W. (2013). A Bayesian object-based approach for estimating vegetation biophysical and biochemical variables from APEX at-sensor radiance data. Remote Sensing of Environment, 139: 6-17. http://doi.org/10.1016/j.rse.2013.07.032

Lauvernet, C., Baret, F., Hasco?t, L., Buis, S., Le Dimet, F.-X. (2008). Multitemporal-patch ensemble inversion of coupled surface–atmosphere radiative transfer models for land surface characterization. Remote Sensing of Environment, 112: 851-861. http://doi.org/10.1016/j.rse.2007.06.027

le Maire, G., Fran?ois, C., Dufrêne, E. (2004). Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89: 1-28. http://doi.org/10.1016/j.rse.2003.09.004

Li, D., Chen, J.M., Zhang, X., Yan, Y., Zhu, J., Zheng, H., et al. (2020). Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time. Remote Sensing of Environment, 248: 111985. http://doi.org/10.1016/j.rse.2020.111985

Li, D., Cheng, T., Jia, M., Zhou, K., Lu, N., Yao, X., et al. (2018a). PROCWT: Coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra. Remote Sensing of Environment, 206: 1-14. http://doi.org/10.1016/j.rse.2017.12.013

Li, D., Cheng, T., Zhou, K., Zheng, H., Yao, X., Tian, Y., et al. (2017a). WREP: A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops. ISPRS Journal of Photogrammetry and Remote Sensing, 129: 103-117. http://doi.org/10.1016/j.isprsjprs.2017.04.024

Li, D., Tian, L., Wan, Z., Jia, M., Yao, X., Tian, Y., et al. (2019). Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra. Remote Sensing of Environment, 231: 111240. http://doi.org/10.1016/j.rse.2019.111240

Li, D., Wang, X., Zheng, H., Zhou, K., Yao, X., Tian, Y., et al. (2018b). Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis. Plant Methods, 14: 76. http://doi.org/10.1186/s13007-018-0344-1

Li, F., Miao, Y., Hennig, S.D., Gnyp, M.L., Chen, X., Jia, L., Bareth, G. (2010). Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture, 11: 335-357. http://doi.org/10.1007/s11119-010-9165-6

Li, F., Mistele, B., Hu, Y., Chen, X., Schmidhalter, U. (2014). Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. European Journal of Agronomy, 52: 198-209. http://doi.org/10.1016/j.eja.2013.09.006

Li, H., Zhao, C., Yang, G., Feng, H. (2015). Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes. Remote Sensing of Environment, 169: 358-374. http://doi.org/https://doi.org/10.1016/j.rse.2015.08.021

Li, P., Wang, Q. (2011). Retrieval of leaf biochemical parameters using PROSPECT inversion: A new approach for alleviating ill-posed problems. IEEE Transactions on Geoscience and Remote Sensing, 49: 2499-2506. http://doi.org/10.1109/tgrs.2011.2109390

Li, P.H., Wang, Q. (2013). Retrieval of chlorophyll for assimilating branches of a typical desert plant through inversed radiative transfer models. International Journal of Remote Sensing, 34: 2402-2416. http://doi.org/10.1080/01431161.2012.744859

Li, Y., Huang, J. (2019). Remote Sensing of Pigment Content at a Leaf Scale: Comparison among Some Specular Removal and Specular Resistance Methods. Remote Sensing, 11: 983. http://doi.org/10.3390/rs11080983

Li, Y., Liu, Y., Wu, S., Wang, C., Xu, A., Pan, X. (2017b). Hyper-spectral estimation of wheat biomass after alleviating of soil effects on spectra by non-negative matrix factorization. European Journal of Agronomy, 84: 58-66. http://doi.org/10.1016/j.eja.2016.12.003

Liu, S., Yang, B., Zhang, Z., Xiang, Y., Wu, T., Zhao, Y., Zhang, F. (2020). Influence of polarized reflection on airborne remote sensing of canopy foliar nitrogen content. International Journal of Remote Sensing, 41: 4879-4900. http://doi.org/10.1080/01431161.2020.1718242

Lord, D., Desjardins, R.L., Dubé, P.A. (1988). Sun-angle effects on the red and near infrared reflectances of five different crop canopies. Canadian Journal of Remote Sensing, 14: 46-55. http://doi.org/10.1080/07038992.1988.10855118

Lu, J., Li, W., Yu, M., Zhang, X., Ma, Y., Su, X., et al. Estimation of rice plant potassium accumulation based on non?negative matrix factorization using hyperspectral reflectance.

Lu, N., Wang, W., Zhang, Q., Li, D., Yao, X., Tian, Y., et al. (2019). Estimation of nitrogen nutrition status in winter wheat from unmanned aerial vehicle based multi-angular multispectral imagery. Frontiers in Plant Science, 10: 1601. http://doi.org/10.3389/fpls.2019.01601

Lu, S., Lu, F., You, W., Wang, Z., Liu, Y., Omasa, K. (2018). A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons. Plant Methods, 14: 15. http://doi.org/10.1186/s13007-018-0281-z

Luo, X., Croft, H., Chen, J.M., He, L., Keenan, T.F. (2019). Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content. Global Change Biology, 25: 2499-2514. http://doi.org/10.1111/gcb.14624

Ma, Z., Chen, X., Wang, Q., Li, P., Jiapaerl, G. (2012). Retrieval of leaf biochemical properties by inversed PROSPECT model and hyperspectral indices: an application to Populus euphratica polymorphic leaves. Journal of Arid Land, 4: 52-62. http://doi.org/10.3724/sp.j.1227.2012.00052

Maccioni, A., Agati, G., Mazzinghi, P. (2001). New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochemistry and Photobiology B: Biology, 61: 52-61. http://doi.org/10.1016/S1011-1344(01)00145-2

Main, R., Cho, M.A., Mathieu, R., O'Kennedy, M.M., Ramoelo, A., Koch, S. (2011). An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 66: 751-761. http://doi.org/10.1016/j.isprsjprs.2011.08.001

Malenovsky, Z., Albrechtova, J., Lhotakova, Z., Zurita-Milla, R., Clevers, J., Schaepman, M.E., Cudlin, P. (2006). Applicability of the PROSPECT model for Norway spruce needles. International Journal of Remote Sensing, 27: 5315-5340. http://doi.org/10.1080/01431160600762990

Markwell, J., Osterman, J.C., Mitchell, J.L. (1995). Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research, 46: 467-472. http://doi.org/10.1007/BF00032301

Meggio, F., Zarco-Tejada, P.J., Miller, J.R., Martín, P., González, M.R., Berjón, A. (2008). Row orientation and viewing geometry effects on row-structured vine crops for chlorophyll content estimation. Canadian Journal of Remote Sensing, 34: 220-234. http://doi.org/10.5589/m08-023

Middleton, E.M. (1991). Solar zenith angle effects on vegetation indices in tallgrass prairie. Remote Sensing of Environment, 38: 45-62. http://doi.org/10.1016/0034-4257(91)90071-D

Moharana, S., Dutta, S. (2016). Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 122: 17-29. http://doi.org/10.1016/j.isprsjprs.2016.09.002

Mohd Asaari, M.S., Mishra, P., Mertens, S., Dhondt, S., Inzé, D., Wuyts, N., Scheunders, P. (2018). Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform. ISPRS Journal of Photogrammetry and Remote Sensing, 138: 121-138. http://doi.org/10.1016/j.isprsjprs.2018.02.003

Mousivand, A., Menenti, M., Gorte, B., Verhoef, W. (2015). Multi-temporal, multi-sensor retrieval of terrestrial vegetation properties from spectral–directional radiometric data. Remote Sensing of Environment, 158: 311-330. http://doi.org/10.1016/j.rse.2014.10.030

Munoz-Huerta, R.F., Guevara-Gonzalez, R.G., Contreras-Medina, L.M., Torres-Pacheco, I., Prado-Olivarez, J., Ocampo-Velazquez, R.V. (2013). A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors, 13: 10823-10843. http://doi.org/10.3390/s130810823

Mutanga, O., Skidmore, A.K. (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25: 3999-4014. http://doi.org/10.1080/01431160310001654923

Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., et al. (2002). Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83: 214-231. http://doi.org/10.1016/S0034-4257(02)00074-3

Pacheco-Labrador, J., Gonzalez-Cascon, R., Pilar Martin, M., Riano, D. (2014). Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 26: 105-118. http://doi.org/10.1016/j.jag.2013.05.013

Pinter, P.J. (1993). Solar angle independence in the relationship between absorbed PAR and remotely sensed data for alfalfa. Remote Sensing of Environment, 46: 19-25. http://doi.org/10.1016/0034-4257(93)90029-W

Pinter, P.J., Jackson, R.D., Elaine Ezra, C., Gausman, H.W. (1985). Sun-angle and canopy-architecture effects on the spectral reflectance of six wheat cultivars. International Journal of Remote Sensing, 6: 1813-1825. http://doi.org/10.1080/01431168508948330

Pu, R.L., Gong, P., Biging, G.S., Larrieu, M.R. (2003). Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index. IEEE Transactions on Geoscience and Remote Sensing, 41: 916-921. http://doi.org/10.1109/tgrs.2003.813555

Qin, Z., Chang, Q., Xie, B., Shen, J. (2016). Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in Yellow River diversion irrigation district. Transactions of the Chinese Society of Agricultural Engineering, 32: 77-85. http://doi.org/10.11975/j.issn.1002-6819.2016.23.011

Rahman, M., Lamb, D., Stanley, J. (2015). The impact of solar illumination angle when using active optical sensing of NDVI to infer fAPAR in a pasture canopy. Agricultural and Forest Meteorology, 202: 39-43. http://doi.org/10.1016/j.agrformet.2014.12.001

Ramoelo, A., Skidmore, A.K., Schlerf, M., Heitkonig, I.M.A., Mathieu, R., Cho, M.A. (2013). Savanna grass nitrogen to phosphorous ratio estimation using field spectroscopy and the potential for estimation with imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 23: 334-343. http://doi.org/10.1016/j.jag.2012.10.009

Ramoelo, A., Skidmore, A.K., Schlerf, M., Mathieu, R., Heitkonig, I.M.A. (2011). Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations. ISPRS Journal of Photogrammetry and Remote Sensing, 66: 408-417. http://doi.org/10.1016/j.isprsjprs.2011.01.008

Ranson, K.J., Daughtry, C.S.T., Biehl, L.L., Bauer, M.E. (1985). Sun-view angle effects on reflectance factors of corn canopies. Remote Sensing of Environment, 18: 147-161. http://doi.org/10.1016/ 0034-4257(85)90045-8

Robinson, B.F., Biehl, L.L. (1979). Calibration procedures for measurement of reflectance factor in remote sensing field research. Proceedings of the Society of Photo-Optical Instrumentation Engineers, 196: 16–26. http://doi.org/10.1117/12.957952

Rondeaux, G., Steven, M., Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55: 95-107. http://doi.org/10.1016/0034-4257(95)00186-7

Saberioon, M.M., Amin, M.S.M., Anuar, A.R., Gholizadeh, A., Wayayok, A., Khairunniza-Bejo, S. (2014). Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation, 32: 35-45. http://doi.org/10.1016/j.jag.2014.03.018

Schaepman-Strub, G., Schaepman, M.E., Painter, T.H., Dangel, S., Martonchik, J.V. (2006). Reflectance quantities in optical remote sensing—definitions and case studies. Remote Sensing of Environment, 103: 27-42. http://doi.org/10.1016/j.rse.2006.03.002

Schlemmer, M., Gitelson, A., Schepers, J., Ferguson, R., Peng, Y., Shanahan, J., Rundquist, D. (2013). Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 25: 47-54. http://doi.org/10.1016/ j.jag.2013.04.003

Schlerf, M., Atzberger, C., Hill, J., Buddenbaum, H., Werner, W., Schüler, G. (2010). Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy. International Journal of Applied Earth Observation & Geoinformation, 12: 17-26. http://doi.org/10.1016/ j.jag.2009.08.006

Shi, H., Xiao, Z., Liang, S., Ma, H. (2017). A method for consistent estimation of multiple land surface parameters from MODIS top-of-atmosphere time series data. IEEE Transactions on Geoscience and Remote Sensing, 55: 5158-5173. http://doi.org/10.1109/TGRS.2017.2702609

Shi, H., Xiao, Z., Liang, S., Zhang, X. (2016). Consistent estimation of multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model. Remote Sensing of Environment, 184: 40-57. http://doi.org/10.1016/j.rse.2016.06.008

Shibayama, M., Wiegand, C.L. (1985). View azimuth and zenith, and solar angle effects on wheat canopy reflectance. Remote Sensing of Environment, 18: 91-103. http://doi.org/10.1016/0034-4257(85)90040-9

Si, Y., Schlerf, M., Zurita-Milla, R., Skidmore, A., Wang, T. (2012). Mapping spatio-temporal variation of grassland quantity and quality using MERIS data and the PROSAIL model. Remote Sensing of Environment, 121: 415-425. http://doi.org/10.1016/j.rse.2012.02.011

Simic, A., Chen, J.M., Leblanc, S.G., Dyk, A., Croft, H., Han, T. (2014). Testing the top-down model inversion method of estimating leaf reflectance used to retrieve vegetation biochemical content within empirical approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7: 92-104. http://doi.org/10.1109/jstars.2013.2271583

Sims, D.A., Gamon, J.A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81: 337-354. http://doi.org/10.1016/s0034-4257(02)00010-x

Spafford, L., le Maire, G., MacDougall, A., de Boissieu, F., Féret, J.-B. (2021). Spectral subdomains and prior estimation of leaf structure improves PROSPECT inversion on reflectance or transmittance alone. Remote Sensing of Environment, 252: 112176. http://doi.org/10.1016/j.rse.2020.112176

Sun, J., Shi, S., Yang, J., Du, L., Gong, W., Chen, B., Song, S. (2018). Analyzing the performance of PROSPECT model inversion based on different spectral information for leaf biochemical properties retrieval. ISPRS Journal of Photogrammetry and Remote Sensing, 135: 74-83. http://doi.org/10.1016/j.isprsjprs.2017.11.010

Tian, Y.-C., Gu, K.-J., Chu, X., Yao, X., Cao, W.-X., Zhu, Y. (2014). Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant and soil, 376: 193-209. http://doi.org/10.1007/s11104-013-1937-0

Vandaele, A.C., Willame, Y., Depiesse, C., Thomas, I.R., Robert, S., Bolsée, D., et al. (2015). Optical and radiometric models of the NOMAD instrument part I: the UVIS channel. Optics Express, 23: 30028-30042. http://doi.org/10.1364/OE.23.030028

Vanderbilt, V., Grant, L., Biehl, L., Robinson, B. (1985). Specular, diffuse, and polarized light scattered by two wheat canopies. Applied Optics, 24: 2408-2418. http://doi.org/10.1364/AO.24.002408

Veefkind, J.P., Aben, I., McMullan, K., F?rster, H., de Vries, J., Otter, G., et al. (2012). TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment, 120: 70-83. http://doi.org/10.1016/j.rse.2011.09.027

Verbrugghe, M., Cierniewski, J. (1996). Effects of sun and view geometrics on cotton bidirectional reflectance. Test of a geometrical model. Remote Sensing of Environment, 54: 189-197. http://doi.org/10.1016/ 0034-4257(95)00174-3

Verhoef, W. (1984). Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, 16: 125-141. http://doi.org/10.1016/0034-4257(84)90057-9

Verhoef, W. (1985). Earth observation modeling based on layer scattering matrices. Remote Sensing of Environment, 17: 165-178. http://doi.org/10.1016/0034-4257(85)90072-0

Verhoef, W., Jia, L., Xiao, Q., Su, Z. (2007). Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies. IEEE Transactions on Geoscience and Remote Sensing, 45: 1808-1822. http://doi.org/10.1109/TGRS.2007.895844

Verrelst, J., Vicent, J., Rivera-Caicedo, J.P., Lumbierres, M., Morcillo-Pallarés, P., Moreno, J. (2019). Global sensitivity analysis of leaf-canopy-atmosphere RTMs: Implications for biophysical variables retrieval from top-of-atmosphere radiance data. Remote Sensing, 11: 1923. http://doi.org/10.3390/rs11161923

Vogelmann, J.E., Rock, B.N., Moss, D.M. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14: 1563-1575. http://doi.org/10.1080/01431169308953986

Walter-Shea, E.A., Norman, J.M., Blad, B.L. (1989). Leaf bidirectional reflectance and transmittance in corn and soybean. Remote Sensing of Environment, 29: 161-174. http://doi.org/10.1016/0034-4257(89)90024-2

Wang, J.F., He, D.X., Song, J.X., Dou, H.J., Du, W.F. (2015a). Non-destructive measurement of chlorophyll in tomato leaves using spectral transmittance. International Journal of Agricultural & Biological Engineering, 8: 73-78. http://doi.org/10.3965/j.ijabe.20150805.1931

Wang, S., Gao, W., Ming, J., Li, L., Xu, D., Liu, S., Lu, J. (2018). A TPE based inversion of PROSAIL for estimating canopy biophysical and biochemical variables of oilseed rape. Computers and Electronics in Agriculture, 152: 350-362. http://doi.org/10.1016/j.compag.2018.07.023

Wang, W., Yao, X., Yao, X., Tian, Y., Liu, X., Ni, J., et al. (2012). Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crops Research, 129: 90-98. http://doi.org/10.1016/j.fcr.2012.01.014

Wang, Z., Skidmore, A.K., Darvishzadeh, R., Heiden, U., Heurich, M., Wang, T. (2015b). Leaf nitrogen content indirectly estimated by leaf traits derived from the PROSPECT model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8: 3172-3182. http://doi.org/10.1109/ jstars.2015.2422734

Wang, Z., Skidmore, A.K., Wang, T., Darvishzadeh, R., Hearne, J. (2015c). Applicability of the PROSPECT model for estimating protein and cellulose plus lignin in fresh leaves. Remote Sensing of Environment, 168: 205-218. http://doi.org/10.1016/j.rse.2015.07.007

Wang, Z., Skidmore, A.K., Wang, T., Darvishzadeh, R., Heiden, U., Heurich, M., et al. (2017). Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects. International Journal of Applied Earth Observation and Geoinformation: 84–94.

Wen, P.-F., He, J., Ning, F., Wang, R., Zhang, Y.-H., Li, J. (2019). Estimating leaf nitrogen concentration considering unsynchronized maize growth stages with canopy hyperspectral technique. Ecological Indicators, 107: 105590. http://doi.org/10.1016/j.ecolind.2019.105590

Wu, C., Niu, Z., Tang, Q., Huang, W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 148: 1230-1241. http://doi.org/10.1016/j.agrformet.2008.03.005

Xiao, Y., Zhao, W., Zhou, D., Gong, H. (2014). Sensitivity analysis of vegetation reflectance to biochemical and biophysical variables at leaf, canopy, and regional scales. IEEE Transactions on Geoscience and Remote Sensing, 52: 4014-4024. http://doi.org/10.1109/tgrs.2013.2278838

Xiong, H., Shi, H., Xiao, Z. (2020). Consistent retrieval of multiple parameters from GOES-R top of atmosphere reflectance data. International Journal of Remote Sensing, 41: 7931-7957. http://doi.org/10.1080/ 01431161.2020.1766151

Xu, C., Fisher, R., Wullschleger, S.D., Wilson, C.J., Cai, M., McDowell, N.G. (2012). Toward a mechanistic modeling of nitrogen limitation on vegetation dynamics. PloS one, 7: e37914.

Xu, M., Liu, R., Chen, J.M., Liu, Y., Shang, R., Ju, W., et al. (2019). Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach. Remote Sensing of Environment, 224: 60-73. http://doi.org/10.1016/j.rse.2019.01.039

Xue, L.H., Cao, W.X., Luo, W.H., Dai, T.B., Zhu, Y. (2004). Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agronomy Journal, 96: 135-142. http://doi.org/10.2134/agronj2004.1350

Yang, B., Knyazikhin, Y., Xie, D., Zhao, H., Zhang, J., Wu, Y. (2018a). Influence of leaf specular reflection on canopy radiative regime using an improved version of the stochastic radiative transfer model. Remote Sensing, 10: 1632. http://doi.org/10.3390/rs10101632

Yang, B., Knyazikhin, Y., Zhao, H., Ma, Y. (2018b). Contribution of leaf specular reflection to canopy reflectance under black soil case using stochastic radiative transfer model. Agricultural and Forest Meteorology, 263: 477-482. http://doi.org/10.1016/j.agrformet.2018.08.024

Yang, P., van der Tol, C., Yin, T., Verhoef, W. (2020). The SPART model: A soil-plant-atmosphere radiative transfer model for satellite measurements in the solar spectrum. Remote Sensing of Environment, 247: 111870. http://doi.org/10.1016/j.rse.2020.111870

Yao, X., Huang, Y., Shang, G., Zhou, C., Cheng, T., Tian, Y., et al. (2015). Evaluation of six algorithms to monitor wheat leaf nitrogen concentration. Remote Sensing, 7: 14939-14966. http://doi.org/10.3390/rs71114939

Yao, X., Ren, H., Cao, Z., Tian, Y., Cao, W., Zhu, Y., Cheng, T. (2014). Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds. International Journal of Applied Earth Observation and Geoinformation, 32: 114-124. http://doi.org/10.1016/j.jag.2014.03.014

Yoder, B.J., Pettigrew-Crosby, R.E. (1995). Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remote Sensing of Environment, 53: 199-211. http://doi.org/10.1016/0034-4257(95)00135-n

Yu, K., Lenz-Wiedemann, V., Chen, X., Bareth, G. (2014). Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects. ISPRS Journal of Photogrammetry and Remote Sensing, 97: 58-77. http://doi.org/10.1016/ j.isprsjprs.2014.08.005

Zarco-Tejada, P., Camino, C., Beck, P., Calderon, R., Hornero, A., Hernández-Clemente, R., et al. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4: 432. http://doi.org/10.1038/s41477-018-0189-7

Zarco-Tejada, P.J., Berjón, A., López-Lozano, R., Miller, J.R., Martín, P., Cachorro, V., et al. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment, 99: 271-287. http://doi.org/10.1016/j.rse.2005.09.002

Zarco-Tejada, P.J., Miller, J.R., Noland, T.L., Mohammed, G.H., Sampson, P.H. (2001). Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 39: 1491-1507. http://doi.org/10.1109/36.934080

Zhang, S., Wang, Q. (2015). Inverse retrieval of chlorophyll from reflected spectra for assimilating branches of drought-tolerant tamarix ramosissima. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 8: 1498-1505. http://doi.org/10.1109/JSTARS.2015.2419225

Zhang, Y., Chen, J.M., Miller, J.R., Noland, T.L. (2008). Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery. Remote Sensing of Environment, 112: 3234-3247. http://doi.org/10.1016/j.rse.2008.04.005

Zhao, F., Gu, X., Verhoef, W., Wang, Q., Yu, T., Liu, Q., et al. (2010). A spectral directional reflectance model of row crops. Remote Sensing of Environment, 114: 265-285. http://doi.org/10.1016/j.rse.2009.09.018

Zheng, H., Cheng, T., Li, D., Yao, X., Tian, Y., Cao, W., Zhu, Y. (2018). Combining Unmanned Aerial Vehicle (UAV)-based multispectral imagery and ground-based hyperspectral data for plant nitrogen concentration estimation in rice. Frontiers in Plant Science, 9: 1-13. http://doi.org/ 10.3389/fpls.2018.00936

Zheng, H., Ma, J., Zhou, M., Li, D., Yao, X., Cao, W., et al. (2020). Enhancing the nitrogen signals of rice canopies across critical growth stages through the integration of textural and spectral information from Unmanned Aerial Vehicle (UAV) multispectral imagery. Remote Sensing, 12: 957. http://doi.org/10.3390/rs12060957

Zhou, K., Cheng, T., Zhu, Y., Cao, W., Ustin, S.L., Zheng, H., et al. (2018). Assessing the impact of spatial resolution on the estimation of leaf nitrogen concentration over the full season of paddy rice using near-surface imaging spectroscopy data. Frontiers in Plant Science, 9. http://doi.org/10.3389/fpls.2018.00964

Zhou, K., Guo, Y., Geng, Y., Zhu, Y., Cao, W., Tian, Y. (2014). Development of a novel bidirectional canopy reflectance model for row-planted rice and wheat. Remote Sensing, 6: 7632-7659. http://doi.org/10.3390/rs6087632

Zhu, Y., Tian, Y., Yao, X., Liu, X., Cao, W. (2007a). Analysis of common canopy reflectance spectra for indicating leaf nitrogen concentrations in wheat and rice. Plant Production Science, 10: 400-411. http://doi.org/10.1626/pps.10.400

Zhu, Y., Zhou, D., Yao, X., Tian, Y., Cao, W. (2007b). Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice. Crop and Pasture Science, 58: 1077-1085. http://doi.org/10.1071/AR06413

曹卫星, 朱艳, 田永超, 姚霞, 汤亮, 刘小军, 倪军 (2011). 作物精确栽培技术的构建与实现. 中国农业科学, 44: 3955-3969.

方圣辉,乐源,梁琦. 基于连续小波分析的混合植被叶绿素反演[J].武汉大学学报(信息科学版),2015:296-302.

冯伟, 姚霞, 朱艳, 田永超, 曹卫星 (2008). 基于高光谱遥感的小麦叶片含氮量监测模型研究. 麦类作物学报: 851-860.

宫兆宁,赵雅莉,赵文吉, 等. 基于光谱指数的植物叶片叶绿素含量的估算模型[J].生态学报,2014(20).

梁亮, 杨敏华, 邓凯东, 张连蓬, 林卉, 刘志霄 (2011). 一种估测小麦冠层氮含量的新高光谱指数. 生态学报, 31: 6594-6605.

田永超, 杨杰, 姚霞, 朱艳, 曹卫星 (2010). 估测水稻叶层氮浓度的新型蓝光氮指数. 应用生态学报, 21: 966-972.

田永超,朱艳,曹卫星. 水稻不同叶位层物理结构与冠层反射光谱的定量研究[J].中国水稻科学,2005(19).

薛利红, 曹卫星, 罗卫红, 张宪 (2004). 小麦叶片氮素状况与光谱特性的相关性研究. 植物生态学报: 172-177.

薛忠财,高辉远,彭涛, 等. 光谱分析在植物生理生态研究中的应用[J].植物生理学报,2011(47):313-320.

姚霞, 朱艳, 田永超, 冯伟, 曹卫星 (2009). 小麦叶层氮含量估测的最佳高光谱参数研究. 中国农业科学, 42: 2716-2725.

张潇元, 张立福, 张霞, 王树东, 田静国, 翟涌光 (2017). 不同光谱植被指数反演冬小麦叶氮含量的敏感性研究. 中国农业科学, 50: 474-485.

赵春江,黄文江,王纪华, 等. 用多角度光谱信息反演冬小麦叶绿素含量垂直分布[J].农业工程学报,2006(22):104-109.

朱艳, 李映雪, 周冬琴, 田永超, 姚霞, 曹卫星 (2006). 稻麦叶片氮含量与冠层反射光谱的定量关系. 作物学报, 26: 3463-3469.