Green Area Index (GAI) serves as an important link in the analysis of the relationship between solar energy radiation and crop grain yield. Voxel-based approach from terrestrial laser scanning (TLS) data has been widely used to measure the canopy structure due to its ability to characterize plant material within a 3D grid. However, few studies have explored how TLS sampling design and voxel size affect the accuracy of crop GAI estimation. In this study, we assessed these effects on wheat GAI estimation based on measured and simulated TLS data. We simulated different TLS sampling designs, including varying numbers of scanning sites (1, 4, 8, 12, 16) and scanning strategies (Strategy 1: peripheral distribution; Strategy 2: uniform distribution). Furthermore, a LiDAR algorithm for GAI estimation based on voxel size optimization, which we call the k-neighborhood voxel approach (KNV), was developed. The results demonstrated that TLS sampling design 2-12 (12-scan sites uniform distribution) was the most effect approach for comprehensively characterizing canopy structure across the wheat growing seasons. Uniform distribution (Strategy 2) resulted in a greater number of laser returns from lower part of the canopy, and enhanced the homogeneity of the distribution of laser points. In addition, the optimal voxel method could greatly mitigate the impacts of changes in TLS sampling design and improve the accuracy of TLS technology for estimating wheat GAI (RMSE = 0.57, RRMSE = 18.26%). This study provides valuable guidance for effective acquisition and accurate estimation of crop attributes in crop breeding and plant phenotyping.

Grain protein concentration (GPC) could be predicted at high accuracy using the reflectance of dried grain powder in the shortwave infrared (SWIR) region. However, the acquisition of grain-level hyperspectral data is time-consuming and such a procedure must be conducted with laboratory conditions. This study proposed an efficient approach to integrating multiscale spectral features as the intermediate variables for improved prediction of GPC in winter wheat by transporting the sensitive features from grain to canopy levels. The performance of the proposed method was compared in the form of ’Dependent variable ~ Independent variable ~ Intermediate variable’ with the traditional canopy-level ’GPC ~ Spectral feature (SF) ~ Leaf nitrogen concentration (LNC)’ models. Our results demonstrated that the models of ’GPC ~ Wavelet feature (WF) ~ LNC’ exhibited higher validation accuracies than the traditional ’GPC ~ Vegetation index (VI) ~ LNC’ models. Specifically, the canopy-level sensitive WFcanopy-720,5(WF at 720 nm and the wavelet decomposition scale of 25) yielded pronounced improvement for GPC prediction, with RMSE values of 1.02% and 0.83% for the anthesis and the post-anthesis stages. Compared with models using the SF- or LNC-based intermediate variables, the model using the intermediate variable of WFgrain-1510,4(grain-level sensitive WF) performed remarkably better, such as the single-feature model of ’GPC ~ WFcanopy-1510,4~ WFgrain-1510,4, at the post-anthesis stage (RMSE = 0.78%) and multi-feature model of ’GPC ~ (WFcanopy-1510,4& WFcanopy-1610,5) ~ (WFgrain-1510,4& WFgrain-1610,5) at the anthesis stage (RMSE = 0.93%). The grain-level spectral features are not expressed in the final model and only canopy-level data are required for model applications. This study represents a first attempt to couple grain and canopy-level SFs to improve the spectroscopic prediction of GPC and provides new insight into the quantitative estimation of grain quality parameters with hyperspectral remote sensing.

The expanding utilization of unmanned aerial vehicle (UAV) remote sensing (RS) technology has significantly advanced crop monitoring and detection. Despite its widespread application, the use of UAVs for examining rice grain starch accumulation (GSA) remains in its infancy. The preflowering nutritional organs’ nonstructural carbohydrate transport and the postflowering plant’s photosynthesis products are the primary sources of GSA. This study constructs a dynamic change curve based on the spectral index (SI) red edge re-normalized different vegetation index (RERDVI) before rice flowering. It introduces a novel indicator, the preflowering biomass accumulation dynamics (PBAD), identified through the dynamic curve’s distinct shape characteristics. Results show that PBAD has a good correlation with the aboveground biomass (AGB) at different preflowering stages. After flowering, a nutrient distribution composite index (NDCI) is developed by combining SIs and color indices (CIs), providing a precise monitoring tool for the nitrogen harvest index (NHI), which is important in GSA. By comprehensively considering preflowering nonstructural carbohydrate accumulation (AGB), postflowering photosynthetic capacity (NHI), canopy temperature depression (CTD) sensitive to GSA, and meteorological factors (sunshine duration (SSD) and precipitation), a GSA estimation model based on multisource RS data fusion was constructed using a multiple linear regression (MLR), random forest regression (RFR), and extreme gradient boosting (XGBoost). This approach significantly improved the accuracy of GSA estimation, with the XGBoost model achieving a validation$R^{2}$of 0.76 and a root mean square error (RMSE) of 0.11 kg/m2 on a multiecological dataset, notably reducing the underestimation observed in traditional linear models.

Spectral remote sensing can effectively, rapidly, and nondestructively detect the nitrogen status of crop plants. Estimation of crop leaf nitrogen concentration (LNC, %) using canopy bidirectional reflectance factor (BRF) is an effective method to diagnose nitrogen deficiency in crops. It is challenging to estimate LNC with empirical remote sensing models because the variability of the canopy structure at different growth stages affects the model accuracy. Over the years, the canopy scattering coefficient [CSC, the ratio of BRF to directional area scattering factor (DASF)] has been used for LNC estimation by suppressing the effect of the canopy structure on BRF. However, this method often regards leaves as the main factor and has less consideration for the canopy structure effects on BRF caused by other organs (e.g., panicles). Incorporating the changes with the emergence of rice panicles into the DASF algorithm may generate reliable results in LNC estimation. Herein, we propose the PROSPECT-P model, which is based on the PROSPECT model and combines the panicle spectra to quantify the structural properties of the panicles and realize the simulation of the panicle albedo at different growth stages. Utilizing the spectral invariants theory, a panicle-leaf structure correction factor (DASFLP) was calculated based on canopy BRF, panicle albedo, leaf albedo, and canopy component fraction. CSC after correction for panicle and leaf structure (CSCLP) from 400–2500 nm can be obtained by the ratio of BRF and DASFLP. The CSCLP was further subjected to continuous wavelet analysis (CWA) and achieved an accurate estimation of the LNC using four machine learning (ML) models. The results showed that when combined with eight wavelet features (WFs), CSCLP can accurately invert rice LNC using the random forest algorithm ($ {R} ^{2} =0.81$, RMSE =0.30, RE =14.02%), which was more exact than CSC ($ {R} ^{2} =0.76$, RMSE =0.33, RE =16.13%) that corrected only for leaf structure. Moreover, the results on UAV multispectral also showed that UAV-CSCLP predicted LNC by XGBoost model ($ {R} ^{2} =0.61$, RMSE =0.35, RE =17.27%) more accurately than the traditional method UAV-CSC ($ {R} ^{2} =0.50$, RMSE =0.40, RE =19.52%) on the independent test set. Herein, we propose the accurate inversion of crop growth parameters by remote sensing using PROSPECT-P to correct for panicle and leaf structure effects.

Leaf angle distribution (LAD) is an important structural attribute of crop canopies as it influences photosynthesis and radiation transport. Terrestrial laser scanning (TLS) has shown promise as a tool for quantifying LAD. However, the current TLS-derived crop canopy LAD estimation lacks automatic segmentation for the special curved leaves of the crop. Furthermore, mutual shading between plants results in an uneven distribution of leaf point density in the crop canopy. We developed a novel voxel segmentation normal vector (VSNV) method for automatically segmenting and spatially normalizing curved leaves to address those concerns. In this methodology, the wheat canopy is divided into voxels, and LAD is derived by averaging the angles from the planes associated with each point within every voxel. The ray-tracing 3D radiative transfer model (LESS) was used to validate the effectiveness of the VSNV method, which produced better LAD results than the normal vector (NV) method. In addition, the mean leaf tilt angle (MTA) of wheat estimated by TLS using the VSNV approach correlated well with the measured value from LAI-2200C, especially at the booting stage (R2= 0.76 and RMSE = 1.40°). The result shows that the improved VSNV method can trace LAD characteristics among cultivars, nitrogen levels, growth stages, and canopy heights. Quantifying the variability of LAD could provide strong technical support for high-throughput phenotyping.

Previous studies on estimating leaf nitrogen concentration (LNC) and grain protein concentration (GPC) from canopy reflectance did not pay particular attention to the nitrogen (N) and protein absorption features, which are mostly located in the shortwave infrared (SWIR) region and challenging to use due to the adverse effect of water absorption. This study aimed to develop a new approach to mitigate the effect of water signals from canopy reflectance spectra of winter wheat and enhance the absorption features of N and protein in the SWIR region. The water effect at the canopy level was removed by using the simulated dry-canopy reflectance (SDR) spectra based on the radiative transfer models and a three-band spectral triangle index (STI) was constructed to capture the chemical-specific absorption variations of N and protein. The results demonstrated that these absorption features became sufficiently apparent by canopy water removal. With the SDR spectra, the STIs with three bands related to N and protein absorption features in SWIR sub-regions exhibited remarkably stronger correlations with LNC and GPC than those with the measured reflectance (MR) spectra. Specifically, STI2060-2180-2240from the SDR spectra achieved the top sensitivity to LNC and GPC. The combination of plant pigment ratio (PPR) and STI2060-2180-2240yielded significantly lower root mean square error (RMSE) values (LNC: RMSE = 0.19%; GPC: RMSE = 0.67%) than either one alone. The integration of water removal and STI could help us better understand the underlying mechanism on the relationships of LNC and GPC with N and protein absorption features.

Unmanned aerial vehicle (UAV) offers an unprecedented observing potential with ultrahigh spatial and temporal resolutions and high flexibility. However, it remains difficult to solve the radiometric inconsistency of multiflight UAV imagery. This study proposed a relative radiometric correction method for multiflight UAV images based on concurrent satellite imagery (MACA) consisting of two steps, i.e., cross-sensor spectral fitting (CSF) and fine-resolution spectral calibration (FSC). In CSF, relationships of multiflight UAV reflectance and the concurrent satellite reflectance were established for generating the fine-resolution reference imagery. Subsequently, a relative radiometric correction model was constructed in the FSC step to correct multiflight UAV imagery. The performance of MACA was evaluated using multiflight UAV datasets acquired on six cropland sites and a concurrent Sentinel-2 image. Compared with four typical or state-of-the-art relative correction methods, the correction using MACA yielded better consistency between UAV and Sentinel-2 data, regardless of individual spectral bands ($\text{R}^{2} =0.79$–0.86, root mean square error (RMSE)$=0.004$–0.019) or vegetation indices (VIs) ($\text{R}^{2} =0.80$–0.86, RMSE$=0.024$–0.054). Moreover, the prediction of plant nitrogen accumulation (PNA) based on the MACA-corrected UAV data had the highest accuracy and showed the spatial variation most significantly within and between fields for all sites. The results demonstrated that MACA was more robust in reducing spectral mismatch across sensors and eliminating the subjective error of pseudo-invariant features (PIFs) selection. MACA has the potential to be used to cross-calibrate multisensor data into a consistent standard, which will benefit multisensor synergies.

Leaf nitrogen content (LNC) can be used to diagnose the nutritional status and guide precise fertilization. Numerous previous researchers estimated LNC on reflectance spectrum or active chlorophyll fluorescence techniques with certain limitations. This study proposed a new technique of sun-induced chlorophyll fluorescence (SIF) for detecting LNC. We conducted an experiment to determine the optimal measurement point at the leaf scale for SIF and the best fluorescence yield indices (FY indices) extracted from SIF for LNC detection. The differences of the upward and downward FY indices were compared to determine the optimal FY indices. The results showed that the 1/3 distance from the leaf base is the optimal position for measuring SIF at the leaf level, and the downward FY indices are much better than the upward one with ratio peak FY687/FY739 as the best to monitor the LNC. The findings demonstrated that SIF can be utilized as a potential method for monitoring the LNC of winter wheat with higher efficiency.

As an important land surface parameter for crop growth model, leaf area index (LAI) is desired to be estimated accurately on regional scale. Before applying LAI estimation model built at local scale to the regional scale, the mismatch between multi-scale sensors should be corrected. We firstly applied the kernel-driven BRDF model to describe the two-dimensional reflection characteristics of Landsat 5-TM data based on the kernel weights from MODIS MCD43A1 products and the angle vectors obtained from TM Collection 1 Level-1 dataset. Then the point spread function (PSF) was used to simulate the spatial response of the sensor. Results show that there is a good agreement between TM observations and corrected MODIS values (Red band: R2= 0.736, RMSE=2.65e-4, Near-infrared band: R2=0.539, RMSE=5.39e-4). If we apply the LAI estimation model that has been validated at local scale (TM) directly to the data at large scale (MODIS) without any correction, more than 50% uncertainty of LAI estimation would be introduced. This study implies that the estimation of LAI in winter wheat could be significantly improved by correcting the differences between multi-scale sensors.

The bidirectional reflectance distribution function (BRDF) effect due to the surface reflectance anisotropy and variations in the solar and viewing geometry has been studied in the remote sensing community for several decades, and most attention was paid to the satellite sensors with large field of view (FOV), such as MODIS with a 110° FOV. With the development of unmanned aerial vehicle (UAV) technique, the imagery acquired at UAV platform provides important information about crop growth status, which is a promising and efficient approach for precise agriculture. However, few studies explored the BRDF effect in UAV images, especially for the sensors with small FOVs. This study investigated the BRDF effect on the estimation of canopy chlorophyll content (CCC) with the UHD 185 hyperspectral imagery (27° FOV) acquired at a UAV platform. Our results from a rice field-plot experiment demonstrated that the CCC was highly correlated to the red-edge chlorophyll index derived at five different view angles. However, the regression models were significantly different among these view angles. This implied that no single CCC estimation model can be applied to the whole image for CCC mapping. The findings suggest the BRDF effect should be considered for providing reliable and consistent CCC estimation.

Rice blast is one of the most devastating crop diseases around the world. Although previous remote sensing studies have examined the spectral variation at leaf and canopy levels in response to disease severity levels, the nonimaging nature of their data makes it difficult to examine the spectral variation related to the disease within a leaf. This study proposes to monitor the spatial and temporal pattern of rice leaf blast on individual leaves with close-range imaging spectroscopy data. Hyperspectral images were acquired from diseased leaves at different infection stages. The image data were converted to reflectance cubes and then processed with a model inversion algorithm PROCWT to retrieve leaf biochemical variables. The biochemical maps were examined to investigate the within-leaf spatial variation and leaf-level temporal variation. Preliminary results demonstrated that the PROCWT algorithm could perform on reflectance image cubes. The retrieved chlorophyll maps exhibited a decline with infection stage and significant within-leaf spatial patterns in response to the disease.

The effect of canopy structure on the remote sensing of foliar nitrogen content has been debated in recent years, due to the uncertain mechanism of estimating foliar nitrogen content through canopy reflectance in the near-infrared region. Although this effect was investigated using the radiative transfer modeling of canopy structural influence, the complicated modeling implementation is still of limited practical use and does not make full use of the spectral details in hyperspectral data. This study proposes to decompose the spectral responses to variations in canopy structure and foliar nitrogen content using a multi-scale spectral analysis tool, called continuous wavelet analysis (CWA). Our results on a rice field-plot experiment demonstrated that the leaf nitrogen content (LNC) were best correlated to the wavelet feature (730 nm, scale 4) with a r2value of 0.62. The wavelet feature (730 nm, scale 6), which was represented with the same wavelength but a higher scale, exhibited strong correlation with the leaf area index (LAI) (r2=0.80). These two wavelet features characterized spectral variation at different scales and could serve as indicators for separating the spectral effects of LAI and LNC. The findings suggest the wavelet tool is promising for better understanding the effect of canopy structure on the spectroscopic estimation of foliar nitrogen and for building structure-insensitive models for LNC prediction.

Chlorophyll fluorescence (CF) is a direct indicator of plant physiology and reflects the photochemical process and its efficiency. CF is difficult to quantify because it is included in the reflected light. In the present study, the interrelationships between chlorophyll fluorescence parameters and spectral reflectance measured in the first, second, third and fourth leaves from the top of plant from heading to 7 days after filling under different nitrogen levels are analyzed. The leaf reflectance spectral indices RVI (440,690), CUR, PRI were highly correlated with leaf chlorophyll fluorescence parameters Fv/Fm, and the CUR and the RVI (440,690) had the best correlation. However due to the low correlation between NDVI and fluorescence parameters, NDVI can't indicate the changes of CF. The first derivative spectral indices Dλρ/D744, D705/D722, D730/D706, REP also were highly correlated with leaf CF parameters Fv/Fm. No relationships were found between Dλρ/D703 and CF parameters. Meanwhile, comparing the regression coefficients among the red edge parameters and chlorophyll fluorescence parameters, the results showed that it is feasible to monitor leaf chlorophyll fluorescence parameters by vegetation indices.

The PROSPECT radiative transfer model was widely used to estimate leaf biochemical parameters, such as chlorophyll, leaf mass per area (LMA) and equivalent water thickness (EWT). Many studies have reported that LMA is the most difficult to retrieve from PROSPECT primarily because the dry matter absorption features on the reflectance spectra of fresh leaves are largely masked by stronger water absorption. This study proposed a new inversion method by integrating the continuous wavelet analysis into the PROSPECT inversion process. Instead of using reflectance directly as for most studies, this method used the wavelet coefficient spectra from continuous wavelet transform for constructing the merit function for inversion. The performance of the new method was evaluated with a multi-species dataset (LOPEX) and a single-species dataset (Wheat). The results demonstrated that the wavelet-based inversion method yielded higher accuracies than traditional methods and better consistency between data sets. This inversion method has great potential for developing portable instruments to predict foliar biomass of crops without the need of calibration models.

Unmanned Aerial Vehicles (UAV)-based remote sensing offers great possibilities to acquire in a fast and convenient way field data for precision agriculture applications. The UAV-based multispectral images with five wavebands (490, 550, 671, 700, 800 nm) were obtained at five growth stages from consecutive two years' wheat field experiments with different combinations in variety, N application rate and planting density. In this paper, we compared systematically leaf nitrogen content (LNC) estimation accuracy and processing speed of a multitude of vegetation indices (VIs), non-parametric and physically-based model retrieval methods. With regard to vegetation indices, most possible band combinations with existing five bands and a linear regression fitting function have been evaluated. The best performing index was optimized three-band combination 2.5(R800-R700)/(R800+6R700-7.5R490) according to EVI (Enhanced vegetation index) for LNC with 10-fold cross-validation determination (R2) of 0.73. This method shows especially fast processing speed (0.03 s). As for non-parametric methods, 14 common regression algorithms have been evaluated. Among these algorithms, Random Forest [TreeBagger] is the best performing method with R2of 0.79 for LNC. This method has the advantage of making use of the full optical spectrum as well as flexible, nonlinear fitting. Additionally, the model is trained and validated relatively fast (2.3 s). Compared with the front two methods, it remains a challenge to estimate the canopy nitrogen through inversion of a PROSAIL based radiative transfer model (RTM). After the generation of a look-up table (LUT), a number of cost functions and regularization options were evaluated for inversion of LCC (leaf chlorophyll content). Inversion of nitrogen indirectly relying on inversion of LCC based on the empirical linear relationship between LCC and LNC. Although this method offered per-pixel estimation, generation of a look-up table and image processing took considerably more time. Besides, the validation performed less reliable. To sum up, Random Forest [TreeBagger] provides fast and accurate estimation of nitrogen using UAV-acquired multispectral imagery which will be good basis for remote sensing of canopy nitrogen status in a wide range of crop.

The accurate prediction of crop yield is of great importance to regional production and food security. Many empirical models for crop production prediction are based on vegetation indices (VI) such as normalized vegetation index (NDVI) or simple ratio (SR) in specific growing period, with little attention paid to the sensitivity of different growing stages to yield prediction. This study investigates the sensitivity of different growing seasons to wheat yield and yield components with time series stacks of Landsat 8 Operational Land Imager (OLI) images over the growing season of winter wheat in a farm of Jiangsu, China. The final yield and yield components including panicles per m2, grains per panicle and 1000-grains weight of winter yield were estimated using three regression methods namely simple linear regression, stepwise multiple linear regression and regression tree. Our experimental results demonstrated that the best estimation of wheat yield was produced using the regression tree method with a R2of 0.87. Among the three yield components, the 1000-grains weight was best estimated from Landsat data acquired at early stages. The estimations of specific yield components from Landsat data are useful for us to better understand the prediction of wheat yield with time series satellite image data.

UAV based hyperspectral imaging is a promising approach to monitor crop growth status rapidly and non-destructively. This paper described a novel instrument to get hyperspectral information from lightweight unmanned aerial vehicles for crop monitoring. The objectives of this study were to assess the data quality of one hyperspectral frame camera and evaluate the ability in rice nitrogen status monitoring. In this study, we introduced one hyperspectral frame camera weighing 470g which could be mounted to low-weight UAVs (<;3 kg). The flight campaign was conducted in a paddy rice field in September 2015. During the flight, two ground-based portable spectrometers (ASD Field Spec Pro spectrometer and GreenSeeker RT 100) were used to collect rice canopy spectra. Later, Normalized difference vegetation index (NDVI) derived from hyperspectral images was compared with that from GreenSeeker and ASD. Also, field sampling was taken at the same day with the flight, and leaf nitrogen concentration (LNC) was obtained through Kjeldahl digestion method. Five existing vegetation indices that were used for N detection were used to estimate LNC. Results are satisfactory, which lay a foundation for the promising application of UAV-based hyperspectral remote sensing on precision agriculture.

It is critical to estimate the biomass for assessing crop growth and predicting yield in crop. The hyperspectral techniques provide a powerful technique for monitoring crop biomass. The previous studies about using hyperspectral data to study crop mainly focused on models based on the full spectra or the manually selected spectra. The stability and prediction ability of full spectra models may be weakened because of involving noises, other unrelated and collinear spectral variables. The manually selected spectra were extracted by vegetation indices, spectral absorption features, derivative spectra and spectral locations in common use, which may ignore the other spectral information, not identify the high biomass and impact the accuracy of model. In order to extract the optimal hyperspectral feature of wheat biomass, several algorithms for sensitive variable selection were compared to determine the spectral variables for estimation model of wheat biomass. Synergy interval partial least squares (SIPLS) [1] and successive projections algorithm (SPA) [2] were employed to eliminate useless variables from the full hyperspectral data. On that basis an approach was proposed by combing SIPLS with SPA to determine the optimal spectra. Then, the optimal features were considered as input variables of the partial least-squares regression (PLSR) method [3],which was the mostly used calibration model and regression method. The determination coefficient of calibration (R2C), the root mean square error (RMSEV), relative root mean square error of validation (RMSEV) and the number of input variables were presented to compare the performance of different methods in extracting sensitive spectral information.

The red edge position (REP) of a reflectance spectrum has been used as means to estimate the foliar chlorophyll content at leaf and canopy level. Most methods for extracting the REPs are based on the first derivative spectra and many studies have shown discontinuities in the REP data due to the existence of a double-peak feature in the first derivative spectra. This study proposes a new technique based on the continuous wavelet transform of foliar reflectance spectra, so that the double-peak problem could be avoided for extracting the REPs. The performance of the REPs extracted by the wavelet-based method was evaluated with data at leaf and canopy levels from a small-plot experiment of wheat crops. Our experimental results demonstrated that the wavelet-based method performed better than the two traditional methods. For the wavelet-based method, the best scale for extracting the REPs from canopy spectral data were higher than that from leaf spectral data. The findings are useful for us to understand the effect of canopy structure on REPs and the scale-dependent spectral contributions of foliar chemistry and canopy structure.