Hengbiao Zheng

Evaluation of a UAV-based hyperspectral frame camera for monitoring the leaf nitrogen concentration in rice

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.

Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices

Crop biomass is a critical variable for characterizing crop growth development, understanding dry matter partitioning, and predicting grain yield. Previous studies on the spectroscopic estimation of crop biomass focused on the use of various spectral indices based on chlorophyll absorption features and found that they often became saturated at high biomass levels. Given that crop biomass is commonly expressed as the dry weight of canopy components per unit ground area, it may be better estimated using the spectral indices that directly characterize dry matter absorption. This study aims to evaluate a group of four dry matter indices (DMIs) by comparison with a group of four chlorophyll indices (CIs) for estimating the biomass of individual components (e.g., leaves, stems) and their combinations with the field data collected from a two-year rice cultivation experiment. The Red-edge Chlorophyll Index (CIRed-edge) of the CI group exhibited the best relationship with leaf biomass (R2 = 0.82) for the whole growing season and with total biomass (R2 = 0.81), but only for the growth stages before heading. However, the Normalized Difference Index for Leaf Mass per Area (NDLMA) of the DMI group showed the best relationships with both stem biomass (R2 = 0.81) and total biomass (R2 = 0.81) for the whole season. This research demonstrated the suitability of dry matter indices and provided physical explanations for the superior performance of dry matter indices over chlorophyll indices for the estimation of whole-season total biomass.

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI=(1+e−15.2829×(RAGDDi−0.1944))−1−(1+e−11.6517×(RAGDDi−1.0267))−1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.

Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice

Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CIred edge), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R2: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m−2) and PNA (R2: 0.81–0.84, RMSE: 2.27–2.38 g m−2). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management.

Estimation of area- and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis

BackgroundThe visible and near infrared region has been widely used to estimate the leaf nitrogen (N) content based on the correlation of N with chlorophyll and deep absorption valleys of chlorophyll in this region. However, most absorption features related to N are located in the shortwave infrared (SWIR) region and the physical mechanism of leaf N estimation from fresh leaf reflectance spectra remains unclear. The use of SWIR region may help us reveal the underlying mechanism of casual relationships and better understand the spectral responses to N variation from fresh leaf reflectance spectra. This study combined continuous wavelet analysis (CWA) and water removal technique to improve the estimation of N content and leaf mass per area (LMA) by reducing the effect of water absorption and enhancing absorption signals in the SWIR region. The performance of the wavelet-based method was evaluated for estimating leaf N content and LMA of rice and wheat crops from fresh leaf reflectance spectra collected over a 2-year field experiment and compared with normalization difference (ND)-based spectral indices.ResultsThe LMA and area-based N content (Narea) exhibited better correlations with the determined wavelet features derived from the water-removed (WR) spectra (LMA: R2 = 0.71, Narea: R2 = 0.77) than those from the measured reflectance (MR) spectra (LMA: R2 = 0.62, Narea: R2 = 0.64). The wavelet features performed remarkably better than the optimized ND indices for the estimations of LMA and Narea with MR spectra or WR spectra. Based on the best estimations of LMA and Narea with wavelet features from WR spectra, the mass-based N content (Nmass) could be retrieved with a high accuracy (R2 = 0.82, RMSE = 0.32%) in the indirect way. This accuracy was higher than that for Nmass obtained in the direct use of a single wavelet feature (R2 = 0.68, RMSE = 0.42%).ConclusionsThe enhancement of absorption features in the SWIR region through the CWA applied to water-removed (WR) spectra was able to improve the spectroscopic estimation of leaf N content and LMA as compared to that obtained with the reflectance spectra of fresh leaves. The success in estimating LMA and N with this method would advance the spectroscopic estimations of grain quality parameters for staple crops and individual dry matter constituents for various vegetation types.

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

Timely monitoring nitrogen status of rice crops with remote sensing can help us optimize nitrogen fertilizer management and reduce environmental pollution. Recently, the use of near-surface imaging spectroscopy is emerging as a promising technology that can collect hyperspectral images with spatial resolutions ranging from millimeters to decimeters. The spatial resolution is crucial for the efficiency in the image sampling across rice plants and the separation of leaf signals from the background. However, the optimal spatial resolution of such images for monitoring the leaf nitrogen concentration (LNC) in rice crops remains unclear. To assess the impact of spatial resolution on the estimation of rice LNC, we collected ground-based hyperspectral images throughout the entire growing season over 2 consecutive years and generated ten sets of images with spatial resolutions ranging from 1.3 to 450 mm. These images were used to determine the sensitivity of LNC prediction to spatial resolution with three groups of vegetation indices (VIs) and two multivariate methods Gaussian Process regression (GPR) and Partial least squares regression (PLSR). The reflectance spectra of sunlit-, shaded-, and all-leaf leaf pixels separated from background pixels at each spatial resolution were used to predict LNC with VIs, GPR and PLSR, respectively. The results demonstrated all-leaf pixels generally exhibited more stable performance than sunlit- and shaded-leaf pixels regardless of estimation approaches. The predictions of LNC required stage-specific LNC~VI models for each vegetative stage but could be performed with a single model for all the reproductive stages. Specifically, most VIs achieved stable performances from all the resolutions finer than 14 mm for the early tillering stage but from all the resolutions finer than 56 mm for the other stages. In contrast, the global models for the prediction of LNC across the entire growing season were successfully established with the approaches of GPR or PLSR. In particular, GPR generally exhibited the best prediction of LNC with the optimal spatial resolution being found at 28 mm. These findings represent significant advances in the application of ground-based imaging spectroscopy as a promising approach to crop monitoring and understanding the effects of spatial resolution on the estimation of rice LNC.

Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R2 = 0.52–0.70), and photochemical reflectance index and blue N index from ground (PRIg and BNIg) performed consistently well across all growth stages (R2 = 0.48–0.65 and 0.39–0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNIg and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring.

Towards decomposing the effects of foliar nitrogen content and canopy structure on rice canopy spectral variability through multi-scale spectral analysis

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 r2 value 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.