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Introduction: Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status. Methods: In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multivegetation index fusion in the estimation of cotton LAI. Results: The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. Discussion: Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications. [ABSTRACT FROM AUTHOR]

In the context of global climate change and increasing human activities, grassland drought has become increasingly severe and complex. The monitoring of grassland drought is crucial for reducing drought-related losses and ensuring national ecological security. This study used the coupled PROSPECT and SAIL radiative transfer models (PROSAIL) to simulate canopy reflectance, considering factors such as grassland growth stages and varying drought conditions. Our objective was to reveal the spectral response characteristics of grasslands to varying drought conditions and identify sensitive spectral bands suitable for drought monitoring during different grassland growth stages. We aligned commonly available satellite bands from moderate resolution imaging spectroradiometer (MODIS), Sentinel 2, Landsat 8, WorldView 2, and Gaofen 2 (GF 2) with these sensitive bands to assess the capabilities of existing satellite data for drought monitoring. Furthermore, this research evaluated the suitability of 16 commonly used remote sensing vegetation indices for grassland drought monitoring, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), Modified Soil Adjusted Vegetation Index (MSAVI), Atmospherically Resistant Vegetation Index (ARVI), Modified Normalized Difference Water Index (MNDWI), Global Vegetation Moisture Index (GVMI), Land Surface Water Index (LSWI), Visible and Shortwave Infrared Drought Index (VSDI), Water index(WI), Moisture Stress Index(MSI), Normalized Difference Water Index(NDWI), Normalized Difference Infrared Index (NDII), Photochemical Reflectance Index (PRI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). The simulation and analysis results revealed: 1) Grassland in different growth stages exhibit similar sensitivities to certain spectral bands, namely those within the ranges of 540 nm–720 nm, 1250 nm–1690 nm, 1805 nm–2190 nm, and 2264 nm–2500 nm, which are more sensitive to various drought conditions. 2) Suitable vegetation indices for both growing and stable stages include NDII, MSI, PRI, LSWI, and GVMI, with silhouette coefficients exceeding 0.6 for the growing stage and 0.7 for the stable stage. The least suitable vegetation index is DVI, with an average silhouette coefficient of 0.15 over the entire growth stage. 3) From the spectral band perspective, among the five assessed satellites, MODIS Band 7 exhibits the highest sensitivity to water content across all satellite bands. MODIS's band configuration is most suitable for monitoring grassland drought during different growth stages, while WorldView 2's band configuration is the least suitable.

Canopy spectral composition significantly affects growth and functional traits of understory plants. In this study, we explored the optimal light condition suitable for enhancing Scutellaria baicalensis's yield and quality, aiming to provide scientific reference for the exploitation and utilization of medicinal plant resources in the understory of forests. We measured the responses of growth, morphology, biomass allocation, physiological traits, and secondary metabolites of S. baicalensis to different light qualities. S. baicalensis was cultured under five LED-light treatments including full spectrum light (control), ultraviolet-A (UV-A) radiation, blue, green, and red light. Results showed that UV-A significantly reduced plant height, base diameter, leaf thickness, leaf area ratio, and biomass of each organ. Red light significantly reduced base diameter, biomass, effective quantum yield of photosystem ΦPS II and total Aavonoid concentration. Under blue light, root length and total biomass of S. baicalensis significantly increased by 48.0% and 10.8%, respectively, while leaf number and chlorophyll content significantly decreased by 20.0% and 31.6%, respectively. The other physiological and biochemical traits were consistent with their responses in control. Our results suggested that blue light promoted photosynthesis, biomass accumulation, and secondary metabolite synthesis of S. baicalensis, while red light and UV-A radiation negatively affected physiological and biochemical metabolic processes. Therefore, the ratio of blue light could be appropriately increased to improve the yield and quality of S. baicalensis. [ABSTRACT FROM AUTHOR]

In order to address the challenge of early detection of cotton verticillium wilt disease, naturally infected cotton plants in the field, which were divided into five categories based on the degree of disease severity, have been investigated in this study. Canopies of infected cotton plants were analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) and MSC-continuous wavelet analysis algorithms, were used to predict the disease severity. With a combination of support vector machine (SVM) models with such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm optimization (PSO), and grey wolf optimizer (GWO), a grading model of cotton verticillium wilt disease was established in this study. The study results show that the MSC-PSO-SVM model outperforms the other three models in terms of classification accuracy, and the accuracy, macro precision, macro recall, and macro F1-score of this model are 80%, 81.26%, 80%, and 79.57%, respectively. Among those eight models constructed on the basis of continuous wavelet analyses using mexh and db3, the MSC-db3(23)-PSO-SVM and MSC-db3(23)-GWO-SVM models perform best, with the latter having a shorter running time. An overall evaluation shows that the MSC-db3(23)-GWO-SVM model is an optimal model, with values of its accuracy, macro precision, macro recall, and macro F1-score indicators being 91.2%, 92.02%, 91.2%, and 91.16%, respectively. Moreover, under this model, the prediction accuracy on disease levels 1 and 5 has achieved the highest rate of 100%, with a prediction accuracy rate of 88% on disease level 2 and the lowest prediction accuracy rate of 84% on both disease levels 3 and 4. These results demonstrate that it is effective to use spectral technology in classifying the cotton verticillium wilt disease and satisfying the needs of field detection and grading. This study provides a new approach for the detection and grading of cotton verticillium wilt disease and offered a theoretical basis for early prevention, precise drug application, and instrument development for the disease. [ABSTRACT FROM AUTHOR]

In the context of global climate change and increasing human activities, grassland drought has become increasingly severe and complex. The monitoring of grassland drought is crucial for reducing drought-related losses and ensuring national ecological security. This study used the coupled PROSPECT and SAIL radiative transfer models (PROSAIL) to simulate canopy reflectance, considering factors such as grassland growth stages and varying drought conditions. Our objective was to reveal the spectral response characteristics of grasslands to varying drought conditions and identify sensitive spectral bands suitable for drought monitoring during different grassland growth stages. We aligned commonly available satellite bands from moderate resolution imaging spectroradiometer (MODIS), Sentinel 2, Landsat 8, WorldView 2, and Gaofen 2 (GF 2) with these sensitive bands to assess the capabilities of existing satellite data for drought monitoring. Furthermore, this research evaluated the suitability of 16 commonly used remote sensing vegetation indices for grassland drought monitoring, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), Modified Soil Adjusted Vegetation Index (MSAVI), Atmospherically Resistant Vegetation Index (ARVI), Modified Normalized Difference Water Index (MNDWI), Global Vegetation Moisture Index (GVMI), Land Surface Water Index (LSWI), Visible and Shortwave Infrared Drought Index (VSDI), Water index(WI), Moisture Stress Index(MSI), Normalized Difference Water Index(NDWI), Normalized Difference Infrared Index (NDII), Photochemical Reflectance Index (PRI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). The simulation and analysis results revealed: 1) Grassland in different growth stages exhibit similar sensitivities to certain spectral bands, namely those within the ranges of 540 nm–720 nm, 1250 nm–1690 nm, 1805 nm–2190 nm, and 2264 nm–2500 nm, which are more sensitive to various drought conditions. 2) Suitable vegetation indices for both growing and stable stages include NDII, MSI, PRI, LSWI, and GVMI, with silhouette coefficients exceeding 0.6 for the growing stage and 0.7 for the stable stage. The least suitable vegetation index is DVI, with an average silhouette coefficient of 0.15 over the entire growth stage. 3) From the spectral band perspective, among the five assessed satellites, MODIS Band 7 exhibits the highest sensitivity to water content across all satellite bands. MODIS's band configuration is most suitable for monitoring grassland drought during different growth stages, while WorldView 2's band configuration is the least suitable. • Four bands sensitive to grassland drought are located in the red and SWIR bands. • NDII, MSI, PRI, LSWI, and GVMI are more suitable for monitoring grassland drought. • Indices calculated using the NIR and SWIR bands are better for drought monitoring. • In terms of band settings, MODIS is the best satellite for drought monitoring. [ABSTRACT FROM AUTHOR]

In order to address the challenge of early detection of cotton verticillium wilt disease, naturally infected cotton plants in the field, which were divided into five categories based on the degree of disease severity, have been investigated in this study. Canopies of infected cotton plants were analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) and MSC-continuous wavelet analysis algorithms, were used to predict the disease severity. With a combination of support vector machine (SVM) models with such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm optimization (PSO), and grey wolf optimizer (GWO), a grading model of cotton verticillium wilt disease was established in this study. The study results show that the MSC-PSO-SVM model outperforms the other three models in terms of classification accuracy, and the accuracy, macro precision, macro recall, and macro F1-score of this model are 80%, 81.26%, 80%, and 79.57%, respectively. Among those eight models constructed on the basis of continuous wavelet analyses using mexh and db3, the MSC-db3(23)-PSO-SVM and MSC-db3(23)-GWO-SVM models perform best, with the latter having a shorter running time. An overall evaluation shows that the MSC-db3(23)-GWO-SVM model is an optimal model, with values of its accuracy, macro precision, macro recall, and macro F1-score indicators being 91.2%, 92.02%, 91.2%, and 91.16%, respectively. Moreover, under this model, the prediction accuracy on disease levels 1 and 5 has achieved the highest rate of 100%, with a prediction accuracy rate of 88% on disease level 2 and the lowest prediction accuracy rate of 84% on both disease levels 3 and 4. These results demonstrate that it is effective to use spectral technology in classifying the cotton verticillium wilt disease and satisfying the needs of field detection and grading. This study provides a new approach for the detection and grading of cotton verticillium wilt disease and offered a theoretical basis for early prevention, precise drug application, and instrument development for the disease.

In order to realize the real-time and nondestructive monitoring of leaf area index (LAI) for flue-cured tobacco at mature stage, a field experiment was conducted to analyze spatial-temporal distribution of LAI at different leaf layers of tobacco plants with different nitrogen application rates. The results showed that the relationships of spectral reflectivity with LAIs of middle layer, middle-lower layers and top-middle-lower layers (canopy layers) of tobacco plants were similar, which were all significantly negatively correlated with spectral reflectivity in the visible light region, while significantly positively correlated within wave band of 760-1 300 nm. The similarity between the correlation curves of spectral reflectivity and LAIs of middle and canopy layers of the plants were high, and LAI of middle layer could reflect the LAI of the whole plant to a large extent. The correlations between RVI (810, 680) and LAIs of middle layer, top-middle layers and canopy layers of the tobacco plants were the highest, while the correlations between λRed and LAIs of lower layer and middle-lower layers were the highest. The quadratic curve model based on RVI (810, 680) could accurately predict the LAIs of middle layer, top-middle layers of the tobacco plants, and the power model based on RVI (810, 680) could efficiently inverse the LAI of canopy layers. The quadratic curve model based on DλRed could efficiently inverse the LAIs of lower and middle-lower layers. Therefore, these spectral parameters could nondestructively monitor LAl of middle-lower layers of tobacco plants in real time. [ABSTRACT FROM AUTHOR]

Introduction: Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status., Methods: In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI., Results: The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively., Discussion: Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Fan, Gao, Zhang, Cang, Zhang, Zhang, Wang, Lv, Zhang and Ma.)

The present study carried out with a field plot experiment and light environment simulation experiment in 2017 to assess the effect of spectral distribution on internode elongation in soybean canopy. Henong 60 and Heinong 48 were used as the experimental materials. The spectral distribution in the soybean canopy was studied under four planting densities 200,000(D20), 300,000(D30), 400,000(D40) and 500,000(D50) plant ha−1. Meanwhile, a pot experiment of light environment simulation with light-emitting diode (LED) was carried out to further analyze the effects of light quantity and quality on the elongation of soybean seedlings. The results showed that the intensity of PAR, blue light, red light and far-red light in soybean canopy decreased by varying degrees with density and canopy depth increasing, and the decrease of PAR and R/FR led to significant elongation of internodes and plant height in high density. The spectral intensity in the middle and base of the canopy was strongly reduced, resulting in the strongest internode elongation in the middle of soybean stem. The increase in soybean plant height was also mainly due to the internode elongation in the middle at high density. In addition, the mono-light of far-red light could promote the internode elongation, while the red light and blue light acted as inhibitors and the inhibitory effect of blue light was even greater.

From September 2020 to January 2021, take "hongyan"(Fragaria×ananassa Duch "Benihope") strawberry as test material to carry out low temperature environmental control test in Nanjing University of Information Science and Technology. Four low temperature treatments 21℃ (daily maximum temperature) / 11 ℃ (daily minimum temperature), 18℃/8℃, 15℃/5℃ and 12℃/2℃ were set, and the duration was set at four levels of 3d, 6d, 9d and 12d, with 25℃/15℃ as the control (CK). The chlorophyll content of strawberry leaves and canopy hyperspectral reflectance were measured. The effects of low temperature stress on chlorophyll content and canopy reflectance spectrum of strawberry were studied. The sensitive bands and characteristic parameters of chlorophyll content estimation model were selected. The results show that: (1) Under the same low temperature condition, Chla, Chlb and Chl(a+b) content of strawberry leaves decreased with the extension of stress days. Under the same stress days, the lower the temperature, the lower Chla, Chlb and Chl(a+b) content, that is, the greater the degree of low temperature stress, the greater the decline of chlorophyll content. (2) The changes of canopy spectral reflectance of strawberry seedlings under different temperatures and the same stress days were roughly the same. In the visible region, there are green peaks and red valleys in the reflection spectrum curve of strawberry canopy. In the near-infrared reflection platform, the spectral reflectance increases gradually with the decrease of temperature, that is, the reflection platform increases gradually. (3) The first-order differential spectrum curve of strawberry canopy changed violently, with obvious peaks and valleys, and occasionally double peaks in the red edge range. With the deepening of low temperature stress, the higher the value of the highest peak of the first-order differential spectrum, the near-infrared reflectance of strawberry canopy spectrum increased and the red edge position shifted blue, and then the peak gradually decreased and the red edge position shifted red. (4) The correlation coefficient between Chl(a+b) content and original spectral reflectance of strawberry canopy is negatively correlated. The correlation between Chl(a+b) content and reflectance in near-infrared band is significantly higher than that in visible band. The correlation between Chl(a+b) content and original spectral reflectance is good, reaching a significant level. The correlation coefficient of 737nm band is the largest, so it can be used as a sensitive band to predict chlorophyll content. The correlation between Chl(a+b) content and DVI, MSAVI, PVI, RDVI, SAVI and TSAVI in vegetation index is very significant, which can be selected as a characteristic parameter to predict chlorophyll content. [ABSTRACT FROM AUTHOR]

The present study carried out with a field plot experiment and light environment simulation experiment in 2017 to assess the effect of spectral distribution on internode elongation in soybean canopy. Henong 60 and Heinong 48 were used as the experimental materials. The spectral distribution in the soybean canopy was studied under four planting densities 200,000(D20), 300,000(D30), 400,000(D40) and 500,000(D50) plant ha−1. Meanwhile, a pot experiment of light environment simulation with light-emitting diode (LED) was carried out to further analyze the effects of light quantity and quality on the elongation of soybean seedlings. The results showed that the intensity of PAR, blue light, red light and far-red light in soybean canopy decreased by varying degrees with density and canopy depth increasing, and the decrease of PAR and R/FR led to significant elongation of internodes and plant height in high density. The spectral intensity in the middle and base of the canopy was strongly reduced, resulting in the strongest internode elongation in the middle of soybean stem. The increase in soybean plant height was also mainly due to the internode elongation in the middle at high density. In addition, the mono-light of far-red light could promote the internode elongation, while the red light and blue light acted as inhibitors and the inhibitory effect of blue light was even greater. [ABSTRACT FROM AUTHOR]

Canopy spectral composition significantly affects growth and functional traits of understory plants. In this study, we explored the optimal light condition suitable for enhancing Scutellaria baicalensis 's yield and quality, aiming to provide scientific reference for the exploitation and utilization of medicinal plant resources in the understory of forests. We measured the responses of growth, morphology, biomass allocation, physiological traits, and secon-dary metabolites of S. baicalensis to different light qualities. S. baicalensis was cultured under five LED-light treatments including full spectrum light (control), ultraviolet-A (UV-A) radiation, blue, green, and red light. Results showed that UV-A significantly reduced plant height, base diameter, leaf thickness, leaf area ratio, and biomass of each organ. Red light significantly reduced base diameter, biomass, effective quantum yield of photosystem Ⅱ ( Ф PSⅡ ), and total flavonoid concentration. Under blue light, root length and total biomass of S. baicalensis significantly increased by 48.0% and 10.8%, respectively, while leaf number and chlorophyll content significantly decreased by 20.0% and 31.6%, respectively. The other physiological and biochemical traits were consistent with their responses in control. Our results suggested that blue light promoted photosynthesis, biomass accumulation, and secondary metabolite synthesis of S. baicalensis , while red light and UV-A radiation negatively affected physiological and biochemical metabolic processes. Therefore, the ratio of blue light could be appropriately increased to improve the yield and quality of S. baicalensis .

[Objective] Based on the hyperspectral reflectance of the canopy damaged by Tetranychustrrncates, a model for estimating the water content of the jujube canopy was constructed. [Method] Based on the spectral reflectance and water content of the jujube (Ziz-iphusjuba) canopy, Analyze the correlation between the first-order differential spectrum of the jujube canopy and the water content under the damage of the spider mite, and screen the sensitive bands of the hyperspectral water content of the jujube canopy, and use the regression models of linear function, quadratic function and exponential function to construct the intercept A hyperspectral estimation model of jujube canopy water content under the damage of spider mites. [Result] The jujube canopy water content decreases with the decrease of spectral reflectance, which is significantly negatively correlated with the level of pests. Using canopy first-order differential spectrum D1102 The multivariate linear stepwise regression model established in the combined bands of D974 and D990 has the best fit, with R2 reaching 0.618, RMSE and RE being 3.428 and 3.900%. [Conclusion] It shows that it is feasible to invert canopy water content by using hyperspectral technology. It has important practical significance in forest pest monitoring. [ABSTRACT FROM AUTHOR]

Analysis of the canopy structure change and spectral response mechanism of rice under flooding stress is an important prerequisite for large-scale monitoring of rice flooding disasters. The leaf area index (LAI) was used as the characterization indicator. The response rule of the canopy spectrum to flooding stress intensity was analyzed. The sensitive spectral characteristic parameters were screened to construct the LAI spectral response model of rice under flooding stress. The results showed that the rice LAI under flooding stress decreased with an increase in waterlogging depth. The spectral reflectance of the rice canopy under flooding stress significantly changed in the near-infrared band and decreased with an increase in waterlogging depth. In 680–760 nm, the double peak in the first-order differential spectrum of the rice canopy was more obvious with advancement of the growth process and a multiple peak appeared during the late growth stage. The blueshift of the red edge parameters was the most obvious in the submerged top during the tillering stage. A power function regression model based on the ratio of the first-order differential spectral amplitude at 737 nm to 719 nm in the red edge range was the optimal LAI response model for rice under flooding stress. A field waterlogging experiment was used to simulate and analyze the influence of flooding on the rice canopy structure and the canopy spectral response rule, providing a reference for the subsequent analysis of rice growth and disaster loss assessment under flooding stress. [ABSTRACT FROM AUTHOR]

Compared to conventional laboratory testing methods, crop nitrogen estimation methods based on canopy spectral characteristics have advantages in terms of timeliness, cost, and practicality. A variety of rapid and non-destructive estimation methods based on the canopy spectrum have been developed on the scale of space, sky, and ground. In order to understand the differences in estimation accuracy and applicability of these methods, as well as for the convenience of users to select the suitable technology, models for estimation of nitrogen status of winter wheat were developed and compared for three methods: drone equipped with a multispectral camera, soil plant analysis development (SPAD) chlorophyll meter, and smartphone photography. Based on the correlations between observed nitrogen status in winter wheat and related vegetation indices, green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected as the sensitive vegetation indices for the drone equipped with a multispectral camera and smartphone photography methods, respectively. The correlation coefficients between GNDVI, SPAD, and VARI were 0.92 ** and 0.89 **, and that between SPAD and VARI was 0.90 **, which indicated that three vegetation indices for these three estimation methods were significantly related to each other. The determination coefficients of the 0−90 cm soil nitrate nitrogen content estimation models for the drone equipped with a multispectral camera, SPAD, and smartphone photography methods were 0.63, 0.54, and 0.81, respectively. In the estimation accuracy evaluation, the method of smartphone photography had the smallest root mean square error (RMSE = 9.80 mg/kg). The accuracy of the smartphone photography method was slightly higher than the other two methods. Due to the limitations of these models, it was found that the crop nitrogen estimation methods based on canopy spectrum were not suitable for the crops under severe phosphate deficiency. In addition, in estimation of soil nitrate nitrogen content, there were saturation responses in the estimation indicators of the three methods. In order to introduce these three methods in the precise management of nitrogen fertilizer, it is necessary to further improve their estimation models.

Compared to conventional laboratory testing methods, crop nitrogen estimation methods based on canopy spectral characteristics have advantages in terms of timeliness, cost, and practicality. A variety of rapid and non-destructive estimation methods based on the canopy spectrum have been developed on the scale of space, sky, and ground. In order to understand the differences in estimation accuracy and applicability of these methods, as well as for the convenience of users to select the suitable technology, models for estimation of nitrogen status of winter wheat were developed and compared for three methods: drone equipped with a multispectral camera, soil plant analysis development (SPAD) chlorophyll meter, and smartphone photography. Based on the correlations between observed nitrogen status in winter wheat and related vegetation indices, green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected as the sensitive vegetation indices for the drone equipped with a multispectral camera and smartphone photography methods, respectively. The correlation coefficients between GNDVI, SPAD, and VARI were 0.92 ** and 0.89 **, and that between SPAD and VARI was 0.90 **, which indicated that three vegetation indices for these three estimation methods were significantly related to each other. The determination coefficients of the 0–90 cm soil nitrate nitrogen content estimation models for the drone equipped with a multispectral camera, SPAD, and smartphone photography methods were 0.63, 0.54, and 0.81, respectively. In the estimation accuracy evaluation, the method of smartphone photography had the smallest root mean square error (RMSE = 9.80 mg/kg). The accuracy of the smartphone photography method was slightly higher than the other two methods. Due to the limitations of these models, it was found that the crop nitrogen estimation methods based on canopy spectrum were not suitable for the crops under severe phosphate deficiency. In addition, in estimation of soil nitrate nitrogen content, there were saturation responses in the estimation indicators of the three methods. In order to introduce these three methods in the precise management of nitrogen fertilizer, it is necessary to further improve their estimation models. [ABSTRACT FROM AUTHOR]