The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI), to estimate the Leaf Area Index (LAI) and above-ground biomass. For LAI estimation using the NDVI, the power function model (y = 10.083x^11.298) demonstrated higher precision, with a multiple correlation coefficient of R² = 0.8184 and the smallest root mean square error (RMSE = 0.3613). These results confirm the strong predictive capacity of NDVI for LAI, with the power function model offering the best estimation accuracy. In estimating above-ground biomass using RVI, the power function model of y = 6.5218x^1.33917 achieved the higher correlation (R² = 0.8851) for fresh biomass with an RMSE of 0.1033, making it the most accurate. For dry biomass, the exponential function model (y = 9.1565 × 10⁻⁵∙exp(1.1146x)) was the most precise, achieving an R² value of 0.8456 and the lowest RMSE value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring.

Red solar-induced chlorophyll fluorescence (SIF_B) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIF_B by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIF_B using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PK_B) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PK_B and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPAR_chl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIF_B by the vegetation canopy. Consequently, we constructed an expression of PK_B to modify SIF_B. To evaluate the modified SIF_B (MSIF_B) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIF_B, MSIF_B, SIF_B-VIs (a fusion of the vegetation index and SIF_B), and MSIF_B-VIs (a fusion of the vegetation index and MSIF_B) with the severity level (SL), respectively. The results show that the correlation between MSIF_B and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIF_B. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIF_B, MSIF_B, SIF_B-VIs, and MSIF_B-VIs as independent variables. PK_B significantly improves the accuracy and robustness of models based on SIF_B and its fusion index SIF_B-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIF_B-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIF_B-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIF_B modified by PK_B can weaken the effects of chlorophyll reabsorption and canopy architecture on SIF_B and improve the ability of SIF_B to detect stress information.

The purpose of this paper is to clarify the quantitative relationship between total nitrogen content of sugar beet and high-resolution vegetation remote sensing, to explore the establishment of an optimal estimation model for total nitrogen content of sugar beet, and to monitor the growth of sugar beet. Xinjiang drip-irrigated sugar beet (Beta356) was selected to collect the reflectance spectra of leaf cluster during the leaves rapid growth period, root expansion period and sugar accumulation period by the ASD field hyperspectral apparatus. The total nitrogen content was also measured and the relationship between original spectral reflectance and total nitrogen content was analyzed. According to the correlation between the first-order differential spectral reflectance and total nitrogen content, a total nitrogen content estimation model was established. The model with spectral characteristic parameter Dr762 power function had a good ability to estimate total nitrogen content in leaves of beet, with the determination coefficient, relative error, and root mean square error of 0.747, 21.635, and 4.914, respectively. Various function estimation models were established based on vegetation index and leaf total nitrogen content. The linear function under vegetation index Dr762–Dr496 had better ability to estimate leaf total nitrogen content. Its determinant coefficient, relative error, and root mean square error were 0.794, 23.008, and 5.372, respectively.

The relationship between the leaf nitrogen content (LNC) and hyperspectral remote sensing imagery (HYP) was determined to construct an estimation model of the LNC of drip-irrigated sugar beets, aiming to provide supports for the in-time monitoring of sugar beet growth and nitrogen management in arid areas. In this study, a field hyperspectrometer was used to collect the leaf reflectance at the 350–2500 nm for each treatment on the 65th, 85th, 104th, 124th, and 140th day after emergence, and the LNC and leaf chlorophyll content (CHL) of sugar beets were also determined. The spectral characteristic parameters were selected to construct the vegetation indices. The LNC estimation model using HYP as the independent variable (HYP-LNC), and that using CHL and HYP as the independent variables (HYP-CHL-LNC), were compared. The results shows that the HYP-CHL-LNC models had a better linear relationship and a higher fitting accuracy than the HYP-LNC models.

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R²=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R²=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R²=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content.