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Rice blast is a highly damaging disease that greatly impacts both the quality and yield of rice. Timely identification and monitoring of this disease are essential for effective agricultural management and for ensuring optimal crop performance. The spectral vegetation index has been widely used in the identification of crop diseases. However, a limitation of these indices is that they cannot identify diseases at different scales. This study aimed to address these issues by developing the rice blast-specific hyperspectral Geometry Ratio Vegetation Index (GRVIRB) for monitoring rice blast disease at the leaf and canopy scales. The sensitive bands for identifying rice blast disease were 688 nm, 756 nm, and 1466 nm using the successive projection algorithm. Based on these three sensitive bands and the spectral response mechanism of rice blast, the GRVIRB was designed. GRVIRB demonstrated high classification accuracy using SVM (support vector machine) and LDA (Linear Discriminant Analysis) models in leaf-scale and canopy-scale datasets from 2020 and 2021, surpassing the current vegetation indices of rice blast detection. It is demonstrated that the GRVIRB has excellent robustness and universality for rice blast detection from leaf to canopy scales in different years. Additionally, the research suggests that the new hyperspectral vegetation index can serve as a valuable reference for studies conducted at both unmanned aerial vehicle and satellite scales.

Vegetation indices play an important role in monitoring variations in vegetation.The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Groupand the Normalized Difference Vegetation Index (NDVI) are both global-based vegetationindices aimed at providing consistent spatial and temporal information regarding globalvegetation. However, many environmental factors such as atmospheric conditions and soilbackground may produce errors in these indices. The topographic effect is another veryimportant factor, especially when the indices are used in areas of rough terrain. In thispaper, we theoretically analyzed differences in the topographic effect on the EVI and theNDVI based on a non-Lambertian model and two airborne-based images acquired from amountainous area covered by high-density Japanese cypress plantation were used as a casestudy. The results indicate that the soil adjustment factor “L” in the EVI makes it moresensitive to topographic conditions than is the NDVI. Based on these results, we stronglyrecommend that the topographic effect should be removed in the reflectance data beforethe EVI was calculated—as well as from other vegetation indices that similarly include a term without a band ratio format (e.g., the PVI and SAVI)—when these indices are used in the area of rough terrain, where the topographic effect on the vegetation indices having only a band ratio format (e.g., the NDVI) can usually be ignored.

Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

In-season determination of corn (Zea mays L.) N requirements via remote sensing may help optimize N application decisions and improve profit, fertilizer use efficiency, and environmental quality. The objective of this study was to use aerial color-infrared (CIR) photography as a remote-sensing technique for predicting in-season N requirements for corn at the V7 growth stage. Field studies were conducted for 2 yr at three locations, each with and without irrigation, in the North Carolina Coastal Plain. Experimental treatments were a complete factorial of four N rates at planting (NPL) and five N rates at V7 (NV7). Aerial CIR photographs were taken at each of the locations at V7 before N application. Optimum NV7 ranged from 0 to 207 kg N ha-1 with a mean of 67 kg N ha-1. Significant but weak correlations were observed between optimum NV7 rates and the band combinations relative green, Relative Green Difference Vegetation Index, and Relative Difference Vegetation Index as measured in CIR photos. High proportions of soil reflectance in the images early in the corn growing season (V7) likely confounded our attempts to relate spectral information to optimum NV7 rates. The primary obstacles to applying this technique early in the season are the use of relative digital counts or indices that require high-N reference strips in the field and strong background reflectance from the soil. When the NPL treatments that were nonresponsive to NV7 (i.e., optimum NV7 = 0) were removed from the analysis, the normalized near infrared, the Green Difference Vegetation Index, the Green Ratio Vegetation Index, and the Green Normalized Difference Vegetation Index were the best predictors of optimum NV7 rate (r2 = 0.33).

Recently the application of spectral reflection data for the prediction of crop parameters for applications in precision agriculture, such as green area index (GAI), total aboveground dry matter (DM), and total aboveground nitrogen content (N content) increases. However, the usability of vegetation indices (VI) for the prediction of crop parameters is strongly limited by the fact that most VI calibrations are only valid for specific crops and growth periods. The results of the presented study based on the differentiation of primary (main driver of the reflectance signal) and secondary (not directly related to reflectance signal) crop parameters. For GAI prediction, a universal (without crop-specific parametrization) simple ratio vegetation index (SR) provided good calibration (R2 adj. = 0.90, MAE = 0.32, rMAE = 22%) and evaluation results (MAE = 0.33, rMAE = 18%). The disentanglement of primary and secondary traits allowed the development of a functional two-step model for the estimation of the N content during vegetative growth (MAE = 19.2 g N m−1, rMAE = 44%). This model was based on fundamental, crop-specific relationships between the crop parameters GAI and N content. Additionally, an advanced functional approach was tested enabling the whole-season prediction of DM and confirming a reliable GAI estimation throughout the whole growing season (R2 = 0.89–0.93).

Drought is a great challenge to agricultural production, and cultivation of drought‐tolerant or water use‐efficient cultivars is important to ensure high biomass yields for bio‐refining and bioenergy. Here, we evaluated drought tolerance of four C3 species, Dactylis glomerata cvs. Sevenop and Amba, Festuca arundinacea cvs. Jordane and Kora, Phalaris arundinacea cvs. Bamse and Chieftain and Festulolium pabulare cv. Hykor, and two C4 species Miscanthus × giganteus and M. lutarioriparius. Control (irrigated) and drought‐treated plants were grown on coarse and loamy sand in 1 m2 lysimeter plots where rain was excluded. Drought periods started after harvest and lasted until 80% of available soil water had been used. Drought caused a decrease in dry matter yield (DM; P < 0.001) for all species and cultivars during the drought period. Cultivars Sevenop, Kora and Jordane produced DM at equal levels and higher than the other C3 cultivars in control and drought‐treated plots both during and after the drought period. Negative correlations were observed between stomatal conductance (gs) and leaf water potential (P < 0.01) and positive correlations between gs and DM (P < 0.05) indicating that gs might be suitable for assessment of drought stress. There were indications of positive associations between plants carbon isotope composition and water use efficiency (WUE) as well as DM under well‐watered conditions. Compared to control, drought‐treated plots showed increased growth in the period after drought stress. Thus, the drought events did not affect total biomass production (DMtotal) of the whole growing season. During drought stress and the whole growing season, WUE was higher in drought‐treated compared to control plots, so it seems possible to save water without loss of biomass. Across soil types, M. lutarioriparius had the highest DMtotal (15.0 t ha−1), WUEtotal (3.6 g L−1) and radiation use efficiency (2.3 g MJ−1) of the evaluated grasses.

Canopy reflectance sensors are a viable technology to optimize the fertilization management of crops. In this research, canopy reflectance was measured through a passive sensor to evaluate the effects of either crop features (N fertilization, soil mulching, appearance of red fruits, and cultivars) or sampling methods (sampling size, sensor position, and hour of sampling) on the reliability of vegetation indices (VIs). Sixteen VIs were derived, including seven simple wavelength reflectance ratios (NIR/R460, NIR/R510, NIR/R560, NIR/R610, NIR/R660, NIR/R710, NIR/R760), seven normalized indices (NDVI, G-NDVI, MCARISAVI, OSAVI, TSAVI, TCARI), and two combined indices (TCARI/OSAVI; MCARI/OSAVI). NIR/560 and G-NDVI (Normalized Difference Vegetation Index on Greenness) were the most reliable in discriminating among fertilization rates, with results unaffected by the appearance of maturing fruits, and the most stable in response to different cultivars. Black mulching film did not affect NIR/560 and G-NDVI behavior at the beginning of the growing season, when the crop is more responsive to N management. Due to a moderate variability of NIR/560 and G-NDVI, a small sample size (5–10 observations) is sufficient to obtain reliable measurements. Performing the measurements at 11:00 and 14:00 and maintaining a greater distance (1.8 m) between plants and instrument enhanced measurement consistency. Accordingly, NIR/560 and G-NDVI resulted in the most reliable VIs.

The accuracy of detecting the chlorophyll content in the canopy and leaves of citrus plants based on sensors with different scales and prediction models was investigated for the establishment of an easy and highly-efficient real-time nutrition diagnosis technology in citrus orchards. The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor, canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and then analyzed. Additionally, the associations of the leaf SPAD (soil and plant analyzer development) value with the ratio vegetation index (RVI) and normalized differential vegetation index (NDVI) were analyzed. The leaf SPAD value predictive model was established by means of univariate and multiple linear regressions and the partial least squares method. Variable distribution maps of the relative canopy chlorophyll content based on spectral reflectance in the orchard were automatically created. The results showed that the correlations of the SPAD values obtained from the Multiplex 3.6 sensor, FieldSpec4 radiometer and low-altitude multispectral remote sensing were highly significant. The measures of goodness of fit of the predictive models were R2=0.7063, RMSECV=3.7892, RE=5.96%, and RMSEP=3.7760 based on RVI(570/800) and R2=0.7343, RMSECV=3.6535, RE=5.49%, and RMSEP=3.3578 based on NDVI[(570,800)(570,950)(700,840)]. The technique to create spatial distribution maps of the relative canopy chlorophyll content in the orchard was established based on sensor information that directly reflected the chlorophyll content of the plants in different parts of the orchard, which in turn provides evidence for implementation of orchard productivity evaluation and precision in fertilization management.

Spatial distribution of canopy N status is the primary information needed for precision management of N fertilizer. This study demonstrated the feasibility of a simple spectral index (SI) using the first derivative of canopy reflectance spectrum at 735 nm (dR/d|735) to assess N concentration of rice (Oryza sativa L.) plants, and then validated the applicability of a simplified imaging system based on the derived spectral model from the dR/d|735 relationship in mapping canopy N status within field. Results showed that values of dR/d|735 were linearly related to plant N concentrations measured at the panicle formation stage. The leaf N accumulation per unit ground area was better fitted than other ratio-based SIs, such as simple ratio vegetation index (SRVI), normalized difference vegetation index (NDVI), R810/R560, and (R1100 - R660)/(R1100 + R660), and remained valid when pooling more data from different cropping seasons in varied years and locations. A simplified imaging system was assembled and mounted on a mobile lifter and a helicopter to take spectral imageries for mapping canopy N status within fields. Results indicated that the imaging system was able to provide field maps of canopy N status with reasonable accuracy (r = 0.465-0.912, root mean standard error [RMSE] = 0.100-0.550) from both remote sensing platforms.

Quantitative spectral reflectance data have the potential to improve the evaluation of turfgrasses in variety trials when management practices are factors in the testing of turf aesthetics and functionality. However, the practical application of this methodology has not been well developed. The objectives of this research were 1) to establish a relationship between spectral reflectance and turfgrass quality (TQ) and percent green cover (PGC) using selected reference plots; 2) to compare aesthetic performance (TQ, PGC, and vegetation indices) and functional performance (surface firmness); and 3) to evaluate lignin content as an alternate means to predict surface firmness in turfgrass variety trials of hybrid bermudagrass [ Cynodon dactylon × C. transvaalensis ]. A field study was conducted on mature stands of three varieties (‘TifTuf’, ‘TifSport’, and ‘Tifway’) and two experimental lines (04-47 and 04-76) at two mowing heights (0.5 and 1.5 inch) and trinexapac-ethyl application (0.15 kg·ha −1 and nontreated control) treatments. Aesthetic performance was estimated by vegetation indices, spectral reflectance, visual TQ, and PGC. The functional performance of each variety/line was measured through surface firmness and fiber analysis. Regression analyses were similar when using only reference plots or all the plots to determine the relationship between individual aesthetic characteristics. Experimental line 04-47 had lower density in Apr. 2010, whereas varieties ‘TifTuf’, ‘TifSport’, and ‘Tifway’ were in the top statistical group for aesthetic performance when differences were found. ‘TifSport’ and ‘Tifway’ produced the firmest surfaces, followed by ‘TifTuf’, and finally 04-76 and 04-47, which provided the least firm surface. Results of leaf fiber analysis were not correlated with turf surface firmness. This study indicates that incorporating quantitative measures of spectral reflectance could reduce time and improve precision of data collection as long as reference plots with adequate range of green cover are present in the trials.