Spectral Characterization of Thrips Infested Mulberry (Morus indica L.) Leaves

Mulberry (Morus spp.) serves as the primary food source for silkworms (Bombyx mori L.). Sucking pests form a major group of destructive fauna affecting mulberry crops. Among these, sap-sucking pests are the most destructive, causing greater losses to mulberry compared to other pest groups. Early identification of pest damage is essential for selecting effective control methods, thereby reducing production costs and minimising crop losses. In contrast, modern techniques such as remote sensing enable rapid and efficient detection over large areas. The study aims to perform spectral analysis of spiralling whitefly-damaged Mulberry plants using hyperspectral radiometry. A randomised block design was employed to analyse the vegetative indices, namely RVI, NDVI, and GRVI, with means compared using the Least Significant Difference method. Furthermore, linear regression models were constructed to assess the relationship between the percentage of damage and each of the vegetation indices. Linear correlation intensity analysis was conducted to identify the wavelengths with the highest positive and negative correlation with spiralling white fly damage. Hyperspectral radiometry, which analyses plant spectral reflectance, is particularly effective in assessing pest-induced stress. In the experiments, the mulberry variety V1, known for its different crop duration and susceptibility to insect pests, was chosen. The damage caused by spiralling whitefly (Aleurodicus disperses) was studied. The percentage of damage was recorded in both healthy and infested plots at 15-day intervals during the active damage stage. Spectral reflectance across various bands was also recorded using a hyperspectral radiometer. Sensitivity analysis of spectral bands and vegetation indices (VIs) was carried out. Different analyses, including correlation and regression of pest damage with VIs, and correlation intensity curve analysis, were conducted in order to detect and discriminate individual pest damage and to estimate the extent of damage caused by this pest. These studies were carried out at the mulberry garden of the Department of Sericulture, Tamil Nadu Agricultural University (TNAU), Coimbatore, during 2018. The results revealed that the spectral reflectance curves of mulberry plants damaged by spiralling whitefly differed from those of healthy plants. In general, damaged plants showed a decrease in near-infrared (NIR) reflectance (770–860 nm), along with an increase in green (520–590 nm) and red reflectance (620–680 nm). The mean values of NDVI, GRVI, and RVI for damaged plants were significantly lower than those of healthy plants. For spiralling whitefly damage, the red band was found to be more sensitive than other bands. Among the vegetation indices, GRVI was most sensitive to spiralling whitefly damage. A significant negative correlation was observed between spiralling whitefly damage and NDVI and GRVI across all observations. The R² values of RVI, NDVI, and GRVI with spiralling whitefly damage were significant, indicating the capability of these indices to estimate damage. To further quantify damage, linear regression equations were developed based on spectral indices. Correlation analysis revealed the highest positive correlation (r = 0.62) in the NIR band at 743 nm, while the least negative correlation (r = –0.40) occurred in the red band at 676.75 nm. The combined use of red-band reflectance and GRVI provides a reliable, non-destructive tool for early detection and monitoring, enabling timely pest management in sericulture.

Accurate, timely, and non-destructive early crop yield prediction at the field scale is essential in addressing changing crop production challenges and mitigating impacts of climate variability. Unmanned aerial vehicles (UAVs) are increasingly popular in recent years for agricultural remote sensing applications such as crop yield forecasting and precision agriculture (PA). The objective of this study was to evaluate the performance of a low-cost UAV-based remote sensing technology for Bambara groundnut yield prediction. A multirotor UAV equipped with a near-infrared sensitive consumer-grade digital camera was used to collect image data during the 2018 growing season (April to August). Flight missions were carried out six times during critical phenological stages of the life-cycle of the monitored crop. Yield was recorded at harvest. Four vegetation indices (VIs) namely normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), green normalized difference vegetation index (GNDVI), and simple ratio (SR) generated from the Red-Green-Near Infrared bands were calculated using the georeferenced orthomosaic UAV images. Pearson’s product-moment correlation coefficient (r) and Bland–Altman testing showed a significant agreement between remotely and proximally sensed VIs. Significant and positive correlations were found between the four VIs and yield, with the strongest relationship observed between SR and yield at podfilling stage (r = 0.81, P < 0.01). Multi-temporal accumulative VIs improved yield prediction significantly with the best index being ∑SR and the best interval being from podfilling to maturity (r = 0.88, P < 0.01). The accumulated ∑SR from podfilling to maturity resulted in higher prediction accuracy with a coefficient of determination (R 2) of 0.71, root mean square error (RMSE) of 0.20 and mean absolute percentage error (MAPE) of 14.2% than SR spectral index at a single stage (R 2 = 0.68, RMSE = 0.24, MAPE = 15.1%). Finally, a yield map was generated using the model developed, to better understand the within-field spatial variations of yield for future site-specific or variable-rate application operations.

Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2–4 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale.

Abstract. Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is of great importance for reducing spatial and temporal uncertainty in estimating terrestrial carbon (C) dynamics. In this study, an approach to estimate daily cropland C fluxes is developed and tested by combining time series of field-scale eddy covariance (EC) CO2 flux data and Sentinel-2 satellite-based vegetation indices (VIs) after appropriately accounting for the spatial alignment between the two time series datasets. The study was carried out for an agricultural field (118 ha) in the lowlands of northeastern Germany. The ability of different VIs to estimate daily net ecosystem exchange (NEE) and gross primary productivity (GPP) based on linear regression models was assessed. Most VIs showed high (&gt;0.9) and statistically significant (p&lt;0.001) correlations with GPP and NEE, although some VIs deviated from the seasonal pattern of CO2 exchange. By contrast, correlations between ecosystem respiration (Reco) and VIs were weak and not statistically significant, and no attempt was made to estimate Reco from VIs. Linear regression models explained generally more than 80 % and 70 % of the variability in NEE and GPP, respectively, with high variability among the individual VIs. The performance in estimating daily C fluxes varied among VIs depending on the C flux component (NEE or GPP) and observation period. Root mean square error (RMSE) values ranged from 1.35 g C m−2 d−1 using the green normalized difference vegetation index (GNDVI) for NEE to 5 g C m−2 d−1 using the simple ratio (SR) for GPP. This equated to an underestimated net C uptake of only 41 g C m−2 (18 %) and an overestimation of gross C uptake of 854 g C m−2 (73 %). Differences between the measured and estimated C fluxes were mainly explained by the diversion of the C flux and VI signal during winter when C uptake remained low, while VI values indicated an increased C uptake due to relatively high crop leaf area. Overall, the results exhibited similar error margins to mechanistic crop models. Thus, they indicated the suitability and expandability of the proposed approach for monitoring cropland C exchange with satellite-derived VIs.

Effects of N application rate on N remobilization and accumulation in maize (Zea mays L.) and estimating of vegetative N remobilization using hyperspectral measurements

The objective of this study was to analyze the relationship between satellite‐derived vegetation indices and CO2 uptake, as an initial step in exploring the possibility of using a satellite‐derived vegetation index as a measure of net photosynthesis. The study area included the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site located on the Konza prairie and adjacent area as well as a transect between Manhattan and Salina. One third of the transect exhibited vegetation and terrain characteristics similar to those on the FIFE site, whereas cultivated land predominated in the remaining portion of the 75‐km‐long flight line. In June, July, August, and October 1987, several CO2 data sets were obtained using the National Research Council of Canada's Twin Otter research aircraft. The normalized difference vegetation index (NDVI) and the simple ratio (SR) were computed from NOAA advanced very high resolution radiometer (AVHRR) data acquired as part of FIFE. Aircraft and satellite data were processed to obtain spatially coincident and locally representative flux values. Results show a linear relationship between NDVI and CO2 uptake during a single day; however, a nonlinear relationship emerged when all data sets were combined. The data from FIFE and the regional transect were consistent for one date but differed for other periods. Overall, about 60% of total variability in CO2 flux was accounted for by the NDVI and 74% by the SR.

Pecan orchards are the largest agricultural water consumer in the lower part of the Mesilla Valley, NM, USA. Knowledge of fractional canopy (FC) cover allows better crop water use assessment and orchard management. FC can be estimated from vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI), the simple ratio (SR), and the triangular vegetation index (TVI), using satellite imagery. The main objective of this research is to develop an approach to determine the FC from a simultaneous imagery campaign consisting of aerial imagery, orchard floor photographs, and satellite images. All the required data were collected based on satellite overpass times at three different times during the initial part of the growing season to enhance the quality of data and reduce errors. The data were processed using the software package Environment for Visualizing Images (ENVI® 4.6.1; ITT Research Systems Inc.). The orchard floor digital photographs were used as a ground truth data set that gave a good correlation to the aerial photography. The aerial images were then used to determine the relationship between the FC and the VIs using these ‘corrected FCs’. The results showed significant correlation between NDVI and FC (R 2 = 0.80; p < 0.0001). Likewise, the calculated SR not only showed good correlation to the FCs but also verified the calculated NDVI. The results indicated that the methodology of this research can be applied to other tree crops as an aid in estimating the FC.

Spatial estimation of urban forest structures with Landsat TM data and field measurements

Fractional vegetation cover (FVC) and aboveground biomass (AGB) are critically important for monitoring grassland degradation, and their accurate estimation can be used as key proxies for assessing land degradation. The main purpose of this study was to estimate the FVC and AGB in the eastern Mongolian steppe using remote sensing and machine learning. In this context, spectral bands and vegetation indices were extracted from the processed Sentinel-2 data and used as predictors. The field vegetation data were derived from the Mongolian pasture-monitoring database, which consisted of 256 plots with FVC and AGB measurements. Consequently, we derived FVC and AGB from Sentinel-2 imagery using 256 field vegetation measurements in the vast eastern Mongolian steppe as a reference for random forest (RF) models (R2 FVC = 0.81, R²AGB = 0.76). Among the variables, the predictor variables derived from spectral vegetation and soil indices, especially NDVI, Simple Ratio (SR), and OSAVI, were highly important for predicting FVC and AGB. As expected, a comparison among the map values showed that the spatial distribution of FVC and AGB was consistent with the landscapes and ecoregions in the study area. As the FVC and AGB maps only showed the current condition of vegetation cover, we also analysed NDVI trends to explain vegetation cover changes. We tested temporal trends in vegetation using Landsat NDVI time series data and the Mann-Kendall trend test. This revealed that in 7.3% of the area, the NDVI significantly increased, whereas a significant decrease was observed in 58% of the area.

This study investigated the relationship between sorghum grain yield over range of soil depth with seasonal crop water stress index based on relative evapotranspiration deficits and spectral vegetation indices. A root zone soil moisture model has been used to evaluate the seasonal soil moisture fluctuation and actual evapotranspiration within a toposequence having varying soil depth of 30 to 75 cm as well as different available water capacity ranging from 6.9% to 12.6% (V/V%). The higher r² values between modeled and observed values of soil water (r²> 0.69 significant at <0.001) and runoff (r² = 0.95, significant at P<0.001) indicated good agreement between model output and observed values. The spectral vegetation indices like simple ratio, normalized difference vegetation index (NDVI), green NDVI, perpendicular vegetation index, soil adjusted vegetation index (SAVI) and modified SAVI (MSAVI) was recorded through out the growth period of sorghum. The vegetation indices except perpendicular vegetation index measured during booting to anthesis stages were positively correlated (P<0.05) with leaf area index and yield. The MSAVI measured during booting to milk-grain stage have the highest positive correlation with yield. Variation was noticed when additive and multiplicative forms of water-production functions calculated from water budget model were used to predict crop yield. But the yield estimation was improved when spectral vegetation indices measured during booting to milk-grain is incorporated along with water production functions. The water budget model along with spectral vegetation indices gave satisfactory estimates of sorghum grain yields and appears to be a useful tool to estimate yield as a function of soil depth and available soil water.

Fumaric acid esters are considered efficacious and safe drugs for the treatment of psoriasis. Renal damage, caused either by acute renal injury or Fanconi syndrome, is a recognized side-effect of this therapy. To investigate whether the measurement of urinary excretion of β2-microglobulin, a marker of renal proximal tubular dysfunction, allows early detection of kidney damage before an increase in serum creatinine or significant proteinuria occurs. Urinary β2-microglobulin excretion was measured regularly in 23 patients undergoing fumaric acid ester therapy. Urinary β2-microglobulin remained normal in all 10 male patients. Three (23%) out of 13 female patients experienced an increase in urinary β2-microglobulin excretion. In two of these patients a sharp increase was observed in association with high doses. One further patient had moderately elevated levels on rather low doses of fumaric acid esters. After discontinuing treatment, urinary β2-microglobulin levels returned to normal within a few weeks. Determination of urinary β2-microglobulin possibly allows early detection of renal damage by fumaric acid esters. Female patients seem to be prone to this side-effect, especially when taking high doses.

Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r2 of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.

Monitoring ecosystem reclamation recovery using optical remote sensing: Comparison with field measurements and eddy covariance

Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands

Satellite remote sensing is considered as an increasingly important technology for detect underground structures. It can be applied to a wide range of applications as shown from various researchers. However, there is a great need to integrate information from a variety of sources, sent at different times and of different qualities using remote sensing tools. This paper presents the results obtained from field spectroradiometric campaigns at âburied' underground structures in Cyprus. A SVC-HR1024 field spectroradiometer was used and in-band reflectances were determined for medium and high resolution satellite sensors, including Landsat. A number of vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Enhanced Vegetation Index (EVI) were utilized for the development of a vegetation index-based procedure aiming at the detection of underground military structures by using existing vegetation indices or other in-band algorithms. In this study, test areas were identified, analyzed and modeled under different scenarios, including: (a) the âñatural state' of the underground structure; (b) the different type of crop over the underground structure and imported soil; (c) the different types of non-natural material over the underground structure. A reference target in the nearby area was selected as a baseline.