SPAD combined with hyperspectral sensors can be an alternative approach to traditional laboratory methods for determining the physiological status of trees. The aim of this work was to assess whether the effectiveness of SPAD predictive models using hyperspectral data might be influenced by where the measurements were carried out. Leaves of apricot trees of two varieties (Farbaly and Farlis) were analysed with SPAD and spectroradiometer, and the data were organized in two different ways: (i) overall dataset (OD), collecting total measurements of trees in each variety; (ii) subset of overall datasets (SOD), collecting the measurements performed on the cardinal points of trees in each variety. Prediction models were built using as regressors: (i) spectral data transformed with Continuum Removal (CR) methodology (CR indices); (ii) vegetation indices (VI) linked to chlorophyll and nitrogen content; (iii) reflectance values associated with chlorophyll content and to wavelengths ranges where (CR) methodology was applied; (iv) reflectance values of full spectrum. The best performances belonged to models using wider ranges of spectrum both in ODs and in SODs. The north cardinal point showed prediction models with the best performances in both varieties. No VI and CR indices showed reliable models. All the reliable prediction models were associated with compounds involved in physiological state and metabolism of leaves in apricot tree.

Few studies document the impacts of conservation management practices such as extensive grazing or mowing on the new ecosystems created by industrial conversions. In southern France, the Rhône channelling led to the construction of dykes to protect the Tricastin industrialized area from floods. Aiming to control plant dynamics for safety reasons and to favour plant biodiversity, mowing or extensive grazing by cattle were recently tested. Monitoring from both permanent plots and aerial photographs shows that three years of extensive grazing and annual mechanical mowing have modified plant composition, significantly increasing plant species richness, evenness and heterogeneity. The increase in evenness and beta-diversity from grazing was significantly higher than from mowing. Only grazing was able to reduce the height and cover of the dominant tussock perennial grass species (Brachypodium phoenicoides), while increasing bare soil cover and thus the contribution of annual species. The Normalized Difference Vegetation Index (NDVI) obtained through aerial photographic analyses confirmed the correlation between NDVI, aboveground biomass and plant species richness for the grazed site alone, allowing the results obtained from quadrats to be generalized to the scale of the grazed site. On the Rhône’s artificial dykes, extensive grazing appears to be a better management tool than mowing to enhance plant biodiversity and meet safety objectives.

Field trials were conducted to compare on-site visual evaluations with color-infrared photography (CIR), and near-infrared (NIR) videography (video) and hand-held radiometry (HHR) for detecting and mapping dicamba injury in cotton. CIR, video, and HHR detected 48%, 42% and 36%, respectively, of the injured crop area as defined by visual evaluation (injury ratings >0 on a scale of 0 to 9). The remote techniques were unable to differentiate crop injury that did not involve the entire plant canopy. Reflectance measurements in the visible red (R) (630–690 nm) and NIR (760–900 nm) wavelengths were taken and used in herbicide dosage prediction equations. Predicted herbicide dosages were significantly, positively correlated (P≤0.01) with physical measures of herbicide present. These studies suggest that remote detection and mapping of moderate and severe herbicide injury is possible. Further, NIR videography, with near-real-time capability and low cost may be the system of choice for this type of application.

The objective of this work was to determine the most appropriate growth stage to make reflectance measurements that would indicate yield in high yielding winter barley crops. The results indicated that where different rates of fertiliser N were applied, at the same crop growth stages, the best relationship between vegetation indices, calculated on the basis of reflectance measurements, and grain yield were found to occur from booting to early grain fill. Where the timing of fertiliser N inputs was different, for a given level of fertiliser N addition, poor correlations between vegetation indices and grain yield during the stem elongation phase were observed.

Intensive agriculture has the objective to increase nutrients use efficiency. Nitrogen (N) is a key nutrient for crops and the estimations of crop N status allow adjusting the fertilization levels to crop requirements, while reducing the environmental costs and optimizing the benefits for farmers. In this work the N status of wheat in a commercial plot has been monitored, varying the N supply taking into account the variability of the soil. The N content in the cover has been monitored simultaneously by sampling at field level and by using vegetation indices based on reflectance in the red-edge band. The results of the field campaign along a crop growth cycle show that the REP, MTCI, AIVI and CCCI calculated from narrow spectral bands show good linear correlations (R2>0.93) with respect to N content (g·m−2). These indices are stable when passing to broad bands as the case of Sentinel 2 with R2>0.9.

The present work investigated the application of detailed airborne images and a resistivity soil sensor (Veris 3100) to detect soil and crop spatial variability to assist in orchard management. The research was carried out in a peach orchard (Prunus persica). Soil apparent electrical conductivity (ECa), NDVI from a multispectral image (0.25 m/pixel) and soil properties at 40 sampling points (0–30 cm) were acquired. The ECa was standardized at 25°C. It showed a strong relationship with former landforms, altered by land levelling. A positive correlation of EC25 with EC1:5, water holding capacity at −1500 kPa and soil depth was found. NDVI was correlated only in the textural fractions coarser than clay. Two types of management zones were proposed: a) to improve the water holding capacity of soils and b) to regulate tree vigour and yield.

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.

Experiments were conducted from 2000 to 2002 at two locations each year to determine if lactofen and imazethapyr injury to soybean could be detected using digital aerial imagery and ground-based optical remote sensing. Lactofen and imazethapyr were applied at base rates of 105 and 71 g/ha, respectively, and at 0, 2X, and 4X rates. Treated plots were evaluated between 7 and 21 d after treatment for crop injury using a ground-based radiometer and a system using computer analysis of digital aerial imagery. Both the ground-based radiometer and the digital aerial imagery were effective in detecting herbicide injury under most conditions. The digital aerial imagery system was found to be more sensitive in detecting herbicide injury than the ground-based radiometer system. Herbicide or herbicide rate had a significant effect on normalized differential vegetation indices (NDVI) derived from digital aerial imagery in four of four site-years. NDVI values derived from a multispectral ground-based radiometer were significant for herbicide or herbicide rate in four of six site-years. NDVI values from treated plots were subtracted from the NDVI value of the untreated check to generate a ΔNDVI. The resulting ΔNDVI values from the ground-based radiometer system were significant for herbicide or herbicide rate in six of six site-years. Neither optical remote-sensing system was effective at estimating actual application rates of lactofen and imazethapyr across a broad range of field and weather conditions due to temporal and spatial variability in crop response to the herbicides.

Hyperspectral remote-sensed data were obtained via a Compact Airborne Spectrographic Imager-II (CASI-II) and used to estimate leaf-area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (YST). In parallel, 34 ground-based field plots were used to measure aboveground biomass and LAI to develop and validate hyperspectral-based models for estimating these measures remotely. Derivatives of individual hyperspectral bands improved the correlations between imaged data and actual on-site measurements. Six derivative-based normalized difference vegetation indices (DNDVI) were developed; three of them were superior to the commonly used normalized difference vegetation index (NDVI) in estimating aboveground biomass of YST, but did not improve estimates of LAI. The locally integrated derivatives-based vegetation indices (LDVI) from adjacent bands within three different spectral regions (the blue, red, and green reflectance ranges) were used to enhance absorption characteristics. Three LDVIs outperformed NDVI in estimating LAI, but not biomass. Multiple regression models were developed to improve the estimation of LAI and aboveground biomass of YST, and explained 75% and 53% of the variance in biomass and LAI, respectively, based on validation assessments with actual ground measurements.