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ABSTRACT The use of vegetation indices has good potential for predicting the productivity of several crops, but factors such as the time of assessment, cultivar, and plant phenology can influence the performance of predictive models. The objective of this study was to evaluate and compare the precision of estimating the common bean grain yield, according to the normalized difference vegetation index (NDVI), using individual models per cultivar and a general model with all cultivars. The cultivars IAC Imperador and IPR Campos Gerais, with determined and indeterminate growth habits, respectively, were evaluated. They were subjected to different nitrogen management methods to provide grain yield variability. NDVI evaluations were conducted throughout the culture cycle on six dates during the vegetative and reproductive stages. The common bean grain yield was estimated with high precision as a function of NDVI, obtaining a precision of up to 78% and an average error close to 350 kg ha-1. The greatest fit of estimation was obtained in the phenological reproductive stages of beans, especially after crop flowering. General models, composed of data from more than one cultivar, had similar precision and, in some cases, superiority to the fitted models for each cultivar, demonstrating the feasibility of using the same model for several genotypes.

The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables.

The availability of satellite images has generated a large number of regional and global studies on vegetation mapping. Such studies have related the growth parameters, nutrient status, physiological responses, and water resources to the yield of agricultural crops or native vegetation. The NDVI (Normalized Difference Vegetation Index) is associated with parameters of growth and yield with readings at several moments of the crop cycle. The objective of this work was to correlate the yield and variability of the NDVI in cotton fields by analyzing Landsat satellite images acquired over nine growing seasons. The study involved the analysis of 101 cotton production fields located in West-Central Brazil. One Landsat image was used during each crop cycle, and the average yield was computed based on total fiber harvested at each field. The fiber yield ranged from 393 to 2,030 kg ha-1, and its correlation with NDVI was 0.37. The coefficient of variation (CV) had a negative correlation with yield, approximating -58.1 kg ha-1 for every one percent increment of the CV. The CV explained the yield variability over the cotton fields more accurately than the average absolute NDVI value.

Vegetation indices (Vis) are one of the standard science products available from the Moderate Resolution Imaging Spectro radiometer (MODIS) instrument on the Earth Observing System (EOS) Terra platform, launched in December 1999. An important requirement of MODIS science products is that they be rigorously validated. In this study, we conducted a site-intensive MODLS VI product validation at the semiarid Jornada Experimental Range, New Mexico, an EOS Land Validation Core Site. Our validation approach involved scaling up independent fine-grained datasets, including ground and airborne radiometry, and high spatial resolution imagery [Enhanced Thematic Mapper Plus (ETM+)], to the coarser MODIS spatial resolutions. The MODIS Vis were evaluated with respect to their radiometric performances, the uncertainties of the composting methodology, and their capabilities to depict seasonal variations in vegetation. The MODIS Quick Airborne Looks (MQUALS) radiometric package was found useful in up-scaling field in situ measurements to coarser spatial resolutions. Both single-day nadir-view and 16-day composited MODIS reflectances and Vis matched well with the nadir-based atmosphere-free MQUALS observations for all the land cover types found at Jornada, with the root mean squared deviations less than 0.03. The MODIS 16-day composited products also performed well with the single-day nadir-view MODIS data, despite some off-nadir view angles and uncertainties with the cloud mask algorithm. The quality assurance (QA)-based constrained view angle-maximum value composite (CV-MVC) algorithm successfully filtered out much of the cloud and aerosol contaminated observations and helped to minimize view angle-related problems. The MODIS seasonal VI profiles also matched quite well with the other multiple sensor datasets obtained at the finer spatial resolutions. The QA information was found to be crucial in achieving consistent spatial and temporal comparisons of global vegetation conditions and for deriving accurate depictions of important phenological features in multitemporal MODIS data. The results of this validation study over the Jornada Experimental Range demonstrated the accuracy, reliability, and science utility of the MODIS VI products in arid and semiarid areas. [ABSTRACT FROM AUTHOR]

I. The study of a desert landscape consisting of C3 shrublands and C4 grasslands demonstrated the feasibility of mapping desertification processes on a regional scale using time-series satellite images, and demonstrated the potential for monitoring desertification processes through time. 2. Fifteen meteorological satellite images of southern New Mexico, spread over the growing season of 1989, were processed to yield images of normalized difference vegetation indices. Computer classification of these images and comparison with extensive ground measurements confirmed the identification of the major classes of shrubland, grassland and mixed shrub and grass areas. 3. Our high-resolution temporal approach is shown to permit the accurate identification of vegetation types even under conditions of sparse vegetation cover. The phenologies of grassland and shrubland were revealed in the time series of satellite images. We used this feature of the data to overcome the tendency of the strong radiation signal from bare soil to mask the biomass signal. 4. Comprehensive comparisons with ground measurements confirmed the accuracy of the biomass identification for local areas within the Jornada Experimental Range, and justified extrapolation of the classification to a larger adjacent area. [ABSTRACT FROM AUTHOR]

Quick non-destructive assessment of leaf chlorophyll content (LCC) is important for studying phenotypes related to plant growth and stress resistance. This study was undertaken to investigate the quantitative relationship between LCC and different vegetation indices (VIs) on both adaxial and abaxial surfaces of white poplar (Populus alba), which has dense tubular hairs on its abaxial surface, and Chinese elm (Ulmus pumila var. pendula), which does not show obvious superficial differences except for lighter colour on the abaxial surface. Some published and newly developed VIs were tested to relate them to LCC. The results showed that most of the published VIs had strong relationships with LCC on the one-surface dataset, but did not show a clear relationship with LCC when both adaxial and abaxial surface reflectance data were included. Among the reflectance indices tested, the modified Datt index, (R719-R726)/(R719-R743), performed best and is proposed as a new index for remote estimation of chlorophyll content in plants with varying leaf surface structures. It explained 92% of LCC variation in this research, and the root mean square error of the LCC prediction was 5.23 μg/cm(2). This new index is insensitive to the effects of adaxial and abaxial leaf surface structures and is strongly related to the variation in reflectance caused by chlorophyll content., (© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Genetic studies of response to water deficit in adult trees are limited by low throughput of the usual phenotyping methods in the field. Here, we aimed at overcoming this bottleneck, applying a new methodology using airborne multispectral imagery and in planta measurements to compare a high number of individuals.An apple tree population, grafted on the same rootstock, was submitted to contrasting summer water regimes over two years. Aerial images acquired in visible, near- and thermal-infrared at three dates each year allowed calculation of vegetation and water stress indices. Tree vigour and fruit production were also assessed. Linear mixed models were built accounting for date and year effects on several variables and including the differential response of genotypes between control and drought conditions.Broad-sense heritability of most variables was high and 18 quantitative trait loci (QTLs) independent of the dates were detected on nine linkage groups of the consensus apple genetic map. For vegetation and stress indices, QTLs were related to the means, the intra-crown heterogeneity, and differences induced by water regimes. Most QTLs explained 15-20% of variance.Airborne multispectral imaging proved relevant to acquire simultaneous information on a whole tree population and to decipher genetic determinisms involved in response to water deficit., (© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.)