Review of spectral vegetation indices and methods for estimation of crop biophysical variables (original) (raw)
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Spectral Data for Determination of Crop Vegetation Indices
The field experiments with four levels of N-fertilizer (0, 30, 60, and 90 kgℎ −1 ) in two repetitions were conducted for three years to select some appropriate vegetation indices for winter wheat. Hyper-spectral reflectance data using a portable field spectroradiometer (351 to 2,500 nm) were recorded from 10 am to 2 pm under cloudless conditions at two different growth stages of winter wheat. All two-band and three band combinations of several vegetation indices were subsequently calculated in an algorithm for determining linear regression analysis against SPAD value, protein content, and grain yield. R square matrices were used to make contour plots and 3-D scatters. Using overlaying in analysis tools of ArcMap the between first and second year results, a number of common hot spots with strong correlations were revealed. The selected hot spots were validated with the dataset of the third year to choose the best vegetation indices for crop variable estimations.
Sensed Vegetation Indices : Theory and Applications for Crop Management
2004
Remote sensing of soil and crop can be an attractive alternative to the traditional methods of field scouting because of the capability of covering large areas rapidly and repeatedly providing spatial and temporal information necessary for a sustainable soil and crop management. The potential of remote sensing in agriculture is very high because it is able to infer about soil and vegetation amount as a non-destructive mean. Numerous spectral vegetation indices (VIs) have been developed to characterize vegetation canopies. Plant canopy reflectance factors and derived multispectral VIs are receiving increased attention in agricultural research as robust surrogates for traditional agronomic parameters. Spectral reflectance and thermal emittance properties of soils and crops have been used extensively to predict ecological variables, such as percent vegetation cover, plant biomass, green leaf area index and other biophysical characteristics. VIs are strongly modulated by interactions of...
Remote Sensing of Environment, 2004
A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]
Applications of Vegetative Indices from Remote Sensing to Agriculture: Past and Future
Inventions
Remote sensing offers the capability of observing an object without being in contact with the object. Throughout the recent history of agriculture, researchers have observed that different wavelengths of light are reflected differently by plant leaves or canopies and that these differences could be used to determine plant biophysical characteristics, e.g., leaf chlorophyll, plant biomass, leaf area, phenological development, type of plant, photosynthetic activity, or amount of ground cover. These reflectance differences could also extend to the soil to determine topsoil properties. The objective of this review is to evaluate how past research can prepare us to utilize remote sensing more effectively in future applications. To estimate plant characteristics, combinations of wavebands may be placed into a vegetative index (VI), i.e., combinations of wavebands related to a specific biophysical characteristic. These VIs can express differences in plant response to their soil, meteorolog...
Remote Sensing of Environment, 2011
Many algorithms have been developed for the remote estimation of biophysical characteristics of vegetation, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. However, the most widespread type of algorithm used is the mathematical combination of visible and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances, their applicability is specific to species, vegetation types or local conditions. The general objective of this study is to evaluate different vegetation indices for the remote estimation of the green leaf area index (Green LAI) of two crop types (maize and soybean) with contrasting canopy architectures and leaf structures. Among the indices tested, the chlorophyll Indices (the CI Green , the CI Red-edge and the MERIS Terrestrial Chlorophyll Index, MTCI) exhibited strong and significant linear relationships with Green LAI, and thus were sensitive across the entire range of Green LAI evaluated (i.e., 0.0 to more than 6.0 m 2 /m 2). However, the CI Red-edge was the only index insensitive to crop type and produced the most accurate estimations of Green LAI in both crops (RMSE = 0.577 m 2 /m 2). These results were obtained using data acquired with close range sensors (i.e., field spectroradiometers mounted 6 m above the canopy) and an aircraft-mounted hyperspectral imaging spectroradiometer (AISA). As the CI Red-edge also exhibited low sensitivity to soil background effects, it constitutes a simple, yet robust tool for the remote and synoptic estimation of Green LAI. Algorithms based on this index may not require re-parameterization when applied to crops with different canopy architectures and leaf structures, but further studies are required for assessing its applicability in other vegetation types (e.g., forests, grasslands).
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 2011
Vegetation indices (VI) combine mathematically a few selected spectral bands to minimize undesired effects of soil background, illumination conditions and atmospheric perturbations. In this way, the relation to vegetation biophysical variables is enhanced. Albeit numerous experiments found close relationships between vegetation indices and several important vegetation biophysical variables, well known shortcomings and drawbacks remain. Important limitations of VIs are illustrated and discussed in this paper. As most of the limitations can be overcome using physically-based radiative transfer models (RTM), advantages and limits of RTM are also presented.
Spectral data source effect on crop state estimation by vegetation indices
Environmental Earth Sciences, 2018
Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. Even though differences in imagery and pointbased spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from spaceborne multispectral imagery and point-based field spectroscopy in application to crop state estimation. For this purpose, irrigated chickpea field was monitored by RapidEye satellite mission and additional measurements by field spectrometer were obtained. Estimated VIs average and coefficient of variation from each observation were compared with physical crop measurements: leaf water content, LAI and chlorophyll level. The results indicate that indices calculated from spaceborne spectral images regardless of the claimed response commonly react on phenology of the irrigated chickpea. This feature makes spaceborne spectral imagery an appropriate data source for monitoring crop development, crop water needs and yield prediction. VIs calculated from field spectrometer were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability (up to 69%) of the calculated values. Consequently, the high spatial variability of field spectral measurements depreciates the estimation agricultural field state by average mean only. Nevertheless, the spatial variability might have certain behavior trend, e.g., a significant increase in the active growth or stress and can be an independent feature for field state assessment.
Hydrological Processes, 2011
Crop coefficients were developed to determine crop water needs based on the evapotranspiration (ET) of a reference crop under a given set of meteorological conditions. Starting in the 1980s, crop coefficients developed through lysimeter studies or set by expert opinion began to be supplemented by remotely sensed vegetation indices (VI) that measured the actual status of the crop on a field-by-field basis. VIs measure the density of green foliage based on the reflectance of visible and near infrared (NIR) light from the canopy, and are highly correlated with plant physiological processes that depend on light absorption by a canopy such as ET and photosynthesis. Reflectance-based crop coefficients have now been developed for numerous individual crops, including corn, wheat, alfalfa, cotton, potato, sugar beet, vegetables, grapes and orchard crops. Other research has shown that VIs can be used to predict ET over fields of mixed crops, allowing them to be used to monitor ET over entire irrigation districts. VI-based crop coefficients can help reduce agricultural water use by matching irrigation rates to the actual water needs of a crop as it grows instead of to a modeled crop growing under optimal conditions. Recently, the concept has been applied to natural ecosystems at the local, regional and continental scales of measurement, using time-series satellite data from the MODIS sensors on the Terra satellite. VIs or other visible-NIR band algorithms are combined with meteorological data to predict ET in numerous biome types, from deserts, to arctic tundra, to tropical rainforests. These methods often closely match ET measured on the ground at the global FluxNet array of eddy covariance moisture and carbon flux towers. The primary advantage of VI methods for estimating ET is that transpiration is closely related to radiation absorbed by the plant canopy, which is closely related to VIs. The primary disadvantage is that they cannot capture stress effects or soil evaporation.
Spectral Data for Plant Chlorophyll Assessment
An increasing role in plant phytodiagnostics becomes to play different spectrometric techniques used as a part of remote sensing applications. The radiation behavior of land covers and the spectral response to changing conditions lies at the root of these studies. The visible and near infrared (400 -900 nm) measurements have proved abilities in vegetation monitoring. The reason is that this wavelength range reveals significant sensitivity to plant biophysical properties. The information is carried by the specific vegetation spectral characteristics which depend on such plant parameters as chlorophyll content, biomass amount, leaf area, etc. These parameters are associated with plant devel-opment and stress factors being closely related to vegetation physiological state. In our study, multispectral data of reflected, transmitted and emitted irradiance have been used to show the possibility for plant chlorophyll assess-ment. Different methods such as vegetation indices, red edge analy...