Weighted Difference Vegetation Index Research Articles

A Case Study on The Evaluation of Maturity Class in Potato Breeding Trials Using UAV Imagery

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI – Green Optimized Soil Adjusted Vegetation Index, MCARI2 – Modified Chlorophyll Absorption Index-Improved, NDRE – Normalized Difference Red Edge, NDVI – Normalized Difference Vegetation Index, and OSAVI – Optimized Soil Adjusted Vegetation Index and WDVI – Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error – MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy’s variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations.

Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data

Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone.

The Evaluation of Spectral Vegetation Indexes and Redundancy Reduction on the Accuracy of Crop Type Detection

Knowledge about crop type distribution is valuable information for effective management of agricultural productivity, food security estimation, and natural resources protection. Algorithms for automatic crop type detection have great potential to positively influence these aspects as well as speed up the process of crop type mapping in larger areas. In the presented study, we used 14 Sentinel-2 images to calculate 12 widely used spectral vegetation indices. Further, to evaluate the effect of reduced dimensionality on the accuracy of crop type mapping, we utilized principal component analysis (PCA). For this purpose, random forest (RF)-supervised classifications were tested for each index separately, as well as for the combinations of various indices and the four initial PCA components. Additionally, for each RF classification feature importance was assessed, which enabled identification of the most relevant period of the year for the differentiation of crop types. We used 34.6% of the ground truth field data to train the classifier and calculate various accuracy measures such as the overall accuracy (OA) or Kappa index. The study showed a high effectiveness of the Modified Chlorophyll Absorption in Reflectance Index (MCARI) (OA = 86%, Kappa = 0.81), Normalized Difference Index 45 (NDI45) (OA = 85%, Kappa = 0.81), and Weighted Difference Vegetation Index (WDVI) (OA = 85%, Kappa = 0.80) in crop type mapping. However, utilization of all of them together did not increase the classification accuracy (OA = 78%, Kappa = 0.72). Additionally, the application of the initial three components of PCA allowed us to achieve an OA of 78% and Kappa of 0.72, which was unfortunately lower than the single-index classification (e.g., based on only NDVI45). This shows that dimensionality reductions did not increase the classification accuracy. Moreover, feature importance from RF indicated that images captured from June and July are the most relevant for differentiating crop types. This shows that this period of the year is crucial to effectively differentiate crop types and should be undeniably used in crop type mapping.

Modeling Carbon Uptake of Dryland Maize Using High Resolution Satellite Imagery

Quantifying carbon uptake or gross primary production (GPP) from agroecosystems is important for understanding the spatial and temporal dynamics of carbon fixation by crops. The availability of high-resolution remote sensing data can significantly improve GPP estimation of small-scale agricultural fields. Multispectral satellite data with 3-m spatial resolution and frequent global coverage are available from the PlanetScope network of satellites. However, this data remains largely unexplored for studying the carbon dynamics of agroecosystems. The overarching goal of this study was to develop a simple empirical method for quantifying the GPP of dryland maize (Zea mays L.) using remotely sensed vegetation indices along with in-situ measurements of photosynthetically active radiation and leaf area index by linking it with carbon uptake data from an eddy covariance flux tower. Four vegetation indices were investigated: the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), the weighted difference vegetation index (WDVI), and the two-band enhanced vegetation index (EVI2). This study was conducted over a three-year period from 2017 to 2019 in East-Central Texas. A total of 12 GPP prediction models were developed using individual yearly data and were used for predicting GPP of the other 2 years. Predicted maize GPP values were then compared against tower-based GPP. The NDVI models were the least successful in predicting GPP and had the highest root mean square error (average: 10.1 3 gC m−2; maximum: 26.3 gC m−2). Models based on SAVI performed especially well with error ranging from 0.05 to 0.94 gC m−2. The slope of the regression between SAVI-based estimated GPP and measured GPP was not different from 1.0 in all combinations of years. The success of the SAVI-based GPP models for predicting dryland maize carbon uptake indicates that it was the least affected vegetation index by changing soil background condition in this row cropping system.

Processing of remote sensing information to retrieve leaf area index in barley: a comparison of methods

Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded, it is necessary to test simultaneously as many methods as possible to understand the advantages and disadvantages of each approach. Ground-based LAI data from three years of barley experiments were related to remote sensing information using vegetation indices (VI), machine learning (ML) and radiative transfer models (RTM), to assess the relative accuracy and efficacy of these methods. The optimized soil adjusted vegetation index and a modified version of the Weighted Difference Vegetation Index performed slightly better than any other retrieval method. However, all methods yielded coefficients of determination of around 0.7 to 0.9. The best performing machine learning algorithms achieved higher accuracies when four Sentinel-2 bands instead of 12 were used. Also, the good performance of VIs and the satisfactory performance of the 4-band RTM, strongly support the synergistic use of satellites and RPAs in precision agriculture. One of the methods used, Sen2-Agri, an open source ML-RTM-based operational system, was also able to accurately retrieve LAI, although it is restricted to Sentinel-2 and Landsat data. This study shows the benefits of testing simultaneously a broad range of retrieval methods to monitor crops for precision agriculture.

Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms

Soil salinization is one of the most important causes of land degradation and desertification, especially in arid and semi-arid areas. The dynamic monitoring of soil salinization is of great significance to land management, agricultural activities, water quality, and sustainable development. The remote sensing images taken by the synthetic aperture radar (SAR) Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area; however, there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization. Therefore, in this study, we used topography indices derived from digital elevation model (DEM), SAR indices generated by Sentinel-1, and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China, and evaluated the potential of multi-source sensors to predict soil salinity. Using the soil electrical conductivity (EC) values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable, we constructed three soil salinity inversion models based on classification and regression tree (CART), random forest (RF), and extreme gradient boosting (XGBoost). Then, we evaluated the prediction ability of different models through the five-fold cross validation. The prediction accuracy of XGBoost model is better than those of CART and RF, and soil salinity predicted by the three models has similar spatial distribution characteristics. Compared with the combination of topography indices and vegetation indices, the addition of SAR indices effectively improves the prediction accuracy of the model. In general, the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor. In addition, SAR indices, vegetation indices, and topography indices are all effective variables for soil salinity prediction. Weighted Difference Vegetation Index (WDVI) is designated as the most important variable in these variables, followed by DEM. The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.

Remotely-sensed assessment of the impact of century-old biochar on chicory crop growth using high-resolution UAV-based imagery

In recent years, special attention has been given to the long-term effects of biochar on the performance of agro-ecosystems owing to its potential for improving soil fertility, harvested crop yields, and aboveground biomass production. The present experiment was set up to identify the effects on soil-plant systems of biochar produced more than 150 years ago in charcoal mound kiln sites in Wallonia (Belgium). Although the impacts of biochar on soil-plant systems are being increasingly discussed, a detailed monitoring of the crop dynamics throughout the growing season has not yet been well addressed. At present there is considerable interest in applying remote sensing for crop growth monitoring in order to improve sustainable agricultural practices. However, studies using high-resolution remote sensing data to focus on century-old biochar effects are not yet available. For the first time, the impacts of century-old biochar on crop growth were investigated at canopy level using high-resolution airborne remote sensing data over a cultivated field. High-resolution RGB, multispectral and thermal sensors mounted on unmanned aerial vehicles (UAVs) were used to generate high frequency remote sensing information on the crop dynamics. UAVs were flown over 11 century-old charcoal-enriched soil patches and the adjacent reference soils of a chicory field. We retrieved crucial crop parameters such as canopy cover, vegetation indices and crop water stress from the UAV imageries. In addition, our study also provides in-situ measurements of soil properties and crop traits. Both UAV-based RGB imagery and in-situ measurements demonstrated that the presence of century-old biochar significantly improved chicory canopy cover, with greater leaf lengths in biochar patches. Weighted difference vegetation index imagery showed a negative influence of biochar presence on plant greenness at the end of the growing season. Chicory crop stress was significantly increased by biochar presence, whereas the harvested crop yield was not affected. The main significant variations observed between reference and century-old biochar patches using in situ measurements of crop traits concerned leaf length. Hence, the output from the present study will be of great interest to help developing climate-smart agriculture practices allowing for adaptation and mitigation to climate.

Study of vegetation cover distribution using DVI, PVI, WDVI indices with 2D-space plot

The present work aims to study the effect of using vegetation indices technique on image segmentation for subdividing an image into the homogeneous regions. Three of these vegetation indices technique has been adopted (i.e. Difference Vegetation-Index (DVI), Perpendicular Vegetation Index (PVI) and Weighted Difference Vegetation Index (WDVI)) for detecting and monitoring vegetation distribution and healthiness. Image binarization method being followed the implementation of the indices to isolating the vegetation areas from the image background. The separated agriculture regions from other land use regions and their percentages are presented for two years (2001 and 2002) of the (ETM+) scenes. The counted areas resulted from 2D-space plot technique and the separated vegetated areas resulted from the using of the vegetation indices are also presented. The separated agriculture regions from the implementation of the DVI-index have proved better than other used indices. Because it showed better coincident approximately with 2D-space plot segmentation.

Habitat suitability mapping for sand cat (Felis margarita) in Central Iran using remote sensing techniques

One of the primary reason of species extinction especially rare species with very specific requirements, is habitat destruction. To protect these species, habitat suitability evaluation plays a central role. Hence, an attempt is made in this study to evaluate the suitability of sand cat’s habitat in a sand dune-dominated landscape in Iran. Four Landsat-derived indices including Normalized Difference Vegetation Index (NDVI), Weighted Difference Vegetation Index, Brightness Index (BI) and Salinity Index were combined to characterize sand cat’s habitat requirements through a land use land cover (LULC) map. Furthermore, a set of landscape metrics were employed to explore the spatial pattern LULC classes. Sand cat’s habitat suitability map was generated by linear combination of the standardized and relatively weighted NDVI and BI indices and then categorized into five classes of most suitable, highly suitable, moderately suitable, least suitable and not suitable. The results showed that about 75% of the total area is suitable for sand cat. Although this region is rich in biodiversity, it has not yet been subject to any conservation planning and should be granted more conservation attentions.

Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010–2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R2 of 0.696; RMSEP of 0.062 g·m−2). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R2 of 0.656; RMSEP of 0.066 g·m−2) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CIgreen) was an accurate and linear estimator of CCC at 10 m (R2 of 0.818; RMSEP of 0.29 g·m−2). Results for CIgreen were better than for the red-edge chlorophyll index (CIred-edge, R2 of 0.576, RMSE of 0.43 g·m−2). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture.

Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?

Data acquisition for parameterization is one of the most important limitations for the use of potato crop growth models. Non-destructive techniques such as remote sensing for gathering required data could circumvent this limitation. Our goal was to analyze the effects of incorporating ground-based spectral canopy reflectance data into two light interception models with different complexity. A dynamic- hourly scale- canopy photosynthesis model (DCPM), based on a non-rectangular hyperbola applied to sunlit and shaded leaf layers and considering carbon losses by respiration, was implemented (complex model). Parameters included the light extinction coefficient, the proportion of light transmitted by leaves, the fraction of incident diffuse photosynthetically active radiation and leaf area index. On the other hand, a simple crop growth model (CGM) based on daily scale of light interception, light use efficiency (LUE) and harvest index was parameterized using either canopy cover (CGMCC) or the weighted difference vegetation index (CGMWDVI). A spectroradiometer, a chlorophyll meter and a multispectral camera were used to derive the required parameters. CGMWDVI improved yield prediction compared to CGMCC. Both CGMWDVI and DCPM showed high degree of accuracy in the yield prediction. Since large LUE variations were detected depending on the diffuse component of radiation, the improvement of simple CGM using remotely sensed data is contingent on an appropriate LUE estimation. Our study suggests that the incorporation of remotely sensed data in models with different temporal resolution and level of complexity improves yield prediction in potato.

Estimating Leaf Area Index by Bayesian Linear Regression Using Terrestrial LiDAR, LAI-2200 Plant Canopy Analyzer, and Landsat TM Spectral Indices

Leaf area index (LAI) is a key biophysical variable and an ecosystem condition indicator that is measured from multiple methods. In this study, LAI was measured by a terrestrial laser scanner (TLS) and Li-Cor LAI-2200 plant canopy analyzer for understanding differences. A total of six different methods that consider leaf clumping, leaf–wood separation, and orthographic and stereographic projection were compared. A reasonable agreement among all methods for LAI estimates was observed (i.e., correlations r > 0.50). The Bayesian linear regression (BLR) approach was used to scale up the six different LAI estimates and to produce continuous field surfaces for the Oak Openings Region in northwest Ohio using Landsat TM–derived spectral vegetation indices (weighted difference vegetation index [WDVI], difference vegetation index [DVI], normalized difference vegetation index [NDVI], soil-adjusted vegetation index [SAVI], and perpendicular vegetation index 3 [PVI3]). The BLR approach provides details about the parameter uncertainties that may arise from foliage and wooden biomass and can be used for model comparisons. In this study, the TLS estimates of foliage derived by orthographic projection had the lowest residual scatter and overall model uncertainty. The deviation departure from the mean BLR estimates revealed that sparse and open areas were associated with the highest error and spatial uncertainties.

Quantifying Responses of Spectral Vegetation Indices to Dead Materials in Mixed Grasslands

Spectral vegetation indices have been the primary resources for characterizing grassland vegetation based on remotely sensed data. However, the use of spectral indices for vegetation characterization in grasslands has been challenged by the confounding effects from external factors, such as soil properties, dead materials, and shadowing of vegetation canopies. Dead materials refer to the dead component of vegetation, including fallen litter and standing dead grasses accumulated from previous years. The abundant dead materials have been presenting challenges to accurately estimate green vegetation using spectral vegetation indices (VIs) derived from remote sensing data in mixed grasslands. Therefore, a close investigation of the relationship between VIs and dead materials is needed. The identified relationships could provide better insight into not only using remote sensing data for quantitative estimation of dead materials, but also the improvement of green vegetation estimation in the mixed grassland that has a high proportion of dead materials. In this article, the spectral reflectance of dead materials and green vegetation mixtures and dead material cover were measured in mixed grasslands located in Grassland National Park (GNP), Saskatchewan, Canada. Nine VIs were derived from the measured spectral reflectance. The relationship between dead material cover and VIs was quantified using the regression model and sensitivity analysis. Results indicated that the relationship between dead material cover and VIs is a function of the amount of dead material cover. Weak positive relationship was found between VIs and dead materials where the cover was less than 50%, and a significant high negative relationship was evident when cover was greater than 50%. When the combined exponential and linear model was applied to fit the negative relationships, more than 90% variation in dead material cover could be explained by VIs. Sensitivity analysis was further applied to the developed models, indicating that sensitivities of all VIs were significant over the entire range of dead material cover except for the triangular vegetation index (TVI), which has insignificant sensitivity when dead material cover was greater than 94%. Among all VIs, the weighted difference vegetation index (WDVI) had the highest sensitivity to changes in dead material cover higher than 50%. The results from this study indicated that vegetation indices based on combination of reflectance in red and NIR bands can be used to estimate dead material cover that is greater than 50%.

Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas

This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R2) between measured and predicted LAI, as well as corresponding confidence intervals. The most suitable approach, which at the same time had the minimum requirements for fieldwork, resulted in a RMSE of 0.407 and R2 of 0.88 for Italy and a RMSE of 0.86 and R2 of 0.64 for the Austrian test site. Considering this procedure, we also evaluated the transferability of the local CLAIR model parameters between the two test sites observing no significant decrease in estimation accuracies. Additionally, we investigated two other statistical methods to estimate LAI based on: (a) Support Vector Machine (SVM) and (b) Random Forest (RF) regressions. Though the accuracy was comparable to the CLAIR model for each test site, we observed severe limitations in the transferability of these statistical methods between test sites with an increase in RMSE up to 24.5% for RF and 38.9% for SVM.

Satellite-based herbicide rate recommendation for potato haulm killing

When using variable-rate application (VRA), tractor-mounted sensors are typically used to measure crop status. Crop status can also be measured with a satellite-based sensor. In both cases a vegetation index derived from the sensor measurements is used as an indicator of the amount of crop biomass. The first objective of this study was to establish a relationship between the Weighted Difference Vegetation Index (WDVI) in potato as measured with a nearby, ground-based crop reflectance meter on the one hand and WDVI as measured with remote, satellite-based sensors on the other hand. It was found that ground-based WDVI and satellite-based WDVI are strongly and linearly related, thus making it feasible to calculate herbicide rates for potato haulm killing on the basis of satellite-based measurements. The scale at which VRA is applied is an important determinant of the reduction in input use. The second objective was to estimate the potential to reduce herbicide use for potato haulm killing as a function of the size of decision units, using the above-mentioned relationship, satellite imagery of 13 potato fields and a previously developed decision rule for herbicide rate. It was found that when the size of the decision unit was 15m×15m (the size of an ASTER pixel), a reduction in herbicide use of at least 50% would be achieved in one out of every two of the fields, and a reduction of at least 33% would be achieved in all fields. When the size of the decision unit was 30m×30m, a reduction of at least 33% would be achieved in one out of every two of the fields. In conclusion, satellite-based crop reflectance measurements can be used instead of ground-based measurements for determining herbicide rate for potato haulm killing. When the size of the decision unit is not larger than 30m×30m, a 50% reduction in herbicide use for potato haulm killing can be achieved with VRA.

Mapping potato crop height and leaf area index through vegetation indices using remote sensing in Cyprus

This paper aims to model leaf area index (LAI) and crop height to spectral vegetation indices (VI), such as normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and weighted difference vegetation index (WDVI). The intended purpose is to create empirical statistical models to support evapotranspiration algorithms applied under the current conditions in the island of Cyprus. Indeed, a traditionally agricultural area was selected in the Mandria Village in the Paphos District area in Cyprus, where one of the island's main exported crops, potatoes, are cultivated. A GER-1500 field spectroradiometer was used in this study in order to retrieve the necessary spectrum data of the different crops for estimating the VI's. A field campaign was undertaken with spectral measurements of LAI and crop height using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric measurements between March and April of 2008 and 2009. Regarding the measurements, the phenological cycle of potatoes was followed. Several regression models have been applied to relate LAI/crop height and the three indices. It was found that the best fitted vegetation index to both LAI and crop height was WDVI. When LAI was regressed against WDVI for potatoes, the determination coefficient (R2) was 0.72, while for crop height R2 reached 0.78. Two Landsat TM-5 images acquired simultaneously during the spectroradiometric and LAI and crop height measurements are used to validate the proposed regression model. From the whole analysis it was found that the modeled results are very close to real values. This fact enables the specific empirical models to be used in the future for hydrological purposes.

Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize

Gross primary production (GPP) is a significant important parameter for carbon cycle and climate change research. Remote sensing combined with other climate and meteorological data offers a convenient tool for large‐scale GPP estimation. GPP was estimated as a product of vegetation indices (VIs) and photosynthetically active radiation (PAR). Four kinds of vegetation indices [the normalized difference vegetation index (NDVI), the weighted difference vegetation index, the soil‐adjusted vegetation index, and the enhanced vegetation index (EVI)] derived from the Moderate Resolution Imaging Spectroradiometer daily surface reflectance product were selected to test our method. The in situ GPP was calculated using the eddy covariance technique and the PAR data were acquired from meteorological measurements. Because VIs were found to be a reliable proxy of both light use efficiency (LUE) and the fraction of absorbed PAR (fAPAR; R2 of 0.63–0.87 for LUE and 0.69–0.76 for fAPAR), the product VI × VI × PAR is used as a measure of GPP according to Monteith logic. Moderate determination coefficients R2 from 0.65 for NDVI to 0.71 for EVI were obtained when GPP was estimated from a single index in maize. When testing our method, calculating GPP as a product of VI × VI × PAR, the determination coefficients R2 largely improved, fluctuating from 0.81 to 0.91. EVI × EVI × PAR provided the best estimation of GPP with the highest R2 of 0.91 because EVI was found to be the best indicator of both LUE and fAPAR (R2 of 0.87 and 0.76, respectively). These results will be helpful for the development of future GPP estimation models.

Fusion of Vegetation Indices Using Continuous Belief Functions and Cautious-Adaptive Combination Rule

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The goal of this paper is to propose a methodology based on vegetation index fusion to provide an accurate estimation of the fraction of vegetation cover (fCover). Because of the partial and imprecise nature of remote-sensing data, we opt for the evidential framework that allows us to handle such kind of information. The defined fCover belief functions are continuous with the interval [0, 1] as a discernment space. Since the vegetation indices are not independent (e.g., perpendicular vegetation index and weighted difference vegetation index are linearly linked), we define a new combination rule called “cautious adaptive” to handle the partial “nondistinctness” between the sources (vegetation indices). In this rule, the “nondistinctness” is modeled by a factor <formula formulatype="inline"><tex>$\varrho$</tex> </formula> varying from zero (distinct sources) to one (totally correlated sources), and the fusion rule varies accordingly from the conjunctive rule to the cautious one. In terms of results, both in the cases of simulated data and actual data, we show the interest of the combination of two or three vegetation indices to improve either the accuracy of fCover estimation or its robustness. </para>

Estimating Grassland Biomass Using SVM Band Shaving of Hyperspectral Data

In this paper, the potential of a band shaving algorithm based on support vector machines (SVM) applied to hyperspectral data for estimating biomass within grasslands is studied. Field spectrometer data and biomass measurements were collected from a homogeneously managed grassland field. The SVM band shaving technique was compared with a partial least squares (PLS) and a stepwise forward selection analysis. Using their results, a range of vegetation indices was used as predictors for grassland biomass. Results from the band shaving showed that one band in the near-infrared region from 859 to 1,006 nm and one in the red-edge region from 668 to 776 nm used in the weighted difference vegetation index (WDVI) had the best predictive power, explaining 61 percent of grassland biomass variation. Indices based on short-wave infrared bands performed worse. Results could subsequently be applied to larger spatial extents using a high-resolution airborne digital camera (for example, Vexcel’s UltraCamTM).

Reflectance characteristics of maize and application of vegetation indices for estimation of leaf area index

An experiment was conducted on maize (Zea mays L), grown as a fodder crop, under irrigated and partially irrigated conditions with Ground Truth Radiometer at the experimental farm of the College of Agriculture, Pune under Mahatma Phule Krishi Vidyapith, Rahuri during pre-kharif season of 1999. Observations were taken at weekly intervals between 1000 and 1100 hrs. (IST) on cloud free days during the crop growing season. The spectral radiance characteristics and vegetation indices like Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Weighted Difference Vegetation Index (WDVI), Perpendicular Vegetation Index (PVI) and their relationship with Leaf Area Index (LAI) have been studied. The paper discusses the significance of spectral variation of radiance and sensitivity of vegetation indices with respect to phenological stages of the crop. The spectral reflectance observations in red and near infrared bands were recorded from the crop field. Vegetation indices were found suitable for crop growth studies. The procedure for estimating LAI using different vegetation indices is also discussed in the paper. It has been found that RVI is a better predictor of LAI during the early stage of growth while NDVI is a better predictor during the later part of growth as compared to other vegetation indices.