Transformed Soil Adjusted Vegetation Index Research Articles

Linking random forest and auxiliary factors for extracting the major economic forests in the mountainous areas of southwestern Yunnan Province, China

Forests are generally extracted from remotely sensed images based on the spectral features, ignoring other important auxiliary information, and the techniques of precise extraction need to be further improved. By using the Sentinel–2 image and auxiliary factors (AFs) including site conditions (SCs) and vegetation indices (VIs), the random forest model with AFs (RF–AFs) was adopted for the extraction of the economic forests in Lancang County, which is a mountainous area with rich biodiversity and is witnessing rapid development of economic forests in Yunnan province of China. The results obtained using the RF–AFs model were compared with those obtained using the random forest model without AFs (RF). The results were as follows: (1) The kappa coefficient for extracting the first–level land use obtained using the RF model was 0.9531. Lancang County is dominated by forests, accounting for 73.76% of the total area. (2) After parameter optimization, the RF–AFs model yielded the highest accuracy in the extraction of the second–level forests, with a kappa coefficient value of 0.9493, which was 14.69% higher than that of the RF model. Thus, the RF–AFs model is more suitable for the precise extraction of economic forests. (3) The evaluation results of the factors’ importance of the RF–AFs model showed that the cumulative importance values of SCs such as temperature (TEM), elevation (EL), precipitation (PRE) and VIs such as plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), transformed soil–adjusted vegetation index (TSAVI) was 76.09%, indicating that they were the main factors for the extraction of economic forests. (4) Economic forests are dominated by Simao pines in Lancang County, which are mainly distributed in the central, southwestern and northern regions, accounting for 31.37% of forests area. The proportion of tea plantations, eucalyptus, and rubber trees is 9.05%, 6.71%, and 3.05% of forests area, respectively. The RF–AFs model is conducive for precisely extracting the economic forests and is thus of great significance in studying the ecological and environmental effects of economic forests, performing forestry management, and maintaining regional ecological security.

Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale

Digital mapping of soil organic carbon (SOC) is crucial to evaluate its spatial variability and also to assess environmental factors controlling it at field scale. The current study was conducted to compare one statistical method include partial least squares regression (PLSR), four individual machine learning(ML) algorithms including random forest (RF), quantile regression forest (QRF), cubist (CB), fuzzy logic (SoLIM) along with two hybrid methods including the random forest-ordinary kriging (RF-OK) and quantile random regression forest-ordinary kriging (QRF-OK) to map SOC. A number of 146 soil samples (0–30 cm) were collected in Khorramabad plain (680 ha). For the quantitative evaluation of SOC spatial variability, two scenarios were considered to use remote sensing data (R) and a combination of geo-morphometric and remote sensing data (GR). The data randomly were split into 80% (117 points) for training and 20% (29 points) for validation. The model performances were evaluated by the statistical indices as the coefficient of determination (R2), root mean square error (RMSE). According to principal component analysis (PCA), nine covariates including transformed soil adjusted vegetation index (TSAVI), relative vegetation index (RVI), Band 10, Band 11,Digital Elevation Model (DEM), Standard height(Standard_he), Valley_Dep, terrain surface texture (texture), and terrain surface convexity (Convexity) were selected as the environmental predictors. Results showed that the hybrid model RF-OK (RMSE = 0.05and R2 = 0.93) and SoLIM model (RMSE = 0.47and R2 = 0.41) with scenario GR had the highest and lowest accurate respectively. TSAVI, DEM, and Band 10 were the most important predictors and explanation more than 50% of SOC spatial variability in the study area. Generally, using hybrid machine learning models in combination with geo-morphometric and remote sensing covariates make it possible to model and predict SOC with acceptable accurate in the field-scale croplands.

Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas.

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs’ saturation in the Apiacás area (i.e., X = −0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = −0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Daljinski indeksi vegetacije izračunati programom SAGA GIS - poređenje vertikalnog i prilagođenog pristupa promena zemljišta za sliku LANDSAT TM

Landsat-TM of 2001 covering Iceland (15.5°W-21°W, 64.5°N-67°N) was processed using SAGA GIS for testing distance-based Vegetation Indices (VIs): four approaches of Perpendicular Vegetation Index (PVI) and two approaches of Transformed Soil Adjusted Vegetation Index TSAVI. The PVI of vegetation from the soil background line indicated healthiness as a leaf area index (LAI). The results showed that the reflectance for vegetation has a linear relation with soil background line. Four PVI models and two TSAVI shown coefficients of determination with LAI. The dataset demonstrate variations in the calculated coefficients. The mode in the histograms of the PVI based on four different algorithms show the difference:-7.1,-8.36, 2.78 and 7.0. The dataset for the two approaches of TSAVI: first case ranges in 4.4.-80.6 with a bell-shape mode of a histogram (8.09 to 23.29) for the first algorithm and an irregular shape for the second algorithm with several modes starting from 0.11 to 0.2 and decreasing to 0.26. SAGA GIS permits the calculation of PVI and TSAVI by computed NDVI based on the intersection of vegetation and soil background. Masking the NIR and R, a linear regression of grids was performed using an equation embedded in SAGA GIS. The advantages of the distance-based PVI and TSAVI consists in the adjusted position of pixels on the soil brightness line which refines it comparing to the slope-based VIs. The paper demonstrates SAGA GIS application in agricultural studies.

Contributions of multispectral images to the study of land cover in wet depressions of eastern Tunisia

Eastern Tunisia is dotted with varied wetlands such as lagoons, sabkhas, garaas… These wetlands, at the interface between land and water, are rich, diverse and dynamic environments. Remote sensing shows potential and efficiency in the detection and characterization of these environments, although these humid depressions are difficult to define. The implementation of new optical satellite sensors characterized by High and Very High Spatial Resolution (HSR and VHSR) and by a high temporal repetitiveness, completes the field data and allows to envisage a delineation of the wet depression and a detailed detection of its land use. The purpose of this article is to determine whether the VHSR image, with its very rich spatial resolution, offers an added value for the detection of these diversified depressions, in comparison with the HSR image with richer spectral resolution. The results of the analysis show that the Ikonos image at VHSR compensates for the spectral richness of Sentinel-2 images at HSR. The “object-oriented” classification method, widely used today in image processing for various applications, appears to be more suitable than the “pixel-based” method in VHSR image classification. The Normalized Difference Water Index (NDWI) and the Transformed Soil Adjusted Vegetation Index (TSAVI) have multiple interests in wetland detection, although moisture masks show some inefficiency in improving the quality of classifications.

A Modified Transformed Soil Adjusted Vegetation Index for Cropland in Jilin Province, China

A Modified Transformed Soil Adjusted Vegetation Index for Cropland in Jilin Province, China

Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

Soil property maps are essential resources for agricultural land use. However, soil properties mapping is costly and time-consuming, especially in the regions with complicated topographic conditions. This study was conducted in a hilly region of Central Vietnam with the following objectives: (i) to evaluate the best environmental variables to estimate soil organic carbon (SOC), total nitrogen (TN), and soil reaction (pH) with a regression kriging (RK) model, and (ii) to compare the accuracy of the ordinary kriging (OK) and RK methods. SOC, TN, and soil pH data were measured at 155 locations within the research area with a sampling grid of 2 km × 2 km for a soil layer from 0 to 30 cm depth. From these samples, 117 were used for interpolation, and the 38 randomly remaining samples were used for evaluating accuracy. The chosen environmental variables are land use type (LUT), topographic wetness index (TWI), and transformed soil adjusted vegetation index (TSAVI). The results indicate that the LUT variable is more effective than TWI and TSAVI for determining TN and pH when using the RK method, with a variance of 7.00% and 18.40%, respectively. In contrast, a combination of the LUT and TWI variables is the best for SOC mapping with the RK method, with a variance of 14.98%. The OK method seemed more accurate than the RK method for SOC mapping by 3.33% and for TN mapping by 10% but the RK method was found more precise than the OK method for soil pH mapping by 1.81%. Further selection of auxiliary variables and higher sampling density should be considered to improve the accuracy of the RK method.

The accuracy of the outer boundary delineation of coral reef area derived from the analyses of various vegetation indices of satellite landsat thematic mapper

Abstract. Sulistyo B. 2017. The accuracy of the outer boundary delineation of coral reef area derived from the analysis of various vegetation indices of satellite Landsat thematic mapper. Biodiversitas 18: 351-358. This research aimed at determining the accuracy of the outer boundary delineation of coral reef area derived from the analysis of various vegetation indices of satellite Landsat Thematic Mapper in Enggano Island, the District of North Bengkulu, Bengkulu Province. The vegetation indices of Landsat Thematic Mapper analyzed were the Ratio Vegetation Index, the Normalized Difference Vegetation Index, the Soil Adjusted Vegetation Index, the Transformed Soil Adjusted Vegetation Index, the Modified Soil Adjusted Vegetation Index and the Transformed Vegetation Index. The result of every analysis of vegetation index (index map) was then used as a base for on-screen digitization of the points assumed to be the outer boundary of coral reefs in order to get digital data. Digitization of shoreline was also done to create an inner boundary of coral reefs. When it is combined with its outer boundary then the calculation of the area is possible. The accuracy of the result was validated by overlaying it onto Map of Coral Reefs derived from a topographic map of Joint Operation Group. To justify whether the accuracy is good (or accepted) or not good (or not accepted), a threshold value proposed by Justice and Townshend was chosen, those are Poor (when the accuracy is < 70%), Moderate (when the accuracy is between 70% and 85%), and Good (when the accuracy is ≥ 85%). The results of the research showed that the accuracy of RVI and NDVI values were Poor, those are 67.11% and 66.42%, respectively, so it could not be used for further analysis. The other results were Moderate, those are 73.33% for SAVI, 74.22% for TSAVI, 77.32 % for MSAVI and 77.33% for TVI, and may be used as a quick alternative when the map is absent.
 Keywords: coral reefs, Landsat, outer boundary delineation, vegetation index

Diachronic analysis of the occupation of the steppe area of the department of Sidi Bel Abbes (Western Algeria)

Modes of occupation of the soil of the steppe area of the department of Sidi Bel Abbes (Western Algeria) know lots of mutations during the period 1987/2013; compromising the future of pastoral activity. This dissection based on supervised classification TSAVI values (Transformed Soil Adjusted Vegetation Index) using images of remote sensing of average spatial resolution of type Landsat-TM 5 and 8. The determination of the state of occupation of the ground and validation of remote sensing map shows that the status of the halophytic/psammophytic steppes and the Matorrals are detected in 38.38 % and 55,71 % of cases, respectively. On the other hand, the steppes chamaephytic mark -9,81 % regression only, agricultural land -24,51 %, and -46,24 % dense vegetation are correctly mapped. The sensing medium resolution is therefore, in the light of these figures, a management tool of the steppe field relevant and effective, which, in addition, allows enriching the field for a proper plan for the fight against desertification.

Discriminant analysis of olive oil mill wastes using spectroradiometers in the visible and near infrared part of the spectrum

This paper aims to introduce an index which is able to assist the recognition of Olive Oil Mill Wastes (OOMW) disposal areas using multispectral satellite images. For this purpose several ground spectroradiometric measurements in the range of 450–900 nm have been collected at a variety of targets. The samples were initially resampled to the GeoEye-1 sensor using the appropriate Relative Spectral Response Filter (RSR) and then scatter plots and spectral signatures were projected. Then an evaluation of nine widely used vegetation indices was performed. This analysis indicated that, Perpendicular Vegetation Index (PVI), Transformed Soil Adjusted Vegetation Index (TSAVI) and Soil and Atmospherically Resistant Vegetation Index (SARVI) are the most efficient indices to enhance the detection of Olive Mill Waste disposal areas. Moreover, correlation as well as separability analysis have shown than blue and near infrared (NIR) spectral bands are the most suitable for interpretation purposes. The Olive Mill Waste index was applied also to GeoEye-1 in different cases studies in the island of Crete.

Are soil‐adjusted vegetation indices better than soil‐unadjusted vegetation indices for above‐ground green biomass estimation in arid and semi‐arid grasslands?

AbstractAccurate estimation of grassland biomass has been a central focus due to its importance in ecosystem processes and carbon cycles. This study aimed to examine whether the performance of soil‐adjusted vegetation indices for estimating above‐ground green biomass was better than that of soil‐unadjusted vegetation indices in arid and semi‐arid grasslands. Above‐ground green biomass in desert steppe of Inner Mongolia and corresponding moderate‐resolution imaging spectroradiometer (MODIS) surface reflectance 8‐day composite MOD09Q1 data were collected during late September of 2013. Results showed that soil‐adjusted SAVI (soil‐adjusted vegetation index), MSAVI (modified soil‐adjusted vegetation index), OSAVI (optimized soil‐adjusted vegetation index), TSAVI (transformed soil‐adjusted vegetation index), ATSAVI (adjusted transformed soil‐adjusted vegetation index) and PVI (perpendicular vegetation index) did not improve estimation accuracy over soil‐unadjusted simple ratio (SR) and normalized difference vegetation index (NDVI), due to low green vegetation cover (<30%) in the study area. Our results suggest that these soil‐adjusted vegetation indices may be not suitable for describing green vegetation information in arid and semi‐arid grasslands with low green vegetation cover (<30%).

Vegetation response to rainfall pulses in the Sonoran Desert as modelled through remotely sensed imageries

In northwestern Mexico in an annual cycle comprised months 2008-2009, we estimated the effects of cumulative rainfall and previous vegetation status on current vegetative greenness adjusted for soil reflectance (via the Transformed Soil Adjusted Vegetation Index (TSAVI)) and canopy water content (via the Normalized Difference Infrared Index (NDII)). Sample kernels (540 of <0.4 ha) were stratified in nine areas by latitude, distance to the Pacific Ocean and slope across a 15 000 km 2 area on the Baja California peninsula. Rainfall data were accumulated over 2-, 4- and 6-week periods from daily satellite estimates (via the Tropical Rainfall Measuring Mission (TRMM)). TSAVI and NDII were calculated from Landsat 5 TM images. When restricted by season, multiple regressions results were 0.59 < r 2 adj < 0.96 for TSAVI and 0.45 < r 2 adj < 0.85 for NDII. Rainfall alone accounted for up to an exceptional 70% of the variance in vegetation indices, while the most widely significant factors were the previous vegetation status and its interaction with precipitation. For TSAVI, direct dependence on precipitation was generally much greater for the 4- and 6-week accumulation than for the 2-week periods. However, none of the factors was universally significant for either vegetation index. Results also showed the importance of scale in the spatial heterogeneity of climate and topography in this arid landscape.

Proximal sensing and vegetation indices for site-specific evaluation on an irrigated crop tomato

The present paper deals with proximal sensing technique applied to a drip irrigated tomato field. The aim of this “on farm” research was the evaluation of three Vegetation indices, WI (Water index) WI/NDVI and TSAVI (Transformed Soil Adjusted Vegetation Index), to analyze the correlation among VIs and tomato yield, to assess the spatial variability of a tomato crop, and finally to identify homogeneous crop area.Until 90 days after transplanting, tomato field was almost uniform, either at a visual assessment or according to spectroradiometric readings. At the end of the vegetative growth stage (from 90 days after transplanting), a spot crop area showed increasing soil moisture conditions due to soil topography. In this spot area, plants first yellowed and after started dying. Among indices, TSAVI appeared the most effective to detect excess soil water conditions, infect at 105 DAT TSAVI index was highly significant correlated to tomato yield, demonstrating to be a good index for early detecting excess crop water status. This study reinforce the possibility of detecting plant water stress by spectroradiometric measurements at field scale (ground-based measurements) and at territorial level.

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

Accurate estimation of green aboveground biomass in arid and semiarid grassland is essential for a variety of studies, such as sustainable grassland management, fire risk assessment, climate change, and environmental degradation. A great need exists for the establishment of robust method for estimating green aboveground biomass in arid and semiarid grassland due to the influences of soil background and litter. In the study, a new index (litter-soil-adjusted vegetation index, L-SAVI) was proposed to estimate green aboveground biomass in arid and semiarid grassland. The L-SAVI was also evaluated based on biomass and spectra in situ measurements in the desert steppe of Inner Mongolia. Results showed that, the performance of the new index was better than that of NDVI (normalized difference vegetation index), SAVI (soil-adjusted vegetation index), MSAVI (modified soil-adjusted vegetation index), OSAVI (optimised soil-adjusted vegetation index), TSAVI (transformed soil-adjusted vegetation index), ATSAVI (adjusted transformed soil-adjusted vegetation index), PVI (perpendicular vegetation index), GSAVI (green-adjusted vegetation index), and L-ATSAVI (litter-corrected ATSAVI) in our study site. The logic behind the L-SAVI was to enable the SAVI to be less sensitive to litter by incorporating the CAI (cellulose absorption index) in the SAVI. In conclusion, the L-SAVI is a suitable predictor for complementing existing vegetation indices on green aboveground biomass estimation in arid and semiarid grassland.

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R 2 > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R 2 > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R 2 = 0.78, accuracy = 73%, root mean square error (RMSE) = 0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy = 79%, RMSE = 0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.

Spectral response of benthic diatoms with different sediment backgrounds

Remote sensing of terrestrial vegetation uses a wide range of vegetation indices (VIs) to monitor plant characteristics, but these indices can be very sensitive to canopy background reflectance. This study investigated background influences on VIs applied to intertidal microphytobenthos, using a synthetic spectral library constituted by a spectral combination of three contrasting types of sediment (sand, fine sand, and mud) and reflectance spectra of benthic diatom monospecific cultures obtained in controlled conditions. The spectral database exhibited, for the same biomass range (3–182 mg chlorophyll a m − 2 ), marked differences in albedo and spectral contrast linked to sediment variability in water content, grain size, and organic matter content. Several VIs were evaluated, from ratios using visible and near infrared wavelengths, to hyperspectral indices (derivative analysis, continuum removal). Among the ratios, the Normalized Difference Vegetation Index (NDVI) appeared less sensitive to background effects than VIs with soil corrections such as the Perpendicular Vegetation Index (PVI), the Soil-Adjusted Vegetation Index (SAVI), the Modified second Soil-Adjusted Vegetation Index (MSAVI2) or the Transformed Soil-Adjusted Vegetation Index (TSAVI). The lower efficacy of soil-corrected VIs may be explained by the structural differences and optical behavior of soil vs. canopies compared to sediment vs. microphytobenthos biofilms. The background effects were minimized using Modified Gaussian Model indices at 632 nm and 675 nm, and the second derivative at 632 nm, while poor results were obtained with the red-edge inflection point (REIP) and the second derivative at 675 nm. The least sensitive index was the Phytobenthos Index which is very similar to the NDVI, but uses a red wavelength at 632 nm instead of 675 nm, to account for the absorption by chlorophyll c. The modified NDVI 705, where the 705 nm wavelength replaces the red band, showed moderate background sensitivity. Moreover, the NDVI 705 and the Phytobenthos Index have the additional relevant property of being less sensitive to the index saturation response with increasing biomass. Unfortunately, these VIs cannot be applied to broad-band multispectral satellite images, and require sensors with a hyperspectral resolution. Nevertheless, this study showed that the background influence was not a limitation to applying the ubiquitous NDVI to map intertidal microphytobenthos using multispectral satellite images.

Assessment of the effects of vegetation on soil erosion risk by water: a case of study of the Batta watershed in Tunisia

Soil erosion by water is a serious environmental problem which affects particularly the agriculture of developing countries. Due to specific factors, such as high rainfall intensity, steep slopes and vegetation scarcity, Tunisia is prone to soil erosion. Taking this into account, the main objective of this study was to estimate the soil erosion risk in the Batta watershed in Tunisia using qualitative and quantitative erosion model with remote sensing data and geographic information system (GIS). Moreover, a developed method that deals with evaluating the impact of vegetation on soil erosion by water is also applied. This method used multi-temporal satellite images with seasonal variability and the transformed soil adjusted vegetation index (TSAVI) which is appropriate in arid and semi-arid areas. For both erosion models, the results show that a large area of the Batta watershed is seriously affected by erosion. This potentially high risk is due especially to severe slopes, poor vegetation coverage and high soil erodibility in this watershed. Furthermore, the use of multi-temporal satellite images and vegetation index show that the effect of vegetation is a significant factor to protect the soil against erosion. The risk is especially serious in the summer season, but it decreases with the growth of vegetation cover in spring. Erosion model, combined with a GIS and remote sensing, is an adequate method to evaluate the soil erosion risk by water. The findings can be used by decision makers as a guideline to plan appropriate strategies for soil and water conservation practices.

A suitable vegetation index for quantifying temporal variation of leaf area index (LAI) in semiarid mixed grassland

Determining a suitable vegetation index (VI) is important for accurately quantifying temporal variation in leaf area index (LAI) in semiarid mixed grassland where performances of VI are highly influenced by complex canopy composition, large amounts of dead vegetation, and bare soil. This study investigates the effects of canopy composition on LAI, evaluates the performances of 16 hyperspectral VIs on LAI estimation at each growing stage, and documents the sensitivity of the VIs to green vegetation and the resistance to dead vegetation and bare soil. Finally, the most suitable VI for quantifying LAI temporal variation is determined. The results indicate that dead vegetation accounts for more variation in LAI than does green vegetation. The study also shows that performances of ratio-based, chlorophyll-independent, soil-line-litter-corrected (L-ATSVI), and soil-line-related VIs are more reliable and stable than chlorophyll-corrected VIs and atmospheric resistant vegetation index (SARVI). Normalized difference vegetation index (NDVI), standardized LAI determining index (SLAIDI), and transformed soil-adjusted vegetation index (TSAVI) are the optimum VIs for LAI estimations in the early, maximum, and late growing seasons, respectively. NDVI is the most suitable VI for quantifying temporal variation in LAI during the entire growing season.

Crop fraction estimation from casi hyperspectral data using linear spectral unmixing and vegetation indices

It is important to estimate vegetation fraction for forecasting regional weather and in precision agriculture for assessing crop performance during emergence and early growth phases. In this study, two approaches, linear spectral unmixing and vegetation indices, were reviewed and evaluated for the estimation of crop fraction from hyperspectral data. Compact Airborne Spectrographic Imager (casi) hyperspectral data were acquired three times in the 2001 growing season over four agricultural fields to monitor crop growth conditions and develop procedures for delineating major subunits for crop management. Crops planted in these fields included corn, soybean, and wheat. End-member spectra were extracted from casi data and used for linear spectral unmixing. Various vegetation indices, including the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), modified soil-adjusted vegetation index (MSAVI), transformed soil-adjusted vegetation index (TSAVI), and recently developed modified triangular vegetation index (MTVI2) and VI700 and VIgreen indices, were evaluated with casi data and with simulated spectra using coupled PROSPECT and SAILH models. All these indices were highly correlated with measured crop fractions. A comparison study based on simulated spectra showed that MTVI2 maintained adequate sensitivity up to a higher crop coverage. A high coefficient of determination (R2 = 0.90) and a low root mean square error (RMSE = 0.10) were obtained between measured and estimated crop fraction using MTVI2. The crop fraction derived from linear spectral unmixing was also highly correlated with the measured crop fraction (R2 = 0.94; RMSE = 0.08). However, determining end-member spectra in the linear spectral unmixing method remains a challenge. Using vegetation indices is a convenient method for crop fraction estimation with satisfactory accuracy.

Evaluation of the potential of various spectral indices and textural features derived from satellite images for surficial deposits mapping

The present paper deals with the relevance of spectral and textural indices to surficial deposits identification and mapping. The study area is located in the Cochabamba valley in central Bolivia. Potential of SPOT‐4, Landsat‐7 and Radarsat‐1 data were compared for surficial deposits mapping. Different spectral indices including NDVI (normalized difference vegetation index) and TSAVI (transformed soil adjusted vegetation index) and textural features (mean, standard deviation, angular second moment, entropy, etc.) were extracted from these datasets and used in the mapping process. The results showed that indices exhibit different level of sensitivities according to surficial deposit types. A discriminant analysis was conducted to extract the most significant indices, which were then used in a three‐step linear combination mathematical model to map surficial deposits. We achieved an overall classification rate of 74% using spectral data of land use map in step 1. By adding information on vegetation and soils obtained from evaluation of spectral indices, this rate was improved to 82% during step 2. Finally, it was further slightly improved to 83% by adding textural data in the final step.