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© 2019 IEEE. The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R2 from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE). Land surface temperature (LST) is a crucial parameter for global climate change studies. LST changes are also directly associated with the large-scale changes in land cover. Previous studies carried out a comparative analysis of satellite-derived LST response between periods before and after homogenous land cover changes. We present an alternative approach that quantifies long-term LST variability in response to various land use/land cover change (LULCC) patterns over Phuket Island, Thailand, from 2003 to 2017. First, four Moderate Resolution Imaging Spectroradiometer (MODIS) overpass times of LST time series were adjusted for seasonal effects using a cubic spline function to preserve the number of original data and enable estimates of LST dynamics and trends using the generalized least squared models. Second, LULCC patterns were classified according to land cover type conversion and spatial pattern transformations between the years 2000 and 2016. Spatial homogeneity and heterogeneity were quantified by the coverage percentage for each land use and land cover (LULC) type within a given location. Finally, the influence of LULCC patterns on the long-term spatiotemporal behavior of LST was assessed using the generalized estimating equation model. Results showed that different land cover transitions influence the dynamics of daytime LST but not the nighttime LST. The proportion of different land cover types within an LST pixel and transition amounts contributed to the quantity of increasing surface temperature, especially over impervious surface types. Diverse LULCC patterns with considerations of spatial heterogeneity improved our insight about a relatively strong effect of combined LULC types on LST responses. The climatic effect through the gradual conversion of heterogeneous land cover is necessary to be considered in climate research studies.

© 2017 Land surface phenology (LSP) has been widely retrieved from satellite data at multiple spatial resolutions, but the spatial scaling effects on LSP detection are poorly understood. In this study, we collected enhanced vegetation index (EVI, 250 m) from collection 6 MOD13Q1 product over the contiguous United States (CONUS) in 2007 and 2008, and generated a set of multiple spatial resolution EVI data by resampling 250 m to 2 × 250 m and 3 × 250 m, 4 × 250 m, …, 35 × 250 m. These EVI time series were then used to detect the start of spring season (SOS) at various spatial resolutions. Further the SOS variation across scales was examined at each coarse resolution grid (35 × 250 m ≈ 8 km, refer to as reference grid) and ecoregion. Finally, the SOS scaling effects were associated with landscape fragment, proportion of primary land cover type, and spatial variability of seasonal greenness variation within each reference grid. The results revealed the influences of satellite spatial resolutions on SOS retrievals and the related impact factors. Specifically, SOS significantly varied lineally or logarithmically across scales although the relationship could be either positive or negative. The overall SOS values averaged from spatial resolutions between 250 m and 35 × 250 m at large ecosystem regions were generally similar with a difference less than 5 days, while the SOS values within the reference grid could differ greatly in some local areas. Moreover, the standard deviation of SOS across scales in the reference grid was less than 5 days in more than 70% of area over the CONUS, which was smaller in northeastern than in southern and western regions. The SOS scaling effect was significantly associated with heterogeneity of vegetation properties characterized using land landscape fragment, proportion of primary land cover type, and spatial variability of seasonal greenness variation, but the latter was the most important impact factor.

© 2016 IEEE. Current bidirectional reflectance distribution function (BRDF) inversions using ordinary least squares (OLS) criterion can be easily contaminated by observations with residual cloud and undetected high aerosols, which leads to abrupt fluctuations in the normalized difference vegetation index (NDVI) time series. The OLS criterion assumes the noise has Gaussian distribution, which is often violated due to positive noise biases caused by clouds and high aerosols. A changing-weight iterative BRDF/NDVI inversion algorithm (CWI) based on a posteriori variance estimation of observation errors is presented to explicitly consider the asymmetrically distributed noise and observations with unequal accuracy in the BRDF retrieval. CWI employs a posteriori variance estimation and an NDVI-based indicator to iteratively adjust the weight of each observation according to its noise level. The validation results suggest CWI performs better than the Li-Gao and OLS approaches. The rmse was reduced from 0.074 to 0.028, and the relative error decreased from 13.4% to 3.8% at the U.S. Department of Agriculture Beltsville Agricultural Research Center site. Similarly, at the Harvard Forest site, the rmse was reduced from 0.086 to 0.031, and the relative error decreased from 9.5% to 2.7%. The average noise and relative noise of the CWI NDVI time series over ten EOS Land Validation Core Sites from 2003-2009 was smaller (0.028, 3.7%) than those of MOD13A2 (0.041, 5.2%), MYD13A2 (0.039, 4.9%) and MCD43B4 (0.030, 4.4%). The results demonstrate the robustness of the CWI approach in suppressing the influence of contaminated observations in BRDF retrievals by producing results that are less affected by undetected clouds and high aerosols.

This study investigated the mechanisms underlying the scaling effects that apply to a fraction of vegetation cover (FVC) estimates derived using two-band spectral vegetation index (VI) isoline-based linear mixture models (VI isoline-based LMM). The VIs included the normalized difference vegetation index, a soil-adjusted vegetation index, and a two-band enhanced vegetation index (EVI2). This study focused in part on the monotonicity of an area-averaged FVC estimate as a function of spatial resolution. The proof of monotonicity yielded measures of the intrinsic area-averaged FVC uncertainties due to scaling effects. The derived results demonstrate that a factor ξ, which was defined as a function of "true" and "estimated" endmember spectra of the vegetated and nonvegetated surfaces, was responsible for conveying monotonicity or nonmonotonicity. The monotonic FVC values displayed a uniform increasing or decreasing trend that was independent of the choice of the two-band VI. Conditions under which scaling effects were eliminated from the FVC were identified. Numerical simulations verifying the monotonicity and the practical utility of the scaling theory were evaluated using numerical experiments applied to Landsat7-Enhanced Thematic Mapper Plus (ETM+) data. The findings contribute to developing scale-invariant FVC estimation algorithms for multisensor and data continuity. © 2014 Society of Photo-Optical Instrumentation Engineers.

In this paper, we developed a more sophisticated method for detection and estimation of mixed paddy rice agriculture from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Previous research demonstrated that MODIS data can be used to map paddy rice fields and to distinguish rice from other crops at large, continental scales with combined Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) analysis during the flooding and rice transplanting stage. Our approach improves upon this methodology by incorporating mixed rice cropping patterns that include single-season rice crops, early-season rice, and late-season rice cropping systems. A variable EVI/LSWI threshold function, calibrated to more local rice management practices, was used to recognize rice fields at the flooding stage. We developed our approach with MODIS data in Hunan Province, China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns. We further mapped the aerial coverage and distribution of early, late, and single paddy rice crops for several years from 2000 to 2007 in order to quantify temporal trends in rice crop coverage, growth and management systems. Our results were validated with finer resolution (2.5m) Satellite Pour l'Observation de la Terre 5 High Resolution Geometric (SPOT 5 HRG) data, land-use data at the scale of 1/10,000 and with county-level rice area statistical data. The results showed that all three paddy rice crop patterns could be discriminated and their spatial distribution quantified. We show the area of single crop rice to have increased annually and almost doubling in extent from 2000 to 2007, with simultaneous, but unique declines in the extent of early and late paddy rice. These results were significantly positive correlated and consistent with agricultural statistical data at the county level (P

In this study we assessed the impacts of forest fragmentation on the Amazon landscape using remote sensing techniques. Landscape disturbance, obtained for an area of approximately 3.5 × 106 km2 through simple spatial metrics (i.e. number of fragments, mean fragment area and border size) and principal component transformation were then compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) seasonal responses. As expected, higher disturbance values prevail in the southern border of the Amazon, near the intensively converted deforestation arc, and close to the major roads. NDVI seasonal responses more closely follow human-induced patterns, i.e. forest remnants from areas more intensively converted were associated with higher NDVI seasonal values. The significant correlation between NDVI seasonal responses and landscape disturbances were corroborated through analysis of geographically weighted regression (GWR) parameters and predictions. On the other hand, EVI seasonal responses were more complex with significant variations found over intact, less fragmented forest patches, thus restricting its utility to assess landscape disturbance. Although further research is needed, our results suggest that the degree of fragmentation of the forest remnants can be remotely sensed with MODIS vegetation indices. Thus, it may become possible to upscale field-based data on overall canopy condition and fragmentation status for basin-wide extrapolations. © 2010 Taylor & Francis.

The Brazilian Amazon has the world's highest absolute rate of forest loss, currently averaging nearly 2 million hectares per year. In this study, we present a near-real-time automated deforestation mapping system for the Brazilian Amazon based on the analysis of spatial context information and MODIS Vegetation Index products and implemented on an ArcGIS 9.0 platform. This system, already validated and operational, was developed as part of the "Integrated Warning Deforestation System for the Amazon" (SIAD), an initiative of the Brazilian government within the scope of the Amazon Protection System (SIPAM), which also comprises: (1) a spatial information module, aimed at the assessment of the causes and impacts of the deforested areas; (2) a prediction module, indicative of deforestation trends; and (3) a data and information gateway based on map server technology.

In this study, the response of vegetation indices (VIs) to the seasonal patterns and spatial distribution of the major vegetation types encountered in the Brazilian Cerrado was investigated. The Cerrado represents the second largest biome in South America and is the most severely threatened biome as a result of rapid land conversions. Our goal was to assess the capability of VIs to effectively monitor the Cerrado and to discriminate among the major types of Cerrado vegetation. A full hydrologic year (1995) of composited AVHRR, local area coverage (LAC) data was converted to Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) values. Temporal extracts were then made over the major Cerrado vegetation communities. Both the NDVI and SAVI temporal profiles corresponded well to the phenological patterns of the natural and converted vegetation formations and depicted three major categories encompassing the savanna formations and pasture sites, the forested areas, and the agricultural crops. Secondary differences in the NDVI and SAVI temporal responses were found to be related to their unique interactions with sun-sensor viewing geometries. An assessment of the functional behaviour of the VIs confirmed SAVI responds primarily to NIR variations, while the NDVI showed a strong dependence on the red reflectance. Based on these results, we expect operational use of the MODIS Enhanced Vegetation Index (EVI) to provide improved discrimination and monitoring capability of the significant Cerrado vegetation types. © 2004 Taylor and Francis Ltd.

For a better evaluation of the accuracy of VIs in estimating biophysical parameters, a "true" VI value attributed only to the vegetation signal and free of any contamination is needed. In this article, pure vegetation spectra were extracted from a set of open and closed canopies by unmixing the green vegetation signal from the background component. Canopy model-simulation and reflectances derived from graph-based linear extrapolation were used to unmix and derive a "true" vegetation signal, equivalent to a perfect absorber (free boundary) canopy background reflectance condition. Optical-biophysical relationships were then derived for a variety of canopy structures with differences in foliage clumping, horizontal heterogeneity, and leaf type. A 3-dimensional canopy radiative transfer model and a hybrid geometric optical-radiative transfer model (GORT) were used to simulate the directional-hemispherical reflectances from agricultural, grassland, and forested canopies (cereal and broadleaf crop, grass, needleleaf, and broadleaf forest). The relationships of the extracted red and near-intimated reflectances and derived vegetation indices (VIs) to various biophysical parameters (leaf area index, fraction of absorbed photosynthetically active radiation, and percent ground cover) were examined for the pure vegetation spectra. The results showed normalized difference vegetation index (NDVI) relationships with biophysical parameters to become more asymptotic over the pure vegetation condition. The extraction of pure vegetation signals had little effect on the soil-adjusted vegetation index (SAVI), which had values equivalent to those obtained with the presence of a background signal. NDVI values were fairly uniform across the different canopy types, whereas the SAVI values had pronounced differences among canopy types, particularly between the broadleaf and cereal/needleleaf structural types. These results were useful not only in selecting suitable vegetation indices to characterize specific canopy biophysical parameters, but also in understanding a "true" VI behavior, free of background noise. ©2000 Elsevier Science Inc.

In this study, the 16-day MODIS (MODerate resolution Imaging Spectroradiometer) vegetation index (VI) compositing algorithm and product were described, evaluated, and compared with the current AVHRR (Advanced Very High Resolution Spectroradiometer) maximum value composite (MVC) approach. The MVC method selects the highest NDVI (normalized difference vegetation index) over a certain time interval. The MODIS VI compositing algorithm emphasizes a global and operational view angle standardization approach: a reflectance-based BRDF (Bidirectional Reflectance Distribution Function) model, succeeded by a back-up MVC algorithm that includes a view angle constraint. A year's worth of daily global AVHRR data was used to prototype the MODIS vegetation index compositing algorithm. The composite scenarios were evaluated with respect to: 1) temporal evolution of the VI for different continents and vegetation types, 2) spatial continuity of the VI, 3) quality flags related to data integrity, cloud cover, and composite method, and 4) view angle distribution of the composited data. On a continental scale, the composited NDVI values from the MODIS algorithm were as much as 30% lower than the mostly, off-nadir NDVI results based on the MVC criterion. The temporal evolution of the NDVI values derived with the MODIS algorithm were similar to the NDVI values derived from the MVC algorithm. A simple BRDF model was adequate to produce nadir equivalent reflectance values from which the NDVI could be computed. Application of the BRDF and 'back-up' components in the MODIS algorithm were dependent on geographic location and season, for example, the BRDF interpolation was most frequently applied in arid and semiarid regions, and during the dry season over humid climate vegetation types. Examples of a MODIS-like global NDVI map and associated quality flags were displayed using a pseudo color bit mapping scheme.

Litter is frequently present within vegetation canopies and thus contributes to the overall spectral response of a canopy. Consequently, litter will affect spectral indices designed to be sensitive to green vegetation, soil brightness or other features. The main objectives of the current research were to 1) evaluate the spectral properties of green vegetation and litter and 2) quantify the effect of standing litter on the performance of spectral indices. The SAIL (scattering by arbitrarily inclined leaves) model was used to generate canopy reflectance "mixtures" and to estimate fractions of absorbed photosynthetically active radiation (fAPAR) with varying leaf area index (LAI), soil background, combinations of vegetation component spectral properties, and one or two horizontal vegetation layers. Spectral measurements of different bare soils and mature green and senescent leaves of representative plant species at the HAPEX-Sahel (Hydrological Atmospheric Pilot Experiment) study sites were used as input. The normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and the modified NDVI (MNDVI) and mixture model spectral indices were selected to evaluate their performance with respect to standing litter and green vegetation mixtures. Spectral reflectance signatures of leaf litter varied significantly, but strongly resembled soil spectral characteristics. The biophysical parameters (LAI, fAPAR), derived from spectral vegetation indices, tended to be overestimated for randomly distributed, sparse green and litter vegetation cover mixtures, and underestimated for randomly distributed dense green and litter vegetation cover mixtures. All spectral indices and their biophysical interpretation were significantly altered by variability in 1) green leaf, leaf litter, and bark optical properties, 2) the amount and position of standing leaf litter, 3) leaf angle distribution, and 4) soil background. The NDVI response to these variables was inconsistent, and was the most affected by litter. The spectral mixture model indices, designed to be sensitive to litter, were shown to be promising for the identification of litter present among different ecosystems.

This article describes the impacts of sensor view and solar zenith angles on two vegetation indices -NDVI (normalized difference vegetation index) and SAVI (soil adjusted vegetation index). An evaluation of these geometric factors on the relationships between these VIs (vegetation indices) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) was performed. To accomplish this, an experiment was conducted in Phoenix, Arizona, over plots of bare soil and low, medium, and high alfalfa. Reflectances were measured from 0.4 μm to 1.0 μm in nine view angles (from -40° to + 40°, in 10° steps) over varying solar zenith angles. This was done simultaneously with fAPAR measurements. Changes in view angle caused variations in the indices to be as high as 50% in relation to nadir. However, there was an opposite view angle behavior between NDVI and SAVI, with the former increasing from antisolar to forward scattering view direction. A derivative analysis of the indices showed the SAVI to exhibit a more linear relationship than NDVI with the individual bands. The relationships between both VIs and fAPAR were, in general, linear. However, view angle variations perturbed these relationships and caused an over- or underestimation of fAPAR, depending on view direction (antisolar or forward), view angle, and vegetation index (NDVI or SAVI). © 1995.

Several soil and atmospheric-correcting variants of the normalized difference vegetation index (NDVI) have been proposed to improve the accuracy in estimating biophysical plant parameters. In this study, a sensitivity analysis, utilizing simulated model data, was conducted on the NDVI and variants by analyzing the atmospheric and soil-perturbed responses as a continuous function of leaf area index. Percent relative error and vegetation equivalent “noise” (VEN) were calculated for soil and atmospheric influences, separately and combined. The NDVI variants included the soil-adjusted vegetation index (SAVI), the atmospherically resistant vegetation index (ARVI), the soil-adjusted and atmospherically resistant vegetation index (SARVI), the modified SAVI (MSAVI), and modified SARVI (MSARVI). Soil and atmospheric error were of similar magnitudes, but varied with the vegetation index. All new variants outperformed the NDVI. The atmospherically resistant versions minimized atmospheric noise, but enhanced soil noise, while the soil adjusted variants minimized soil noise, but remained sensitive to the atmosphere. The SARVI, which had both a soil and atmosphere calibration term, performed the best with a relative error of 10 percent and VEN of ±0.33 LAI. By contrast, the NDVI had a relative error of 20 percent and VEN of ±0.97 LAI. © 1994 IEEE

The effects of soil optical properties on vegetation index imagery are analysed with ground-based spectral measurements and both simulated and actual AVHRR data from the NOAA satellites. Soil effects on vegetation indices were divided into primary variations associated with the brightness of bare soils, secondary variations attributed to ‘colour’ differences among bare soils, and soil- vegetation spectral mixing. Primary variations were attributed to shifts in the soil line owing to atmosphere or soil composition. Secondary soil variance was responsible for the Saharan desert ‘artefact’ areas of increased vegetation index response in AVHRR imagery. The impact of soil effects is discussed with a transect of vegetation index data derived from NOAA data from desert to equatorial forest. © 1991 Taylor & Francis Ltd.

The optical properties of a partially vegetated cotton field with spatially and temporally dynamic soil water conditions were analyzed with coincident aircraft and satellite data. The study was conducted at the Maricopa Agricultural Center in Arizona during June 1988. The dynamics of soil surface drying made it difficult to evaluate plant cover and assess soil condition with combined thermal, brightness, and normalized difference vegetation index (NDVI) spectral parameters as all three parameters were sensitive to the three stages of soil drying. A soil-adjusted vegetation index (SAVI) minimized both spatial and temporal variations in soil spectral behavior and was found useful in vegetation analysis and in further qualitative assessment of soil condition. Due to the complex dynamics of soil surface drying and variability in soil properties, soil water content at the surface (0 - 5 cm) could not be determined with the Thematic Mapper moisture bands, or with the various "wetness" indicators. © 1990.

A factor-analytic inversion model is presented which enables a data set of spectral mixtures to be decomposed into the sum of unique reflecting components weighted by their corresponding amounts. Spectral mixtures are decomposed into abstract eigenspectra and eigenvector matrices. The eigenspectra are then transformed into pure component spectral signatures through a target testing procedure that allows one to individually search for the presence of ground reflecting features. Soil-plant mixtures with variable soil moisture and plant densities were successfully decomposed into dry soil, wet soil, and vegetation components and their respective amounts in all spectral mixtures were determined. Potential uses of factor analysis in soil identification and biomass assessment are discussed. © 1986.

A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton (Gossypium hirsutum L. var DPI-70) and range grass (Eragrostics lehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple °lobal" that can describe dynamic soil-vegetation systems from remotely sensed data. © 1988.

An atmospheric radiant transfer model was used to compare ground-measured radiances over partially vegetated canopies with their simulated responses at the top of a clear (100 km meteorological range) and a turbid (10 km) atmosphere. Radiance measurements in the first four bands of the Thematic Mapper were taken over incomplete cotton (Gossypium hirsutum L.) and Lehmann lovegrass (Eragrostis lehmanniana Nees) canopies with different soil backgrounds separately inserted underneath. Atmospheric influences on the spectra of partial canopies were found to be significantly dependent on the "brightness" of the underlying soil. The change in canopy red and near-infrared radiance between the ground and the top of the atmosphere was such that an increase, decrease, or no change could be observed, depending on the magnitude of the canopy substrate contribution. Both increasing soil "brightness" and atmospheric turbidity lowered the ratio (RVI) and normalized difference vegetation index values (NDVI). Consequently, atmospheric-induced RVI and NDVI degradation were greatest over canopies with darker soils and were not detectable over canopies with light-colored soils. In contrast, soil and atmospheric effects on the perpendicular vegetation index were independent with atmosphere degradation being similar across all soil backgrounds. Soil influences on vegetation indices from partial canopies were found to be of similar magnitude to those attributed to the atmosphere for the range of soil and atmosphere conditions examined here. © 1988.

The spectral behavior of an arid, Lehmann lovegrass (Eragrostis lehmanniana), range canopy with varying quantities of live, green grass, senesced, yellow grass, weathered, gray litter, and different soil backgrounds was analyzed with a ground based radiometer. The analysis included rangeland field plots and artificial mixtures of live and dead grass. Senesced grass and weathered litter were found to significantly alter the spectral response of the range canopy in the first four Thematic Mapper wavebands (0.45-0.52; 0.52-0.60; 0.63-0.69; 0.76-0.90 μm). These influences seriously hampered the utility of spectral vegetation indices in assessing green phytomass levels. Gray litter lowered the response of the green vegetation index (GVI) and perpendicular vegetation index (PVI) while minimally influencing the ratio vegetation index (RVI) and the normalized difference vegetation index (NDVI). Yellow, senesced grass increased the greenness response of plots without green vegetation and decreased the greenness response of plots with green vegetation. Higher reflecting soils increased the GVI and PVI response and decreased the RVI and NDVI response of comparable range canopy mixtures. Small amounts of 30 cm tall, green grass (750 kg/ha) could not be detected within a 75 cm tall, senesced grass stand (5000 kg/ha). The results of this study show spectral vegetation indices to be unreliable measures of green phytomass in arid rangelands. A mixture model employing principal component analysis was used to extract a green vegetation signal, but green phytomass detection was not improved. Apparently, the green vegetation signal emerging from range canopies is diminished by the scattering influences of the vertically oriented elements of the senesced grass phytomass. © 1987.