Soil-adjusted Vegetation Indices Research Articles

Spatial-Temporal Characteristics and Driving Forces of Aboveground Biomass in Desert Steppes of Inner Mongolia, China in the Past 20 Years

The desert steppe serves as a transitional zone between grasslands and deserts, and long-term monitoring of aboveground biomass (AGB) in the desert steppe is essential for understanding grassland changes. While AGB observation techniques based on multisource remote-sensing data and machine-learning algorithms have been widely applied, research on monitoring methods specifically for the desert steppe remains limited. In this study, we focused on the desert steppe of Inner Mongolia, China, as the study area and used field sampling data, MODIS data, MODIS-based vegetation indices (VI), and environmental factors (topography, climate, and soil) to compare the performance of four commonly used machine-learning algorithms: multiple linear regression (MLR), partial least-squares regression (PLS), random forest (RF), and support vector machine (SVM) in AGB estimation. Based on the optimal model, the spatial–temporal characteristics of AGB from 2000 to 2020 were calculated, and the driving forces of climate change and human activities on AGB changes were quantitatively analyzed using the random forest algorithm. The results are as follows: (1) RF demonstrated outstanding performance in terms of prediction accuracy and model robustness, making it suitable for AGB estimation in the desert steppe of Inner Mongolia; (2) VI contributed the most to the model, and no significant difference was found between soil-adjusted VIs and traditional VIs. Elevation, slope, precipitation, and temperature all had positive effects on the model; (3) from 2000 to 2020, the multiyear average AGB in the study area was 58.34 g/m2, exhibiting a gradually increasing distribution pattern from the inner region to the outer region (from north to south); (4) from 2000 to 2020, the proportions of grassland with AGB slightly and significantly increasing trend in the study area were 87.08% and 5.13%, respectively, while the proportions of grassland with AGB slightly and significantly decreasing trend were 7.76% and 0.05%, respectively; and (5) over the past 20 years, climate change, particularly precipitation, has been the primary driving force behind AGB changes of the study area. This research holds reference value for improving desert steppe monitoring capabilities and the rational planning of grassland resources.

Algorithms and Predictors for Land Cover Classification of Polar Deserts: A Case Study Highlighting Challenges and Recommendations for Future Applications

The use of remote sensing for developing land cover maps in the Arctic has grown considerably in the last two decades, especially for monitoring the effects of climate change. The main challenge is to link information extracted from satellite imagery to ground covers due to the fine-scale spatial heterogeneity of Arctic ecosystems. There is currently no commonly accepted methodological scheme for high-latitude land cover mapping, but the use of remote sensing in Arctic ecosystem mapping would benefit from a coordinated sharing of lessons learned and best practices. Here, we aimed to produce a highly accurate land cover map of the surroundings of the Canadian Forces Station Alert, a polar desert on the northeastern tip of Ellesmere Island (Nunavut, Canada) by testing different predictors and classifiers. To account for the effect of the bare soil background and water limitations that are omnipresent at these latitudes, we included as predictors soil-adjusted vegetation indices and several hydrological predictors related to waterbodies and snowbanks. We compared the results obtained from an ensemble classifier based on a majority voting algorithm to eight commonly used classifiers. The distance to the nearest snowbank and soil-adjusted indices were the top predictors allowing the discrimination of land cover classes in our study area. The overall accuracy of the classifiers ranged between 75 and 88%, with the ensemble classifier also yielding a high accuracy (85%) and producing less bias than the individual classifiers. Some challenges remained, such as shadows created by boulders and snow covered by soil material. We provide recommendations for further improving classification methodology in the High Arctic, which is important for the monitoring of Arctic ecosystems exposed to ongoing polar amplification.

Evaluation of the impact of crop residue on fractional vegetation cover estimation by vegetation indices over conservation tillage cropland: a simulation study

ABSTRACT Accurate estimation of fractional vegetation cover (FVC) is of great significance to agricultural production. Crop residue management affect crop residue cover (CRC) over croplands. Crop and crop residue on the soil surface both contribute to overall canopy reflectance. Few studies, however, have examined the effect of crop residue on vegetation indices (VIs) and estimated FVC. The present study evaluated the response of eight commonly used VIs to crop residues and FVC uncertainty caused by crop residue based on the dimidiate pixel model (DPM) by using simulated reflectance of low-tilled cropland via a three-dimensional radiative transfer model. The absolute difference (AD) was used to quantify the spectral difference between crop residues and soils in red and near infrared wavelengths. Increases in normalized difference VI (NDVI), ratio VI (RVI), transformed soil-adjusted VI (TSAVI), and normalized difference phenology index (NDPI) were observed when green crops were mixed with crop residue that had negative ADs with soils, but decreases in enhance VI (EVI), perpendicular VI (PVI), SAVI, and litter-soil-adjusted VI (L-SAVI) were observed when crop residue was present under medium and high vegetation cover. The presence of crop residue with a positive AD with soils reduced NDVI, RVI, TSAVI, and NDPI while increased the other VIs. Crop residue had the least impact on EVI- and SAVI-based DPMs, with FVC-estimated uncertainty less than 0.1, followed by the NDPI- and L-SAVI-based model, while DPMs based on NDVI- and RVI performed poorly. Each VI-based DPM’s estimated uncertainty was highly correlated with AD values. Furthermore, the majority of the VI-based models were sensitive to solar position except for the NDPI-based model. Our findings highlight the need of considering the impact of crop residue on FVC retrieval over low-tilled cropland in future research.

Temperature sensitivities of vegetation indices and aboveground biomass are primarily linked with warming magnitude in high-cold grasslands

Incessant argument on whether temperature sensitivity of aboveground biomass is primarily linked with temperature itself (local air temperature and increased magnitudes in air temperature) or other environmental variables restrains our capability to exactly forecast upcoming alterations in grass yield and high-quality development of animal husbandry in high-cold areas. Consequently, since May 2010, a field warming trial with open-top containers was achieved in high-cold grasslands at three elevations (i.e. 4313, 4513 and 4693 m) of the Tibet. The temperature sensitivities of normalized different vegetation indices (Q1_NDVI), soil adjusted vegetation indices (Q1_SAVI) and aboveground biomass (Q1_AGB) were detected in 2014–2015 and 2017–2018. Temperature itself had the greatest exclusive impacts on the Q1_NDVI, Q1_SAVI and Q1_AGB. Vegetation indices itself or aboveground biomass itself had the second greatest exclusive impacts on the Q1_NDVI, Q1_SAVI or Q1_AGB. The exclusive impact of vegetation indices itself or aboveground biomass itself was less than one-tenth that of temperature itself. Water availability and elevation & duration (elevation and warming length) only had exclusive impacts on the Q1_NDVI and Q1_SAVI. The total exclusive impact of water availability and elevation & duration on the Q1_NDVI or Q1_SAVI was around 11–12 % equivalent to that of temperature itself. Vegetation indices/aboveground biomass itself, water availability and elevation & duration had interactive impacts with temperature itself on the Q1_NDVI, Q1_SAVI or Q1_AGB. Compared to local air temperature, increased magnitudes in air temperature had the greater exclusive effects on the Q1_NDVI, Q1_SAVI and Q1_AGB. Consequently, temperature sensitivities of vegetation indices, and aboveground biomass were primarily linked with temperature itself (especially warming magnitude), and adjusted by water availability, vegetation indices/aboveground biomass itself, elevation and warming length in high-cold grasslands of the Tibet.

Assessment of cotton and sorghum stand establishment using UAV‐based multispectral and DSLR‐based RGB imagery

AbstractPlant density and canopy cover are key agronomic traits for cotton (Gossypium hirsutum L.) and sorghum [Sorghum bicolor (L.) Moench] phenotypic evaluation. The objective of this study was to evaluate utility of broadband red–green–blue (RGB) and narrowband green, red, red‐edge, and near‐infrared spectral data taken by an unmanned aerial vehicle (UAV), and RGB taken by a digital single‐lens reflex camera for assessing the cotton and sorghum stands. Support Vector Machine was used to analyze UAV images, whereas ImageJ was used for RGB images. Fifteen vegetation indices (VIs) were evaluated for their accuracy, predictability, and residual yield. All VIs had Cohen's k > .65, F score > .63, and User and Producer accuracy of more than 71 and 69%, respectively. Soil‐adjusted vegetation indices (SAVIs) among narrowband VIs and excess green minus excess red (ExG–ExR) among broadband VIs provided more agreeable estimates of cotton and sorghum density than the remaining VIs with R2 and index of agreement (IoA) up to .79 and .92, respectively. The estimated canopy cover explained up to 83 and 82% variability in leaf area index (LAI) of cotton and sorghum, respectively. The ImageJ produced R2 from .79 to .90 and .83 to .86 and IoA .89 to .97 and ∼.91 between estimated and observed cotton and sorghum density, respectively. ImageJ explained up to 82 and 79% variability in cotton and sorghum LAI, respectively. Although ImageJ can give close estimates of crop density and cover, UAV‐based narrowband VIs still can provide an agreeable, reliable, and time‐efficient estimate of these attributes.

Assessment and Monitoring of Salinity of Soils After Tsunami in Nagapattinam Area Using Fuzzy Logic-Based Classification

Abstract The research attempts to know the salinity level and how much agricultural land was affected by the 2004 Indian Ocean tsunami in the study area. Nagapattinam province of Tamilnadu, which was strongly hit by the 2004 Indian Ocean tsunami, is selected as the demonstration site. IKONOS and QuickBird image for the periods 2003 (pre-tsunami, IKONOS), 2004 (immediate tsunami, IKONOS) and 2006 (post-tsunami, QuickBird) have been used in this study. The purpose of this research is to detect the effects of the tsunami in the study area. Multispectral images were obtained in order to detect the salt-affected soils and compare the data with images after the tsunami. The near-infrared reflectance spectra contain significant information related to soil components. The development of remote sensing techniques can support the monitoring and efficient mapping of salinity level in soil for environmental assessment. In this research, we have implemented the techniques in four levels: image preprocessing, extracting land region, feature extraction and image classification. Image preprocessing is the first step in the image processing chain and is usually necessary prior to image classification and analysis. In the second level, land regions are extracted from the study area using the Soil-Adjusted Vegetation Indices. In the feature extraction level, we have extracted 15 salinity features from the image data and performed the feature selection method to select the best five features of soil. In the classification stage, the salt-affected land region is classified into three classes (C1, highly saline; C2, saline; and C3, non-saline) using the fuzzy logic method. In the immediate tsunami and after-tsunami data, 95.15- and 121.4-hectare area, respectively, were detected as a salt-affected area.

Estimation and counter- validation of LISS-III derived leaf area index in Deltaic vegetation

Leaf area index (LAI), a dimensionless biophysical variable is considered as one of the most important factors in characterizing canopy structure. It estimates the amount of foliage area per unit of ground area and helps indirectly to assess biomass and energy balance in an ecosystem. Remote sensing techniques established a strong correlation between the vegetation reflectance characteristics in red and near infra-red bands and LAI. Good number of image derived vegetation indices has been applied so far to estimate LAI successfully. In this paper correlation is established between field-collected LAI and three soil adjusted vegetation indices, i.e., SAVI, MSAVI and OSAVI derived from IRS-LISS-III data in deltaic ecosystem in Sagar Island of West Bengal, India. LAI was estimated from OSAVI for the whole island as OSAVI yielded best result (R2= 0.92). Coarse resolution MODIS LAI (MOD 15A3) product was counter-validated with respect to the LISS-III derived LAI image following the upscale validation approach. Out of the six best-fit models applied, the logistic regression showed strong positive correspondence between the two products (R2 = 0.71). Uncertainty of the model was also assessed and probable reasons were identified.

Estimation and counter- validation of LISS-III derived leaf area index in Deltaic vegetation

Leaf area index (LAI), a dimensionless biophysical variable is considered as one of the most important factors in characterizing canopy structure. It estimates the amount of foliage area per unit of ground area and helps indirectly to assess biomass and energy balance in an ecosystem. Remote sensing techniques established a strong correlation between the vegetation reflectance characteristics in red and near infra-red bands and LAI. Good number of image derived vegetation indices has been applied so far to estimate LAI successfully. In this paper correlation is established between field-collected LAI and three soil adjusted vegetation indices, i.e., SAVI, MSAVI and OSAVI derived from IRS-LISS-III data in deltaic ecosystem in Sagar Island of West Bengal, India. LAI was estimated from OSAVI for the whole island as OSAVI yielded best result (R2= 0.92). Coarse resolution MODIS LAI (MOD 15A3) product was counter-validated with respect to the LISS-III derived LAI image following the upscale validation approach. Out of the six best-fit models applied, the logistic regression showed strong positive correspondence between the two products (R2 = 0.71). Uncertainty of the model was also assessed and probable reasons were identified.

Assessment of unified models for estimating potato leaf area index under water stress conditions across ground-based hyperspectral data

Accurate simulation of potato leaf area index (LAI) under water stress is of great significance for rational selection of planting location and extraction of planting area. To obtain the simulation model of potato LAI under different water conditions [water saturation (WS), water deficiency (WD), and control check (CK)], several spectral parameters of vegetation indices (VIs), first-order differential parameters and spectral parameters of the continuum removal method were derived from canopy spectra, first-order differential spectra and continuum removal spectra for correlation with the corresponding LAI, respectively. Furthermore, three different types of parameters with high LAI correlation coefficients were used to build the simulation models and verify the accuracy. As a result, the selected modeling parameters were normalized difference VI, soil-adjusted VI, quotient of red edge area (SDr) and blue edge area (SDb), ( SDr − SDb ) / ( SDr + SDb ) , depth area ratio (W), and gross area of absorption peak (S). The most suitable models are as follows: the exponential model of W under the WS condition [coefficient of determination ( R2 ) = 0.8262, root mean square error ( RMSE ) = 0.4338, and mean absolute error ( MAE ) = 0.3750], the power function model of S under the CK condition (R2 = 0.8133, RMSE = 0.4695, and MAE = 0.3798), and the power function model of SDr / SDb under the WD condition (R2 = 0.8407, RMSE = 0.4459, and MAE = 0.3882).

Globally standardized MODIS spectral mixture models

ABSTRACTStandardized substrate, vegetation, and dark (S, V, & D) spectral endmembers (EMs) are presented for spectral mixture analysis (SMA) of MODIS version 6 daily nadir looking BRDF-adjusted reflectance (daily NBAR). EMs are derived from a diverse collection of over 43,000,000MODIS spectra spanning 6 continents and all non-polar biomes. EM spectra are similar to those from decametre studies. Sensitivity analysis using 351 pairs of 27 EM combinations shows mean differences in SVD fraction estimates of 4±3%, 3±2%, and 3±2%, respectively. An additional snow EM is identified, but deemed tentative pending further cryospheric analysis. Vicarious validation with Landsat shows under 0.5% bias in V fraction, −7% and +11% biases in S and D fractions, and comparable dispersion (standard deviation 6% to 7%) in all fractions. Model misfit is low (root-mean-square error under 5% for 99.9% pixels). When V is compared to Normalized Difference, Enhanced, and Soil Adjusted Vegetation Indices (NDVI, EVI, SAVI), NDVI shows over twice the dispersion (standard deviation 29% vs 13–14%) and over 4 times the bias (+18% vs 4%) of EVI and SAVI. Combined with previous studies, these results extend the scaling linearity and low misfit of the global SVD linear mixture model from 2m up to 500m.

Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid, saline rangelands in North-East Iran?

ABSTRACTSatellite remote sensing has greatly facilitated the assessment of aboveground biomass in rangelands. Soil-adjusted vegetation indices have been developed to provide better predictions of aboveground biomass, especially for dryland regions. Semi-arid rangelands often complicate a remote sensing based assessment of aboveground biomass due to bright reflecting soils combined with sparse vegetation cover. We aim at evaluating whether soil-adjusted vegetation indices perform better than standard, i.e. unadjusted, vegetation indices in predicting dry aboveground biomass of a saline and semi-arid rangeland in NE-Iran. 672 biomass plots of 2 × 2 m were gathered and aggregated into 13 sites. Generalized Linear Regression Models (GLM) were compared for six different vegetation indices, three standard and three soil-adjusted vegetation indices. Vegetation indices were calculated from the MODIS MCD43A4 product. Model comparison was done using Akaike Information Criterion (AICc), Akaike weights and pseudo R2. Model fits for dry biomass showed that transformed NDVI and NDVI fitted best with R2 = 0.47 and R2 = 0.33, respectively. The optimized soil-adjusted vegetation index (OSAVI) behaved similar to NDVI but less precise. The soil-adjusted vegetation index (SAVI), the modified soil-adjusted vegetation index (MSAVI2) and the enhanced vegetation index (EVI) performed worse than a null model. Hence, soil-adjusted indices based on the soil-line concept performed worse than a simple square root transformation of the NDVI. However, more studies that compare MODIS based vegetation indices for rangeland biomass estimation are required to support our findings. We suggest applying a similar model comparison approach as performed in this study instead of relying on single vegetation indices in order to find optimal relationships with aboveground biomass estimation in rangelands.

Reduced Prediction Saturation and View Effects for Estimating the Leaf Area Index of Winter Wheat

Relationships between vegetation indices (VIs) and leaf area index (LAI) tend to saturate in the nadir direction, and vary with crop canopy structure and view zenith angles (VZAs). The objective of this paper was to improve the monitoring accuracy and angular stability of VIs for estimating LAI using multiangular remote sensing data. The relationship between LAI and ground-based hyperspectral spectral reflectance was quantified in winter wheat (Triticum aestivum L.) exhibiting erectophile and planophile growth habits. To further reduce the saturation, species specificity, and angular sensitivity, we developed a saturation factor (SF), based on near-infrared and green bands. Multiplying all VIs by SF greatly improved the association with LAI across all VZAs ( $R^{2} = 0.73{-}0.82$ ). Most VI $\times$ SF values, particularly optimized soil-adjusted VI $\times$ SF, were able to construct universal algorithms across VZAs for accurate estimation of LAI due to the sensitivity of SF to LAI in a dense canopy, and the insensitivity of SF to view effects with larger VZAs. This approach is also promising for the exploitation of multiangular satellite data for the design and calibration of nonview-angle-corrected spectral reflectance, for which the sensor is only deployed at fixed observation direction.

Assessing the capability of broadband indices derived from Landsat 8 Operational Land Imager to monitor above ground biomass and salinity in semiarid saline environments of the Bahía Blanca Estuary, Argentina

ABSTRACTIn arid and semi-arid ecosystems, salinisation and desertification are the most common processes of land degradation, and satellite data may provide a valuable tool to assess land surface condition and vegetation status. The aim of this study was to evaluate the capability of Landsat 8 OLI (Operational Land Imager) remote sensing information and broadband indices derived from it, to monitor above ground biomass (AGB) and salinity in two different semiarid saline environments (unit a and unit b) in the Bahía Blanca Estuary. Unit a (Ua) is composed of bushes of Cyclolepis genistoides in association with Atriplex undulata and 41% of bare soil. Unit b (Ub) is composed of dense thickets of Allenrolfea patagonica in association with C. genistoides and 34% of bare soil. Pearson’s correlation analyses were performed between field estimates of AGB and salinity (soil salinity and interstitial water salinity) and remote sensing estimates. Satellite data include surface reflectance of individual bands, vegetation indices (NDVI [normalised difference vegetation index], SAVI [soil-adjusted vegetation index], MSAVI2 [modified soil-adjusted vegetation index], NDII [normalised difference infrared index], GNDVI [green normalised difference vegetation index], GRNDI [green-red normalised difference index], OSAVI [optimised soil-adjusted vegetation index], SR [simple ratio]), and salinity indices (SI1, SI2, SI3 [salinity index 1, 2 and 3, respectively] and BI [brightness index]). Correlation analyses involving AGB were performed twice; first considering all months and then again excluding the months with higher soil salinities. In Ua, soil adjusted vegetation indices SAVI and MSAVI2 showed to be suitable to detect changes in the total green AGB and C. genistoides green AGB (the major contributor to total green AGB). After excluding data from December and January (the months with the highest soil salinity), green AGB of A. undulata also showed a significant positive correlation with soil adjusted indices SAVI, MSAVI2 and OSAVI. Although proportionally this species was not a large contributor to the total biomass, it is characterised by a high leaf reflectance, which makes it suitable for biomass retrieval. In Ub, significant positive correlations were obtained between NDVI, SAVI, NDII, OSAVI and SR indices and the AGB green ratio, but significant negative correlations were obtained between A. patagonica red AGB and these vegetation indices. When December and January were excluded from the analysis the negative correlations between vegetation indices NDVI, OSAVI and SR and red AGB remained significant (r = −0.68, −0.76 and −0.7, respectively). The positive correlations between these indices and AGB green ratio (r = 0.73, 0.78 and 0.75, respectively) remained significant as well. Significant negative correlations were also found between NDVI, NDII, GNDVI, OSAVI and SR indices and field salinity estimates. As soil salinisation induces A. patagonica reddening, red AGB and soil salinity covariate in the field, and the negative correlation with vegetation indices may be useful to retrieve information on both variables combined, which are indicative of water stress. Correlation analysis between field estimates of salinity and spectral salinity indices showed significant positive correlation for all the tested indices. The obtained results highlight the importance of a thoughtful selection of remote sensing indices to account for changes in vegetation biomass, especially in arid and semiarid environments particularly sensitive to desertification and salinisation. Also, ground truth cannot be overlooked, and field work is necessary to test index performance in every case.

Advanced land observing satellite data to identify ground vegetation in a juniper forest, northeast Iran

Juniperus excelsa subsp. polycarpos, (Persian juniper), is found in northeast Iran. In this study, the relationship between ground cover and vegetation indices have been investigated using remote sensing data for a Persian juniper forest. Multispectral data were analyzed based on the Advanced Visible and Near Infrared Radiometer type 2 and panchromatic data obtained by the Panchromatic Remote-sensing Instrument for Stereo Mapping sensors, both on board the advanced land observing satellite (ALOS). The ground cover was calculated using field survey data from 25 sub-sample plots and the vegetation indices were derived with 5 × 5 maximum filtering algorithm from ALOS data. R2 values were calculated for the normalized difference vegetation index (NDVI) and various soil-adjusted vegetation indices (SAVI) with soil-brightness-dependent correction factors equal to 1 and 0.5, a modified SAVI (MSAVI) and an optimized SAVI (OSAVI). R2 values for the NDVI, MSAVI, OSAVI, SAVI (1), and SAVI (0.5) were 0.566, 0.545, 0.619, 0.603, and 0.607, respectively. Total ratio vegetation index for arid and semi-arid regions based on spectral wavelengths of ALOS data with an R2 value 0.633 was considered. Results of the current study will be useful for forest inventories in arid and semi-arid regions in addition to assisting decision-making for natural resource managers.

Quad and compact multitemporal C-band PolSAR observations for crop characterization and monitoring

Monitoring crop condition using optical satellite indices has a legacy of several decades. Early warning of variances in crop production is vital in mitigating regional and global food insecurity. Adoption of optical vegetation indices for this purpose is widespread, yet cloud cover impedes the acquisition of these data. Although early research using scatterometers and aircraft hinted at the sensitivity of Synthetic Aperture Radar (SAR) responses to crop development, the implementation of satellite SAR observations in operational crop condition monitoring is limited. In the research presented here, volume-to-surface (V/S) scattering ratios derived from C-band RADARSAT-2 quad and simulated compact polarimetric (QP and CP) imagery are assessed for their potential to monitor crop growth. Both V/S ratios were strongly correlated with optical vegetation indices, including the widely adopted Normalized Difference and Soil Adjusted Vegetation Indices. The changes in the ratio of volume to surface scattering were correlated with variations in crop biomass. The results support the potential of a SAR scattering ratio for crop condition monitoring. In particular, encouraging results were reported for compact polarimetry, a mode that can be implemented to deliver broader swath coverage conducive to regional and national monitoring.

Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

Abstract. Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.

Reducing background effects in orchards through spectral vegetation index correction

Satellite remote sensing provides an alternative to time-consuming and labor intensive in situ measurements of biophysical variables in agricultural crops required for precision agriculture applications. In orchards, however, the spatial resolution causes mixtures of canopies and background (i.e. soil, grass and shadow), hampering the estimation of these biophysical variables. Furthermore, variable background mixtures obstruct meaningful comparisons between different orchard blocks, rows or within each row. Current correction methodologies use spectral differences between canopies and background, but struggle with a vegetated orchard floor. This background influence and the lack of a generic solution are addressed in this study.Firstly, the problem was demonstrated in a controlled environment for vegetation indices sensitive to chlorophyll content, water content and leaf area index. Afterwards, traditional background correction methods (i.e. soil-adjusted vegetation indices and signal unmixing) were compared to the proposed vegetation index correction. This correction was based on the mixing degree of each pixel (i.e. tree cover fraction) to rescale the vegetation indices accordingly and was applied to synthetic and WorldView-2 satellite imagery. Through the correction, the effect of background admixture for vegetation indices was reduced, and the estimation of biophysical variables was improved (ΔR2=0.2–0.31).

Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

In the past 40years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.

Developing Hyperspectral Vegetation Indices for Identifying Seagrass Species and Cover Classes

Pu, R.; Bell, S., and English, D., 2015. Developing hyperspectral vegetation indices for identifying seagrass species and cover classes.Seagrass habitats are characteristic features of shallow waters worldwide and provide a variety of ecosystem functions. To date, few studies have evaluated the efficiency of spectral vegetation indices (VIs) for characterizing aquatic plants. Here we evaluate the use of in situ hyperspectral data and hyperspectral VIs for distinguishing among seagrass species and levels of percentage submerged aquatic vegetation (%SAV) cover in a subtropical shallow water setting. Analysis procedures include (1) retrieving bottom reflectance, (2) calculating correlation matrices of VIs with %SAV cover and F value matrices from analysis of variance among species, (3) testing the difference of VIs between levels of %SAV cover and between species, and (4) discriminating levels of %SAV cover and species by using linear discriminant analysis (LDA) and classification and regression trees (CART) classifiers with selected VIs as input. The experimental results indicated that (1) the best VIs for discriminating the four levels of %SAV cover were simple ratio (SR) VI, normalized difference VI (NDVI), modified simple ratio VI, and NDVI × SR, whereas the best VIs for distinguishing the three seagrass species included the weighted difference VI, soil-adjusted VI (SAVI), SAVI × SR and transformed SAVI; (2) the optimal central wavelengths for constructing the best VIs were 460, 500, 610, 640, 660, and 690 nm with spectral regions ranging from 3 to 20 nm at band width 3 nm, most of which were associated with absorption bands by photosynthetic and other accessory pigments in the visible spectral range. Compared with LDA, CART performed better in discriminating the four levels of %SAV cover and identifying the three seagrass species.

Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages

Normalized Difference Vegetation Index and Ratio Vegetation Index obtained with the fixed band GreenSeeker active multispectral canopy sensor (GS-NDVI and GS-RVI) have been commonly used to non-destructively estimate crop growth parameters and support precision crop management, but their performance has been influenced by soil and/or water backgrounds at early crop growth stages and saturation effects at moderate to high biomass conditions. Our objective is to improve estimation of rice (Oryza sativa L.) aboveground biomass (AGB) with hyperspectral canopy sensing by identifying more optimal measurements using one or more strategies: (a) soil adjusted Vegetation Indices (VIs); (b) optimized narrow band RVI and NDVI; and (c) Optimum Multiple Narrow Band Reflectance (OMNBR) models based on raw reflectance, and its first and second derivatives (FDR and SDR).Six rice nitrogen (N) rate experiments were conducted in Jiansanjiang, Heilongjiang province of Northeast China from 2007 to 2009 to create different biomass conditions. Hyperspectral field data and AGB samples were collected at four growth stages from tillering through heading from both experimental and farmers’ fields. The results indicate that six-band OMNBR models (R2=0.44–0.73) explained 21–35% more AGB variability relative to the best performing fixed band RVI or NDVI at different growth stages. The FDR-based 6-band OMNBR models explained 4%, 6% and 8% more variability of AGB than raw reflectance-based 6-band OMNBR models at the stem elongation (R2=0.77), booting (R2=0.50), and heading stages (R2=0.57), respectively. The SDR-based 6-band OMNBR models made no further improvements, except for the stem elongation stage. Optimized RVI and NDVI for each growth stage (R2=0.34–0.69) explained 18–26% more variability in AGB than the best performing fixed band RVI or NDVI. The FDR- and SDR-based optimized VIs made no further improvements. These results were consistent across different sites and years. It is concluded that with suitable band combinations, optimized narrow band RVI or NDVI could significantly improve estimation of rice AGB at different growth stages, without the need of derivative analysis. Six-band OMNBR models can further improve the estimation of AGB over optimized 2-band VIs, with the best performance using SDR at the stem elongation stage and FDR at other growth stages.