Leaf area and clumping indices for a boreal mixed-wood forest: lidar,hyperspectral, and Landsat models

The leaf area index (LAI) and the clumping index (CI) provide valuable insight into the spatial patterns of forest canopies, the canopy light regime and forest productivity. This study examines the spatial patterns of LAI and CI in a boreal mixed-wood forest, using extensive field measurements and remote sensing analysis. The objectives of this study are to: (1) examine the utility of airborne lidar (light detection and ranging) and hyperspectral data to model LAI and clumping indices; (2) compare these results to those found from commonly used Landsat vegetation indices (i.e. the normalized difference vegetation index (NDVI) and the simple ratio (SR)); (3) determine whether the fusion of lidar data with Landsat and/or hyperspectral data will improve the ability to model clumping and LAI; and (4) assess the relationships between clumping, LAI and canopy biochemistry.

Regression models to predict CI were much stronger than those for LAI at the site. Lidar was the single best predictor of CI (r ² > 0.8). Landsat NDVI and SR also had a moderately strong predictive performance for CI (r ² > 0.68 with simple linear and non-linear regression forms), suggesting that canopy clumping can be predicted operationally from satellite platforms, at least in boreal mixed-wood environments. Foliar biochemistry, specifically canopy chlorophyll, carotenoids, magnesium, phosphorus and nitrogen, was strongly related to the clumping index. Combined, these results suggest that Landsat models of clumping could provide insight into the spatial distribution of foliar biochemistry, and thereby photosynthetic capacity, for boreal mixed-wood canopies. LAI models were weak (r ² < 0.4) unless separate models were used for deciduous and coniferous plots. Coniferous LAI was easier to model than deciduous LAI (r ² > 0.8 for several indices). Deciduous models of LAI were weaker for all remote sensing indices (r ² < 0.67). There was a strong, linear relationship between foliar biochemistry and LAI for the deciduous plots. Overall, our results suggest that broadband satellite indices have strong predictive performance for clumping, but that airborne hyperspectral or lidar data are required to develop strong models of LAI at this boreal mixed-wood site.     

Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery

Microwave-based remote sensing algorithms for mapping soil moisture are sensitive to water contained in surface vegetation at moderate levels of canopy cover. Correction schemes require spatially distributed estimates of vegetation water content at scales comparable to that of the microwave sensor footprint (10¹ to 10⁴ m). This study compares the relative utility of high-resolution (1.5 m) aircraft and coarser-resolution (30 m) Landsat imagery in upscaling an extensive set of ground-based measurements of canopy biophysical properties collected during the Soil Moisture Experiment of 2002 (SMEX02) within the Walnut Creek Watershed. The upscaling was accomplished using expolinear relationships developed between spectral vegetation indices and measurements of leaf area index, canopy height, and vegetation water content. Of the various indices examined, a Normalized Difference Water Index (NDWI), derived from near- and shortwave-infrared reflectances, was found to be least susceptible to saturation at high levels of leaf area index. With the aircraft data set, which did not include a short-wave infrared water absorption band, the Optimized Soil Adjusted Vegetation Index (OSAVI) yielded best correlations with observations and highest saturation levels. At the observation scale (10 m), LAI was retrieved from both NDWI and OSAVI imagery with an accuracy of 0.6, vegetation water content at 0.7 kg m⁻², and canopy height to within 0.2 m. Both indices were used to estimate field-scale mean canopy properties and variability for each of the intensive soil-moisture-sampling sites within the watershed study area. Results regarding scale invariance over the SMEX02 study area in transformations from band reflectance and vegetation indices to canopy biophysical properties are also presented.     

Retrieval of Leaf area index and stress conditions for Sundarban mangroves using Sentinel-2 data

ABSTRACT

The potential of Sentinel-2 (S2) data in mapping Leaf area index (LAI) of mangroves having heterogeneous species composition, variable canopy density, and complex backgrounds was studied. Out of the three available near-infrared bands in S2, band-8 of 10 m spatial resolution was found to be the most suitable one for deriving the Normalized Difference Vegetation Index (NDVI) for mangroves. The LAI-NDVI relation did not accord apparently with the earlier reports and the underlying complex background effect was validated with Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data. It simulated spectral and spatial conditions of S2 by linear mixing of canopy and background that confirmed the effect of background contributions to the canopy reflectance decorrelating the NDVI from LAI. The compensation for diverse backgrounds was accomplished with optimum-scaled NDVI (scNDVI_m) obtained from the mean of scaled NDVIs derived with different backgrounds in the mangroves. LAI was well correlated with composite NDVI (NDVI_com), derived empirically from the most appropriate NDVI (NDVI_S2) and scNDVI_m where ground observation controlled the threshold arbitration in extracting the range of scNDVI_m. It was shown that an improved LAI estimate with a coefficient of determination (R²) of 0.69 and root-mean-square error (RMSE) of 0.02 could be obtained with NDVI_com. This method has the advantage of compensating the contaminations due to background reflectance. While the relation between LAI and NDVI_com was found to be consistent, the application of the same methodology in similar mangroves should be site-specific with ample ground observation. The fusion of NDVI and scNDVI obtained from S2 yields better LAI retrieval for mixed mangroves, such as that of Sundarban.     

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.     

Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations

Like other human-induced landcover changes, urbanization represents a response to specific economic, demographic, or environmental conditions. We use the Washington D.C. area as a case study to relate satellite-derived estimates of urban growth to these economic and demographic drivers. Using the Landsat data archive we have created a three epoch timeseries for urban growth for the period 1973-1996. This map is based on a NDVI-differencing approach for establishing urban change filtered with a landcover classification to minimize confusion with agriculture. Results show that the built-up area surrounding Washington DC has expanded at a rate of ～22km² per year during this period, with notably higher growth during the late-1980s. Comparisons with census data indicate that the physical growth of the urban plan, observable from space, can be reasonably correlated with regional and national economic patterns.     

A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery

Plant foliage density expressed as leaf area index (LAI) is used in many ecological, meteorological, and agronomic models, and as a means of quantifying crop spatial variability for precision farming. LAI retrieval using spectral vegetation indices (SVI) from optical remotely sensed data usually requires site-specific calibration values from the surface or the use of within-scene image information without surface calibrations to invert radiative transfer models. An evaluation of LAI retrieval methods was conducted using (1) empirical methods employing the normalized difference vegetation index (NDVI) and a new SVI that uses green wavelength reflectance, (2) a scaled NDVI approach that uses no calibration measurements, and (3) a hybrid approach that uses a neural network (NN) and a radiative transfer model without site-specific calibration measurements. While research has shown that under a variety of conditions NDVI is not optimal for LAI retrieval, its continued use for remote sensing applications and in analysis seeking to develop improved parameter retrieval algorithms based on NDVI suggests its value as a “benchmark” or standard against which other methods can be compared. Landsat-7 ETM+ data for July 1 and July 8 from the Soil Moisture EXperiment 2002 (SMEX02) field campaign in the Walnut Creek watershed south of Ames, IA, were used for the analysis. Sun photometer data collected from a site within the watershed were used to atmospherically correct the imagery to surface reflectance. LAI validation measurements of corn and soybeans were collected close to the dates of the Landsat-7 overpasses. Comparable results were obtained with the empirical SVI methods and the scaled SVI method within each date. The hybrid method, although promising, did not account for as much of the variability as the SVI methods. Higher atmospheric optical depths for July 8 leading to surface reflectance errors are believed to have resulted in overall poorer performance for this date. Use of SVIs employing green wavelengths, improved method for the definition of image minimum and maximum clusters used by the scaled NDVI method, and further development of a soil reflectance index used by the hybrid NN approach are warranted. More importantly, the results demonstrate that reasonable LAI estimates are possible using optical remote sensing methods without in situ, site-specific calibration measurements.     

A consistency analysis of surface reflectance and leaf area index retrieval from overlapping clear-sky Landsat ETM+ imagery

In this study, the consistency of systematic retrievals of surface reflectance and leaf area index was assessed using overlap regions in adjacent Landsat Enhanced Thematic Mapper-Plus (ETM+) scenes. Adjacent scenes were acquired within 7-25 days apart to minimize variations in the land surface reflectance between acquisition dates. Each Landsat ETM+ scene was independently geo-referenced and atmospherically corrected using a variety of standard approaches. Leaf area index (LAI) models were then applied to the surface reflectance data and the difference in LAI between overlapping scenes was evaluated. The results from this analysis show that systematic LAI retrieval from Landsat ETM+ imagery using a baseline atmospheric correction approach that assumes a constant aerosol optical depth equal to 0.06 is consistent to within ±0.61 LAI units. The average absolute difference in LAI retrieval over all 10 image pairs was 26% for a mean LAI of 2.05 and the maximum absolute difference over any one pair was 61% for a mean LAI of 1.13. When no atmospheric correction was performed on the data, the consistency in LAI retrieval was improved by 1%. When a scene-based dense, dark vegetation atmospheric correction algorithm was used, the LAI retrieval differences increased to 28% for a mean LAI of 2.32. This implies that a scene-based atmospheric correction procedure may improve the absolute accuracy of LAI retrieval without having a major impact on retrieval consistency. Such consistency trials provide insight into the current limits concerning surface reflectance and LAI retrieval from fine spatial resolution remote sensing imagery with respect to the variability in clear-sky atmospheric conditions.     

Exploring the temporal density of Landsat observations for cropland mapping: experiments from Egypt,Ethiopia, and South Africa

ABSTRACT

Accurate land-use/land-cover mapping based on remote-sensing images depends on clear and frequent observations. This study aimed to explore how many Landsat images were needed within a year and when they should be acquired, for cropland mapping in Africa. Three Landsat footprints in Egypt (Path/Row: 177/039, 127 images), Ethiopia (Path/Row: 168/054, 98 images), and South Africa (Path/Row: 170/078, 207 images) from 1984 to 2016 were used together with spectral indices and a 30-m digital elevation model in a random forest-based supervised classification. Detailed exploration was conducted into the number and temporal distribution of Landsat images required. Our results indicated that average cropland mapping accuracies for these three sites ranged from 81.17% to 87.59% (Egypt), 54.43% to 79.72% (Ethiopia), and 28.11% to 59.35% (South Africa) using different numbers of images within a year. The overall cropland accuracies were improved with an increase in available Landsat images within a year and reached a relatively stable stage when more than five images were acquired in all three sites. Growing season images played a key role in identifying cropland, accounting for a 13.22% average accuracy improvement compared with non-growing season images. Therefore, at least five images are recommended from a computational efficiency perspective, although fewer images, as low as two growing season images, can also achieve good results in specific regions.     

Potentiality of Landsat,SPOT and IRS satellite imagery,for recognition of salt affected soils in Indian Arid Zone

Landsat, SPOT and IRS data, black and white and false colour composite (FCC) imagery of the summer (April, May), rainfed crop season (October) and winter irrigated crop season (January, February) of Indian Arid Zone were interpreted for recognition or three types of salt affected soils, viz. (1) natural salt affected; slight, moderate and severe, (2) saline soils due to saline water irrigation, (3) sodic soils due to high residual sodium carbonate (RSC) water irrigation. These were field checked and supported by analytical data. The Landsat-MSS band 4 could only provide the overall extent of salinity. The moderate and severe natural salt affected soils were identified by Landsat-MSS band 2, Landsat-MSS and TM, IRS LISS-I and LISS-II and SPOT HRV2 data for April and January. But the differentiation between the saline and sodic soils was possible only by the use of multi-date imagery (October and January) and the clue provided by the cropping pattern. The potentiality of remote sensing data products for identification of the types and degree of salt affected soils is discussed.     

Bayesian maximum entropy data fusion of field-observed leaf area index (LAI) and Landsat Enhanced Thematic Mapper Plus-derived LAI

Accurate high-resolution leaf area index (LAI) reference maps are necessary for the validation of coarser-resolution satellite-derived LAI products. In this article, we propose an efficient method based on the Bayesian Maximum Entropy (BME) paradigm to combine field observations and Landsat Enhanced Thematic Mapper Plus (ETM+)-derived LAI surfaces in order to produce more accurate LAI reference maps. This method takes into account the uncertainties associated with field observations and with the regression relationship between ETM+-derived LAI and field measurements to perform a non-linear prediction of LAI, the variable of interest. In order to demonstrate the difference by soft data and hard data, we estimate the LAI reference maps by three BME interpolation methods, BME1, BME2, and BME3. BME1 and BME2 perform maximum estimation and mean estimation, respectively, by taking the ETM+-derived LAI as interval soft data and the field LAI measurements as hard data. BME3 is utilized when ETM+-derived LAI surfaces are processed as uniform probability soft data and field measurements are processed as Gaussian probability soft data. Three study sites are selected from the BigFoot project (NASA's Earth Observing System validation programme) (http://www.fsl.orst.edu/larse/bigfoot/index.html). In regard to the mean and standard deviation of LAI surfaces, standard deviation predicted by BME methods has lower values than that derived by ETM+. The mean value of the BME-predicted LAI, which takes into account the uncertainties of field measurements, is lower than that of ETM+-derived LAI at each study site. A comparison with field measurements shows that BME1, BME2, and BME3 have root mean square errors (RMSE) of 0.455, 0.485, and 0.517 and average biases of??0.017,??0.010, and??0.304, respectively. The RMSEs and biases of the predicted LAI surfaces are less when compared to the ETM+-derived LAI, which has the average RMSE and bias of 0.642 and??0.080. When the field measurements are processed as soft data, the predicted LAI by BME3 has more bias than those of the predictions by BME1 and BME2, but has less RMSE than that of the ETM+-derived LAI by 0.125. In summary, BME is capable of incorporating the spatial autocorrelation and the uncertainties in the field LAI measurements into the LAI surface estimation to produce a more accurate LAI surface with less RMSE in validation. The maximum estimation has relatively better accuracy than the mean estimation. The results indicate that the BME is a promising method for fusing point-scale and area-scale data.     

Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery

This paper compares the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to estimate the LST from four different seasons for the Twin Cities, Minnesota, metropolitan area. A map of percent impervious surface with a standard error of 7.95% was generated using a normalized spectral mixture analysis of July 2002 Landsat TM imagery. Our analysis indicates there is a strong linear relationship between LST and percent impervious surface for all seasons, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests percent impervious surface provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.     

Mapping shrubland biomass along Mediterranean climatic gradients: The synergy of rainfall-based and NDVI-based models

Two approaches to biomass mapping of shrublands across sub-humid and arid transition zones are integrated. The first generalizes relationships between biomass and precipitation from sites in the Mediterranean Basin, California, Namibia and Mongolia. The second represents existing Normalized Difference Vegetation Index (NDVI)-based models for biomass estimation on a regional scale. A new modified NDVI-based model is presented that uses relative rain availability as the ratio between the mean annual precipitation and the threshold rain level representing the transition from herbaceous growth to shrub dominance. While the data accounts for the actual vegetation cover, the relative rain parameter accounts for the potential biomass production. Implementation of the modified relative rain model with Landsat imagery of climatic gradients (the east-west gradient between the Judean Mountains and the Judean desert and the north-south gradient between the Judean Mountains and the Negev Desert) yielded realistic estimates of biomass in areas of high human disturbance to the natural ecosystems. These results support the possibility that the modified model can be used to map biomass across wide Mediterranean and desert-fringe ecosystems.     

Assessment of different models for bathymetry calculation using SPOT multispectral images in a high-turbidity area: the mouth of the Guadiana Estuary

Periodic calculation of coastal bathymetries can show the evolution of geomorphological features in active areas such as mesotidal estuary mouths. Bathymetries in shallow coastal areas have been addressed mainly by two technologies, lidar and optical remote sensing. Lidar provides good accuracy, but is an expensive technique, requiring planned flights for each region and dates of interest. Optical remote sensing acquires images periodically but its results are limited by water turbidity. Here we use a lidar bathymetry to compare different bathymetry computation methods using a SPOT optical image from a nearby date. Three statistical models (green-band, PCA correlations, and GLM) were applied to obtain mathematical expressions to estimate bathymetry from that image: all gave errors lower than 1 m in an area with depths ranging from 0 to 6 m. These algorithms were then applied to images from three different dates, correcting the effects caused by different tidal and atmospheric conditions. We show how this allows the study of morphological changes. We discuss the accuracy obtained with respect to the reference bathymetry (0.9 m on average, but less than 0.5 m in low-turbidity areas), the effects of the turbidity on our estimations, and compare both with previously published results. The results show that this approach is effective and allows identification of known features of coastal dynamics, and thus it would be an important step towards short-term bathymetry monitoring based on optical satellite remote sensing.     

Vegetation mapping of a tropical freshwater swamp in the Northern Territory,Australia: A comparison of aerial photography,Landsat TM and SPOT satellite imagery

The tropical wetland environments of northern Australia have ecological, social, cultural and economic values. Additionally, these areas are relatively pristine compared to the many other wetland environments in Australia, and around the world, that have been extensively altered by humans. However, as the remote northern coastline of Australia becomes more populated, environmental problems are beginning to emerge that highlight the need to manage the tropical wetland environments. Lack of information is currently considered to be a major factor restricting the effective management of many ecosystems and for the expansive wetlands of the Northern Territory, this is especially the case, as these areas are generally remote and inaccessible. Remote sensing is therefore an attractive technique for obtaining relevant information on variables such as land cover and vegetation status. In the current study, Landsat TM, SPOT (XS and PAN) and large-scale, true-colour aerial photography were evaluated for mapping the vegetation of a tropical freshwater swamp in Australia's Top End. Extensive ground truth data were obtained, using a helicopter survey method. Fourteen cover types were delineated from 1:15 000 air photos (enlarged to 1:5000 in an image processing system) using manual interpretation techniques, with 89% accuracy. This level of detail could not be extracted from any of the satellite image data sets, with only three broad land-cover types identified with accuracy above 80%. The Landsat TM and SPOT XS data provided similar results although superior accuracy was obtained from Landsat, where the additional spectral information appeared to compensate in part for the coarser spatial resolution. Two different classification algorithms produced similar results.     

Integrating Corine Land Cover data and Landsat TM for surface emissivity definition: application to the urban area of Athens,Greece

This study examines the potential of the combined use of the land cover/land use information provided by the Corine Land Cover (CLC) database with Landsat satellite data for the definition and quantitative correlation of emissivity with various land covers and land uses that describe a certain territory. Surface emissivity in the 10.5–12.5 µm wavelength range is derived using Landsat data and the Normalized Difference Vegetation Index Thresholds method (NDVI^THM), whereas mean emissivity values for selected urban/non‐urban land cover types are estimated by integrating the emissivity image with the land cover vector data. The method is applied to the greater Athens area, Greece, in order to estimate the emissivity of various land cover types found within the urban setting. Analysis of variance (ANOVA) indicates statistically significant differences in emissivity associated with different land cover types. Furthermore, statistical results demonstrate that the method is very effective and can provide emissivity values of different land cover types with good accuracy and therefore can quantitatively link emissivity with surface type.     

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and inter-annually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R² = 0.80 and R² = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages.     

Detecting and evaluating the influence of water depth,volume and altitude on the variations in the surface temperature of lakes using Landsat imagery

Abstract

The Landsat thermal band has been used to map the thermal pattern in three lakes of the English Lake District, Cumbria. The patterns arc clearly associated with the variations in the depth. The cause of the formation of this pattern is thought to be solar warming of water. The influence of water volume and altitude and the surface temperature and the relation among them were formalised in an equation involving 16 lakes.     

Assessing the information content of multiangle satellite data for mapping biomes: II. Theory

The insights gained from present land cover classification activities suggest integration of multiangle data into classification attempts for future progress. Land cover types that exhibit distinct signatures in the space of remote sensing data facilitate unambiguous identification of cover types. In this two-part series, we develop a theme for consistency among cover type definitions, uniqueness of their signatures, and physics of the remote sensing data. In the first part, Zhang et al.'s [Remote Sens. Environ., in press.] empirical arguments in support of the consistency principle were presented. This part provides a theoretical justification of the consistency requirements. Radiative transfer best explains the physics of the processes operative in the generation of the signal in the optical remote sensing data. Biome definitions given in terms of variables that this theory admits and the use of the transport equation to interpret biome signatures guarantee the consistency requirements. It is shown in this paper that three metrics of the biome angular signature in the spectral space—location, angular signature slope (ASSI), and length (ASLI) indices—are related to eigenvalues and eigenvectors of the transport equation. These variables allow a novel parameterization of canopy structure based on the partitioning of the incident radiation among canopy absorption, transmission, and reflection. Consistency between cover type definitions and uniqueness of their signatures with the physics of the remote sensing data is required not only to reduce ambiguity in land cover identification, but also to directly relate land cover type to biophysical and biogeochemical processes in vegetation canopies.     

Passive microwave observations of inundation area and the area/stage relation in the Amazon River floodplain

Inundation patterns in Amazon River floodplains are revealed by analysis of the 37 GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer on the Nimbus-7 satellite. Flooded area is estimated at monthly intervals for January 1979 through August 1987 using mixing models that account for the major landscape units with distinctive microwave emission characteristics. Results are presented separately for 12 longitudinal reaches along the Amazon River main stem in Brazil as well as for three major tributaries (the Jurua, Purus and Madeira rivers). The total area along the Amazon River main stem that was flooded (including both floodplain and open water) varied between 19000 and 91 000km2. The correlation between flooded area and river stage is used to develop a predictive relationship and reconstruct regional inundation patterns in the floodplain of the Amazon River main stem over the past 94 years of stage records (1903-1996). The mean flooded area along the Amazon River during this 94-year period was 46800km2, of which the openwater surfaces of river channels and floodplain lakes comprised about 20 700km2.     

A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat

The normalized difference vegetation index (NDVI) is a commonly used index for monitoring crop growth status. Previous studies have shown that the leaf area index (LAI) estimation based on NDVI is limited by saturation that occurs under conditions of relatively dense canopies (LAI > 2 m² m^–2). To reduce the saturation effect, we suggested new spectral indices through the spectral indices approach. The results suggested that the two-band normalized difference spectral index (NDSI = ((ρ_{940 –} ρ₇₃₀) /(ρ₉₄₀ + ρ₇₃₀))) resulted from the two-band spectral indices approach and the three-band modified normalized difference spectral index (mNDSI = ((ρ_{940 –} 0.8 × ρ₉₅₀) – ρ₇₃₀) /((ρ_{940 –} 0.8 × ρ₉₅₀) + ρ₇₃₀)) resulted from the three-band spectral indices approach, and they were able to mitigate saturation and improve the LAI prediction with a determination coefficient (R²) of 0.77 and 0.78, respectively. In the validation based on data from independent experiments, these new indices exhibited an accuracy with relative root mean square error (RRMSE) lower than 23.38% and bias higher than –0.40. These accuracies were significantly higher than those obtained with some existing indices with good performance in LAI estimation, such as the enhanced vegetation index (EVI) (RRMSE = 30.19%, bias = –0.34) and the modified triangular vegetation index 2 (MTVI2) (RRMSE = 29.30%, bias = –0.28), and the indices with the ability to mitigate the saturation, such as the wide dynamic range vegetation index (WDRVI) (RRMSE = 31.37%, bias = –0.54), the red-edge wide dynamic range vegetation index (red-edge WDRVI) (RRMSE = 26.34%, bias = –0.54), and the normalized difference red-edge index (NDRE) (RRMSE = 28.41%, bias = –0.56). Additionally, these new indices were more sensitive under moderate to high LAI conditions (between 2 and 8 m² m^–2). Between these two new developed spectral indices, there was no significant difference in the accuracy and sensitivity assessments. Considering the index structure and convenience in application, we demonstrated that the two-band spectral index NDSI((ρ_{940 –} ρ₇₃₀) /(ρ₉₄₀ + ρ₇₃₀)) is efficient in mitigating saturation and has considerable potential for estimating the LAI of canopies throughout the entire growing season of wheat (Triticum aestivum L.), whereas the three-band spectral index contributes lesser in the saturation mitigation provided the red-edge band has been contained.