Shortwave Infrared Reflectance Research Articles

Satellite Detection of Water Stress Effects on Terrestrial Latent Heat Flux With MODIS Shortwave Infrared Reflectance Data

AbstractThe MODerate‐resolution Imaging Spectroradiometer (MODIS) provides spatially contiguous measurements of terrestrial biophysical variables, which can be used to estimate the terrestrial latent heat flux (LE). MODIS‐derived shortwave infrared reflectance (SWIR) metrics (SWIRs) are sensitive to the soil moisture and vegetation water stress. In this study, we used the MODIS‐derived SWIRs with eddy covariance flux measurements obtained from 25 flux tower sites representing 10 different land cover types within China to evaluate the sensitivity of SWIRs to ground‐measured evaporation fraction and LE. The water constraint metrics determined using the MODIS‐derived SWIR generally corresponded better with the ground‐measured evaporation fraction values than those obtained without using SWIR. The MODIS‐derived SWIRs were used as proxies for the soil and vegetation water supply constraints in a revised Priestley‐Taylor algorithm to estimate the terrestrial LE. The estimated LE using the MODIS‐derived SWIRs generally corresponded well with the ground‐measured LE (0.56 ≤ R2 ≤ 0.97) for most of the flux tower sites. Regional algorithm sensitivity analysis using the MODIS‐derived SWIRs as water supply proxies demonstrated that water limitations reduce LE by more than 53% over China, and the atmospheric vapor pressure deficit and relative humidity are not sufficient to characterize both the atmosphere demand and water supply for LE estimation. Our results demonstrate the potential of using MODIS‐derived SWIRs to characterize soil and vegetation water supply factors for determining LE, where the relatively high spatial and temporal resolutions (500 m and daily) are closer to the scale of the eddy covariance ground measurements.

Mapping Crop Residue and Tillage Intensity Using WorldView-3 Satellite Shortwave Infrared Residue Indices

Crop residues serve many important functions in agricultural conservation including preserving soil moisture, building soil organic carbon, and preventing erosion. Percent crop residue cover on a field surface reflects the outcome of tillage intensity and crop management practices. Previous studies using proximal hyperspectral remote sensing have demonstrated accurate measurement of percent residue cover using residue indices that characterize cellulose and lignin absorption features found between 2100 nm and 2300 nm in the shortwave infrared (SWIR) region of the electromagnetic spectrum. The 2014 launch of the WorldView-3 (WV3) satellite has now provided a space-borne platform for the collection of narrow band SWIR reflectance imagery capable of measuring these cellulose and lignin absorption features. In this study, WorldView-3 SWIR imagery (14 May 2015) was acquired over farmland on the Eastern Shore of Chesapeake Bay (Maryland, USA), was converted to surface reflectance, and eight different SWIR reflectance indices were calculated. On-farm photographic sampling was used to measure percent residue cover at a total of 174 locations in 10 agricultural fields, ranging from plow-till to continuous no-till management, and these in situ measurements were used to develop percent residue cover prediction models from the SWIR indices using both polynomial and linear least squares regressions. Analysis was limited to agricultural fields with minimal green vegetation (Normalized Difference Vegetation Index < 0.3) due to expected interference of vegetation with the SWIR indices. In the resulting residue prediction models, spectrally narrow residue indices including the Shortwave Infrared Normalized Difference Residue Index (SINDRI) and the Lignin Cellulose Absorption Index (LCA) were determined to be more accurate than spectrally broad Landsat-compatible indices such as the Normalized Difference Tillage Index (NDTI), as determined by respective R2 values of 0.94, 0.92, and 0.84 and respective residual mean squared errors (RMSE) of 7.15, 8.40, and 12.00. Additionally, SINDRI and LCA were more resistant to interference from low levels of green vegetation. The model with the highest correlation (2nd order polynomial SINDRI, R2 = 0.94) was used to convert the SWIR imagery into a map of crop residue cover for non-vegetated agricultural fields throughout the imagery extent, describing the distribution of tillage intensity within the farm landscape. WorldView-3 satellite imagery provides spectrally narrow SWIR reflectance measurements that show utility for a robust mapping of crop residue cover.

Retrieval of leaf water content from remotely sensed data using a vegetation index model constructed with shortwave infrared reflectances

ABSTRACTThe timely and accurate estimation of leaf water content (LWC) is of great practical significance for monitoring the state of vegetation growth and forecasting crop yield. As the spectral capabilities of terrestrial spectroscopy instruments and high-spatial-resolution satellite sensors steadily increase, people pay more and more attentions to the application potential for the spectral characteristics of the plant in shortwave infrared (SWIR) domain. The major purpose of this article was to investigate the relationship between leaf spectral reflectance and LWC in SWIR. A normalized vegetation index model constructed with SWIR reflectances centred at 1725 and 2200 nm yielded a high overall performance compared to the previous normalized difference water index (NDWI). The Leaf Optical Properties Experiment 1993 data set was introduced to validate the LWC estimated with the proposed model. The results showed that the accuracy of LWC estimation from this index can reach 0.0046 g cm−2, which is better than that from traditional NDWI with root mean square error equalled to 0.0104 g cm−2. The effects of band width and random noise on the LWC estimation from these two indices were also analysed and the results showed that LWC retrieved from both indices were insensitive to band width choices and random noise variations.

High‐Resolution Shortwave Infrared Imaging of Water Infiltration into Dry Soil

Core Ideas SWIR imaging for quantification of soil moisture dynamics is introduced. High spatiotemporal resolution images yield accurate soil moisture profile. Flux density profiles during water infiltration were quantified with this method. The new technique opens potential avenues for soil hydraulic properties estimation. A novel proximal sensing framework for high‐resolution soil water content profile retrieval under laboratory conditions has been developed. Constant‐head upward‐flow experiments were conducted for a number of soils that cover a wide textural range. The soils were packed into quartz Hele–Shaw cells, and the profile was imaged at high temporal frequency with a shortwave infrared (SWIR) camera in the 900‐ to 1700‐nm electromagnetic domain during upward infiltration of water. The SWIR reflectance recorded for each spatial pixel was converted to soil water content with a recently developed linear physical model. Because of the linearity of the model, its parameters were assumed to be identical at both the pixel and column scales, and this allowed simple self‐calibration during the experiment. The obtained water content profiles were in good agreement with soil water content data measured independently with a recently developed time domain reflectometry array with 1‐cm depth resolution. In addition, the accuracy of the soil water content profiles was verified based on the water mass balance. The high‐spatiotemporal‐resolution SWIR reflectance‐derived water content profiles allow calculation of water flux densities, which provides a potential new avenue for the rapid estimation of soil hydraulic properties and processes via inverse numerical or analytical modeling.

Updating the Grassland Vegetation Inventory Using Change Vector Analysis and Functionally-Based Vegetation Indices

The Grassland Vegetation Inventory (GVI), which represents a comprehensive biophysical, anthropogenic, and land-use inventory of grasslands in Alberta, is widely used as a baseline for grassland conditions. An up-to-date GVI is essential for understanding grassland changes and for planning management or conservation actions on grasslands. In this study, a hybrid change detection method is proposed that incorporates change vector analysis and a set of vegetation indices (VIs) measuring different vegetation attributes for mapping the conversion of native grassland to cultivated agriculture, and ultimately to update the GVI based on multiseasonal and multiyear Landsat images. Vegetation indices that contribute significantly to differentiation between existing native grassland and land recently converted from native grassland to cultivated cropland were identified by using stepwise regression analyses and were used as inputs for mapping the conversion between 2006 and 2011 or 2015. The results showed that land conversion can be detected using a single image acquired during the growing season, but that the accuracy of identification is affected by the date of image collection and the nature of the VIs used. The greatest accuracy in detecting land conversion between 2006 and 2011 was achieved using the difference in VI between years (dVI) for the Shortwave Infrared Reflectance 3/2 Ratio (SWIR32) and the Enhanced Vegetation Difference Index (EVI) derived from July imagery (accuracy = 95.2 %; Kappa = 0.86). The same combination of SWIR32 and EVI was also effective, although with lower accuracy (accuracy = 86.0 %; Kappa = 0.64) when tested on a larger geographical area and for detecting land use change between 2006 and 2015. The method proposed here could be applied to detect the land cover conversion in other grassland regions, although the optimal VIs and image acquisition date may need to be modified depending on the type of land use activities implemented in each region.

Hyperspectral logging of middle Cambrian marine sediments with hydrocarbon prospectivity: a case study from the southern Georgina Basin, northern Australia

ABSTRACTThe Georgina Basin is a Neoproterozoic–Paleozoic basin that spans parts of the Northern Territory and Queensland in northern Australia. The basin is prospective for petroleum, phosphate and base metals (copper, lead and zinc). The Dulcie and Toko synclines in the southern part of the basin are prospective for petroleum, where a thick Cambro-Ordovician succession of marine carbonates hosts several source rocks and associated oil and gas shows. The key source rock units occur within the middle Cambrian Narpa Group, including both the Thorntonia Limestone (Series 2 and 3) and the Arthur Creek Formation (Series 3). The base of the Arthur Creek Formation is characterised by organic-rich ‘hot’ shales (associated with a prominent gamma spike in well logs) that have been targeted by petroleum explorers for both conventional and unconventional oil and gas. For this study, hyperspectral logging data collected by HyLogger™ instruments were evaluated from 13 wells in the southern Georgina Basin, including petroleum, mineral and stratigraphic wells. Formation boundaries are commonly (but not always) characterised by distinctive changes in mineralogy, as determined by spectral and X-ray diffraction data. Key source rock units in the southern Georgina Basin were characterised and mapped in terms of their mineralogy, and other spectral properties (e.g. Short-Wave Infrared (SWIR) reflectance and spectral contrast). Interpretation of the hyperspectral data alongside wireline log data supports the differentiation of two successions within the Arthur Creek Formation that are each characterised by basal organic-rich shales, previously distinguished on the basis of biostratigraphic and well-log data. The older succession in the Dulcie Syncline is spectrally characterised as being quartz and carbonate dominated. The younger succession, distributed across the eastern part of the Dulcie Syncline and fully across the Toko Syncline, is spectrally characterised as quartz and carbonate dominated, with variable white-mica contributions. Key associations are observed between the HyLogger mineralogy and geophysical-log data. Peaks in the gamma log intensity in the middle Cambrian sediments commonly correspond to elevated measured total organic carbon contents, decreased carbonate contribution, SWIR reflectance and spectral contrast, and relatively increased proportions of white micas and quartz. This study demonstrates that HyLogging data can provide an improved understanding of the sedimentological, mineralogical and diagenetic characteristics, as well as associated spatial heterogeneity, of prospective hydrocarbon formations in sedimentary basins.

Delineation of Debris-Covered Glaciers Based on a Combination of Geomorphometric Parameters and a TIR/NIR/SWIR Band Ratio

Accurate mapping of glaciers is important for the management of water resources, studying glacier mass-balance, and monitoring glacier health. However, many glaciers are partially covered by debris of varying thicknesses, and these debris-covered glacier areas are difficult to distinguish clearly from the adjacent valley rock using optical remote sensing data due to their similar reflectance in the visible-to-near-infrared (VNIR) wavelength region. For better detection of the debris-covered glacier margin, a new method was proposed here which considers glacier terrain features (concave and convex landscapes) as well as VNIR reflectance, shortwave-infrared reflectance (SWIR), and thermal infrared (TIR) information. The proposed method uses several geomorphometric parameters (slope, plan, and profile curvature) and a Landsat Thematic Mapper (TM) band ratio image TM TIR/(TM NIR/TM SWIR) for mapping the Yengisogat glacier in the Shaksgam valley of China. The final debris-covered glacier boundary is generated by thresholding the band ratio image and overlaying it onto the result derived from cluster analysis performed using plan, profile curvature, and combined with mean of slope information. Evaluation of the final debris-covered glacier outline was carried out through comparison with GIS data from several reference data sets, and the final Yengisogat glacier outline had a difference of less than 1% in area compared to the reference data sets.

A SWIR based algorithm to retrieve total suspended matter in extremely turbid waters

In ocean colour remote sensing, the use of Near Infra Red (NIR) spectral bands for the retrieval of Total Suspended Matter (TSM) concentration in turbid and highly turbid waters has proven to be successful. In extremely turbid waters (TSM>100mgL−1) however, these bands are less sensitive to increases in TSM. Here it is proposed to use Short Wave Infra Red (SWIR) spectral bands between 1000 and 1300nm for these extreme cases. This SWIR spectral region is subdivided into two regions, SWIR-I (1000nm to 1200nm) and SWIR-II (1200nm to 1300nm) which correspond to local minima in the pure water absorption spectrum. For both spectral regions the water reflectance signal was measured in situ with an ASD spectrometer in three different extremely turbid estuarine sites: Scheldt (Belgium), Gironde (France), and Río de la Plata (Argentina), along with the TSM concentration. A measurable water reflectance was observed for all sites in SWIR-I, while in the SWIR-II region the signal was not significant compared to the Signal-to-Noise Ratio (SNR) of current Ocean Colour (OC) sensors. For the spectral band at 1020nm (present in Ocean and Land Colour Instrument — OLCI, onboard Sentinel-3) and at 1071nm, an empirical single band TSM algorithm is defined which is valid for both the Gironde and Scheldt estuarine sites. This means that a single algorithm can be applied for both sites without expensive recalibration. The relationship between TSM and SWIR reflectance at 1020 and 1071nm is linear and did not show any saturation for the concentrations measured here (up to 1400mgL−1), while saturation was observed for the NIR wavelengths, as expected. Hence, for extremely turbid waters it is advised to switch from NIR to SWIR-I wavelengths to estimate TSM concentration. This was demonstrated for an airborne hyperspectral dataset (Airborne Prism Experiment, APEX) from the Gironde estuary having several spectral bands in the SWIR-I. The empirical single band SWIR TSM algorithm was applied to the atmospherically corrected scene providing a TSM concentration map of the Gironde from mouth to more upstream with concentrations expected in this region ranging from a few to several hundreds mgL−1. These results, i.e. the existence of a single relationship for the Scheldt and Gironde, not showing any decrease of sensitivity, highlights the importance of having SWIR bands in future ocean colour sensors for studying extremely turbid rivers, coastal areas and estuaries in the world. A further implication of these results is that there is a TSM limit for application of atmospheric correction algorithms which assume zero SWIR marine reflectance. That limit is defined here as function of wavelength and sensor noise level.

Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy

We used imaging spectroscopy to perform a top-down mesoscale analysis of tropical tree species richness across a bioclimatic gradient in Panama. The expressed precipitation gradient from the wet Caribbean side to the dry Pacific side makes Panama an excellent study area for performing a mesoscale assessment of climate effects on tropical tree species richness. Spatial patterns in local spectral variability (expressed as the coefficient of variation) and spectral similarity (expressed as the spectral similarity index) were used as proxies for species area curves and species distance decay curves. Our analysis revealed significant spectral changes along the precipitation gradient. Highest spectral diversity was observed for moist forest sites while lowest diversity was observed for the driest forest sites. Most of the spectral variation came from changes in the visible (VIS) and shortwave-infrared (SWIR) reflectance. Variation in the VIS was significantly higher for the dry compared to the moist and wet forests, while the opposite was true for the NIR and SWIR reflectance. Our spectral mesoscale analysis extends previous results suggesting that niche differentiation with respect to soil water availability is a direct determinant of both local- and regional-scale distributions of tropical trees. A next step would be to test the accuracy and scalability of our results with lower spatial resolution spectrometer data, simulating the observing conditions that will be achieved with future satellite missions such as the European Union's EnMap and NASA's HyspIRI missions.

Retrieval of leaf water content spanning the visible to thermal infrared spectra

The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390–14.0μm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R2) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR–SWIR), the most accurate spectral index yielded R2 of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R2 was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R2=0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R2=0.96, RMSE=4.74% and RMSE cross validation RMSECV=6.17%) followed by VNIR–SWIR reflectance spectra (R2=0.91, RMSE=6.90% and RMSECV=7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R2=0.67, RMSE=13.27% and RMSECV=16.39%). This study demonstrated that the mid infrared (MIR) and shortwave infrared (SWIR) domains were the most sensitive spectral region for the retrieval of leaf water content.

Relation between Seasonally Detrended Shortwave Infrared Reflectance Data and Land Surface Moisture in Semi‑Arid Sahel

In the Sudano-Sahelian areas of Africa droughts can have serious impacts on natural resources, and therefore land surface moisture is an important factor. Insufficient conventional sites for monitoring land surface moisture make the use of Earth Observation data for this purpose a key issue. In this study we explored the potential of using reflectance data in the Red, Near Infrared (NIR), and Shortwave Infrared (SWIR) spectral regions for detecting short term variations in land surface moisture in the Sahel, by analyzing data from three test sites and observations from the geostationary Meteosat Second Generation (MSG) satellite. We focused on responses in surface reflectance to soil- and surface moisture for bare soil and early to mid- growing season. A method for implementing detrended time series of the Shortwave Infrared Water Stress Index (SIWSI) is examined for detecting variations in vegetation moisture status, and is compared to detrended time series of the Normalized Difference Vegetation Index (NDVI). It was found that when plant available water is low, the SIWSI anomalies increase over time, while the NDVI anomalies decrease over time, but less systematically. Therefore SIWSI may carry important complementary information to NDVI in terms of vegetation water status, and can provide this information with the unique combination of temporal and spatial resolution from optical geostationary observations over Sahel. However, the relation between SIWSI anomalies and periods of water stress were not found to be sufficiently robust to be used for water stress detection.

Estimation of Herbaceous Fuel Moisture Content Using Vegetation Indices and Land Surface Temperature from MODIS Data

The monitoring of herbaceous fuel moisture content is a crucial activity in order to assess savanna fire risks. Faced with the difficulty of managing wide areas of vegetated surfaces, remote sensing appears an attractive alternative for terrestrial measurements because of its advantages related to temporal resolution and spatial coverage. Earth observation (EO)-based vegetation indices (VIs) and the ratio between Normalized Difference Vegetation Index (NDVI) and surface temperature (ST) were used for assessment of herbaceous fuel moisture content estimates and validated against herbaceous data collected in 2010 at three open savanna sites located in Senegal, West Africa. EO-based estimates of water content were more consistent with the use of VI as compared to the ratio NDVI/ST. Different VIs based on near-infrared (NIR) and shortwave infrared (SWIR) reflectance were tested and a consistent relationship was found between field measurements of leaf equivalent water thickness (EWT) from all test sites and Normalized Difference Infrared Index (NDII), Global Vegetation Moisture Index (GVMI) and Moisture Stress Index (MSI). Also, strong relationships were found between fuel moisture content (FMC) and VIs for the sites separately; however, they were weaker for the pooled data. The correlations between EWT/FMC and VIs were found to decrease progressively as the woody cover increased. Although these results suggest that NIR and SWIR reflectance can be used for the estimation of herbaceous water content, additional validation from an increased number of study sites is necessary to study the robustness of such indices for a larger variety of savanna vegetation types.

Retrieval of the aerosol direct radiative effect over clouds from spaceborne spectrometry

The solar radiative absorption by an aerosol layer above clouds is quantified using passive satellite spectrometry from the ultraviolet (UV) to the shortwave infrared (SWIR). UV‐absorbing aerosols have a strong signature that can be detected using UV reflectance measurements, even when above clouds. Since the aerosol extinction optical thickness decreases rapidly with increasing wavelength for biomass burning aerosols, the properties of the clouds below the aerosol layer can be retrieved in the SWIR, where aerosol extinction optical thickness is sufficiently small. Using radiative transfer computations, the contribution of the clouds to the reflected radiation can be modeled for the entire solar spectrum. In this way, cloud and aerosol effects can be separated for a scene with aerosols above clouds. Aerosol microphysical assumptions and retrievals are avoided by modeling only the pure (aerosol‐free) cloud spectra. An algorithm was developed using the spaceborne spectrometer Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). The aerosol direct radiative effect (DRE) over clouds over the South Atlantic Ocean west of Africa, averaged through August 2006 was found to be 23 ± 8 Wm−2 with a mean variation over the region in this month of 22 Wm−2. The largest aerosol DRE over clouds found in that month was 132 ± 8 Wm−2. The algorithm can be applied to any instrument, or a combination of instruments, that measures UV, visible and SWIR reflectances at the top of the atmosphere (TOA) simultaneously.

Estimating fraction of photosynthetically active radiation of corn with vegetation indices and neural network from hyperspectral data

The fraction of photosynthetically active radiation (FPAR) is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China, the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed, and the FPAR estimation performances using vegetation index (VI) and neural network (NN) methods with different two-band-combination hyperspectral reflectance were investigated. The results indicated that the corn-canopy FPAR retained almost a constant value in an entire day. The negative correlations between FPAR and visible and shortwave infrared reflectance (SWIR) bands are stronger than the positive correlations between FPAR and near-infrared band reflectance (NIR). For the six VIs, the normalized difference vegetation index (NDVI) and simple ratio (SR) performed best for estimating corn FPAR (the maximum R2 of 0.8849 and 0.8852, respectively). However, the NN method estimated results (the maximum R2 is 0.9417) were obviously better than all of the VIs. For NN method, the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands; for VIs, however, they were from the SWIR and NIR bands. As for both the methods, the SWIR band performed exceptionally well for corn FPAR estimation. This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content, which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation (APAR), and makes further impact on corn-canopy FPAR.

Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data

Soil characteristics provide important support for understanding transformations that occur in environmental systems. Physical characteristics and chemical compositions of soils controlled by pedogenetic processes, climatic changes and land use imply different types of environmental transformations. Reflectance spectroscopy is an alternative soil mapping technique that uses spectral absorption features between visible (VIS) and short-wave infrared (SWIR) wavelengths (0.3–2.5 μm) for determining soil mineralogy. Soil analysis by means of reflectance spectroscopy and orbital optical sensors have provided favorable results in mapping transformation processes in environmental systems, particularly in arid and semiarid climates in extra-tropical terrains. In the case of inter-tropical environments, these methods cannot be readily applied due to local factors such as lack of exposed regolith, high amounts of soil moisture and the presence of dense vegetation. This study uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and reflectance spectroscopy data to map mineral components of soils covering a part of the state of São Paulo, Brazil, which could be linked to key aspects of environmental transformations in this tropical area (e.g., climate change, shifts in agriculture fronts, ph, and soil characteristics). We collected forty-two (42) soil samples at a depth of 0–20 cm, considering that this superficial layer corresponds to the highest correlation with soil properties detected by the ASTER sensor. These samples were measured using a FieldSpec FR spectrometer, and the derived spectra were interpreted for mineral composition. Interpretation was supported by X-ray diffraction analysis on the same samples. The spectral signatures were re-sampled to ASTER VNIR (AST1-4: 0.52–0.86 μm) and SWIR (AST5-9: 1.60–2.43 μm) spectral bandwidths and validated by comparing reflectance spectra of field samples with those extracted from atmospherically corrected and calibrated ASTER pixels. The agreement between spectral signatures measured from soil samples and those derived from ASTER imagery pixels proved plausible, with R 2 correlation values ranging from 0.6493 to 0.7886. This signifies that diagnostic spectral features of key minerals in tropical soils can be mapped at the spectral resolution of 9-band ASTER VNIR through SWIR reflectance. We used these spectral signatures as end-members in hyperspectral routine classifications adapted for use with ASTER data. Results proved possible the identification and remote mapping of minerals such as kaolinite, montmorillonite and gibbsite, as well as the distinction between iron-rich and iron-poor soils.

Detecting Canopy Water Status Using Shortwave Infrared Reflectance Data From Polar Orbiting and Geostationary Platforms

Various canopy water status estimates have been developed from recent advances in Earth Observation (EO) technology. A promising methodology is based on the sensitivity of shortwave infrared (SWIR) reflectance to variations in leaf water content. This study explores the potential of SWIR-based canopy water status detection from geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data as compared to polar orbiting environmental satellite (POES)-based moderate resolution imaging spectroradiometer (MODIS) data. The EO-based SWIR water stress index (SIWSI) is evaluated against in situ measured canopy water content indicators at a semi-arid grassland savanna site in Senegal 2008. Daily SIWSI from both MODIS and SEVIRI data show an overall inverse relation to Normalized Difference Vegetation Index (NDVI) throughout the growing season. SIWSI observations from SEVIRI are furthermore sensitive to short-term variations of in situ measured plant water content indicators when aboveground biomass increases from 500 to 900 gm-2 (LAI ≈ 1-2). MODIS SIWSI observations in contrast do not covary with in situ measured moisture indicators. Spatio-temporal trend analyses performed on SEVIRI SIWSI during a dry period within the growing season support these findings. These results suggest that the combined advantage of an improved temporal resolution and a fixed viewing angle potentially makes the SEVIRI sensor an interesting complementary data source to POES data for SWIR-based canopy water status and stress monitoring in a semi-arid environment.

Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product

For more than 20 years the Normalized Difference Vegetation Index (NDVI) has been widely used to monitor vegetation stress. It takes advantage of the differential reflection of green vegetation in the visible and near-infrared (NIR) portions of the spectrum and provides information on the vegetation condition. The Land Surface Water Index (LSWI) uses the shortwave infrared (SWIR) and the NIR regions of the electromagnetic spectrum. There is strong light absorption by liquid water in the SWIR, and the LSWI is known to be sensitive to the total amount of liquid water in vegetation and its soil background. In this study we investigated the LSWI characteristics relative to conventional NDVI-based drought assessment, particularly in the early crop season. The area chosen for the study was the state of Andhra Pradesh located in the Indian peninsular. The Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index (VI) product from the Aqua satellite was used in the study. The analysis was carried out for the years 2002 (deficit year) and 2005 (normal year) using the NDVI from the MODIS VI product and deriving the LSWI using the NIR and SWIR reflectance available with the MODIS VI product. The response of LSWI to rainfall, observed in the rate of increase in LSWI in the subsequent fortnights, shows that this index could be used to monitor the increase in soil and vegetation liquid water content, especially during the early part of the season. The relationship between the cumulative rainfall and the current fortnight LSWI is stronger in the low rainfall region (<500 mm), while the one-fortnight lagged LSWI had a stronger relationship in the high rainfall region (>500 mm). The relationship between LSWI and the cumulative rainfall for the entire state was mixed in 2002 and 2005. The strength of the relationship was weak in the high rainfall region. When LSWI was regressed directly with NDVI for three LSWI ranges, it was observed that the NDVI with the one-fortnight lag had a strong relationship with the LSWI in most of the categories.

The combined use of reflectance, emissivity and elevation Aster/Terra data for tropical soil studies

Reflectance, emissivity and elevation data of the sensor ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)/Terra were used to characterize soil composition variations according to the toposequence position. Normalized data of SWIR (shortwave infrared) reflectance and TIR (thermal infrared) emissivity, coupled to a soil-fraction image from a spectral mixture model, were evaluated to separate bare soils from nonphotosynthetic vegetation. Regression relationships of some soil properties with reflectance and emissivity data were then applied on the exposed soil pixels. The resulting estimated values were plotted on the ASTER-derived digital elevation model. Results showed that the SWIR bands 5 and 6 and the TIR bands 10 and 14 measured the clay mineral absorption band and the quartz emissivity feature, respectively. These bands improved also the discrimination between nonphotosynthetic vegetation and soils. Despite the differences in pixel size and field sampling size, some soil properties were correlated with reflectance (R² of 0.65 for Al2O3 in band 6; 0.61 for Fe2O3 in band 3) and emissivity (R² of 0.65 for total sand fraction in the 10/14 band ratio). The combined use of reflectance, emissivity and elevation data revealed variations in soil composition with topography in specific parts of the landscape. From higher to lower slope positions, a general decrease in Al2O3 and increase in total sand fraction was observed, due to the prevalence of Rhodic Acrustox at the top and its gradual transition to Typic Acrustox at the bottom.

Detection of Ice and Mixed Ice–Water Pixels for MODIS Ocean Color Data Processing

Current data processing for deriving ocean color products from the Moderate Resolution Imaging Spectroradiometer (MODIS) has no specific ice and mixed ice-water pixel detection procedure. The near-infrared (NIR) reflectance threshold at the MODIS 869-nm band, which has been used to discriminate clear sky from clouds (cloud masking) for standard ocean color data processing, can eliminate most of the ice pixels. However, there are still many cases for which the ice and mixed ice-water pixels have been misidentified as ocean waters in current ocean color data processing, leading to errors in the MODIS-derived ocean color product (e.g., chlorophyll-a concentration). This is particularly true for most of the mixed ice-water cases. For atmospheric correction using the short-wave infrared (SWIR) method, which also uses SWIR reflectance for cloud masking, the problem of ice misidentification is even worse. In this paper, we describe a method for detection of ice and mixed ice-water pixels for MODIS ocean color data processing. Using the MODIS-derived normalized water-leaving radiances at 412, 555, and 859 nm, a scheme for ice and mixed ice-water detection has been developed and tested for producing MODIS global ocean color products. In fact, the proposed algorithm is a by-product calculated from the MODIS-derived normalized water-leaving radiance spectra data. Thus, the MODIS-derived ice surface radiance data can be used to study sea ice physical and optical properties. With the new ice detection scheme, pixels with ice and/or mixed ice-water can be discriminated, flagged, or masked out. The ice detection results are compared with the MODIS ice map product produced from the MODIS land discipline team, as well as the ice product data obtained from the NOAA National Ice Center. We show improved results from the new masking algorithm for the purpose of MODIS ocean color data processing, particularly for detection of mixed ice-water pixels.

Sub‐pixel reflectance unmixing in estimating vegetation water content and dry biomass of corn and soybeans cropland using normalized difference water index (NDWI) from satellites

Estimating vegetation cover, water content, and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modelling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often by the contamination of the remote sensing signal by the atmosphere and soil reflectance variations at the surface. This study used Landsat TM/ETM+ and MODIS data to investigate how sub‐pixel atmospheric and soil reflectance contamination can be removed from the remotely sensed vegetation growth signals. The sensitivity of spectral bands and vegetation indices to such contamination was evaluated. Combining the strengths of atmospheric models and empirical approaches, a hybrid atmospheric correction scheme was proposed. With simplicity, it can achieve reasonable accuracy in comparison with the 6S model. Insufficient vegetation coverage information and poor evaluation of fractional sub‐pixel bare soil reflectance are major difficulties in sub‐pixel soil reflectance unmixing. Vegetation coverage was estimated by the Normalized Difference Water Index (NDWI). Sub‐pixel soil reflectance was approximated from the nearest bare soil pixel. A linear reflectance mixture model was employed to unmix sub‐pixel soil reflectance from vegetation reflectance. Without sub‐pixel reflectance contamination, results demonstrate the true linkage between the growth of sub‐pixel vegetation and the corresponding change in satellite spectral signals. Results suggest that the sub‐pixel soil reflectance contamination is particularly high when vegetation coverage is low. After unmixing, the visible and shortwave infrared reflectances decrease and the near‐infrared reflectances increase. Vegetation water content and dry biomass were estimated using the unmixed vegetation indices. Superior to the NDVI and the other NDWIs, the SWIR (1650 nm) band‐based NDWI showed the best overall performance. The use of the NIR (1240 nm), which is a unique band of MODIS, was also discussed.