Normalized Difference Water Index

The normalized difference vegetation index (INDVI) has been widely used for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel around 0.66 pm and a near-JR channel around 0.86 pm. The red channel is located in the strong chlorophyll absorption region, while the near-IR channel is located in the high reflectance plateau of vegetation canopies. The two channels sense very different depths through vegetation canopies. In this article, another index. namely, the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space. NDII is defined as (p(0.86 m) -p(l.24 um))/(p(0.86 ,um) + p(1.24 pm)), where p represents the radiance in reflectance units. Both the 0.86-pm and the 1.24-pmn channels are located in the high reflectance plateau of vegetation canopies. They sense similar depths through vegetation canopies. Absorption by vegetation liquid water near 0.86 um is negligible. Weak liquid absorption at 1.24 pm is present. Canopy scattering enhances the water absorption. As a result, NDWIJ is sensitive to changes in liquid water content of vegetation canopies. Atmospheric aerosol scattering effects in the 0.86-1.24 pm region are weak. NMDWI is less sensitive to atmospheric effects than ND Vi. X,'DW`I does not remove completely the background soil reflectance effects, similar to ND17I. Because the informiation about vegetation canopies contained in the 1.24-pn channel is very different from that contained in the red channel near 0.66 um, NDWI should be considered as an independent vegetation index. It is complementary to, not a substitutefor NDVI. Laboratory-measured reflectance spectra of stacked green leaves, and spectral imaging data acquired with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) overJasper Ridge in California and the High Plains in northern Colorado, are used to demonstrate the usefulness of ANDWI. Comparisons between NDWI and NDVI images are also given. © Elsevier Science Inc., 1996 REMOTE SENS. ENVIRON. 5:257-266 (1996) (©)Elsevier Science Inc., 1996 655 Axeuole of tie Americas. Next ork N I(01 0 0)0)34-4257 96 / $15.00 P1 S)034-4257(96)00067-3

Statistical and radiative-transfer physically based studies have previously demonstrated the relationship between leaf water content and leaf-level reflectance in the near-infrared spectral region. The successful scaling up of such methods to the canopy level requires modeling the effect of canopy structure and viewing geometry on reflectance bands and optical indices used for estimation of water content, such as normalized difference water index (NDWI), simple ratio water index (SRWI) and plant water index (PWI). This study conducts a radiative transfer simulation, linking leaf and canopy models, to study the effects of leaf structure, dry matter content, leaf area index (LAI), and the viewing geometry, on the estimation of leaf equivalent water thickness from canopy-level reflectance. The applicability of radiative transfer model inversion methods to MODIS is studied, investigating its spectral capability for water content estimation. A modeling study is conducted, simulating leaf and canopy MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions. A field sampling campaign to assess the investigated simulation methods was undertaken for analysis of leaf water content from leaf samples in 10 study sites of chaparral vegetation in California, USA, between March and September 2000. MODIS reflectance data were processed from the same period for equivalent water thickness estimation by model inversion linking the PROSPECT leaf model and SAILH canopy reflectance model. MODIS reflectance data, viewing geometry values, and LAI were used as inputs in the model inversion for estimation of leaf equivalent water thickness, dry matter, and leaf structure. Results showed good correlation between the time series of MODIS-estimated equivalent water thickness and ground measured leaf fuel moisture (LFM) content (r 2 = 0.7), demonstrating that these inversion methods could potentially be used for global monitoring of leaf water content in vegetation.

on water thickness. The relationship between WI and the canopy RWC signifi-Infrared Imaging Spectrometer over Point Dume (Los cantly improved when only data from plots with green Angeles County, CA, USA) were used to assess the ability vegetation cover Ͼ70% were considered (r 2 ϭ0.88, pϽ of hyperspectral reflectance data to estimate canopy Rel-0.001). All the indices studied had an important strucative Water Content (RWC) at the landscape level. The tural component (as indicated by the strong correlation study was performed on 23 vegetation stands comprised with NDVI), yet only the indices WI and NDWI addiof three characteristic chaparral plant communities, with tionally responded to water content. These results indicontrasting phenological stages and canopy cover. Sevcate that the WI and NDWI are sensitive to variations eral estimates of water content based on the near-infrain canopy relative water content at the landscape scale. red (NIR; reflectance indices and water thickness derived ©Elsevier Science Inc., 2000 from reflectance and radiance data) and shortwave infrared (SWIR) water absorption bands were compared to measurements of vegetation structure and water content INTRODUCTION made on the ground. The Water Index (WI) and Normalized Difference Water Index (NDWI), reflectance indices

Measurements of spring phenological dates in boreal regions using NDVI can be affected by snowmelt. This impacts the analysis of interannual variations in phenology and the estimates of annual carbon fluxes. For these two objectives, snowmelt effect must be removed from the phenological detection. We propose a methodology for determining the date of onset of greening in the 1982 -2004 period using SPOT-VEGETATION (VGT) and NOAA Advanced Very High Resolution Radiometer (AVHRR) data. From 1998 onwards, the date of onset of greening is taken as the date at which the Normalized Difference Water Index (NDWI), calculated from SPOT-VGT near and short-wave infrared bands, starts increasing. This index decreases with snowmelt but increases with vegetation greening. For the 1982 -2001 period, the date of onset of greening is the date at which AVHRR-NDVI equals a pixel specific threshold (PST), determined using the results of the NDWI method in the years common to the two datasets. The methods are validated using in situ measurements of the dates of leaf appearance. RMSE of 6.7 and 7.8 days, respectively, is found using NDWI-VGT and PST-NOAA methodologies, and the difference between the two methodologies in the common years is small. Very importantly, the dates are not biased. The interannual variations of the 23-year spring phenology dataset on the study area in northern Eurasia are analysed. In average over the study area, an advance of 8 days and a delay of 3.6 days are, respectively, found over the periods 1982 -1991 and 1993 -2004. These results confirm and complete previous studies about the greening trend, remove the uncertainty due to snow, and may improve carbon budget calculations. D

Poyang Lake is a seasonal lake, exchanging water with the lower branch of the Yangtze River. During the spring and summer flooding season it inundates a large area while in the winter it shrinks considerably, creating a large tract of marshland for wild migratory birds. A better knowledge of the water coverage duration and the beginning and ending dates for the vast range of marshlands surrounding the lake is important for the measurement, modelling and management of marshland ecosystems. In addition, the abundance of a special type of snail (Oncomelania hupensis), the intermediate host of parasite schistosome (Schistosoma japonicum) in this region, is also heavily dependent on the water coverage information. However, there is no accurate digital elevation model (DEM) for the lake bottom and the inundated marshland, nor is there sufficient water level information over this area. In this study, we assess the feasibility of the use of multitemporal Landsat images for mapping the spatialtemporal change of Poyang Lake water body and the temporal process of water inundation of marshlands. Eight cloud-free Landsat Thematic Mapper images taken during a period of one year were used in this study. We used the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) methods to map water bodies. We then examined the annual spatial-temporal change of the Poyang Lake water body. Finally we attempted to obtain the duration of water inundation of marshlands based on the temporal sequence of water extent determined from the Landsat images. The results showed that although the images can be used to capture the snapshots of water coverage in this area, they are insufficient to provide accurate estimation of the spatial-temporal process of water inundation over the marshlands through linear interpolation.

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 (10 1 to 10 4 m). This study compares the relative utility of high-resolution (1.5 m) aircraft and coarserresolution (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 À2 , 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. D

1] The evaluation of the relationship between satellitederived vegetation indices (normalized difference vegetation index and normalized difference water index) and soil moisture improves our understanding of how these indices respond to soil moisture fluctuations. Soil moisture deficits are ultimately tied to drought stress on plants. The diverse terrain and climate of Oklahoma, the extensive soil moisture network of the Oklahoma Mesonet, and satellitederived indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) provided an opportunity to study correlations between soil moisture and vegetation indices over the 2002 -2006 growing seasons. Results showed that the correlation between both indices and the fractional water index (FWI) was highly dependent on land cover heterogeneity and soil type. Sites surrounded by relatively homogeneous vegetation cover with silt loam soils had the highest correlation between the FWI and both vegetation-related indices (r$0.73), while sites with heterogeneous vegetation cover and loam soils had the lowest correlation (r$0.22). Citation:

Ebinur Lake is located in a typical arid region in the north-west of China. It is an area with the lowest elevation in the Junggar Basin in the Province of Xinjiang. Recent monitoring indicates that the lake surface area has increased. To obtain a continuous record of the change in lake area, a radiometric analysis of SPOT/ VEGETATION (VGT) imagery was carried out based on methodology developed for regional lake area mapping. Two indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were selected to identify the water body of Ebinur Lake. The indices are calculated based on the spectral reflectances in the red and near infrared bands of VGT sensor. If the NDVI is less than a critical value (0) and if the NDWI is larger than a critical value (0), the pixel is flagged as a water body. Validation indicates that the methodology to identify water bodies based on multi-spectral VGT data is applicable in our study area achieving an overall accuracy of 91.4%. Independent monitoring results elicit that the lake surface area was at its lowest in 1998. The yearly average surface area is about 503 km 2 . The lake area increased to 603 km 2 during 1999. In the period 1999-2001 the area changes are marginal. A large area increase occurred from 2001 to 2002 till the lake area reached a surface area of 791 km 2 . The lake area peaks to 903 km 2 in 2003 and subsequently decreased to areas of 847 km 2 in 2004 and 746 km 2 in 2005. Similar area change dynamics are observed when applying the remote sensing based technique. Seasonally, the typical dynamics elicit a larger surface area in spring and winter and a smaller one during summer.

Methods to accurately estimate the biophysical and biochemical properties of vegetation are a major research objective of remote sensing. We assess the capability of the MODIS satellite sensor to measure canopy water content and evaluate its relationship to ecosystem exchange (NEE) for an evergreen forest canopy. A time-series of three vegetation indexes were derived from MODIS data, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII), which were compared to physically based estimates of equivalent water thickness (EWT) from the airborne AVIRIS hyperspectral instrument over a temperate conifer forest in southwestern Washington. After cross-calibration of the imagery, water indexes derived from MODIS showed good agreement with AVIRIS EWT, while the NDVI was insensitive to water content variation. Three years of NEE data from eddy covariance measurements at the Wind River AmeriFlux tower were compared with the time series of MODIS indexes, which show seasonal water content has similar trajectory with NEE. In contrast, the MODIS NDVI time series did not yield a good relationship with NEE. This study demonstrates the potential to use MODIS water indexes for spatial and temporal NEE estimation at regional and global scales in appropriate ecosystems.

Imaging spectrometry has the potential to provide improved discrimination of crop types and better estimates of crop yield. Here we investigate the potential of Hyperion to discriminate three Brazilian soybean varieties and to evaluate the relationship between grain yield and 17 narrow-band vegetation indices. Hyperion analysis focused on two datasets acquired from opposite off-nadir viewing directions but similar solar geometry: one acquired on 08 February 2005 (forward scattering) and the other on 14 January 2006 (back scattering). In 2005, the soybean canopies were observed by Hyperion at later reproductive stages than in 2006. Additional Hyperion datasets were not available due to cloud cover. To further examine the impact of viewing geometry within the same season, Hyperion data were complemented by 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) images (bands 1 and 2) acquired in consecutive days (05-06 February 2005) with opposite viewing geometries (−42°and + 44°, respectively). MODIS data analysis was used to keep reproductive stage as a constant factor while isolating the impact of viewing geometry. For discrimination purposes, multiple discriminant analysis (MDA) was applied over each dataset using surface reflectance values as input variables and a stepwise procedure for band selection. All possible Hyperion band ratios and the 17 narrow-band vegetation indices with soybean grain yield were evaluated across years through Pearson's correlation coefficients and linear regression. MODIS-derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) were evaluated within the same growing season. Results showed that: (1) the three soybean varieties were discriminated with highest accuracy in the back scattering direction, as deduced from MDA classification results from Hyperion and MODIS data; (2) the highest correlation between Hyperion vegetation indices and soybean yield was observed for the Normalized Difference Water Index (NDWI) (r = +0.74) in the back scattering direction and this result was consistent with band ratio analysis; (3) higher Hyperion correlation results were observed in the back scattering direction when compared to the forward scattering image. For the same reproductive stage, stronger shadowing effects were observed over the MODIS red band in the forward scattering direction producing lower and lesser variable reflectance for the sensor. As a result, the relationship between MODIS-derived NDVI and soybean yield improved from the forward (r of +0.21) to the back scattering view (r of +0.60). The same trend was observed for SR that increased from + 0.22 to +0.58.

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity ( = 0 78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satelliteand meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.

Remote sensing methods for locating and monitoring temporary ponds over large areas in arid lands were tested on a study site in Northern Senegal. Three main results are presented, validated with field data and intended to highlight different spectral, spatial and temporal characteristics of the methods: 1) Among several water indices tested, two Middle Infra-Red-based indices (MNDWI -Modified Difference Water Index and NDWI 2 -Normalized Difference Water Index) are found to be most efficient; 2) an objective method is given prescribing the necessary sensor spatial resolution in terms of minimal detected pond area;

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multiresolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990 m and 90 m resolutions, respectively. Secondly, the relationship between the 990 m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990 m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90 m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90 m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90 m data (R 2 = 0.709 and RMSE = 2.702 K).

1] Reliable monitoring of the leaf area index (LAI) is required to further understand the carbon, water, and energy cycles of forests. In this study, we proposed a new satellite-based method to estimate the overstory LAI (LAI o ) separately from the understory LAI (LAI u ) for larch forests covering eastern Siberia. We modeled forest scenes representative of larch forest structure, with particular consideration of the typical clumped shoot structure of larch. Three-dimensional radiative transfer simulations were then conducted under various forest conditions to establish the relationships between LAI o and seasonal increases in the normalized difference water index after leaf appearance. Model-based sensitivity analyses indicated a maximum error of up to 26% under known noise levels. Averaged at the continental scale, total LAI from our estimates, the CYCLOPES version 3.1, and the Moderate Resolution Imaging Spectroradiometer MOD15 Collection 5 (main algorithm) showed similar ranges in summer. However, spatial pattern of LAI was slightly different, with smoother variability for CYCLOPES LAI. Our LAI and CYLOPES effective LAI reproduced a realistic seasonal variation with exact timing of spring increase in LAI o . The main drawbacks of MOD15 Collection 5 were unrealistically strong temporal variability, and the fact that LAI began to increase earlier than the overstory leaf appearance date. Overall, the results show that our new method is a good alternative to MOD15 Collection 5 and CYCLOPES, as it provides separate estimates of LAI o and LAI u and true LAI instead of effective LAI. Citation: Kobayashi, H., N. Delbart, R. Suzuki, and K. Kushida (2010), A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest,

The spatial distribution of saturated areas is an important consideration in numerous applications, such as water resource planning or sighting of management practices. However, in humid well vegetated climates where runoff is produced by saturation excess processes on hydrologically active areas (HAA) the delineation of these areas can 5 be difficult and time consuming. Much of the non-point source pollution in these watersheds originates from these HAAs. Thus, a technique that can simply and reliably predict these areas would be a powerful tool for scientists and watershed managers tasked with implementing practices to improve water quality. Remotely sensed data is a source of spatial information and could be used to identify HAAs, should a proper 10 technique be developed. The objective of this study is to develop a methodology to determine the spatial variability of saturated areas using a temporal sequence of remotely sensed images. The Normalized Difference Water Index (NDWI) was derived from medium resolution LANDSAT 7 ETM+ imagery collected over seven months in the Town Brook watershed in the Catskill Mountains of New York State and used to 15 characterize the areas that were susceptible to saturation. We found that within a single landcover type, saturated areas were characterized by the soil surface water content when the vegetation was dormant and leaf water content of vegetation during the growing season. The resulting HAA map agreed well with both observed and spatially distributed computer simulated saturated areas. This methodology appears promising 20 for delineating saturated areas in the landscape. 25 best management practices in agricultural watersheds. Remote sensing techniques 1664 HESSD

The spectral vegetation index (ρ NIR − ρ SWIR )/(ρ NIR + ρ SWIR ), where ρ NIR and ρ SWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI) and normalized burn ratio (NBR). After reviewing each term's definition, associated sensors and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2-1.3, 1.55-1.75 or 2.05-2.45 µm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the term's popularity and the 'rule of priority' in scientific nomenclature, NDWI, NDII and NBR, each corresponding to the three SWIR regions, are more preferable terms.

Following the severe drought in 1999–2000 there was a widespread outbreak of oak decline in the Ozark Highlands. Over 400,000ha of dead and dying oak trees were observed by the USDA Forest Service in this region. Although oak forests that are dead can be easily interpreted from air photos or classified from satellite images, it is difficult to detect dying trees that are still green but will die back or recover in the following years. In this study, we applied a normalized difference water index (NDWI) to map the continuous forest dynamics related to oak decline. The Landsat TM image in 1992 and the ETM+ image in 2000 were processed to calculate the differential NDWI which revealed moisture variation primarily caused by the drought and the associated red oak borers. A simple thresholding method was used to map oak dying back, recovery and non-change areas in the study area. The died-back areas were extracted from the modified land use/land cover maps created by the Missouri Resource...

The main aim of this research is to highlight the environment change indicators during the last 20 years in a representative area of the southern part of Iraq (Basrah Province was taken as a case) to understand the main causes which led to widespread environment degradation phenomena using a 1:250000 mapping scale. Remote sensing and GIS’s software were used to

We examined the relationships between two satellite-derived vegetation indices and foliar δ15N values obtained from dominant canopy species in a set of tree islands located in the Everglades National Park in South Florida, USA. These tree islands constitute important nutrient hotspots in an otherwise P-limited wetland environment. Foliar δ15N values obtained from a previous study of 17 tree islands in both slough (perennially wet) and prairie (seasonally wet) locations served as a proxy of P availability at the stand level. We utilized five cloud-free SPOT 4 multispectral images (20 m spatial resolution) from different times of the seasonal cycle to derive two atmospherically corrected vegetation indices: the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), averaged for each tree island. NDWI, which incorporates a shortwave infrared (SWIR) band that provides information on leaf water content, showed consistently higher linear fits with island foliar δ15N values than did NDVI. In addition, NDWI showed greater variation throughout the seasonal cycle than did NDVI, and was significantly correlated with average water stage, which suggests that the SWIR band captures important information on seasonally variable water status. Tree islands in slough locations showed higher NDWI than prairie islands during the dry season, which is consistent with higher levels of transpiration and nutrient harvesting and accumulation for perennially wet locations. Overall, the results suggest that water availability is closely related to P availability in subtropical tree islands, and that NDWI may provide a robust indicator of community-level water and nutrient status.►We establish a novel link between remote sensed vegetation indices and stable isotope analysis. ►We support the transpiration driven nutrient accumulation hypothesis of Everglades tree islands. ►We show NDWI is a more sensitive index than NDVI for water and nutrient content in tree island plants.

In this study, spectral indices were calculated from single date HyMap (3 m; 126 bands), Hyperion (30 m; 242 bands), ASTER (15/30 m; 9 bands), and a time series of MODIS nadir BRDF-adjusted reflectance (NBAR; 1 km, 7 bands) for a study area surrounding the Tumbarumba flux tower site in eastern Australia. The study involved: a) the calculation of a range of physiologically-based vegetation indices from ASTER, HyMap, Hyperion and MOD43B NBAR imagery over the flux tower site; b) comparison across scales between HyMap, Hyperion and MODIS for the normalized difference water index (NDWI) and the Red-Green ratio; c) analysis of relationships between tower-based flux and light use efficiency (LUE) measurements and seasonal and climatic constraints on growth; and d) examination of relationships between fluxes, LUE and time series of NDVI, NDWI and Red-Green ratio. Strong seasonal patterns of variation were observed in NDWI and Red/Green ratio from MODIS NBAR. Correlations between fine (3 and 30 m) and coarse (1 km) scale indices for a small region around the flux tower site were moderately good for Red/Green ratio, but poor for NDWI. Hymap NDWI values for the understorey canopy were much lower than values for the tree canopy. MODIS NDWI was negatively correlated with CO 2 fluxes during warm and cool seasons. The correlation indicated that surface reflectance, affected by a spectrally bright grassland understorey canopy, was decoupled from growth of trees with access to deep soil moisture. The application of physiologically-based indices at earth observation scale requires careful attention to applicability of band configurations, contribution of vegetation components to reflectance signals, mechanistic relationships between biochemical processes and spectral indexes, and incorporation of ancillary information into any analysis.

Wildfire hazard is a worldwide problem with enormous environmental, economic, and social costs. Better methods are needed to assess risk to more effectively manage natural resources. Current models need spatially explicit, timely, information about surface conditions. Meeting this need requires development of remotely sensed model inputs. Critical remotely sensed parameters needed for wildfire hazard assessment include surface reflectance, canopy water content, species composition and fuel state. Semiarid shrublands have been difficult to assess and monitor using multispectral satellite data because their spectral bands and wavelength locations are not sensitive to these input parameters, however, such inputs have been demonstrated using AVIRIS data. We compare near simultaneous acquisition of Hyperion (12/06/01) and AVIRIS (14/06/01) data for retrieval of surface reflectance. For both instruments, field spectra were acquired to support reflectance retrieval. Hyperion and AVIRIS spectra are similar in shape and albedo but SNR is 5 times higher in AVIRIS. Fuel condition was assessed using the endmember fractions from spectral mixture analysis, with Hyperion and AVIRIS providing similar fractions. Canopy water content was compared using the 1200 nm liquid water band and two water indices, the Water Index and the Normalized Difference Water Index (NDWI). Results show poor Hyperion performance relative to AVIRIS.

The objective in this study was to develop proper vegetation indices for prediction of soil irrigation demanding under vegetation covering conditions. The four tested winter wheat varieties were under four different irrigation treatment levels at two different growth stages, one at booting stage and another at grain filling stage, the winter wheat canopy and bare soil reflected spectrum, foliar and soil moisture condition were tested in the field at an interval of 7 days from erecting stage to ripening stage. The combined spectral indices of photochemical reflectance index (PRI) and normalized difference water index (NDWI) were chosen in this study. Regression equations between the ration of NPRI to NDWI and soil water content(SWC) were found to be most appropriate for assessing soil water condition under vegetation covering, and relationship between the ration of PRI to NDWI and soil irrigation demanding conditions were also studied. It indicated that soil irrigation demanding condition under vegetation covering conditions could be evaluated by a pragmatic approach for canopy level estimation with proper combined vegetation index.

Normalized Difference Water Index (NDWI) and MODIS Normalized Difference Vegetation Index (NDVI) showed a relationship with annual rainfall and soil moisture. A very significant relationship found in polynomial relationship between annual rainfall with NDVI and MODIS NDVI (r2 = 0.830 and 0.983). Higher rainfall was associated with higher NDVI value for both, surface reflectance imagery and MODIS NDVI. This study found no significant relationship in in situ measurement between soil moisture content and mean NDWI. This study suggests the insignificant was associated with insufficient in-situ data. Whereas annual rainfall and mean NDWI for long term period was r2 = 0.349 which indicate moderate relationship. Further study is require to investigate the NDWI value for long term period and in situ measurements in order to provide insufficient in situ analysis data.

This work proposes an automatic technique to be applied to water body segmentation in images acquired with low spectral resolution sensor as the newest digital photogrammetric cameras (DMC, HRSC-AX and ADS40) and satellite systems like CBERS and IKONOS, which have only four spectral bands, in the visible spectrum. To achieve that, two indices are used: NDVI - Normalized Difference Vegetation

Preliminary results from an ongoing project to study the impact of off-nadir hyperspectral data acquisition on the relationships between narrow-band vegetation indices and soybean yield are presented. Narrow-band vegetation indices may perform better than conventional broadband indices to estimate biophysical parameters of vegetation and crop yield. Off-nadir Hyperion/EO-1 images were collected in the back scattering direction on January 14, 2006 over a farm located in the Mato Grosso state showing initial soybean reproductive stages. Pearson's correlation coefficients and the regression relationships of all possible Hyperion band ratios and 17 narrow-band vegetation indices with soybean grain yield were then analyzed for three selected varieties (Monsoy 9010, Tabarana and Perdiz). Results showed that the best indices to estimate soybean yield were mainly associated with leaf water content, which were consistent with the largest r values observed for near infrared/near infrared band ratios (e.g., 1290/770 nm). Statistically significant Pearson's correlation coefficients were observed for the Normalized Difference Water Index (NDWI) (r = +0.74), the Water Band Index (WBI) (r = +0.47) and the Carotenoid Reflectance Index (CRI) (r = +0.53). Due to the single off-nadir Hyperion imagery acquisition in the backscattering direction, further research is still necessary to confirm the role played by NDWI to predict soybean yield at early reproductive stages and to establish the influence of viewing geometry on the correlation results.

Drought affects all human activities and ecosystems. Nearly 40 percent of the world's population inhabit Drylands, and they depend on agriculture for their food, security and livelihoods. Among the remote sensing indices developed, the Land Surface General Drought Index (LSGDI) was recently proposed. This paper proposes an improved model of LSGDI to face the issue of drought in semi-arid and arid regions. The experiment was conducted for the Maga's floodplain, in North-Cameroon. The method uses satellite images of Landsat, corresponding to the middle and the end of the dry season. A Vegetation Moisture Index (VMI) and a Normalized Difference Soil Drought Index (NDSoDI) are both developed. On an orthogonal plan, their projections give a drought line that expresses the improved LSGDI (LSGDI2) as the root sum square of the NDSoDI and the VMI. The LSGDI2 results are ranged in [0.09-0.14] interval, which is used to define the threshold and ease the qualifiers for drought classes. The visual patterns easily match the sandy areas of the original Landsat images with the highest values, while the vegetation and water areas match the lowest values. Compared with the LSGDI and Second Modified Perpendicular drought Index (MPDI1), the new index reflectance values are higher. Finally, although LSGDI2 curve's evolution follows the NDSoDI one at 94%, the new spectral index values depends on the both components, helping to map highest values of drought and moisture in Maga's floodplain, for a sustainable rice culture expansion.

The Prairie Pothole Region (PPR), covered with thousands of shallow ponds known as potholes, is a large wetland area in central North America. The PPR wetlands are valuable because of the ecosystem services they provide, including water balance and flood mediation, habitat for migratory birds and waterfowl production, and carbon sequestration. During the last century, impacts of intense land use