Normalized Difference Water Index

In the last decades, the detection of drought occurrences and assessment of its severity using satellite data are becoming popular in disaster, desertification, crop production, phenology, land cover change and climate change studies. To detect the drought effects on different vegetation types, many methodologies have been developed, mostly relying on the use of vegetation indices. This communication reports the first attempt to assess the capability of MODIS NDVI, Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) from 2000 to 2006 time-series to detect the 2005 drought in Amazonia. To reach this objective, monthly composites of the MOD13A2 product were generated from period. Then, monthly anomalies were calculated, considering anomalous values when lower than-1 standard deviation (sd) or higher than 1 sd. Rainfall data provided by the Tropical Rainfall Measuring Mission (TRMM) was also acquired for the same time-series with the objective of supporting the understanding of vegetation response with the precipitation. Water deficit data calculated based on the TRMM data were also used to guide the sampling scheme. A land cover map for South America updated with natural land cover changes detected by the Near Real Time Deforestation Detection Project (DETER) was used as a mask to avoid false anomalies in the Brazilian Amazon. In general, NDWI and EVI showed to be sensitive and consistent for the temporal series used. NDVI presented a high variability and though a difficult interpretation. Critical months in the NDWI and EVI series coincided with the months with higher water stress calculated based on the TRMM data. EVI also showed to detect changes in the canopy structure. These preliminary results suggest that this is a strong methodology to be used in the spatial analysis of the extent of the drought effects in the vegetation. Literfall data will be incorporate in this research for validation purposes.

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.

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

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).

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

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.

Lake Namak is a remnant of the Paratethys sea, which forms Lake Urmia, the Caspian Sea, and other water bodies in Iran. Besides being a small salt lake, the lake is located 790 meters above sea level and is fed by the Qom river. However, with the effect of drought in recent years, decreasing surface water and increasing salinity have caused the lake to dry up. In this study, the spatial-temporal changes of Lake Namak between the years 2001-2021 -in three decades-Calculated using Landsat 5-TM, Landsat 7-ETM+ and Landsat 8-OLI images. In this study, Normalized Differential Water Index (NDWI), Modified Difference Water Index (MNDWI), Water Ratio Index (WRI) and Automatic Water Extraction Index (AWEI), which allows surface water extraction from Landsat data, were examined. As a result, the changes in the water surface that occurred in the 20-year period were compared spatially (km²) and it was determined that the NDWI index, which has a relatively high accuracy rate, can be used as a more useful method in determining the surface water compared to other indexes.

Pre-monsoon and post-monsoon surface waterlogged areas were delineated using satellite remote sensing data for Muzaffarpur, Vaishali and Saran districts of North Bihar. Digital data of IRS-1C LISS-III sensor acquired on December 7, 1998 and April 6, 1999 were analyzed using digital image processing software-ERDAS Imagine 8.3.1. The surface waterlogged areas were delineated using modeling technique which is the most advanced and accurate method. Using the modeling technique, a pixel is classified as water if the digital number (DN) value of its Near Infra Red (NIR) band is less than the DN value of the Red band and the Green band, and the Normalized Difference Water Index (NDWI) is greater than or equal to 0.32. The pre-monsoon surface waterlogged areas are found to be 14.02, 23.61 and 9.61 km2 while the post-monsoon surface waterlogged areas are found to be 231.83, 118.19 and 176.06 km2 for Muzaffarpur, Vaishali and Saran districts, respectively. Also, land use/land cover maps were prepared.

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

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

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 7 5 4 -7 6 6 (N. Neeti). a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a g w a t 0378-3774/$ -see front matter #

In the present study, the multi-temporal satellite images of IRS P6 LISS III were used to map waterlogging dynamics over different seasons. An area of 594.36 km2 (6.75%) and 4.17 km2 (0.04%) was affected by surface waterlogging during pre and postmonsoon season, respectively. The average annual groundwater level fluctuations were calculated using 18 years (1990–2007) pre and postmonsoon groundwater level data to identify the areas which are under groundwater induced waterlogging conditions. The soil map clearly indicates that salinity and sodicity exhibit the highest severity and occur in areas with shallow groundwater levels. The hydrogeomorphical units mapped using IRS P6 LISS III satellite images are flood plain, alluvial plain, paleochannels, and oxbow lakes. The study revealed that 44.65% areas have very good to excellent groundwater resources. The litholog data clearly indicate an alternating sequence of clay and sand in which deep aquifers made up of coarse sand would be best suited for adequate water supply and good groundwater quality. The integrated study utilizing digital spatial data pertaining to waterlogging, soil salinity, water level fluctuation, and lithological variation proved that planning of any surface and subsurface water resources development activity should be taken up after assessments of said parameters.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 7 5 4 -7 6 6 (N. Neeti). a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a g w a t 0378-3774/$ -see front matter #

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

Due to the importance of coastline detection in coastal studies, different methods have been developed in recent decades in accordance with the evolution of measuring techniques such as remote sensing. This work proposes an automatic methodology with new water indexes to detect the coastline from different multispectral Landsat images; the methodology is applied to three Spanish deltas in the Mediterranean Sea. The new water indexes use surface reflectance rather than top-of-atmosphere reflectance from blue and shortwave infrared (SWIR 2) Landsat bands. A total of 621 sets of images were analyzed from three different Landsat sensors with a moderate spatial resolution of 30 m. Our proposal, which was compared to the most commonly used water indexes, showed outstanding performance in automatic detection of the coastline in 96% of the data analyzed, which also reached the minimum value of bias of −0.91 m and a standard deviation ranging from ±4.7 and ±7.29 m in some cases in contrast to the existing values. Bicubic interpolation was evaluated for a simple sub-pixel analysis to assess its capability in improving the accuracy of coastline extraction. Our methodology represents a step forward in automatic coastline detection that can be applied to micro-tidal coastal sites with different land covers using many multi-sensor satellite images.

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:

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.

In this study, we explored the potential of multi-temporal SPOT-4 VEGETATION (VGT) sensor data for characterization of temperate and boreal forests in Northeastern China. As the VGT sensor has a short-wave infrared (SWIR) band that is sensitive to vegetation, soil moisture and leaf water content, the Normalized Difference Water Index (NDWI) was calculated in addition to the Normalized Difference Vegetation Index (NDVI). A forest map of Northeast China was generated from an unsupervised classification of 25 10-day VGT composite data (NDVI and NDWI) over the period of March 11 -20, 1999 to November 11 -20, 1999. Seven different forest categories were distinguished from the 1-km spatial resolution VGT data. The VGT forest map was compared to estimates of forest area derived from Landsat 7 Enhanced Thematic Mapper (ETM+) images. There was a good agreement on spatial distribution and area of forest between the VGT product and the TM product, however, the VGT product provided additional information on forest type. Analysis of NDVI and NDWI over the plant growing season allows for the identification of distinct growth patterns between the different forest types. It is evident that VGT data can be used to provide timely and detailed forest maps with limited ancillary data needed. The VGT-derived forest maps could be very useful as input to biogeochemical models (particularly carbon cycle models) that require timely estimates of forest area and type. D

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 7 5 4 -7 6 6 (N. Neeti). a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a g w a t 0378-3774/$ -see front matter #

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.

a g r i c u l t u r a l w a t e r m a n a g e m e n t 9 5 ( 2 0 0 8 ) 7 5 4 -7 6 6 (N. Neeti). a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a g w a t 0378-3774/$ -see front matter #

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.

Wetlands are among the most productive ecosystems in the world and any alterations might lead to changes in their bio-physical, socioeconomic and climatic conditions. Wetland dynamics as an index of land use change were studied. Satellite remote sensing was utilized to understand the periodic and seasonal dynamics of Samaspur wetlands using Landsat and RESOURCESAT-1 temporal data. Index-based (i.e., Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI)) classification resulted in meaningful discrimination of wetland classes. Results indicate (i) effective water spread areas have increased to optimum capacity at 1990 due to the influence of Sharda canal, (ii) expansion of the agricultural area has led to reduction of the wetland buffer area, and (iii) increase in vegetation biomass due to pesticide-fertilizer runoff and sedimentation load. We also reiterate (i) free availability of the Landsat satellite data in public domain facilitating such monitoring studies and (ii) availability and utility of SWIR band information in wetland classification exercise. The study concludes that policy-driven measures have both long and short term impacts on land use and its natural wetland ecosystems; and the characterizing the later serves as indictor of the former and perhaps vice versa.

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.

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.

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.

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.

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;

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.

INFLUENCE OF DATA ACQUISITION GEOMETRY ON VEGETATION INDICES AND LAI ESTIMATES WITH MODIS, HYPERION AND PROSAIL SIMULATIONS FOR SOYBEAN ABSTRACT In order to reduce cloud cover effects on the acquisition of optical images, the temporal resolution of the satellites can be improved by using large swath width sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS acquires data over

Improvement in remote sensing techniques in spatial/spectral resolution strengthens their applicability for urban environmental study. Unfortunately, high spatial resolution imagery also increases internal variability in land cover units and can cause a 'salt-and-pepper' effect, resulting in decreased accuracy using pixel-based classification results. Region-based classification techniques, using an image object (IO) rather than a pixel as a classification unit, appear to hold promise as a method for overcoming this problem. Using IKONOS high spatial resolution imagery, we examined whether the IO technique could significantly improve classification accuracy compared to the pixel-based method when applied to urban land cover mapping in Tampa Bay, FL, USA. We further compared the performance of an artificial neural network (ANN) and a minimum distance classifier (MDC) in urban detailed land cover classification and evaluated whether the classification accuracy was affected by the number of extracted IO features. Our analysis methods included IKONOS image data calibration, data fusion with the pansharpening (PS) process, Hue-Intensity-Saturation (HIS) transferred indices and textural feature extraction, and feature selection using a stepwise discriminant analysis (SDA). The classification results were evaluated with visually interpreted data from high-resolution (0.3 m) digital aerial photographs. Our results indicate a statistically significant difference in classification accuracy between pixel-and object-based techniques; ANN outperforms MDC as an objectbased classifier; and the use of more features (27 vs. 9 features) increases the IO classification accuracy, although the increase is statistically significant for the MDC but not for the ANN.

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.

1] A five-year (2001 -2005) history of moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data was analyzed for grassland drought assessment within the central United States, specifically for the Flint Hills of Kansas and Oklahoma. Initial results show strong relationships among NDVI, NDWI, and drought conditions. During the summer over the Tallgrass Prairie National Preserve, the average NDVI and NDWI were consistently lower (NDVI < 0.5 and NDWI < 0.3) under drought conditions than under non-drought conditions (NDVI>0.6 and NDWI>0.4). NDWI values exhibited a quicker response to drought conditions than NDVI. Analysis revealed that combining information from visible, near infrared, and short wave infrared channels improved sensitivity to drought severity. The proposed normalized difference drought index (NDDI) had a stronger response to summer drought conditions than a simple difference between NDVI and NDWI, and is therefore a more sensitive indicator of drought in grasslands than NDVI alone. Citation:

A five-year (2001-2005) history of moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data was analyzed for grassland drought assessment within the central United States, specifically for the Flint Hills of Kansas and Oklahoma. Initial results show strong relationships among NDVI, NDWI, and drought conditions. During the summer over the Tallgrass Prairie National Preserve, the average NDVI and NDWI were consistently lower (NDVI < 0.5 and NDWI < 0.3) under drought conditions than under non-drought conditions (NDVI>0.6 and NDWI>0.4). NDWI values exhibited a quicker response to drought conditions than NDVI. Analysis revealed that combining information from visible, near infrared, and short wave infrared channels improved sensitivity to drought severity. The proposed normalized difference drought index (NDDI) had a stronger response to summer drought conditions than a simple difference between NDVI and NDWI, and is therefore a more sensitive indicator of drought in grasslands than NDVI alone.

Monitoring and understanding plant phenology is important in the context of studies of terrestrial productivity and global change. Vegetation phenology such as dates of onsets of greening up and leaf senescence have been determined by remote sensing using mainly the Normalized Difference Vegetation Index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, Normalized Difference Snow Index (NDSI) and Normalized Difference Water Index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In Spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the Fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of Abstract leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed.

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

Monitoring and understanding plant phenology is important in the context of studies of terrestrial productivity and global change. Vegetation phenology such as dates of onsets of greening up and leaf senescence have been determined by remote sensing using mainly the Normalized Difference Vegetation Index (NDVI). In boreal regions, the results suffer from significant uncertainties because of the effect of snow on NDVI. In this paper, SPOT VEGETATION S10 data over Siberia have been analysed to define a more appropriate method. The analysis of time series of NDVI, Normalized Difference Snow Index (NDSI) and Normalized Difference Water Index (NDWI), together with an analysis of in situ phenological records in Siberia, shows that the vegetation phenology can be detected using NDWI, with small effect of snow. In Spring, the date of onset of greening up is taken as the date at which NDWI starts increasing, since NDWI decreases with snowmelt and increases with greening up. In the Fall, the date of onset of leaf coloring is taken as the date at which NDWI starts decreasing, since NDWI decreases with senescence and increases with snow accumulation. The results are compared to the results obtained using NDVI-based methods, taking in situ phenological records as the reference. NDWI gives better estimations of the start of greening up than NDVI (reduced RMSE, bias and dispersions, and higher correlation), whereas it does not improve the determination of the start of Abstract leaf coloring. A map of greening up dates in central Siberia obtained from NDWI is shown for year 2002 and the reliability of the method is discussed.

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...

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.

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.

Due to the information gap between the VEGETATION sensors and Sentinel-3 mission, the Belgian state decided to build a small satellite, Project for Onboard Autonomy-Vegetation (PROBA-V), to ensure the continuity of the data record for vegetation studies. In this study, simulated PROBA-V data generated by the Landsat Thematic Mapper (TM) were used to evaluate the potential of this mission to assess winter wheat status. The root mean square error (RMSE) of PROBA-V&amp;amp;amp;amp;amp;amp;#x27;s leaf area index (LAI), which was generated using the ...