Function Of Normalized Difference Vegetation Index Research Articles

Estimation of irrigated oats yield using spectral indices

Spectral indices such as normalized difference vegetation index (NDVI) and Falker chlorophyll index (FCI) are valuable tools for estimating crop yield. However, there remains a need to define the best phenological stages for the evaluation and identification of the indices' critical limits to achieve better agricultural management practices. We aimed to compare the level of accuracy in estimating grain and biomass yield of white oats (Avena sativa L.) as a function of NDVI and FCI at four phenological stages and to define the critical limits of the two indices. Five experimental treatments were performed in four replicates; each treatment received a different irrigation level: 11%, 31%, 60%, 87%, and 100% of crop evapotranspiration volume, respectively. The critical ranges of NDVI and FCI were set when the relative grain yield was between 90% and 110%.The mean NDVI and FCI values in each plot were determined at four phenological stages, which correlated to grain and biomass yield. Active sensors were used and the measured values were later used in regression analysis. The grain and biomass yield of white oats can be estimated with high accuracy. The NDVI presents greater accuracy of grain and biomass yield estimates of white oats than FCI. The critical ranges of the spectral indices for the “Average” class of oat grain yield as a function of the NDVI were 0.71-0.77, 0.66-0.73, and 0.63-0.71 for the phenological stages 8, 10, and 10.5.4, respectively, and of FCI were 58.4–60.2 and 58.2–61.3 for phenological stages 10 and 10.5.4, respectively.

Development of a remote sensing-based rice yield forecasting model

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

NDVI-Based Lacunarity Texture for Improving Identification of Torreya Using Object-Oriented Method

Normalized Difference Vegetation Index (NDVI) is a very useful feature for differentiating vegetation and non-vegetation in remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the vegetation landscapes, we proposed the lacunarity texture derived from NDVI to characterize the spatial patterns of vegetation landscapes concerning the “gappiness” or “emptiness” characteristics. The NDVI-based lacunarity texture was incorporated into object-oriented classification for improving the identification of vegetation categories, especially Torreya which was the targeted tree species in the present research. A three-level hierarchical network of image objects was defined and the proposed texture was integrated as potential sources of information in the rules base. A knowledge base of rules created by classifier C5.0 indicated that the texture could potentially be applied in object-oriented classification. It was found that the addition of such texture improved the identification of every vegetation category. The results demonstrated that the texture could characterize the spatial patterns of vegetation structures, which could be a promising approach for vegetation identification.

Application of Satellite Microwave Images in Estimating Snow Water Equivalent1

Abstract: Flood forecast and water resource management requires reliable estimates of snow pack properties [snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM/I) radiometer along with information on the Normalized Difference Vegetation Index (NDVI).The algorithm was developed and tested over 19 test sites characterized by different seasonal average snow depth and land cover type. Three spectral signatures derived from SSM/I data, namely T19V‐T37V (GTV), T19H‐T37H (GTH), and T22V‐T85V (SSI), were examined for correlation with the snow depth and SWE. To avoid melting snow conditions, we have used observations taken only during the period from December 1‐February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth/SWE and are most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the new technique was evaluated through its comparison with ground‐based measurements and with results of SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the National Weather Service. The proposed algorithm was found to be superior to previously developed global microwave SWE retrieval techniques.

A Wetness Index Using Terrain-Corrected Surface Temperature and Normalized Difference Vegetation Index Derived from Standard MODIS Products: An Evaluation of Its Use in a Humid Forest-Dominated Region of Eastern Canada.

In this paper we develop a method to estimate land-surface water content in amostly forest-dominated (humid) and topographically-varied region of eastern Canada. Theapproach is centered on a temperature-vegetation wetness index (TVWI) that uses standard 8-day MODIS-based image composites of land surface temperature (TS) and surface reflectanceas primary input. In an attempt to improve estimates of TVWI in high elevation areas, terrain-induced variations in TS are removed by applying grid, digital elevation model-basedcalculations of vertical atmospheric pressure to calculations of surface potential temperature(θS). Here, θS corrects TS to the temperature value to what it would be at mean sea level (i.e.,~101.3 kPa) in a neutral atmosphere. The vegetation component of the TVWI uses 8-daycomposites of surface reflectance in the calculation of normalized difference vegetation index(NDVI) values. TVWI and corresponding wet and dry edges are based on an interpretation ofscatterplots generated by plotting θS as a function of NDVI. A comparison of spatially-averaged field measurements of volumetric soil water content (VSWC) and TVWI for the 2003-2005 period revealed that variation with time to both was similar in magnitudes. Growing season, point mean measurements of VSWC and TVWI were 31.0% and 28.8% for 2003, 28.6% and 29.4% for 2004, and 40.0% and 38.4% for 2005, respectively. An evaluation of the long-term spatial distribution of land-surface wetness generated with the new θS-NDVI function and a process-based model of soil water content showed a strong relationship (i.e., r² = 95.7%).

Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index

Crop coefficients are a widely used and universally accepted method for estimating the crop evapotranspiration (ETc) component in irrigation scheduling programs. However, uncertainties of generalized basal crop coefficient (Kcb) curves can contribute to ETc estimates that are substantially different from actual ETc. Limited research with corn has shown improvements to irrigation scheduling due to better water-use estimation and more appropriate timing of irrigations when Kcb estimates derived from remotely sensed multispectral vegetation indices (VIs) were incorporated into irrigation-scheduling algorithms. The purpose of this article was to develop and evaluate a Kcb estimation model based on observations of the normalized difference vegetation index (NDVI) for a full-season cotton grown in the desert southwestern USA. The Kcb data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop coefficient procedures using field data obtained during two cotton experiments conducted during 1990 and 1991 at a site in central Arizona. The estimation model consisted of two regression relations: a linear function of Kcb versus NDVI (r2=0.97, n=68) used to estimate Kcb from early vegetative growth to effective full cover, and a multiple regression of Kcb as a function of NDVI and cumulative growing-degree-days (GDD) (r2=0.82, n=64) used to estimate Kcb after effective full cover was attained. The NDVI for cotton at effective full cover was ~0.80; this value was used to mark the point at which the model transferred from the linear to the multiple regression function. An initial evaluation of the performance of the model was made by incorporating Kcb estimates, based on NDVI measurements and the developed regression functions, within the FAO-56 dual procedures and comparing the estimated ETc with field observations from two cotton plots collected during an experiment in central Arizona in 1998. Preliminary results indicate that the ETc based on the NDVI-Kcb model provided close estimates of actual ETc.