The Normalized Difference Vegetation Index (NDVI) was used to characterize the seasonal variation of land cover over the Northeastern Brazil (NEB) with the NOAA/NASA Pathfinder AVHRR Land (PAL) data set. Twenty years of monthly spatial-aggregated NDVI climatology was determined by calculating lower, mean, and upper NDVI values using a 0.5 lat by 0.5 log grid cell size. The results indicated tha...

The data used in the present study is a 21-year GIMMS-NDVI dataset, surface data on phenology of birch, and temperature data from meteorological stations, all for the period 1982 to 2002. For each pixel a 21-year mean and a mean of peak NDVI value was computed. Threshold values related to this mean, and mean of peak NDVI values, which show the best correlations with the phenological field data ...

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend e...

Sampling of herbivorous stink bugs in southeastern U.S. cotton remains problematic. Remote sensing was explored to improve sampling of these pests and associated boll injury. Two adjacent 14.5-ha cotton fields were grid sampled in 2011 and 2012 by collecting stink bug adults and bolls every week during the third, fourth, and fifth weeks of bloom. Satellite remote sensing data were collected dur...

Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectivel...

Soil moisture is critical parameters in climatic and hydrological modeling and vegetation growth. Ground-based point measurement of soil moisture is liable but expensive and impractical for watershed measurement. Remote sensing measurement has provided thriving perspective for spatial and instantaneous measurement of soil moisture. Microwave measurement is effective and successful for surface s...

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g., climate related-changes) versus direct and rapid changes (e.g., fire, land development) is challenging as changes can be confounded by time-dependent ...

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI (NDVIo) and full vegetation NDVI (NDVI∞). Usually it is assumed that NDVIo is close to zero (NDVIo∼0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soi...

[1] This work evaluates whether continuity between Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) is achievable for monitoring phenological changes in Alaska. This work also evaluates whether NDVI can detect changes in start of the growing season (SOS) in this region. Six quadratic regressi...