High Resolution Normalized Difference Vegetation Index Research Articles

Assessment of Pasture Biomass with the Normalized Difference Vegetation Index from Active Ground‐Based Sensors

Calculating forage availability is challenging for managers of grazing systems due to the spatial heterogeneity of swards. Remote sensing applications may help to overcome this problem through estimates of biomass made with reflectance data. The objectives of this study were to (i) estimate herbage mass using an active, on‐the‐go, ground‐based, narrow band sensor to calculate the normalized difference vegetation index (NDVI), (ii) determine if NDVI may be used to assess spatial variability of herbage mass of grasslands, and (iii) determine if NDVI may be used to evaluate management of grazing systems. The NDVI was measured using an active ground‐based sensor, the GreenSeeker (Ntech Industries, Ukiah, CA). In tall fescue [Schedonorus arundinaceus (Schreb.) Dumort], NDVI was correlated with biomass determined by destructive harvesting (r2 = 0.68) and also with a calibrated rising plate meter (RPM) (r2 = 0.54). Semivariograms revealed that NDVI sampling intervals of 0.76 m adequately described the spatial variability structure of grazed swards. The frequency distributions of sward biomass derived from NDVI may reflect the foraging strategies of cattle. Negative skewness and high kurtosis are consistent with selective grazing, while positive skewness and low kurtosis are consistent with less diet selectivity. Frequency distributions also improved definition of available forage within each field. We concluded that spatial properties of grassland biomass may be derived from high resolution NDVI and RPM data and could be used to evaluate conditions of grassland landscapes and to aid decision‐making of managed grazing systems.

Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach

An approach is evaluated for the estimation of soil moisture at high resolution using satellite microwave and optical/infrared (IR) data. This approach can be applied to data acquired by the Visible/Infrared Imager Radiometer Sensor Suite (VIIRS) and a Conical Scanning Microwave Imager/Sounder (CMIS), planned for launch in the 2009–2010 time frame under the National Polar-Orbiting Operational Environmental Satellite System (NPOESS). The approach for soil moisture estimation involves two steps. In the first step, a passive microwave remote sensing technique is employed to estimate soil moisture at low resolution (∼25 km). This involves use of a simplified radiative transfer model to invert dual-polarized microwave brightness temperature. In the second step, the microwave-derived low-resolution soil moisture is linked to the scene optical/IR parameters, such as Normalized Difference Vegetation Index (NDVI), surface albedo, and Land Surface Temperature (LST). The linking is based on the ‘Universal Triangle’ approach of relating land surface parameters to soil moisture. The optical/IR parameters are available at high resolution (∼1 km) but are aggregated to the microwave resolution for the purpose of building the linkage model. The linkage model in conjunction with high-resolution NDVI, surface albedo and LST is then used to disaggregate microwave soil moisture into high-resolution soil moisture. The technique is applied to data from the Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) acquired for the Southern Great Plains (SGP-97) experiment conducted in Oklahoma in June–July 1997. An error budget analysis performed on the estimation procedure shows that the rms error in the estimation of soil moisture is of the order of 5%. Predicted soil moisture results at high resolution agree reasonably well with low resolution results in both magnitude and spatio-temporal patterns. The high resolution results are also compared with in situ (0–5 cm deep) point measurements. While the trends are similar, the soil moisture estimates in the two cases are different. Issues involving comparison of satellite derived soil moisture with in situ point measurements are also discussed.

High temporal resolution NDVI phenology from micrometeorological radiation sensors

The boreal forest is a region characterized by wide swings in temperature and light levels over the course of a year. This seasonal variability strongly effects the vegetation of this biome. Normalized difference vegetation index (NDVI) values were observed at daily timescales for key land cover types of the boreal forest, developing a more detailed description of seasonal changes in NDVI than could be produced from satellite data. NDVI values were calculated from tower‐mounted photosynthetically active radiation (PAR) and global solar sensors measuring both incoming and reflected radiation above the canopies at four Boreal Ecosystem‐Atmosphere Study (BOREAS) sites. Comparisons were made between the tower‐based broadband hemispherical NDVI values and the narrowband nadir‐viewed NDVI values from helicopter modular multiband radiometer (MMR). The comparisons indicate that the tower NDVI values are close to the MMR NDVIs in value for the BOREAS sites, but the range in tower NDVIs is not so great as in the MMR NDVIs. In 1996, BOREAS towers operated from before thaw to freeze‐up, allowing a complete picture of growing season NDVI for fen, young jack pine, black spruce, and aspen sites. The tower‐based NDVI time series display different patterns for each vegetation type, showing the effects of snow cover and vegetation green‐up and senescence. Changes in solar zenith angles are shown to have little effect on the seasonal NDVI patterns.