Search Results (3)

The Long Term Data Record (LTDR) project has the goal of developing a quality and consistent surface reflectance product from coarse resolution optical sensors. This paper focuses on the Advanced Very High Resolution Radiometer (AVHRR) part of the record, using the Moderate Resolution Imaging Spectrometer (MODIS) instrument as a reference. When a surface reflectance time series is acquired from satellites with variable observation geometry, the directional variation generates an apparent noise which can be corrected by modeling the bidirectional reflectance distribution function (BRDF). The VJB (Vermote, Justice and Bréon, 2009) method estimates a target’s BRDF shape using 5 years of observation and corrects for directional effects maintaining the high temporal resolution of the measurement using the instantaneous Normalized Difference Vegetation Index (NDVI). The method was originally established on MODIS data but its viability and optimization for AVHRR data have not been fully explored. In this study we analyze different approaches to find the most robust way of applying the VJB correction to AVHRR data, considering that high noise in the red band (B1) caused by atmospheric effect makes the VJB method unstable. Firstly, our results show that for coarse spatial resolution, where the vegetation dynamics of the target don’t change significantly, deriving BRDF parameters from 15+ years of observations reduces the average noise by up to 7% in the Near Infrared (NIR) band and 6% in the NDVI, in comparison to using 3-year windows. Secondly, we find that the VJB method can be modified for AVHRR data to improve the robustness of the correction parameters and decrease the noise by an extra 8% and 9% in the red and NIR bands with respect to using the classical VJB inversion. We do this by using the Stable method, which obtains the volumetric BRDF parameter (V) based on its NDVI dependency, and then obtains the geometric BRDF parameter (R) through the inversion of just one parameter. Full article

The Iraqi Marshes in Southern Iraq are considered one of the most important wetlands in the world. From 1982 to the present, their area has varied between 10,500 km² and 20,000 km². The marshes support a variety of plants, such as reeds and papyrus, and are home to many species of birds. These marshes are Al-Hammar, Central or Al-Amarah, and Al-Huwaiza. Freshwater supplies to the marshes come from the Tigris and Euphrates rivers in Iraq and from the Karkha River from Iran. For this analysis, we used the Land Long-Term Data Record Version 5 (LTDR V5) Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) sensor dataset. This dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution to monitor the spatial and temporal variability of vegetation along with other hydrological variables such as land surface temperature, precipitation, and evapotranspiration. In our analysis, we considered three time periods: 1982–1992; 1993–2003; and 2004–2017 due to anthropogenic activities and climate changes. Furthermore, we examined the relationships between various water cycle variables through the investigation of vegetation and water coverage changes, and studied the impacts of climate change and anthropogenic activities on the Iraqi Marshes and considered additional ground observations along with the satellite datasets. Statistical analyses over the last 36 years show significant deterioration in the vegetation: 68.78%, 98.73, and 83.71% of the green biomass has declined for Al-Hammar, The Central marshes, and Al-Huwaiza, respectively. The AVHRR and Landsat images illustrate a decrease in water and vegetation coverage, which in turn has led to an increase in barren lands. Unfortunately, statistical analyses show that marshland degradation is mainly induced by human actions. The shrinkage in water supplies taken by Iraq’s local neighbors (i.e., Turkey, Syria, and Iran) has had a sharp impact on water levels. The annual discharge of the Tigris declined from ~2500–3000 m³/s to ~500 m³/s, and the annual discharge of the Euphrates River declined from ~1500 m³/s to less than 500 m³/s. Full article

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics. Full article