Moderate Resolution Imaging Spectroradiometer Data Products Research Articles

Comparing the accuracy of MODIS data products for vegetation detection between two environmentally dissimilar ecoregions: the Chocó-Darien of South America and the Great Basin of North America

The daily images produced by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor aboard the Terra and Aqua satellites have been widely used to monitor global vegetation. Using these data, the Earth Observing System operated by US National Aeronautics and Space Administration (NASA) has developed a variety of MODIS products focused on the monitoring and evaluation of vegetation condition. These products have three possible sources of variation that can affect the sensitivity of vegetation detection: 1) orbital and mechanical differences between MODIS sensors aboard Aqua or Terra, 2) the preprocessing algorithms used to generate multitemporal cloud-free mosaics (MAIAC or original MODIS algorithm), and/or 3) post-processing algorithms applied by users to optimize vegetation index values derived from temporal sequences of imagery. We evaluated these sources of variation by comparing the results of a vegetation classification for two different ecoregions. The accuracies of vegetation classifications utilizing either the Aqua or Terra MODIS sensors, the MAIAC or original MODIS preprocessing algorithms, and two common post-processing techniques (Asymmetric Gaussian or Savitzky and Golay function) were compared to determine which set of techniques or sensors yielded the best results. The ecoregions we chose to use were the Great Basin of North America and Chocó-Darien of South America. We compared four different MODIS data products (MOD13Q1, MYD13Q1, MOD09Q1, and MYD09Q1) as predictor variables using Random Forest as the classification algorithm to generate a land cover map. We found that the accuracy of the vegetation classifications (using Kappa as measure of accuracy) changed significantly depending on the MODIS platform (Terra or Aqua), the preprocessing algorithm (MAIAC or MODIS), and the two postprocessing algorithms for both ecoregions. Our result suggests that comparative analyses are needed to optimize the results when equivalent MODIS products are used in vegetation detection and classification.

Multi-threaded compression of Earth observation time-series data

ABSTRACTEarth observation data are typically compressed using general-purpose single-threaded compression algorithms that operate at a fraction of the bandwidth of modern storage and processing systems. We present evidence that recently developed multi-threaded compression codecs offer substantial benefits over widely used single-threaded codecs in terms of compression efficiency when applied to a selection of moderate resolution imaging spectroradiometer (MODIS) datasets stored in the HDF5 format. Compression codecs from the LZ77 and Rice families are shown to vary in efficacy when applied to different MODIS data products, highlighting the need for compression strategies to be tailored to different classes of data. We also introduce LPC-Rice, a new multi-threaded codec, that performs particularly well when applied to time-series data.

Assessment of vulnerability of Indian agriculture to rainfall variability – Use of NOAA-AVHRR (8 km) and MODIS (250 m) time-series NDVI data products

Advanced Very High Resolution Radiometer (AVHRR) (8 km) Normalized Differential Vegetation Index (NDVI) data and Moderate Resolution Imaging Spectroradiometer (MODIS) (16-day, 250m) NDVI data products were considered to analyze vulnerability of Indian agriculture to rainfall variability under climate change impact studies. Predicted higher temperature and altered rainfall patterns accompanied by extreme weather events would impact vegetation growth in natural forest, open scrub, agricultural land and plantations. NDVI derived from 2-band information (Red and Near-infra Red) of multi-spectral imagery of AVHRR (1982 to 2006) and from MODIS (20002010) were analysed to understand spatial and temporal variability. Coefficient of Variation (CV) of maximum NDVI from 15-day composites for the total length of the study period was used to assess vulnerability of rain-fed agriculture and results were corroborated with the Standard Precipitation Index (SPI) rather than actual rainfall received during the study period. AVHRR time-series data helped to identify vulnerable areas at regional-scale, i.e., agro-ecological subregions (AESR) due to coarser ground resolution while MODIS data products with 250m pixel resolution helped identify vulnerability at the district level. It was estimated that over 241 Mha areas in the country may not be vulnerable to rainfall variability-induced climate change, whereas over 81.3 Mha in arid, semi-arid and dry sub-humid regions in the country may be vulnerable to extreme weather events. Study indicated that over 12.1 and 1.81 Mha of Kharif cropland would be mildly and severely vulnerable, whereas 6.86 and 0.5 Mha of Rabi cropland may be adversely affected in a similar manner. Of the remaining agricultural lands, 29.93 and 5.24 Mha would also be vulnerable to climate change in a similar manner. Studies also indicated a decrease in length of Kharif and Rabi seasons and a delay in the start of Kharif season based on preliminary findings.

Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance

We compared estimates of actual evapotranspiration (ET) produced with six different vegetation measures derived from the MODerate resolution Imaging Spectroradiometer (MODIS) and three contrasting estimation approaches using measurements from eddy covariance flux towers at 16 FLUXNET sites located over six different land cover types. The aim was to assess optimal approaches in using optical remote sensing to estimate ET. The first two approaches directly regressed various MODIS vegetation indices (VIs) and products such as leaf area index (LAI) and fraction of photosynthetically active radiation (fPAR) with ET and evaporative fraction (EF). In the third approach, the Penman–Monteith (PM) equation was inverted to obtain surface conductance (Gs), for dry plant canopies. The Gs values were then regressed against the MODIS data products and used to parameterize the PM equation for retrievals of ET. Jack-Knife cross-validation was used to evaluate the various regression models against observed ET. The PM-Gs approach provided the lowest root mean square error (RMSE), and highest determination coefficients (R2) across all sites, with an average RMSE=38Wm−2 and R2=0.72. Direct regressions of observed ET against the VIs resulted in an average RMSE=60Wm−2 and R2=0.22, while the EF regressions an average RMSE=42Wm−2 and R2=0.64. The MODIS LAI and fPAR product produced the poorest estimates of ET (RMSE>44Wm−2 and R2<0.6); while the VIs each performed best for some of the land cover types. The enhanced vegetation index (EVI) produced the best ET estimates for evergreen needleleaf forest (RMSE=28.4Wm−2, R2=0.66). The normalized difference vegetation index (NDVI) best estimated ET in grassland (RMSE=23.8Wm−2 and R2=0.68), cropland (RMSE=29.2Wm−2 and R2=0.86) and woody savannas (RMSE=25.4Wm−2 and R2=0.82), while the VI-based crop coefficient (Kc) yielded the best estimates for evergreen and deciduous broadleaf forests (RMSE=27Wm−2 and R2=0.7 in both cases). Using the ensemble-average of ET as estimated using NDVI, EVI and Kc we computed global grids of dry canopy conductance (Gc) from which annual statistics were extracted to characterise different functional types. The resulting Gc values can be used to parameterize land surface models.

A MODIS approach to predicting stream water quality in Wisconsin

Efforts to predict water quality often rely heavily on static landuse/land-cover (LULC) classifications derived from remote sensing imagery. However, LULC classifications are infrequently updated, and the development of regular (annual) land cover maps that accurately capture intra- and inter-annual change may be expensive in terms of data and labor costs. In addition, existing land cover products may not include classes relevant to the assessment of water quality. Conversely, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are freely available, preprocessed, frequently updated, and available in a range of useful data products. Our goal was to predict water quality for streams in Wisconsin, USA using a model constructed exclusively from MODIS products. We used streamwater nitrate and dissolved phosphorus data from 2001 to 2004 from two previous studies (Robertson et al., 2006; Stanley & Maxted, 2008) to evaluate the potential for using MODIS in an empirical model to predict water quality, thereby reducing reliance on possibly dated land cover maps. Using predictors derived exclusively from MODIS data products, we successfully predicted 80% of the variation in measured nitrate concentrations. Remote sensing based models explained 51% of the variation in dissolved phosphorus concentrations. Predictions of water quality were developed on both a per-pixel and a watershed basis. The remote sensing approach offers the potential for spatially continuous estimates of stream nutrient concentrations, and complements the suite of approaches currently used to assess water quality; including periodic stream surveys models, and point-source data.

Spatiotemporal variability of grassland vegetation cover in a catchment in Inner Mongolia, China, derived from MODIS data products

Conditions, distribution and development of vegetation in semi arid regions are highly variable. In this study we detected the temporal and spatial variability of vegetation in the Xilin river catchment from 2000 to 2008 by analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data products of that period. The study is based on LAI (Leaf area index) and supported by NDVI (Normalized difference vegetation index) and LST (Land surface temperature) data with a spatial resolution of 1 km. The mean LAI of the catchment from 2000 to 2008 is 0.59. Precipitation data of the study period governs the conditions and distribution of vegetation in the catchment. In dry years, e.g. 2001 and 2005, LAI was clearly lower (0.52) compared to 2003 (LAI = 0.72) which was a wet year. As precipitation generally decreases from south-east to north-west, LAI values decrease according to this gradient. The influence of heavy grazing in the vicinity of the Xilin river is obvious as LAI is low (0.4) in these areas. The high temporal variability of the LAI is displayed by its high mean CV (coefficient of variation) which is 48% for the observed years. The analysis of sample areas illustrates temporal and spatial differences in vegetation development within the catchment and shows a generally delayed growth start in the north-west of the catchment.

Inferences of all‐sky solar irradiance using Terra and Aqua MODIS satellite data

Solar irradiance is a key environmental control, and accurate spatial and temporal solar irradiance data are important for a wide range of applications related to energy and carbon cycling, weather prediction, and climate change. This study presents a satellite‐based scheme for the retrieval of all‐sky solar irradiance components, which links a physically based clear‐sky model with a neural network version of a rigorous radiative transfer model. The scheme exploits the improved cloud characterization and retrieval capabilities of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and employs a cloud motion tracking scheme for the production of hourly solar irradiance data throughout the day. The scheme was implemented for the Island of Zealand, Denmark (56° N, 12° E) and Southern Arizona, USA (31° N, 110° W) permitting model evaluation for two highly contrasting climates and cloud environments. Information on the atmospheric state was provided by MODIS data products and verifications against AErosol RObotic NETwork (AERONET) data demonstrated usefulness of MODIS aerosol optical depth and total precipitable water vapour retrievals for the delineation of spatial gradients. However, aerosol retrievals were significantly biased for the semi‐arid region, and water‐vapour retrievals were characterized by systematic deviations from the measurements. Hourly global solar irradiance data were retrieved with overall root mean square deviations of 11.5% (60 W m−2) and 26.6% (72 W m−2) for Southern Arizona and the Island of Zealand, respectively. For both regions, hourly satellite estimates were shown to be more reliable than pyranometer measurements from ground stations only 15 km away from the point of interest, which is comparable to the accuracy level obtainable from geostationary satellites with image acquisitions every 15–30 min. The proposed scheme is particularly useful for solar irradiance mapping in high‐latitude regions as data from geostationary satellites experience a gradual degradation in spatial resolution and overall quality with latitude and become unusable above approximately 60° latitude. However, in principle, the scheme can be applied anywhere on the globe, and a synergistic use of MODIS and geostationary satellite datasets may be envisaged for some applications.