Moderate Resolution Satellite Sensors Research Articles

Quantifying the Effects of Hurricanes Irma and Maria on Coastal Water Quality in Puerto Rico using Moderate Resolution Satellite Sensors

Coastal, benthic communities, such as coral reefs, are at particular risk due to poor water quality caused by hurricanes. In addition to the physical impacts from wave action and storm surge, hurricanes bring significant rainfall resulting in increased runoff from land. Hurricanes Irma and Maria caused record or near-record floods at many locations across Puerto Rico and resulted in major impacts on coastal and benthic ecosystems from heavy rainfall and river discharge. In this study, we use imagery from the moderate resolution Visible Infrared Imaging Radiometer Suite (VIIRS) satellite to quantify the impacts of hurricanes Irma and Maria, which struck Puerto Rico during September 2017, on the water quality of the coastal waters of Puerto Rico using the chlorophyll-a (Chl-a) and the diffuse attenuation coefficient at 490 nm (Kd490) products. The objectives include: (1) quantify the water quality and light attenuation after the hurricanes; (2) compare this event to the climatology of these parameters, and 3) evaluate long-term exposure and exceedances of various coastal areas to low levels of turbidity. The Chl-a inner shelf values increased in 2017 during the months of June (8% above baseline), July (17%), August (5%), September (8%), October (19%), and November (28%) when compared to 2012–2016 baseline data. The values for Chl-a concentration reached and exceeded 0.45 µg/L by August 2017 and persisted above that value until December 2017. The Kd490 inner shelf values for 2017 increased (in percent) for the months of June (4% above baseline), July (9%), August (10%), September (5%), October (12%), and November (7%) when compared to 2012–2016 baseline data. The values of Kd490 in August, September, and December 2017 were the highest seen during 2012–2017. Even with the limitations of spatial resolution and loss of data to cloud cover, the 6-year imagery time-series analysis can provide a useful evaluation of the effects of these two hurricanes on the coastal water quality in Puerto Rico, and quantify the exposure of benthic habitats to higher nutrient and turbidity levels.

Global Analysis of Burned Area Persistence Time with MODIS Data

Biomass burning causes a non-permanent land cover change (burned area) through the removal of vegetation, the deposition of charcoal and ashes, and the exposure of soil; the temporal persistence of these changes is highly variable, ranging from a few weeks in savannas to years in forests. Algorithms for the generation of moderate-resolution (10–30 m) continental and global burned area maps have been prototyped in an effort to meet the needs of diverse users of fire information. Nevertheless, moderate-resolution sensors have reduced the temporal resolution (e.g., to 16 days for Landsat), which could potentially lead to omission errors, especially in ecosystems where the spectral signal associated with burning disappears quickly and cloud cover limits the number of valid observations. This study presents a global analysis of the burned area persistence time, defined as the duration of the spectral separability of the burned/unburned areas mapped by the MODIS MCD64 Global Burned Area Product. The separability was computed by analyzing time series of normalized burn ratio (NBR) from nadir BRDF-adjusted MODIS reflectances (MCD43 product). Results showed that, globally, the median burned area persistence time was estimated at 29 days, and 86.6% of the global area, as detected by MODIS, can only be detected accurately for up to 48 days. Thus, results indicate that burned area persistence time can be a limiting factor for global burned area mapping from moderate-resolution satellite sensors, which have a low temporal resolution (e.g., Landsat 16 days, Sentinel-2A/B 5 days).

A global study of NDVI difference among moderate-resolution satellite sensors

Moderate-resolution sensors, including AVHRR (Advanced Very High Resolution Radiometer), MODIS (MODerate-resolution Imaging Spectroradiometer) and VIIRS (Visible-Infrared Imager-Radiometer Suite), have provided over forty years of global scientific data. In the form of NDVI (Normalized Difference Vegetation Index), these data greatly benefit environmental studies. However, their usefulness is compromised by sensor differences. This study investigates the global NDVI difference and its spatio-temporal patterns among typical moderate-resolution sensors, as supported by state-of-the-art remote sensing derived products. Our study demonstrates that the atmosphere plays a secondary role to LULC (Land Use/Land Cover) in inter-sensor NDVI differences. With reference to AVHRR/3, AVHRR/1 and 2 exhibit negative NDVI biases for vegetated land cover types. In summer (July), the area of negative bias shifts northward, and the magnitude increases in the Northern Hemisphere. For most LULC types, the bias generally shifts in the negative direction from winter (January) to summer. A linear regression of the NDVI difference versus NDVI shows a close correlation between the slope value and vegetation phenology. Overall, NDVI differences are controlled by LULC type and vegetation phenology. Our study can be used to generate a long-term, consistent NDVI data set from composite MODIS and AVHRR NDVI data. LULC-dependent and temporally variable correction equations are recommended to reduce inter-sensor NDVI differences.

A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products

Abstract. Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000–2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the various methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with a % gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with a % gap < 10% and increases rapidly with the % gap. TSGF provides more accurate estimates of phenological timing up to a % gap < 60%.

Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1–18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%–2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.

An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery

The advent of new high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover changes from space. Satellite observations are carried out regularly and continuously, and provide a great deal of insight into the temporal changes of land cover use. High spatial resolution imagery better resolves the details of these changes and makes it possible to overcome the "mixed-pixel" problem that is inherent with more moderate resolution satellite sensors. At the same time, high-resolution imagery presents a new challenge over other satellite systems, in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify the land cover changes. To obtain the accuracies that are required by many applications to large areas, very extensive manual work is commonly required to remove the classification errors that are introduced by most methods. To improve on this situation, we have developed a new method for land surface change detection that greatly reduces the human effort that is needed to remove the errors that occur with many classification methods that are applied to high-resolution imagery. This change detection algorithm is based on neural networks, and it is able to exploit in parallel both the multiband and the multitemporal data to discriminate between real changes and false alarms. In general, the classification errors are reduced by a factor of 2-3 using our new method over a simple postclassification comparison based on a neural-network classification of the same images.

Validation of active fire detection from moderate-resolution satellite sensors: the MODIS example in northern eurasia

This paper discusses the process of validating active yes/no binary detection products from moderate-resolution satellite sensors. General concepts and practical issues are illustrated by the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) active product in Siberia. Coincident Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery is used to characterize spatial patterns of flaming at sub-MODIS pixel scale. It is shown that for proper evaluation reference observations are needed at the scale of the satellite pixel, as only 60% of the MODIS footprints contain single contiguous clusters of ASTER pixels. In Siberia the size of a single ASTER cluster within the MODIS footprint that has a 50% probability of being flagged as fire is ~60, compared to ~45 in the Brazilian Amazon, whereas previous radiative transfer simulations suggested similar detection probabilities. The lower-than-expected detection rates in Siberia are largely attributable to flaming underneath heavy smoke, which is not detected by the current MODIS algorithm. Pixel-based and cluster-based omission error rates are derived, and it is shown that the probability of flagging as fire a MODIS pixel which contains a given number of 30-m ASTER pixels is typically 3-5 times lower than detecting a contiguous cluster with the same number of ASTER pixels. The procedures described are recommended for a consensus active validation protocol, but with the inclusion of multiplatform sensor configurations to complement the near-nadir angular sampling from single-platform observations such as MODIS and ASTER on Terra

Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors

We report the results of a modeling study on the sensitivity of normalized difference vegetation index (NDVI) and surface reflectance to differences in instrument spectral response functions (SRF) for various Advanced Very High Resolution Radiometers (AVHRR) onboard the National Oceanic and Atmospheric Administration's (NOAA) satellites NOAA-6–16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Vegetation sensor (VGT), and the Global Imager (GLI). Modeling results were validated against real satellite observations employing AVHRR/NOAA-14 and -15 and MODIS, with a very good agreement. It is shown that for identical atmospheric state and similar surface spectral reflectance, the NDVI and spectral reflectances are sensitive to the sensor's SRF. Relative to a reference SRF for AVHRR/NOAA-9, the differences in reflectance among the AVHRRs range from −25% to 12% for visible channel (red) and from −2% to 4% for near-infrared (NIR) channel. Absolute change in NDVI among various AVHRRs ranged from −0.02 to 0.06. The most significant difference was observed for the AVHRR/3. Consistent results were obtained with the AVHRR sensors aboard the following afternoon satellites: NOAA-9, -11, and -12, whereas important discrepancies were found for other AVHRRs aboard NOAA-6 and -10 and especially those launched more recently (NOAA-15 and -16). Reflectance and NDVI measured by MODIS channels 1 and 2 also exhibit significant differences (up to 30–40%) relative to AVHRR. GLI and VGT have some specific features that should be taken into account when intercomparing surface or top of the atmosphere (TOA) reflectance as well as NDVI. Sensitivity of the SRF effect to variable atmospheric state (water vapor, aerosol, and ozone) was also investigated. Polynomial approximations are provided for bulk spectral correction with respect to AVHRR/NOAA-9.