Moderate Resolution Imaging Spectroradiometer Imagery Research Articles

Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery

Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (Tm) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI×Tm parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R2 of 0.51 (p<0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI×Tm correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R2=0.68, p=0.003) and the minimum land surface temperature (LST_min, R2=0.81, p=0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055gC per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6gCm−2month−1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.

The use of global synthetic data for pre-launch tuning of the VIIRS cloud mask algorithm

A methodology is presented to perform pre-launch tuning of thresholds used in the Visible Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The approach relies upon several data sources, including global synthetic data (GSD), Moderate Resolution Imaging Spectroradiometer (MODIS) and VIIRS relative spectral responses (RSRs) and MODIS top-of-atmosphere (TOA) radiance, reflectance and brightness temperature data. The GSD are used first to derive cloud cover distributions, that is, 0%, 50% and 100%, at VIIRS moderate resolution (800 m) for each VCM cloud detection test, based on inputs to the radiative transfer models. These cloud distributions are then used to update the values of the low, mid and high cloud-free confidence thresholds in the VCM algorithm. The approach is demonstrated by using MODIS RSRs with the GSD to set these thresholds and then analysing granules of MODIS data with the updated VCM. Performance is quantified through comparisons with manually generated cloud masks created from the MODIS imagery. The performance of the tuned VCM with MODIS data improved substantially for all major background conditions. The probability of correct typing (PCT) improved nearly 4% over the ocean to 97.5% and nearly 20% over snow-covered surfaces to 95.1%. The PCT values over land improved from 87.1% to 93.4% and over desert from 87.2% to 93.9%. The process was then repeated using VIIRS RSRs and the updated thresholds were forwarded to the National Polar-Orbiting Operational Satellite System (NPOESS) Preparatory Project (NPP) ground segment for incorporation into the operational system. It is concluded that GSD are invaluable for the pre-launch tuning of the VCM algorithm, which is now expected to exceed system requirements soon after the launch of the NPP satellite.

Partial blind model inversion of mountain forest structure from MODIS imagery

Blind model inversion of forest structure allows the user to run powerful physically based canopy reflectance models (CRMs) without having to specify any input model parameters as these are instead automatically derived. This is particularly important for large areas and regional scales where obtaining these model parameters may be costly, impractical, not representative or impossible. This is especially challenging in high-relief mountainous terrain. This article presents the multiple-forward mode-partial blind (MFM-PB) inversion capability as an important advancement from MFM-User and MFM adaptive full-blind (AFB) processing in that MFM-PB permits all available user input data to be utilized, while facilitating PB analyses for model parameters that are missing, a more typical operational-level requirement. MFM-PB was compared with MFM-User analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Canadian Rocky Mountains and was shown to be comparable in terms of both generated inputs and all biophysical structural outputs, with differences for stand density of ±42 stems/ha, crown radii ±0.08 m, height to crown centre (HCC) ±0.10 m and tree height (HGT) ±0.37 m. These mountain results were further compared with MFM results from flat, boreal forest terrain and were found to be comparable. MFM-PB provides full flexibility for CRM inversion, and is particularly important for (but not limited to) larger area, regional-scale studies for which user input data are typically constrained.

Estimating winter crop area across seasons and regions using time-sequential MODIS imagery

The wheat grain industry is Australia's second largest agricultural export commodity. There is an increasing demand for accurate, objective and near real-time crop production information by industry. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to capture reflectance over large areas at acceptable pixel scale, cost and accuracy. The use of multi-temporal MODIS-enhanced vegetation index (EVI) imagery to determine crop area was investigated in this article. Here the rigour of the harmonic analysis of time-series (HANTS) and early-season metric approaches was assessed when extrapolating over the entire Queensland (QLD) cropping region for the 2005 and 2006 seasons. Early-season crop area estimates, at least 4 months before harvest, produced high accuracy at pixel and regional scales with percent errors of −8.6% and −26% for the 2005 and 2006 seasons, respectively. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. The errors for specific area estimates for wheat, barley and chickpea were 9.9%, −5.2% and 10.9% (for 2005) and −2.8%, −78% and 64% (for 2006), respectively. Area estimates of total winter crop, wheat, barley and chickpea resulted in coefficient of determination (R 2) values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high coefficient of determination (0.87) was achieved for total winter crop area estimates in August across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps. The extrapolability of these approaches to determine total and specific winter crop area estimates, well before flowering, showed good utility across larger areas and seasons. Hence, it is envisaged that this technology might be transferable to different regions across Australia.

The Potential Link Between El Nino and Water Hyacinth Blooms in Winam Gulf of Lake Victoria, East Africa: Evidence from Satellite Imagery

This study evaluates the link between the occurrence of El Nino events in East Africa and water hyacinth blooms in Winam Gulf of Lake Victoria using remote sensing technology. A time-series of the Normalized Difference Vegetation Index (NDVI) analyzed from data acquired by the multispectral Aqua/Terra sensors aboard the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite are used to monitor areal extent and density of the aquatic plants. We related the NDVI derived from MODIS imagery to data on El Nino South Oscillation (ENSO) events that were obtained from NOAA and rainfall data from the study site. Our results from the 11-year time-series data show a statistically significant positive correlation (R = 0.6, P = 0.021) between the occurrence of El Nino events and water hyacinth blooms in Winam Gulf. The proliferation of water hyacinth rafts in Winam Gulf may be a response to dramatic environmental and water quality changes in Lake Victoria. The patterns of impact of ENSO events and rainfall show spatial and temporal variation patterns in the region, depending on the time and space evolution of each individual ENSO event that could explain the differences in general patterns of water hyacinth cover in Lake Victoria during different El Nino events. Although the problems eutrophication and water hyacinth are severe in the Winam Gulf of Lake Victoria some of the solutions to the problem are located several hundreds of kilometers away in the rich agricultural farmlands of the Lake Victoria catchment area. These include better land management practices and strategies aimed at limiting pollution and soil erosion within the lake’s drainage basin.

A simple method for mapping bathymetry over turbid coastal waters from MODIS data: possibilities and limitations

Timely mapping of underwater topography over turbid coastal waters is very important to navigation. Such a task is ideally accomplished through moderate- resolution imaging spectroradiometer (MODIS) satellite data that have a temporal resolution measured in hours. In this article we propose a simple regression method for retrieving bathymetry from MODIS bands. It only requires concurrently collected total suspended solids and water depth samples at limited spots, without considering the downward attenuation coefficient, surface reflectance or bottom reflectance. Regression analysis of the observed spot depth (D) against individual bands and their transformation enables an empirical model to be established. The model in which band 3 (M 3) is the exploratory variable is the most accurate, with an r value (correlation coefficient) of only 0.654. Correction of this model by the concentration level of suspended solids using bands 2 (M 2) and 5 (M 5) improves the prediction accuracy from 3.26 to 1.52 m, or from 39% to 24%. The best model takes the form of D = –7.833 + 0.0326M 3 / (M 2 M 5) (r = 0.815, n = 3318). Application of this model to the MODIS imagery led to the generation of a bathymetric map over the 15 000 km2 study area. Assessed against four profiles, the retrieved bathymetry has a mean absolute accuracy of around 2 m or a relative inaccuracy of 10% to 18%. The remotely sensed bathymetry contains many minor relief features absent from its in situ-surveyed counterpart. It is concluded that this proposed simple method can produce reasonably accurate results without the need to consider atmospheric effects or bottom reflectance over the range of 5–20 m. However, it may not work so well in clear oceanic Case I waters.

Multi-index-based soil moisture estimation using MODIS images

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.

Enhancement of a fire-detection algorithm by eliminating solar contamination effects and atmospheric path radiance: application to MODIS data

Traditional fire-detection algorithms with either fixed or contextual thresholds mainly rely on the temperature contrast between a fire pixel and its surrounding pixels in the mid-infrared (MIR) and thermal-infrared (TIR) bands. Solar contamination and thermal atmospheric path radiance in the MIR band can weaken the contrast between the high- and low-temperature objects and undermine the capability of detecting fires during daytime. The degree of solar contamination in the MIR band depends on variable surface albedo, solar zenith angle and atmospheric conditions. A method is proposed to eliminate the solar radiation and thermal path radiance received by the MODerate Resolution Imaging Spectroradiometer (MODIS) sensor in the MIR band. The modified MIR brightness temperature is incorporated into the existing fire-detection algorithm (referred to as ‘MOD14’) after re-tuning the daytime thresholds. The performance of the revised algorithm (referred to as ‘MOD-MOD’) was tested using coincident data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard the Terra satellite and visual inspection of large quantities of MODIS imageries. Moderate improvements are achieved in the detection rate while retaining low commission errors. Improvement of the detection by MOD-MOD depends on land-cover type. The majority of the false detections occur over deforested area.

A study of sediment transport in a shallow estuary using MODIS imagery and particle tracking simulation

The study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) red-channel reflectance with a spatial resolution of 250 m to estimate suspended sediment concentration (SSC) in the Mobile Bay estuary, Alabama. Based on monthly in situ sampling, a new algorithm was developed using an exponential regression model. The concentration of inorganic suspended sediments (ISS) in Mobile Bay and Mississippi Sound was concerned and mapped by applying the new algorithm. The ISS maps during a cold front passage have revealed how the resuspension and transport of sediments respond to the variable wind forcing in this micro-tidal system. Particle tracking based on a three-dimensional hydrodynamic model was utilized to explain what was observed from the satellite imagery. It has been found that the rapid disappearance of the surface ISS after a cold front passage was mainly caused by settling of sediments rather than flushing out of the estuary. The study demonstrates that a combination of ISS mapped from the MODIS band-1 reflectance and three-dimensional numerical modelling is an effective tool to analyse sediment dynamics in the Mobile Bay estuary and other similar estuaries.

Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data

Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security.

Monitoring Rice Agriculture in the Sacramento Valley, USA With Multitemporal PALSAR and MODIS Imagery

Rice agriculture is an important crop that influences land-atmosphere interactions and requires substantial resources for flood management. Multitemporal acquisition strategies provide an opportunity to improve rice mapping and monitoring of hydroperiod. The objectives of this study were to 1) delineate rice paddies with Phased Array L-band Synthetic Aperture Radar (PALSAR) fine-beam single/dual (FBS/D) mode measurements and 2) integrate multitemporal, ScanSAR Wide-Beam 1 (WB1)- and Moderate Resolution Imaging Spectroradiometer (MODIS)- observations for flood frequency mapping. Multitemporal and multiscale PALSAR and MODIS imagery were collected over the study region in the Sacramento Valley, California, USA. A decision-tree approach utilized multitemporal FBS (HH polarization) data to classify rice fields and WB1 measurements to assess paddy flood status. High temporal frequency MODIS products further characterized hydroperiod for each individual rice paddy using a relationship between the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI). Validation found the PALSAR-derived rice paddy extent maps and hydroperiod products to possess very high overall accuracies (95% overall accuracy). Agreement between MODIS and PALSAR flood products was strong with agreement between 85-94% at four comparison dates. By using complementing products and the strengths of each instrument, image acquisition strategies and monitoring protocol can be enhanced. The results highlight how the integration of multitemporal PALSAR and MODIS can be used to generate valuable agro-ecological information products in an operational context.

Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005

Cropping intensity, defined as the numbers of crops (single, double and triple cropping) per year in a unit cropland area, is one of the major factors in crop production and agriculture intensification. Changes in cropping intensity are driven by a number of socio-economic and climate factors. Agricultural census statistics of India contain much information on the distribution of crop types and total cropland area but contain no detailed spatially explicit information on the spatial distribution of double and triple cropping fields. In this study, we used multi-temporal images from the Moderate Resolution Imaging Spectroradiometer (MODIS) in 2005 to identify and map double-and triple-cropping land in India. The phenology algorithm we developed is based on the temporal profile analysis of three vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI). The MODIS-based phenology algorithm estimates that India has a total area of 57.8 million ha of multiple- (including both double and triple-) cropping fields in 2005, which is about 6.4% higher than the estimate (54.3 million ha) from the agriculture census data in 2004/2005. The resultant MODIS-derived map of multiple-cropping croplands at 500-m spatial resolution in 2005 was compared with the state and district levels using the agricultural census data. We also evaluated the MODIS-derived map using in situ observation data collected in India. The results of this study demonstrate the potential of the phenology algorithm in delineating double-and triple-cropping practice in India. The resultant geospatial dataset of multiple-cropping croplands in India is useful for the study of irrigation, food security and climate.

A simple, binary classification algorithm for the detection of Trichodesmium spp. within the Great Barrier Reef using MODIS imagery

A binary classification algorithm to detect the presence of high surface concentrations of the nitrogen‐fixing cyanobacterium Trichodesmium spp. was developed for high spatial resolution (250 m) imagery of the Moderate‐resolution Imaging Spectroradiometer (MODIS). Above‐water hyperspectral radiometric measurements of dense Trichodesmium surface aggregations (&gt;10 µg/L Chlorophyll a) showed that the water‐leaving radiance Lw at wavelengths greater than 700 nm were much higher in magnitude (&gt;0.05 W m−2sr−1) relative to the visible wavelengths 400–700 nm (&lt;0.03 W m−2sr−1). The binary classification algorithm is based on three criteria. The first criteria relied on the difference in magnitude between the MODIS normalized water‐leaving radiance (nLw) at the 859 and 678 nm wavebands. The magnitude of the nLw at the 555 and 645 nm wavebands relative to nLw 678 nm waveband formed the second and third criteria, respectively. The classification algorithm was tested on a small subset of 13 MODIS images with corresponding Trichodesmium sea‐truths and yielded an 85% accuracy. Fine‐scale features consistent with dense Trichodesmium surface aggregations, such as eddy swirls and windrows, appear to be well represented with the algorithm results. The algorithm was also found to be robust in the presence of highly reflective, potential confounding effects.

A practical DOS model-based atmospheric correction algorithm

Atmospheric correction is of great importance in quantitative remote sensing studies. However, many of the atmospheric correction algorithms proposed in the literature are not easily applicable in real cases. In order to develop a practical atmospheric correction algorithm, Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is employed to obtain aerosol optical depth and the total atmospheric water vapour content, which are used to compute the transmittances in a dark object subtraction (DOS) model. An improved DOS atmospheric correction method combining MODIS imagery with the conventional DOS technique is proposed. A Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image acquired on 21 October 2001 in Wuyi mountain, south-eastern China, and a CBERS 02 CCD image acquired on 24 August 2005 in Dunhuang, north-western China, were atmospherically corrected with this new approach. Various tests are performed, from spectral signature analysis, to vegetation index spatial profile and image information content comparisons, and by direct comparison with ground-measured reflectances, to evaluate the performance of the improved DOS model. The evaluation shows it can generally achieve a good atmospheric correction result.

Differentiating between Clouds and Heavy Aerosols in Sun-Glint Regions

Abstract An approach is presented to distinguish between clouds and heavy aerosols in sun-glint regions with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. The approach extends the applicability of an algorithm that has already been applied successfully in areas outside the geometric and wind-induced sun-glint areas of the earth over both land and water surfaces. The successful application of this approach to include sun-glint regions requires an accurate cloud phase analysis, which can be degraded, especially in regions of sun glint, because of poorly calibrated radiances of the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Consequently, procedures have been developed to replace bad MODIS level 1B (L1B) data, which may result from saturation, dead/noisy detectors, or data dropouts, with radiometrically reliable values to create the Visible Infrared Imager Radiometer Suite (VIIRS) proxy sensor data records (SDRs). Cloud phase analyses produced by the NPOESS VIIRS cloud mask (VCM) algorithm using these modified VIIRS proxy SDRs show excellent agreement with features observed in color composites of MODIS imagery. In addition, the improved logic in the VCM algorithm provides a new capability to differentiate between clouds and heavy aerosols within the sun-glint cone. This ability to differentiate between clouds and heavy aerosols in strong sun-glint regions is demonstrated using MODIS data collected during the recent fires that burned extensive areas in southern Australia. Comparisons between heavy aerosols identified by the VCM algorithm with imagery and heritage data products show the effectiveness of the new procedures using the modified VIIRS proxy SDRs. It is concluded that it is feasible to accurately detect clouds, identify cloud phase, and distinguish between clouds and heavy aerosol using a single cloud mask algorithm, even in extensive sun-glint regions.

Southeast Pacific Stratocumulus: High-Frequency Variability and Mesoscale Structures over San Félix Island

Abstract Stratocumulus cloud cover patterns and their relationship to drizzle were characterized at San Felix Island (SFI; 26.5°S, 80°W) in the southeast Pacific Ocean. Small closed, large closed, and open cells were identified in about 65% of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images during 2003. The MODIS imagery was combined with ceilometer and surface meteorological measurements, human observations of cloud types and drizzle, and large-scale meteorological analyses for January through June. The authors identified two drizzle regimes: a synoptically quiescent summer (January–March) regime characterized by a strong anticyclone, large closed cells, and frequent drizzle, and an autumn (April–June) regime characterized by a weaker anticyclone, small closed cells and open cells, and precipitation that was mainly associated with synoptic activity. The large closed cells had higher mean cloud bases and tops than the small closed cells and accounted for 45% of the cumulus-under-stratocumulus reports and 29% of the total drizzle and rain reports. Large closed cells occupied more intermittently coupled boundary layers than did the small closed cells. Open cells also occurred in more decoupled conditions but only accounted for 18% of the total reports of drizzle and rain. The atmospheric stability of large and small closed cells was similar, but large closed cells were more commonly associated with a strong anticyclone, and small closed cells with wave activity superimposed upon a weakened anticyclone. The increased drizzle and occurrence of cumulus-under-stratocumulus in the summer rather than autumn is consistent with higher nighttime liquid water paths. A contribution of this study is the documentation of the ways in which synoptic activity can affect stratocumulus decks.

Detecting the spatial and temporal variability of chlorophyll-a concentration and total suspended solids in Apalachicola Bay, Florida using MODIS imagery

Apalachicola Bay, Florida, accounts for 90% of Florida's and 10% of the nation's eastern oyster (Crassostrea virginica) harvesting. Chlorophyll-a concentration and total suspended solids (TSS) are two important water quality variables, among other environmental factors such as salinity, for eastern oyster production in Apalachicola Bay. In this research, we developed regression models of the relationships between the reflectance of the Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra 250 m data and the two water quality variables based on the Bay-wide field data collected during 14–17 October 2002, a relatively dry period, and 3–5 April 2006, a relatively wet period, respectively. Then we selected the best regression models (highest coefficient of determination, R 2) to derive Bay-wide maps of chlorophyll-a concentration and TSS for the two periods. The MODIS-derived maps revealed large spatial and temporal variations in chlorophyll-a concentration and TSS across the entire Apalachicola Bay.

Regional‐scale Assessment of Soil Salinity in the Red River Valley Using Multi‐year MODIS EVI and NDVI

The ability to inventory and map soil salinity at regional scales remains a significant challenge to scientists concerned with the salinization of agricultural soils throughout the world. Previous attempts to use satellite or aerial imagery to assess soil salinity have found limited success in part because of the inability of methods to isolate the effects of soil salinity on vegetative growth from other factors. This study evaluated the use of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in conjunction with directed soil sampling to assess and map soil salinity at a regional scale (i.e., 10-10(5) km(2)) in a parsimonious manner. Correlations with three soil salinity ground truth datasets differing in scale were made in Kittson County within the Red River Valley (RRV) of North Dakota and Minnesota, an area where soil salinity assessment is a top priority for the Natural Resource Conservation Service (NRCS). Multi-year MODIS imagery was used to mitigate the influence of temporally dynamic factors such as weather, pests, disease, and management influences. The average of the MODIS enhanced vegetation index (EVI) for a 7-yr period exhibited a strong relationship with soil salinity in all three datasets, and outperformed the normalized difference vegetation index (NDVI). One-third to one-half of the spatial variability in soil salinity could be captured by measuring average MODIS EVI and whether the land qualified for the Conservation Reserve Program (a USDA program that sets aside marginally productive land based on conservation principles). The approach has the practical simplicity to allow broad application in areas where limited resources are available for salinity assessment.

MODIS imagery of turbid plumes in San Diego coastal waters during rainstorm events

Data of normalized water-leaving radiance, nLw, obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite at spatial resolution of 250 m (band 1 centered at 645 nm) and 500 m (band 4 at 555 nm) are used to study turbid plumes in coastal waters of southern California during rainstorm events in winter of 2004–2005. Our study area includes San Diego coastal waters, which extend approximately 25 km offshore between Point Loma and 10 km south of the US–Mexican border. These waters are influenced by terrigenous input of particulate and dissolved materials from San Diego and Tijuana watersheds and non-point sources along the shore. Optimum threshold values of satellite-derived normalized water-leaving radiances at both wavebands were established for distinguishing the plume from ambient ocean waters. These threshold values were determined by searching for a maximum correlation between the estimates of satellite-derived plume area calculated using a broad range of nLw values and the environmental variables characterizing rainfall, river discharge, wind, and tides. A correlation analysis involving the amount of precipitated water accumulated during a storm event over the San Diego and Tijuana watersheds was selected as the basis for final determinations of the optimum threshold nLwthr and subsequent calculations of the plume area. By applying this method to a sequence of MODIS imagery, we demonstrate the spatial extent and evolution of the plume during rainstorm events under various conditions of precipitation, river discharge, wind forcing, and coastal currents.

Evaluation of sun glint models using MODIS measurements

To understand the performance of various sun glint models, we have carried out comparison studies with measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) for several popular sun glitter models, including the Cox–Munk models, Ebuchi–Kizu models, Breon–Henriot models, etc., over the open oceans using MODIS data at the near-infrared (NIR) (859 nm) and shortwave infrared (SWIR) bands (1240 and 2130 nm). Sun glint models with and without a wind direction dependence were evaluated. To obtain the MODIS-measured glint reflectance from the total reflectance at the top of the atmosphere, we performed atmospheric corrections to remove the effects of atmospheric absorption as well as radiance contributions from molecules (Rayleigh scattering) and aerosols. We have selected 12 MODIS sun glint scenes over various open ocean regions for this study. Our results indicate that overall, the Cox–Munk [1] model with the wind direction dependence has the best performance in terms of correlation coefficients with the MODIS measurements. The Breon–Henriot [8] model performed similarly as from the Cox–Munk model as two models are in fact very close. Findings from these evaluations can improve our ability to accurately remove the sun glint contamination in MODIS imagery and produce robust satellite ocean color and atmosphere products.