Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions.

Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world.

Synergistic integration of optical and microwave satellite data for crop yield estimation.

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R ≥ 2 0.76 ; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation.

Spaceborne Satellite for Snow Cover and Hydrological Characteristic of the Gilgit River Basin, Hindukush⁻Karakoram Mountains, Pakistan.

The Indus River, which flows through China, India, and Pakistan, is mainly fed by melting snow and glaciers that are spread across the Hindukush⁻Karakoram⁻Himalaya Mountains. The downstream population of the Indus Plain heavily relies on this water resource for drinking, irrigation, and hydropower generation. Therefore, its river runoff variability must be properly monitored. Gilgit Basin, the northwestern part of the Upper Indus Basin, is selected for studying cryosphere dynamics and its implications on river runoff. In this study, 8-day snow products (MOD10A2) of moderate resolution imaging spectroradiometer, from 2001 to 2015 are selected to access the snow-covered area (SCA) in the catchment. A non-parametric Mann⁻Kendall test and Sen's slope are calculated to assess whether a significant trend exists in the SCA time series data. Then, data from ground observatories for 1995⁻2013 are analyzed to demonstrate annual and seasonal signals in air temperature and precipitation. Results indicate that the annual and seasonal mean of SCA show a non-significant decreasing trend, but the autumn season shows a statistically significant decreasing SCA with a slope of -198.36 km²/year. The annual mean temperature and precipitation show an increasing trend with highest values of slope 0.05 °C/year and 14.98 mm/year, respectively. Furthermore, Pearson correlation coefficients are calculated for the hydro-meteorological data to demonstrate any possible relationship. The SCA is affirmed to have a highly negative correlation with mean temperature and runoff. Meanwhile, SCA has a very weak relation with precipitation data. The Pearson correlation coefficient between SCA and runoff is -0.82, which confirms that the Gilgit River runoff largely depends on the melting of snow cover rather than direct precipitation. The study indicates that the SCA slightly decreased for the study period, which depicts a possible impact of global warming on this mountainous region.

Calibration Changes to Terra MODIS Collection-5 Radiances for CERES Edition 4 Cloud Retrievals.

Previous research has revealed inconsistencies between the Collection 5 (C5) calibrations of certain channels common to the Terra and Aqua MODerate-resolution Imaging Spectroradiometers (MODIS). To achieve consistency between the Terra and Aqua MODIS radiances used in the Clouds and the Earth's Radiant Energy System (CERES) Edition 4 (Ed4) cloud property retrieval system, adjustments were developed and applied to the Terra C5 calibrations for channels 1-5, 7, 20, and 26. These calibration corrections were developed independently of those used for MODIS Collection 6 (C6) data, which became available after the CERES Ed4 processing had commenced. The comparisons demonstrate that the corrections applied to the Terra C5 data for CERES Edition 4 generally resulted in Terra-Aqua radiance consistency that is as good as or better than that of the C6 datasets. The C5 adjustments resulted in more consistent Aqua and Terra cloud property retrievals than seen in the previous CERES edition. Other calibration artifacts were found in one of the corrected channels and in some of the uncorrected thermal channels after Ed4 began. Where corrections were neither developed nor applied, some artifacts are likely to have been introduced into the Ed4 cloud property record. For example, the degradation in the Aqua MODIS 0.65-µm channel in both the C5 and C6 datasets affects trends in cloud optical depth retrievals. Thus, despite the much-improved consistency achieved for the Terra and Aqua datasets in Ed4, the CERES Ed4 cloud property datasets should be used cautiously for cloud trend studies because of those remaining calibration artifacts.

SWCTI: Surface Water Content Temperature Index for Assessment of Surface Soil Moisture Status.

The vegetation supply water index (VSWI = NDVI/LST) is an effective metric estimating soil moisture in areas with moderate to dense vegetation cover. However, the normalized difference vegetation index (NDVI) exhibits a long water stress lag and the land surface temperature (LST), sensitive to water stress, does not contribute considerably to surface soil moisture monitoring due to the constraints of the mathematical characteristics of VSWI: LST influences VSWI less when LST value is sufficiently high. This paper mathematically analyzes the characteristics of VSWI and proposes a new operational dryness index (surface water content temperature index, SWCTI) for the assessment of surface soil moisture status. SWCTI uses the surface water content index (SWCI), which provides a more accurate estimation of surface soil moisture than that of NDVI, as the numerator and the modified surface temperature, which has a greater influence on SWCTI than that of LST, as the denominator. The validation work includes comparison of SWCTI with in situ soil moisture and other remote sensing indices. The results show SWCTI demonstrates the highest correlation with in situ soil moisture; the highest correlation R = 0.801 is found between SWCTI and the 0⁻5 cm soil moisture in a sandy loam. SWCTI is a functional and effective method that has a great potential in surface soil moisture monitoring.

Challenges in Complementing Data from Ground-Based Sensors with Satellite-Derived Products to Measure Ecological Changes in Relation to Climate-Lessons from Temperate Wetland-Upland Landscapes.

Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relations between temperature and precipitation and a set of variables measured on the ground at individual wetlands and another set measured via satellite sensors within surrounding 4 km² landscape blocks. At the block scale, we used evapotranspiration and vegetation greenness as remotely sensed proxies for water availability and to estimate seasonal photosynthetic activity. We used sensors on the ground to coincidentally measure surface-water availability and amphibian calling activity at individual wetlands within blocks. Responses of landscape blocks generally paralleled changes in conditions measured on the ground, but the latter were more dynamic, and changes in ecological conditions on the ground that were critical for biota were not always apparent in measurements of related parameters in blocks. Here, we evaluate the effectiveness of decisions and assumptions we made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines.

The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua.

The MODIS Level-2 cloud product (Earth Science Data Set names MOD06 and MYD06 for Terra and Aqua MODIS, respectively) provides pixel-level retrievals of cloud-top properties (day and night pressure, temperature, and height) and cloud optical properties (optical thickness, effective particle radius, and water path for both liquid water and ice cloud thermodynamic phases-daytime only). Collection 6 (C6) reprocessing of the product was completed in May 2014 and March 2015 for MODIS Aqua and Terra, respectively. Here we provide an overview of major C6 optical property algorithm changes relative to the previous Collection 5 (C5) product. Notable C6 optical and microphysical algorithm changes include: (i) new ice cloud optical property models and a more extensive cloud radiative transfer code lookup table (LUT) approach, (ii) improvement in the skill of the shortwave-derived cloud thermodynamic phase, (iii) separate cloud effective radius retrieval datasets for each spectral combination used in previous collections, (iv) separate retrievals for partly cloudy pixels and those associated with cloud edges, (v) failure metrics that provide diagnostic information for pixels having observations that fall outside the LUT solution space, and (vi) enhanced pixel-level retrieval uncertainty calculations. The C6 algorithm changes collectively can result in significant changes relative to C5, though the magnitude depends on the dataset and the pixel's retrieval location in the cloud parameter space. Example Level-2 granule and Level-3 gridded dataset differences between the two collections are shown. While the emphasis is on the suite of cloud optical property datasets, other MODIS cloud datasets are discussed when relevant.

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products.

With the objective of reducing the large uncertainties in the estimations of emissions from crop residue open burning, an improved method for establishing emission inventories of crop residue open burning at a high spatial resolution of 0.25°×0.25° and a temporal resolution of 1month was established based on the moderate resolution imaging spectroradiometer (MODIS) Thermal Anomalies/Fire Daily Level3 Global Product (MOD/MYD14A1). Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to improve the accuracy of related activity data. Locally observed emission factors were used to calculate the primary pollutant emissions. MODIS satellite data were modified by combining them with county-level agricultural statistical data, which reduced the influence of missing fire counts caused by their small size and cloud cover. The annual emissions of CO2, CO, CH4, nonmethane volatile organic compounds (NMVOCs), N2O, NOx, NH3, SO2, fine particles (PM2.5), organic carbon (OC), and black carbon (BC) were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43, 1.09, 0.34, and 0.06Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June (37%). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ±126% for N2O to a high of within ±169% for NH3.

Decadal changes in premature mortality associated with exposure to outdoor PM2.5 in mainland Southeast Asia and the impacts of biomass burning and anthropogenic emissions.

In mainland Southeast Asia (SEA), a rapid increase of fossil fuel consumption and massive particulate matter emissions from biomass burning (BB) are severely threatening the health of local inhabitants. In this study, surface PM2.5 data, satellite fire observations and emission inventories were integrated with the Global Exposure Mortality Model (GEMM) to estimate premature mortality attributable to PM2.5 exposure from 1990 through 2019 and to explore and quantify the health burden associated with BB and anthropogenic emissions in mainland SEA. BB in mainland SEA has remained intense over the past decades. Owing to a lack of effective control measures, emission inventory and satellite-observed data both showed that BB has markedly intensified in several regions, including northern Cambodia and northern Laos. The multiannual average (1997-2015) BB PM2.5 emission was 1.6 × 106 t/yr, which is much higher than that of anthropogenic (fossil fuel combustion) PM2.5 emission. GEMM results indicated that PM2.5-related premature mortality in mainland SEA more than doubled from 100 (95 % confidence interval [CI], 88-112) thousand in 1990 to 257 (95 % CI, 228-286) thousand in 2019. Decomposition analysis revealed that variations in population size and age structure also promoted this increase of PM2.5-related deaths. Given that mainland SEA is a rapidly developing region, it is expected that local public health will face increasing challenges due to population growth, population ageing, and increased anthropogenic emissions. Therefore, it is imperative for policymakers to consider these influential factors, set practical mitigation targets, and explore how to effectively and systematically combine BB with anthropogenic emission controls to maximize the health benefits.

A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images.

The Medium Resolution Spectral Imager-II (MERSI-II) onboard the recently launched Chinese Fengyun-3D (FY-3D) satellite has great capability in detecting global aerosols as it includes aerosol bands similar to Moderate Resolution Imaging Spectroradiometer (MODIS). However, to date, aerosol retrieval based on MERSI-II is still limited to dark target regions and there is no official aerosol products for the MERSI-II. This study focuses on developing a high-precision algorithm to retrieve aerosol optical depth (AOD) suitable for entire land areas (except snow/ice and inland waters) based on MERSI-II measurements. Considering both the accuracy and retrieval efficiency, a new cost function is constructed based on (1) the fact that the AOD (550 nm) retrieved independently from different bands should be the same with the correct aerosol model, and (2) the assumption that the aerosol types are the same in the 5 × 5 km spatial range. The retrieval method based on the new cost function is nearly 50 times faster than most current methods using iterative calculations. To extend the application adaption of the FY-3D MERSI-II AOD retrieval and avoid the errors caused by the surface Lambertian hypothesis, a bidirectional reflectance distribution function (BRDF) database is built using MODIS products. Eight candidate aerosol models in different natural zones of China are constructed based on AERONET aerosol products from 2010 - 2021. The new method is applied to MERSI-II images over China and validated against ground-based measurements at 14 stations from 2020 to 2021. MODIS aerosol products from three operational algorithms are also used for comparison purposes. The results show that MERSI-II AOD retrievals agree well with the ground-based measurements with correlation coefficient (R), root mean square error (RMSE), and relative mean bias (RMB) of 0.913, 0.123, and 0.955, respectively. In addition, 72.19 % of AOD matchups fall within the expected error (EE) envelopes. The MERSI-II retrievals show higher accuracy than that of MODIS dark target (DT) and deep blue (DB) products and comparable accuracy of the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. MERSI-II AOD also shows higher stability in terms of spatial and temporal and better performance under heavy aerosol loading conditions than MODIS products. A good AOD agreement with R from 0.777 to 0.863 between MERSI-II and MODIS products is found over the land of China. The new method showing high retrieval efficiency and accuracy has great potential to be operationally applied on AOD retrieval for MERSI-II.

Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria.

Globally, ambient air pollution claims ~9 million lives yearly, prompting researchers to investigate changes in air quality. Of special interest is the impact of COVID-19 lockdown. Many studies reported substantial improvements in air quality during lockdowns compared with pre-lockdown or as compared with baseline values. Since the lockdown period coincided with the onset of the rainy season in some tropical countries such as Nigeria, it is unclear if such improvements can be fully attributed to the lockdown. We investigate whether significant changes in air quality in Nigeria occurred primarily due to statewide COVID-19 lockdown. We applied a neural network approach to derive monthly average ground-level fine aerosol optical depth (AODf) across Nigeria from year 2001-2020, using the Multi-angle Implementation of Atmospheric Correction (MAIAC) AODs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, AERONET aerosol optical properties, meteorological and spatial parameters. During the year 2020, we found a 21% or 26% decline in average AODf level across Nigeria during lockdown (April) as compared to pre-lockdown (March), or during the easing phase-1 (May) as compared to lockdown, respectively. Throughout the 20-year period, AODf levels were highest in January and lowest in May or June, but not April. Comparison of AODf levels between 2020 and 2019 shows a small decline (1%) in pollution level in April of 2020 compare to 2019. Using a linear time-lag model to compare changes in AODf levels for similar months from 2002 to 2020, we found no significant difference (Levene's test and ANCOVA; α = 0.05) in the pollution levels by year, which indicates that the lockdown did not significantly improve air quality in Nigeria. Impact analysis using multiple linear regression revealed that favorable meteorological conditions due to seasonal change in temperature, relative humidity, planetary boundary layer height, wind speed and rainfall improved air quality during the lockdown.

[Applicability of MODIS C006 Aerosol Products in a Typical Environmental Area of the Beijing-Tianjin-Hebei Region].

By fitting with the aerosol optical depth (AOD) from AERONET ground observations at sites in Beijing, Xianghe, and Xinglong with different environmental backgrounds, MODIS C051 Dark Target (DT C051), C006 Dark Target (DT C006), C006 Deep Blue (DB C006), and C006 Deep Blue/Dark Target merged AOD products were compared and evaluated to understand their applicability in the Beijing-Tianjin-Hebei region. The main conclusions are as follows:① The comparison of the C051 and C006 algorithms shows that the accuracy of the AOD at the Beijing and Xianghe sites notably improved, while an improvement was not observed at the Xinglong site; the DB C006 AOD is closest to the AERONET AOD at the Beijing site and the DT C006 AOD is closest to the AERONET AOD at the Xianghe site; the combined C006 AOD is closest to the AERONET AOD at the Xinglong site. ② The inversion error of the MODIS DT C006 at the Beijing site is caused by the improper selection of the aerosol model and surface reflectance; the inversion error of the MODIS DB C006 is mainly due to surface reflectance in spring and the aerosol model in winter. ③ Compared with the DT C051 AOD, the effective data coverage of the DT C006 is reduced, but that of DB C006 and the combined C006 increased; the combined C006 AOD data have the largest coverage. The results show that the application of the combined AOD product is best for the Beijing-Tianjin-Hebei region.

[Spatial and Temporal Characteristics of Air Quality and Cause Analysis of Heavy Pollution in Northeast China].

Northeastern China experiences severe atmospheric pollution, with an increasing occurrence of heavy haze episodes. Based on ground monitoring data, satellite products and meteorological products of atmospheric pollutants in northeast China from 2013 to 2017, the characteristics of spatial and temporal distribution of air quality and the causes of heavy haze events in northeast China were discussed. It was found that the "Shenyang-Changchun-Harbin" city belt was the most polluted area in the region on an annual scale. The spatial distribution of air quality index (AQI) values had a clear seasonality, with the worst pollution occurring in winter, an approximately oval-shaped polluted area around western Jilin Province in spring, and the best air quality occurring in summer and most of autumn. The three periods that typically experienced intense haze events were Period I from late-October to early-November (i. e., late autumn and early winter), Period Ⅱ from late-December to January (i. e., the coldest time in winter), and Period Ⅲ from April to mid-May (i. e., spring). During Period I, strong PM2.5 emissions from seasonal crop residue burning and coal burning for winter heating were the dominant reasons for the occurrence of extreme haze events (AQI>300). Period Ⅱ had frequent heavy haze events (200 < AQI < 300) in the coldest months of January and February(200 < AQI < 300), which were due to high PM2.5 emissions from coal burning and vehicle fuel consumption, a lower atmospheric boundary layer, and stagnant atmospheric conditions. Haze events in Period Ⅲ, with high PM10 concentrations, were primarily caused by the regional transportation of windblown dust from degraded grassland in central Inner Mongolia and bare soil in western Jilin Province. Local agricultural tilling could also release PM10 and enhance the levels of windblown dust from tilled soil.

Radiometric Measurement Comparison on the Integrating Sphere Source Used to Calibrate the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Landsat 7 Enhanced Thematic Mapper Plus (ETM+).

As part of a continuing effort to validate the radiometric scales assigned to integrating sphere sources used in the calibration of Earth Observing System (EOS) instruments, a radiometric measurement comparison was held in May 1998 at Raytheon/Santa Barbara Remote Sensing (SBRS). This comparison was conducted in support of the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) instruments. The radiometric scale assigned to the Spherical Integrating Source (SIS100) by SBRS was validated through a comparison with radiometric measurements made by a number of stable, well-characterized transfer radiometers from the National Institute of Standards and Technology (NIST), the National Aeronautics and Space Administration's Goddard Space Flight Center (NASA's GSFC), and the University of Arizona Optical Sciences Center (UA). The measured radiances from the radiometers differed by ±3 % in the visible to near infrared when compared to the SBRS calibration of the sphere, and the overall agreement was within the combined uncertainties of the individual measurements. In general, the transfer radiometers gave higher values than the SBRS calibration in the near infrared and lower values in the blue. The measurements of the radiometers differed by ±4 % from 800 nm to 1800 nm compared to the SBRS calibration of the sphere, and the overall agreement was within the combined uncertainties of the individual measurements for wavelengths less than 2200 nm. The results of the radiometric measurement comparison presented here supplement the results of previous measurement comparisons on the integrating sphere sources used to calibrate the Multi-angle Imaging SpectroRadiometer (MISR) at NASA's Jet Propulsion Laboratory (JPL), Pasadena, CA and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) at NEC Corporation, Yokohama, Japan.

Caracterização espectral multitemporal dos cereais de estação fria em imagens de satélite com média resolução espacial / Multitemporal spectral characterization of cool season cereals in satellite images with moderate spatial resolution

A fim de avaliar os padrões de resposta de áreas cultivadas com cereais de estação fria destinados para pastagens e para produção de grãos em imagens de satélite, foram analisados perfis temporais de índice de vegetação por diferença normalizada (NDVI), adquiridos em 29 áreas cultivadas com trigo e azevém anual, nos Estados do Rio Grande do Sul e Paraná. Para cada área foi informada a espécie cultivada (trigo ou azevém anual) e a coordenada do ponto central da área adquirido por meio do Global Positioning System (GPS). Foram usadas as imagens do sensor MODIS (Moderate Resolution Imaging Spectroradiometer), com resolução espacial de 250 metros, sobre cada área monitorada, de onde os valores de NDVI foram extraídos. Os perfis temporais de NDVI mostraram que os cultivos de produção de grãos têm um comportamento espectral típico de cultivos agrícolas, enquanto que, nas áreas cultivadas para a produção de pastagem, não foi observado esse mesmo padrão. As diferenças nos padrões temporais observadas se devem a modificações que o pastoreio impõe na fenologia e na morfologia dessas plantas.

In order to evaluate the satellite image patterns between such cool season cereals cultivated areas intended for grazing or grain production, Normalized Difference Vegetation Index (NDVI) temporal profiles were analyzed. This data was acquired from twenty nine wheat and annual ryegrass cultivated areas in the states of Rio Grande do Sul and Paraná. For each area, the cultivated species (wheat or ryegrass), as well as the respective central point coordinates, acquired via Global Positioning System (GPS) was informed. NDVI values were extracted over each monitored area from MODIS (Moderate Resolution Imaging Spectroradiometer) sensor images, with spatial resolution of 250 meters. The NDVI temporal profiles showed that grain production areas have a typical agricultural field spectral pattern. The same pattern was not observed for the grazing pasture areas. The differences observed in these temporal patterns are defined by the changes that grazing has imposed on the phenology and morphology of these plants.