MODIS-Based AVHRR Cloud and Snow Separation Algorithm

Abstract : The long-term data record (LTDR) has the goal of developing a quality and consistent Advanced Very High Resolution Radiometer (AVHRR) surface reflectance and albedo products dating back to 1982 at 0.05° spatial resolution. Distinguishing between cloud and snow is of critical importance when analyzing global albedo trends, for they influence the Earth's energy balance. However, this task is specially challenging when working with AVHRR given its limited spectral bands. Therefore, the current version of the LTDR does not distinguish between snow and clouds. To this end, we propose the Moderate Resolution Imaging Spectroradiometer (MODIS)-based AVHRR Class Separation Algorithm (MACSSA), whose goal is to identify clear land and snow pixels using AVHRR data. We make use of a combination of optical and thermal information from satellite and reanalysis data, along with monthly climatology information. These are used as inputs for two different support vector machine (SVM) models, which are then applied to AVHRR data to retrieve the MACSSA predicted tags. These are compared first against reference tags retrieved from the MYD10C1 product over pixels with less than 2-min overpass time difference between MODIS Aqua and NOAA16-19, distributed all around the world, and second against the Climate Change Initiative Cloud (Cloud_cci AVHRR) project. We found the product to be highly accurate in identifying clear land pixels, with a probability of detection of clear pixels (PODclear) of 97%. The discrimination of snow and clouds shows a PODsnow of 89%, which is encouraging given the spectral limitations of the AVHRR sensor.

 EXISTING SYSTEM :

 ? In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. ? A common shortcoming of existing datasets is the absence of uncertainty information for pixel-level retrievals (Level2 data) as well as for daily and monthly averages (Level-3 data). ? Spectral consistency is not maintained in existing cloud retrievals (e.g. Ham et al., 2009) despite being of particular importance to, for example, studies investigating the impact of cloud properties and their change on TOA broadband fluxes and latent heating rates. ? Various cloud types exist for either liquid or ice phase, which allows the simplification to a binary CPH information.

 DISADVANTAGE :

 ? The attraction of neural networks is that they are best suited to solving the problems that are the most difficult to solve by traditional computational methods. ? At this point we say that the network has learned the problem "well enough" - the network will never exactly learn the ideal function, but rather it will asymptotically approach the ideal function. ? The detection of cloud shadows is a problem. Clear-sky scenes that are potentially affected by shadows can be theoretically computed given the viewing geometry, solar azimuth and zenith angles, cloud edges distribution and cloud altitude. ? The issues of shadows caused by mountainous terrain also need to study.

 PROPOSED SYSTEM :

 • The purpose of this document is to provide a description and discussion of the physical principles and practical considerations behind the remote sensing and retrieval algorithms for cloud properties that we are developing for MODIS. • During the post-launch stage, it is anticipated that one full day of MODIS data at the 1 km pixel resolution per month will be sufficient for algorithm testing and validation purposes. • Therefore, MODIS is ideally suited to cloud remote sensing applications and retrieval purposes. • Set ECS core metadata which contains information about a MODIS product that is used for catalog, search, and archival purposes.

 ADVANTAGE :

 ? The chosen method should be efficient in term of computing time, make the maximum use of channels, be easily adapted (e.g., if one channel is missing), and be mature. ? The data set used in this study are mainly from Advanced Very High Resolution Radiometer (AVHRR), the Geostationary Meteorological Satellite (GMS) imageries. ? We use the Back-Propagation (BP) neural network in this cloud classification study, which is used wildly in many fields. ? Observation data consisting of cloud distribution pictures sent from these satellites is used in many fields including TV and newspaper weather forecasts. ? Theory analysis and experiment show that not only four channels data can be used to distinguish clouds and lands and water but also the difference between channels can do so.