Time resolution refers to the minimum time interval between two adjacent remote sensing observations in the same area.For orbiting satellites, it is also called coverage period.The time interval is large and the time resolution is low, otherwise, the time resolution is high.
When the same target is repeatedly detected, the time interval between two adjacent detections is called the time resolution of remote sensing image. It can provide information on the dynamic changes of ground objects, which can be used to monitor the changes of ground objects, and can also provide additional information for the accurate classification of some thematic elements.Time resolution includes two situations. One is the time resolution designed by the sensor itself, which cannot be changed due to the influence of satellite operation rules.The other is the time resolution artificially designed according to the application requirements, which must be equal to or less than the time resolution of the satellite sensor itself.
Cycle classification
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According to the length of regression cycle, time resolution can be divided into three types:
(1) Ultra short (short) cycle time resolution, which can observe changes within a day, in hours.
(2) Medium period time resolution, which can observe changes within a year, in days.
(3) Long period time resolution, usually in years.
The data required by the meteorological satellite is in hours, so the regression period of the meteorological satellite is, ultra short (short) period.Satellites that monitor atmospheric, oceanic and physical changes, as well as man-made satellites designed to monitor natural disasters, are generally short cycle or ultra short cycle satellites as required.When observing the dynamic change law of vegetation, the period of satellite is determined according to the biological change rhythm, which is generally medium period.If the cycle becomes shorter, there will be too much investment, which will make the investment out of proportion to the output.Some aerial photographs and satellite images are used to study the evolution of natural phenomena. Data can meet the needs in years, so it is a long period.Landsat image information sent to the ground on a day by day basis can meet the human demand for remote sensing information of resources and environment.Landsat satellite has continuously sent back data to the ground since 1972. People can extract data every few years to study things that change slowly in nature, such as land use change.[1]
function
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Time resolution is of great significance in remote sensing.Time resolution can be used for dynamic monitoring and forecasting, such as vegetation dynamic monitoringDynamic monitoring of land useIt can also find the ground object movement law through prediction, and summarize the model or formula to serve the practice.The time resolution can be used for natural historical changes and dynamic analysis, such as observing the trend of changes in the main island and cities in the estuary, and further studying why such changes occur and what dynamic mechanisms exist.The imaging rate and resolution rate can be improved by using the time resolution, and the data obtained in the past can be stacked and analyzed, so as to improve the accuracy of surface feature recognition.
Impact on subjects
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Time resolution has a wide range of applications, not only in geographical information, remote sensing, GIS, meteorology and other research fields, but also in medical, biological, chemical and other research fields that require time intervals as variables.In meteorology, the study of the impact of appropriate time resolution on some meteorological elements has always been a topic of frequent discussion.
For example, the daily meteorological element values in the meteorological data database often have some deviations from the true values. One of the sources of these deviations is related to the time resolution of daily observations, because the daily values are usually the daily average of multiple observations.If the observation data once every 1min is taken as the true value, only the influence of the observation time resolution of 2, 5, 10, 15, 30 and 60 min (i.e. 24 times/d) on the estimation of daily average value of each meteorological element is discussed. It is found that the shorter the observation time interval is, the smaller the error is, indicating that the fine time resolution will narrow the gap between the daily average value and the true value, thus improving the estimation accuracy.[2]
However, no matter what the time resolution value is, there must be errors between the true value and the element value. How to reduce errors by adjusting the time resolution is an important part of meteorological research.In order to evaluate the impact of different observation time resolutions on the estimation effect of meteorological elements and the accuracy of reference crop evapotranspiration estimation, the absolute error (E) between the estimated values of meteorological elements based on various time resolutions and their corresponding true values (based on 1-min observation data, i.e. daily meteorological element values obtained from 1440 observations per day)Relative error (PE), mean bias error (MBE), mean absolute error (MAE) and mean absolute relative error (MAPE), as shown in the Formula.
formula
Pi and Oi are estimated and true values of meteorological elements respectively, and n represents the number of samples in the study period.The closer the above five indicators are to zero, the smaller the error, the higher the precision.[3]
