In simple word, spatial resolution is the pixel dimension of satellite images (e.g., 30 m for Landsat). And temporal resolution is the frequency of data collection over particular location (e.g., 16 days for Landsat). I hope it is useful.

Dr Avinash Kumar Ranjan thank you for your contribution to the discussion

Spatial resolution of images is enhanced by short spatial pulse length and focusing. Compared with low-frequency pulses, high-frequency pulses have shallow depth of penetration owing to increased attenuation. Temporal resolution of a two-dimensional image is improved when frame rate is high. Temporal resolution is a measure of the repeat cycle or frequency with which a sensor revisits the same part of the Earth's surface. Spatial resolution is a measure of the smallest object that can be resolved by the sensor, or the ground area imaged for the instantaneous field of view (IFOV) of the sensor, or the linear dimension on the ground represented by each pixel. Landsat 7 data has eight spectral bands with spatial resolutions ranging from 15 to 60 m (49 to 197 ft); the temporal resolution is 16 days. Landsat images are usually divided into scenes for easy downloading. Spatial resolution is a measurement of how detailed objects are in an image based on pixels. Whereas spectral resolution is the amount of spectral detail in a band based on the number and width of spectral bands. Let's get into both concepts of image resolution with a bit more detail. On that account, two terms are introduced: spatial resolution, and temporal resolution. The spatial resolution is the amount of spatial detail in an observation, and the temporal resolution is the amount of temporal detail in an observation. Spatial refers to space. Temporal refers to time. Spatiotemporal, or spatial temporal, is used in data analysis when data is collected across both space and time. It describes a phenomenon in a certain location and time as, shipping movements across a geographic area over time. Spatial data can analyze on many levels, zip codes, census tract, state, geocode, etc. Temporal data is often analyzed as multiple data points per observation over time and can be measured by just as many ways as the spatial data, if not more. Spatial means space, whereas temporal means time. Spatial Data Mining refers to the process of extraction of knowledge, spatial relationships, and exciting patterns that are not explicitly stored in a spatial database. Spatial distribution, also called spatial pattern analysis, is an analysis tool used in many fields to measure the physical location in which things occur. Spatial pattern analysis differs from temporal distribution, which measures the change in patterns according to time. Under pure spatial variation, factors vary across a spatial transect but are constant from one time period to another and under pure temporal variation, factors vary from one time to another but are constant across space. Spatial synthesis includes any of the formal techniques which study entities using their topological, geometric, or geographic properties. Temporal Synthesis can be described as 'automated construction' of a system whereby we develop a temporary specification and then try to prove it. In a nutshell, spatial resolution refers to the capacity a technique has to tell you exactly which area of the brain is active, while temporal resolution describes its ability to tell you exactly when the activation happened.

The spatial resolution is the amount of spatial detail in an observation, and the temporal resolution is the amount of temporal detail in an observation

Dr Suraj Patil thank you for your contribution to the discussion

Spatial Resolution refers to the size of the smallest feature that can be detected by a satellite sensor or displayed in a satellite image. It is usually presented as a single value representing the length of one side of a square. Temporal resolution as the amount of time needed to revisit and acquire data for the exact same location. When applied to remote sensing, this amount of time depends on the orbital characteristics of the sensor platform as well as sensor characteristics. On that account, two terms are introduced: spatial resolution, and temporal resolution. The spatial resolution is the amount of spatial detail in an observation, and the temporal resolution is the amount of temporal detail in an observation. Most commercial imagery falls between 2 and 5 meter resolution, with high-resolution sensors capturing at 70, 50 and 30 centimeter resolution. Each increase in resolution results in an exponential increase in the amount of critical information held in each pixel.There are two ways for a cell to sense these external chemicals: temporal sensing, where the cell senses the external chemical at two different time points after it has moved through a certain distance, or spatial sensing, where the cell senses the external chemical at two different locations on its cellular surface.Spatial resolution states that the clarity of an image cannot be determined by the pixel resolution. The number of pixels in an image does not matter. Or in other way we can define spatial resolution as the number of independent pixels values per inch. The spatial resolution is the amount of spatial detail in an observation, and the temporal resolution is the amount of temporal detail in an observation. Spatial resolution refers to the ability to distinguish between two regions of the brain. Temporal resolution refers to the ability to distinguish between two events in the brain taking place at different times. Spatiotemporal, or spatial temporal, is used in data analysis when data is collected across both space and time. It describes a phenomenon in a certain location and time. Spatial analysis is how we understand the space around us and the world. It helps us solve complex location-analytics problems. A temporal understanding of data helps us see if patterns are consistent over time and to detect unusual patterns if any. Spatial means space, whereas temporal means time. Spatial Data Mining refers to the process of extraction of knowledge, spatial relationships, and exciting patterns that are not explicitly stored in a spatial database. Spatial data mining deals with data types such as points, lines, and polygons, while temporal data mining deals with data types such as time series, events, and sequences.