To measure the daily average water surface temperature in small reservoirs using remote sensing, select appropriate satellite sensors like MODIS, Landsat, or Sentinel-3, considering their spatial and temporal resolution. Download thermal infrared (TIR) band data and pre-process it by applying atmospheric and geometric corrections, along with cloud masking. Retrieve surface temperatures using algorithms like the split-window for MODIS or single-channel for Landsat, then extract temperatures specific to the reservoir. Calculate the daily average by temporally and spatially averaging the data, and validate with ground measurements. Automating the process and accounting for reservoir size, seasonal, and diurnal variations will enhance accuracy and allow for effective monitoring of thermal dynamics.

Measuring the daily average water surface temperature of small reservoirs using remote sensing involves several steps:

Step 1. Selection of Remote Sensing Data.

-Satellite Selection: Choose an appropriate satellite that provides thermal infrared (TIR) data, such as:

-Landsat series: Landsat 8 and 9 have thermal infrared sensors (TIRS) that provide TIR data at 100 m resolution.

-MODIS: Onboard the Terra and Aqua satellites, MODIS provides daily thermal data but at a coarser spatial resolution (1 km).

-Sentinel-3:It has the Sea and Land Surface Temperature Radiometer (SLSTR) for measuring water surface temperatures.

-VIIRS: Aboard the Suomi NPP and NOAA-20 satellites, VIIRS offers TIR data at 750 m resolution.

-Temporal Resolution:Consider the revisit time of the satellite, as this will determine how frequently you can obtain temperature data.

Step 2.Data Acquisition

-Download Data: Obtain the necessary thermal infrared imagery from the selected satellite. This can be done via platforms like USGS EarthExplorer (for Landsat), NASA's Worldview (for MODIS), or Copernicus Open Access Hub (for Sentinel).

-PreProcessing:Depending on the satellite you may need to perform atmospheric correction, georeferencing, and calibration to ensure the data's accuracy.

Step 3. Extraction of Water Surface Temperature

-Radiance to Temperature Conversion:Convert the TIR band radiance data to temperature using the Planck function or the satellite-specific thermal constants.

-Water Masking:Apply a water mask to isolate the water bodies from the rest of the image.

-Temperature Averaging:Calculate the average temperature for the reservoir by averaging the temperature values of the pixels that correspond to the water surface.

Step 4. Temporal Averaging

-Daily Averaging: If you have multiple images of the reservoir on the same day (from different satellites or overpasses), average the temperatures to obtain the daily average surface temperature.

Step 5. Validation

- Insitu Measurements: Compare the remote sensing data with in-situ measurements (if available) to validate the accuracy of the remote sensing-derived temperatures.

Step 6. Considerations for Small Reservoirs

-Spatial Resolution. For small reservoirs, ensure that the spatial resolution of the satellite data is fine enough to capture the details of the reservoir without significant mixed-pixel issues.

- Cloud Cover: Be cautious of cloud cover, which can obscure thermal data. Consider using data from multiple satellites to mitigate this issue.

Tools and Software

- QGIS/ArcGIS: For processing and analyzing the remote sensing data.

-Google Earth Engine:For accessing and processing large amounts of remote sensing data efficiently.

-Python/R:For custom data processing and statistical analysis.

By following these steps, you can effectively measure and analyze the daily average water surface temperature of small reservoirs using remote sensing techniques.

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The daily average SST of the ocean near bays from remote sensors is different of the SST inside data as continous record and after calculate the daily average. The tendecy correspond at least on time scales > 7 days, according many years of skills