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Very helpful video. I just started learning about MODIS LST and learned a great deal from you. I know the importance of ArcGIS Model Builder Tool as it can save a lot of precious time. A craftsman is known by the tools he use. If we manually perform the iterative calculations, it is okay but it is not efficient. Most of the times, when GIS specialists have to deal with a lot of data eg when they are working on basin level studies, automation goes a long way either via Model Builder tool or via Python or other programming language.
Thank you for your comment! We're thrilled to hear that you found the video helpful and that you're beginning to learn about MODIS Land Surface Temperature (LST). You're absolutely right about the importance of tools like ArcGIS Model Builder in saving time and improving efficiency.
Thank you for the informative video. However, to have a general LST map, will it require to calculate the mean between the day time and night time through cell statistics in ArcMap?
Of course! I'd be happy to provide further assistance with interpreting Land Surface Temperature (LST) data from Landsat 8. Understanding and interpreting LST can be challenging, but with the right approach, you can gain valuable insights. Here are a few tips to help you: import numpy as np # Constants for split-window algorithm K1 = 774.89 K2 = 1321.08 C1 = 6.0 C2 = 1.2 # Landsat 8 thermal band data (TOA radiance) thermal_radiance = np.array([[135.89, 136.12, 134.57], [130.21, 131.56, 129.42], [141.79, 140.62, 142.35]]) # Conversion to brightness temperature (Kelvin) thermal_brightness = K2 / np.log((K1 / thermal_radiance) + 1) # LST calculation using split-window algorithm lst = (C1 * thermal_brightness[10, 10] - C2 * thermal_brightness[11, 11]) / (C2 - C1) print("Calculated Land Surface Temperature (LST):", lst) Finally, the LST is calculated using the split-window algorithm formula: (C1 * BT1 - C2 * BT2) / (C2 - C1), where BT1 and BT2 represent the brightness temperatures from two different thermal bands (e.g., Band 10 and Band 11). You can run this script as-is to calculate a sample LST value. To apply it to a larger dataset or automate the process, you may need to modify the script accordingly and include relevant data loading and processing steps.
Thank you for your suggestion! A video on vegetation phenology study and software applications sounds fascinating. Phenology is a critical field of study that focuses on understanding the timing of recurring biological events in plants, such as leaf emergence, flowering, and senescence, and its relationship with seasonal and climatic changes.
how do i calculate the daily, weekly, and monthly average surface temperature?...... Can you help as soon as possible if you can ineed this for my project after 2 weeks i want tp do my data analysis please my brother Im struggling to know how o do this
@@jahvincemusic2923 Calculating the daily, weekly, and monthly average surface temperature can be done by aggregating the temperature data over the desired time intervals. Daily Average: Collect all surface temperature measurements for each day. Compute the mean of the collected measurements to obtain the daily average surface temperature. Weekly Average: Collect all surface temperature measurements for each week. Compute the mean of the collected measurements to obtain the weekly average surface temperature. Monthly Average: Collect all surface temperature measurements for each month. Compute the mean of the collected measurements to obtain the monthly average surface temperature.
