Hello Titus. Your question is interesting. In fact, if low resolution images were obtained by aggregating (averaging) high resolution images, the values of the former could not be higher. Therefore, could there be any tips in the way aggregation has been made (for instance not simple averages?). Any metadata from the source of your dataset about the way aggregation has been performed?

Best regards,

Koffi

Hello Titus. Your question is interesting. In fact, if low resolution images were obtained by aggregating (averaging) high resolution images, the values of the former could not be higher. Therefore, could there be any tips in the way aggregation has been made (for instance not simple averages?). Any metadata from the source of your dataset about the way aggregation has been performed?

Best regards,

Koffi

I think this is possible in two separate cases.

• First, let say that the actual data was captured using the low resolution (250m), then for some reasons the company decided to resample the data and produce higher resolution (30m). The process itself may lead to losses from rounding. This would be just a mathematical/numerical issues.
• Second, let say that the data was collected with two different sensors (which would be the case, if the resolution is direct and comes from data collection). In this case, the sensitivity of the sensors to the energy from an area of 30*30m is much lower than the sensitivity when considering a bigger area of 250*250m. To illustrate my point, let say that under a given energy threshold e, the sensor just says there is nothing and records 0. You are going to have such cases when the area from which the sensor collects is smaller. So you will have few of those cases when the resolution is 250 and more when the resolution increases. So for instance if you go for a resolution of let say 1m, you may not record anything.
• The previous argument suppose that the components/sensibility of the sensor is the same though the resolution changes, in practice companies try to have better instruments and better calibration when looking at higher resolutions. However, this effort does not eliminate the limitation the argument 2 makes.
• Finally, the case you are experiencing is quite normal. Many companies don't collect data at very high spatial and radiometric resolutions because of the second argument. When they go to higher spatial resolution they tend to compensate that low energy recordable by reducing the radiometric resolution. An example of such a product is the Landsat imagery. The panchromatic (expanding all the visible spectrum is 15m resolution while the spectral bands are 30m and the thermal band is 60m).

I hope this helps

This is a possibility to consider. The 250 m resolution is picking up more trees, riparian areas, streams and perhaps wetlands. 30 m resolution may miss areas of higher ET potential unless the number of samples and random approach of sampling adequately samples the diversity. In N.C., USA at Coweeta hydrologic station, one study summarized found pine, hardwood and grass covers transpiring annually 30, 20, and 10 inches respectively, so sample location would be important.

i am not familiar with WATPOR, but review the assumptions and limitations for potential issues. There are about 69 30 by 30 m pixels in one 250 by 250 m pixel. It may be difficult to compare the two for their differences in size, variation in cover, topography, soils, etc.

Consider a tree, it pulls water from soil through roots and transpires through leaves, the surface boundaries are not well defined or exact. If the tree leaves are sampled on the edge of a 30 m plot, but the tree and roots are mostly outside the plot, how does the program deal with transpiration boundary issues? Who is to say that the leaf coverage orer a portion of the 30 m plot is transpiring water from within the 30 m plot or has transpired water from areas outside the plot? The 30 m dimension may be too small for viable estimates unless confined in lysimeter.

Thank you all for the explanation .

Zhu, L., Suomalainen, J., Liu, J., Hyyppä, J., Kaartinen, H., & Haggren, H. (2018). A Review: Remote Sensing Sensors. IntecOpen.