PRSS-1: from pixels to useful maps

PRSS-1 launched from Jiuquan on July 9, 2018, sharing the flight with PakTES-1A. SUPARCO describes PRSS-1 as a sun-synchronous low-orbit imaging satellite serving mapping, agriculture, infrastructure, water management and disaster applications. Its ground facilities in Islamabad and Karachi support planning, reception, processing and archiving. An observation becomes useful through this entire sequence, not simply when a camera records light.

A sun-synchronous orbit maintains approximately consistent local solar time for repeated passes, which helps comparisons under similar illumination. It does not keep the spacecraft above one location all day. The spacecraft travels over different ground areas, and clouds can prevent a useful optical view even when an imaging opportunity occurs. Revisit opportunity and delivery of a clear, usable observation are therefore different measures.

SUPARCO lists approximately 0.98-meter panchromatic and 2.89-meter multispectral image products for PRSS-1. A panchromatic measurement combines light over a broad band; multispectral measurements separate selected wavelength ranges. Finer spatial sampling can help distinguish small patterns, while spectral differences can help classify materials or vegetation. Neither property alone supplies a complete description of the landscape or the condition of every object within it.

The agency also offers pansharpened products, which combine information from the finer panchromatic and multispectral measurements. Processing can create a useful display, but it does not turn every color value into an independent native high-resolution measurement. When comparing products, preserve their sensor, date, processing and scale. The two Landsat flood images shown here are a separate international example of change mapping in Pakistan, not PRSS-1 observations.

Two kinds of image information

Suppose a fictional analyst marks 500 square pixels as water on a map with 10-meter pixel spacing. Each pixel covers 100 square meters, so the marked area totals 50,000 square meters, or five hectares. This is a simplified area estimate; mixed pixels, shadows, cloud masks and classification errors can change the result. Counting pixels without their scale would not establish a flooded area.

A response team also needs to know when the image was acquired and whether roads or settlements have changed since then. A detailed image delivered too late may be less useful for a particular decision than a coarser but recent map. PRSS-1 illustrates why image products need interpretation and distribution as well as a capable sensor. A map of water extent does not by itself reveal water depth, road safety or the number of people affected.