# Processing with SNAP [BurstProcessor](../reference/processor.BurstProcessor.md#s1bursts.processor.BurstProcessor) turns downloaded bursts into geocoded GeoTIFFs. It runs SNAP once for every burst, or once for every pair of bursts, and skips products that already exist. # How a processing call works Every `create_*` method takes the bursts, the UTM zone of the output, and optionally a settings object. From the settings, s1bursts builds the SNAP processing graph, the chain of SNAP steps that turns a burst into a product. The settings describe how to process and suit every burst alike. The UTM zone depends on where the bursts are, so it is passed separately as `crs`, the coordinate reference system (CRS): ``` python from s1bursts import BurstProcessor, RTCConfig processor = BurstProcessor(fm) rtc = processor.create_backscatter(bursts, crs="auto", config=RTCConfig(grid_spacing=20)) ``` Products are always projected in a WGS 84 / UTM zone. `crs="auto"` takes each burst's own zone, its [utm_zone](../reference/burst.Burst.md#s1bursts.burst.Burst.utm_zone), which suits bursts used on their own, such as a time series: the zone depends only on the footprint, so every date of a burst lands in the same one. To fix the zone instead, pass it as a string, an EPSG code or a `pyproj.CRS`, such as `crs="EPSG:32632"` for zone 32 North. If you plan to [composite](compositing.md), choose the zone before processing: a composite accepts only products in the UTM zone and pixel size of its grid square, so process them with `crs=square.crs`. Without `config`, the defaults apply, and they suit most uses. Each product has its own method and settings object: | Method | Input | Settings | Product | |----|----|----|----| | [create_backscatter()](../reference/snap_client.SNAPClient.md#s1bursts.snap_client.SNAPClient.create_backscatter) | Bursts | [RTCConfig](../reference/snap_graph.RTCConfig.md#s1bursts.snap_graph.RTCConfig) or [GTCConfig](../reference/snap_graph.GTCConfig.md#s1bursts.snap_graph.GTCConfig) | Backscatter of each acquisition | | [create_coherences()](../reference/processor.BurstProcessor.md#s1bursts.processor.BurstProcessor.create_coherences) | Pairs | [CoherenceConfig](../reference/snap_graph.CoherenceConfig.md#s1bursts.snap_graph.CoherenceConfig) | Coherence between two acquisitions | The settings objects share most fields. The one you are most likely to change is the pixel size, `grid_spacing`, which is 10 m by default. A smaller pixel size does not add detail beyond what the radar resolves. The [API reference](../reference/snap_graph.RTCConfig.md) lists every setting and its default. [SNAP processing](snap-processing.md) explains the steps each graph runs and how to inspect the graph your settings produce. # Backscatter Backscatter describes how strongly the ground reflects the radar signal on one date. In mountains, it also depends on the angle of each slope to the satellite: slopes facing the satellite look bright, and slopes facing away look dark. By default, s1bursts removes this effect with radiometric terrain correction (RTC), following the terrain-flattening method of Small (2011)[^1]. RTC divides each pixel by the area of ground that contributed to it, so the remaining differences in brightness come from the surface itself. The result is called gamma-0. Use RTC unless you have a reason not to. Only RTC products can be composited with [resolution weighting](resolution-weighting.md). The settings object selects the product: [RTCConfig](../reference/snap_graph.RTCConfig.md#s1bursts.snap_graph.RTCConfig), the default, gives RTC, and [GTCConfig](../reference/snap_graph.GTCConfig.md#s1bursts.snap_graph.GTCConfig) gives geometrically terrain-corrected (GTC) backscatter, called beta-0, which places pixels correctly on the map but keeps the slope brightness: ``` python from s1bursts import GTCConfig gtc = processor.create_backscatter(bursts, crs="auto", config=GTCConfig()) ``` # Coherence Coherence measures how similar two acquisitions of the same footprint are, from 0 to 1. It drops wherever the surface changed between the two dates, for example through snowfall, melt, vegetation growth or movement, such as the flow of a glacier. The pairs come from [`find_pairs()`](finding-and-downloading.md#pair-acquisitions): ``` python pairs = bursts.find_pairs(baseline=12) coherence = processor.create_coherences(pairs, crs="auto") ``` The product contains the coherence magnitude: the absolute value of the complex correlation between the two acquisitions, estimated over a small window of neighboring pixels set by `CoherenceConfig(window=...)`. A larger window gives smoother values at a coarser resolution. The phase of the correlation is not kept, and s1bursts makes no interferograms. ASF's [HyP3 burst InSAR](https://hyp3-docs.asf.alaska.edu/guides/burst_insar_product_guide) products cover the same bursts with wrapped and unwrapped phase. # Run several jobs at once [SNAPConfig](../reference/snap_client.SNAPConfig.md#s1bursts.snap_client.SNAPConfig) sets how many SNAP processes run in parallel and how much memory they share. Pass it to the processor to override the [environment variables](environment-variables.md#snap): ``` python from s1bursts import SNAPConfig processor = BurstProcessor(fm, config=SNAPConfig(max_workers=2, max_ram_gb=16)) ``` More processes finish a batch sooner but split the memory budget between them. If SNAP runs out of memory, use fewer processes or a larger budget. # Reprocessing and failures A product whose file already exists is not processed again, so a script can be rerun after an interruption. > **Warning: Changed settings raise an error** > > Filenames describe the burst and date, not the settings, so an existing file is checked before it is skipped. If it was made with other settings, such as another `grid_spacing` or DEM, the call raises a [ProductSettingsMismatchError](../reference/errors.ProductSettingsMismatchError.md#s1bursts.errors.ProductSettingsMismatchError) naming each difference, before SNAP runs. The same error is raised for a file that does not record every setting, such as one written by an earlier s1bursts version, and for a file made by another SNAP version, since the versions give slightly different results (see [SNAP versions](snap-processing.md#snap-versions)). A SNAP version that could not be determined is not compared. Write each experiment to its own [FileManager](../reference/filemanager.FileManager.md#s1bursts.filemanager.FileManager) directory, or pass `overwrite=True` to replace the old products. A failed burst does not stop the batch by default. Each call returns an [OperationSummary](../reference/summary.OperationSummary.md#s1bursts.summary.OperationSummary). Check it before using the results, since a time series with silent gaps can mislead. [raise_on_failure()](../reference/summary.OperationSummary.md#s1bursts.summary.OperationSummary.raise_on_failure) raises an error listing every failed burst, and otherwise returns the summary, so its products are one step away: ``` python rtc = processor.create_backscatter(bursts, crs="auto").raise_on_failure().products ``` Pass `continue_on_error=False` to [BurstProcessor](../reference/processor.BurstProcessor.md#s1bursts.processor.BurstProcessor) to stop at the first failure instead. ## Footnotes [^1]: Small, D. (2011). Flattening gamma: Radiometric terrain correction for SAR imagery. *IEEE Transactions on Geoscience and Remote Sensing*, 49(8), 3081-3093.