Important Information to Understand and Use Crop Imagery LAI, RLAI, and Yield Downloads
GIS AG Maps developed algorithms for the rasters based on published research and its own research. Values are based on normalized relationships earlier in season, and other (not normalized) relationships later in season, as it should be (when to apply the different rasters included in the downloads is described in detail below). LAI and RLAI crop rasters are multiplied by 100 then converted to an integer raster to reduce file size (for example, LAI 2.50 is 250, 2.51 is 251, etc.). Corn yield rasters are rounded to near whole number (bu/ac). Soybean yield rasters are multiplied by 10 then converted to an integer raster to reduce file size (for example, yield LAI 55.0 is 550, 55.1 is 551, etc.).
Downloads include:
1) True Color Image.
2) Atmospheric Clearness Image (QA file for Landsat 8 and SCL file for Sentinel-2 [SLC file may not be available for Sentinel-2; if it is not available, separate images for clouds and shadows will be included).
3) Green Crop Canopy Relative Leaf Area Index (RLAI) rasters (pixels have RLAI values) for earlier and later crop stages that can be used for different crops with green canopies (such as wheat, potatoes, etc.). RALI is a generalized LAI value; however, the value of 100 (1.00) should be viewed as actual LAI 1.00 for any crop it is applied to. RLAI values can be scaled/calibrated in custom ways to specific green crop canopies by the user once in GIS. RLAI differences within a field can be correlated to yield difference based on publicly available information.
4) Corn and soybean leaf area index (LAI) or yield (bushels per acre) image (separated for each crop; pixels have LAI or yield values) for Corn Belt areas, or any area when requested. During transition times during the season, it may be necessary to include corn and/or soybean LAI imagery for both earlier and later times; in this case, corn and soybean imagery (along with the TCI and atmospheric clearness images) with be in a separate folder because file size is limited to under 300 GB. Otherwise, all files will be in one folder. Corn and soybean imagery from the beginning to middle of August are converted to a forecasted yield amount; for corn the value applies to field that are at the R3-R4 stages, for soybean value applies to fields at the R4-R5 stages. Corn yield is rounded to the nearest whole bu/ac; soybean yield is multiplied by 10 and rounded to the nearest whole bu/ac [for example, 55.2 would be 552]; eliminating decimal (floating point) raster significantly reduces file/download size. GIS Ag Maps welcome feedback about the values and can adjust values based on feedback.
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In all cases, pixel values aside, images can show higher and lower area of crop condition within a field just by brighter and darker, respectively, shades of gray. An effective way to display the various shades of gray within a field, and therefore view higher and lower levels of crop condition, is to symbolize the raster based on the low and high pixel values of a particular field. To determine the different pixel values within field, first click the layer (raster) of interest in the Layers window so it is highlighted, then click the Identify Features tool on the upper menu (a letter "i" in a circle with a cursor next to it), then click on pixels throughout the field to have a window appear with the pixel value. To symbolize the raster based on the Min and Max value of a field, right-click on the raster in the Layers window then select Properties then Symbology and enter the low and high values into Min and Max boxes and click OK. It is necessary to check to make sure the area of interest is clear and not in shadow using the QA (Landsat) and SCL (Sentinel-2) layers in the download, this can be done largely by viewing the QA and SCL layers; if checking by pixel value, for the Sentinel-2 SLC file the pixel value 4 represents clear vegetation, while for Landsat 8 QA file the pixel value 322 represents clear vegetation. You should also use the TCI to check for clouds and shadows.
The name of the crop image file describes when a particular image should be used (except in the situation listed at end of paragraph*). The file name generally tells you the range of RLAI, LAI, and/or growth stages that the file should be used for. To estimate the RLAI or LAI value of a field, first click the layer (raster) of interest in the Layers window to highlight it, then click the Identify Features tool on the upper menu (a letter "i" in a circle with a cursor next to it), then click on pixels throughout the field to have a window appear with the value to estimate the field RLAI or LAI (and, hence, which image to use).
The file names (showing when an image should be used) are as follows: Green_Crop_Canopy_RLAI_100_to_RLAI_400 (designed to be used during greenup), Green_Crop_Canopy_RLAI_400_to_Senescence, Soybeans_LAI_100_to_LAI_325, Soybeans_LAI_325_to_R5, Corn_LAI_100_to_350, Corn_LAI_350_to_R4, Corn_R3R4_Yield_Forecast, and Soybean_R4R5_Yield_Forecast.
*AS FAR AS WHEN TO CHANGE FROM LOWER TO HIGHER LAI OR RLAI IMAGES, if there is a large decrease in LAI or RLAI values (approximately 50 TO 75 or more [RLAI 0.50 to 0.75]) from lower to higher LAI or RLAI images (for example, from Green_Crop_Canopy_RLAI_100_to_RLAI_400 image to Green_Crop_Canopy_RLAI_400_to_Senescence), continue to use the lower LAI image (Green_Crop_Canopy_RLAI_100_to_RLAI_400, in this case) even if the average field value is beyond 400. However, if the LAI variability within a field is very low (there should be about 0.5 to 1.0 LAI difference, or more, within a field), it is a sign that you should use the later image because it will increase the variability. If the values between the two rasters are about even on the same image date or there is an increase of any kind, then use the higher LAI image (for this example, use the Green_Crop_Canopy_RLAI_400_to_Senescence image as opposed to the Green_Crop_Canopy_RLAI_100_to_RLAI_400 image). THE SAME RULES APPLY TO SOYBEAN AND CORN IMAGES. It is important to make the change in the images in order to maintain variability, as the earlier image start loosing variability and becoming insensitive to change as LAI increases.
After downloading then decompressing the folder (free decompression software can be downloaded from this website here), open free QGIS (click link to download it from this website), then click Project (upper left on menu) > Open > then navigate to the decompressed folder and click green QGIS Project Icon. The map will open with state and county symbolized; the True Color Image (TCI) and clearness image (QA for Landsat and SCL for Sentinel-2) will also be turn on. The crop layers will be turned off. To turn on and off different layers click the box next to the layer. To reorder layers, click and drag them up or down. It is important to analyzed imagery over high resolution imagery. The recommended method of adding free background imagery (if you have sufficient internet speed) is to enable constant high resolution background imagery (Google or Bing). To add Google imagery (while in QGIS), click Browser>right-click on XYZ Tiles>click New Connection>Name it Google Imagery and paste the following into the URL window: http://mt0.google.com/vt/lyrs=s&hl=en&x={x}&y={y}&z={z} . Now if you double click on Google Imagery, the imagery will appear (zoom in somewhere to see the high-resolution imagery). The URL for Bing Imagery is: http://ecn.t3.tiles.virtualearth.net/tiles/a{q}.jpeg?g=1 . The URL for OpenStreetMap (roads and more) is: https://a.tile.openstreetmap.org/{z}/{x}/{y}.png . If you do not have sufficient internet speed, free high resolution imagery can be downloaded from the The National Map Viewer (offsite page; opens in new tab). IT IS IMPORTANT TO KNOW that if you have the high resolution image as the viewable layer but the crop layer selected (by shading it by clicking it with the cursor), when you click on the high resolution layer with the Identify Features tool, the value that will appear with be the crop raster value (so you can view the high resolution image and click on it, yet have an RLAI, LAi, or yield value appear).
Once the canopy has closed, or virtually closed, the correlation between satellite imagery (calculated in particular ways) and green crop canopies typically have a high statistically significant spatial correlation to crop condition and, hence, future yield patterns (corn in reproductive stages being an exception); it is possible, but uncommon, for circumstances after imagery to significantly alter yield patterns. See the Landsat Correlation to Crop Yield folder for examples. The canopy needs to be closed enough to sufficiently reduce the effect of the background soil reflectance being averaged into pixel values.
Corn in reproductive stages is an exception to high statistically significant spatial correlation to crop condition. Cornfields in the vegetative stages after the canopy has closed, do have a high statistically significant level of correlation to crop condition; however, during reproductive stages, due to the change in the appearance and structure of the crop, cornfields have much less of a significant spatial correlation to inter-field crop condition. Corn imagery shows large differing areas of crop condition but does not reliable show subtle differences, such as imagery does for soybean fields and other green crop canopies. Look at corn field data more as an overall and generalized value with significant difference being shown.
Usable areas of fields should be at least 80 meters wide, as pixels used from the two satellites for the data downloads are 20 and 30 meters square. Only use pixels that are at least a half pixel distance from non-field areas as there is, on average, about a half-pixel horizontal error per pixel (so the surface represented by the pixel may represent the surface about a half-pixel away in various directions). Use with high resolution background imagery to make sure a pixel solely represents cropfield area (high resolution imagery is available free online, as previously described). Overall, the imagery can give you a useful idea of higher and lower areas of crop condition, LAI, and yield within a field. Feel free to contact us with questions about using the data.