Crop Yield Estimation using Remote Sensing and GIS ArcGIS

This lecture presents an exponential model for crop yield estimation, using coefficients A and B to fit extreme values, and explains why a linear equation is unsuitable.

Explore your study area using Google Earth Pro to identify field clusters, compare time-spanned imagery, and build a crop yield model validated by sampling and remote sensing data.

download crop data from government sources, select district and years for crops like wheat, export as csv or excel, and download district shapefiles with boundary verification.

Identify your area of interest and verify it with official shapefiles, then download a Landsat 8 image with near-infrared and red bands from Earth Explorer for crop yield analysis.

Master ArcGIS workflows to process satellite images by stacking bands and creating composite rasters, perform pre-processing, and prepare for crop classification of the study area.

Learn how to extract a district shapefile from a satellite image, align coordinate systems, and export district-specific shapefiles for a crop yield estimation workflow.

Cut the study area with extraction by mask in ArcGIS, using a shapefile as the mask on a satellite image, then adjust band visualization for later classification.

Classify wheat fields using the image classification tool, validating with Google Earth to define study area, separate vegetation from non-vegetation, and ensure accuracy using infrared, standard deviation, and non-stretch display.

Classify crops and non-crop areas using training samples in ArcGIS, selecting diverse samples for wheat, vegetation, urban areas, and water to achieve 80–97% accuracy.

Learn to classify crops in ArcGIS using a support vector machine classifier, training with labeled samples, performing segmentation and classification, and generating a reusable output classifier raster.

Generate NDVI in ArcGIS by selecting red and infrared bands, export a permanent output, and interpret NDVI values to separate crops from urban and vegetation for yield modeling.

Verify crop area by saving the project, transferring raster counts to Excel, and comparing estimated hectares with records, then compute accuracy using pixel-based area and validation.

Isolate wheat-specific NDVI from a classified raster using conditional raster analysis and SQL, mask urban and forest areas, and prepare the wheat NDVI for regression in Excel.

Develop a regression equation linking index values to crop yield using Excel to compute averages, identify max/min, and fit an exponential trend line, then apply it in ArcGIS.

Develop a reusable crop yield model using the driver equation and ArcGIS tools (raster calculator, map algebra) to generalize across nearby areas and classify yield into five color classes.

Convert 30-meter pixels to hectares using linear interpolation, then compute total crop production in kilograms and convert to tons, while assessing accuracy around 98% with a simple formula.

Classify yield data in ArcGIS using natural breaks to create five yield classes, reclassify pixels, and calculate the area in square kilometers per class, then export to Excel for visualization.

Validate by applying same trained model to area within ten days; use same band combination, separate crop and DBA; compare average yield, crop area, and total production with observer data.

Learn to extract a study area from a satellite image using mask, adjust raster bands (5,4,3), and apply stretching, histogram equalization, and standard deviation for better display.

Arrange layers and create a validation data frame for the study area, then calculate NDVI using the infrared and red bands, export the data, and classify the satellite image.

Classify the study area with a trained ArcGIS model, separate corn pixels using a conditional raster, and feed the result into the regression model to estimate wheat yield.

Calculate validation matrices and yield accuracy in ArcGIS by converting pixels to hectares and using a validation table to compute area and production accuracy. Display results with color maps.

Learn to validate survey data with 100 GPS points, plot them in ArcGIS, and build a regression model using pre-calculated yields and an exponential trend line achieving 96% accuracy.

Learn common GIS mistakes that hinder ArcGIS, such as long folder paths, spaces in folder names, data on the C drive, and missing raster extensions like .tiff or .img.

Learn to create publication-quality maps of crop unit data using GIS, interpret satellite images, and understand land-use classifications and common mapping errors.