Friday, January 27, 2023

Crop yield estimation using remote sensing and GIS Tutorial

Crop yield estimation is a crucial aspect of agricultural management and planning. Accurate and timely yield estimates can help farmers make informed decisions about planting, fertilization, irrigation, and harvest timing. Remote sensing and geographic information systems (GIS) are powerful tools that can be used to estimate crop yields with a high degree of accuracy. One of the most commonly used indices in remote sensing for crop yield estimation is the normalized difference vegetation index (NDVI).  we will explore the use of NDVI in conjunction with remote sensing and GIS to estimate crop yields.
Remote sensing is the process of collecting and analyzing data about the earth's surface using sensors mounted on aircraft or satellites. These sensors can detect and measure various characteristics of the earth's surface, such as temperature, humidity, and vegetation cover. NDVI is a commonly used index in remote sensing that measures the amount of vegetation cover in an area. NDVI is calculated by taking the difference between the near-infrared and red bands of a multispectral image and dividing that difference by the sum of the near-infrared and red bands. NDVI values range from -1 to 1, with higher values indicating more vegetation cover.
GIS is a collection of software and data that can be used to analyze, visualize, and manage geographic data. GIS can be used to analyze remote sensing data, such as NDVI images, to estimate crop yields.By developing a regression model on NDVI and crop yield. GIS can also be used to create maps that show crop yields across an entire region, making it easier to identify areas that may need additional resources or attention.
ArcGIS is a popular GIS software that can be used to analyze remote sensing data and estimate crop yields. It can be used to process NDVI images and create maps that show crop yields across an entire region. The software also has a wide range of tools that can be used to analyze and manipulate spatial data, making it an ideal tool for crop yield estimation.
Model development required a number of tools and logic to use together.  

Tutorial Highlights :

  1. Use Machine learning method for crop classification in ArcGIS, separate crops from natural vegetation

  2. The model was developed using the minimum observed data available online

  3. Crop NDVI separation

  4. Crop Yield model development

  5. Crop production calculation from GIS model data

  6. Identify the low and high-yield zones and area calculation

  7. Calculate the total production of the region

  8. Validation of developed model on another study area

  9. Validate production and yield of other areas using a developed model of another area

  10. Convert the model to the ArcGIS toolbox