Pursue Your Passion for Remote Sensing with Lovely Professional University India

Are you fascinated by the world of remote sensing and eager to further your knowledge and expertise in this field? Look no further, as Lovely Professional University India (LPU India) is thrilled to announce the launch of its Master's program in Remote Sensing. This exciting opportunity is tailored for students like you who aspire to make a difference in the realm of spatial data analysis and environmental monitoring. Read on to discover how this program can help shape your future and pave the way for a successful career in remote sensing.

Why Choose LPU India?

LPU India, renowned for its academic excellence and holistic approach to education, has established itself as a leader in providing industry-relevant programs. With the launch of the Master's program in Remote Sensing, LPU continues its commitment to staying at the forefront of educational innovation and offering students the opportunity to excel in their chosen fields.

Unparalleled Faculty and Resources:

At LPU India, you will learn from a distinguished faculty comprising experienced professionals and renowned experts in the field of remote sensing. Their guidance and expertise will equip you with the necessary skills and knowledge to thrive in this rapidly evolving domain. Additionally, LPU India is equipped with state-of-the-art facilities, laboratories, and software resources, ensuring that you receive a comprehensive learning experience that blends theoretical concepts with practical applications.

Hands-on Training and Research Opportunities:

LPU India believes in learning by doing. Through hands-on training and practical sessions, you will have the opportunity to work with cutting-edge technologies and software used in remote sensing applications. The university also encourages research and innovation, providing you with a platform to contribute to the field through meaningful projects and collaborations.

Join LPU India's Master's Program in Remote Sensing:

If you are passionate about exploring the vast opportunities in remote sensing and wish to pursue a Master's degree in this exciting field, we invite you to take the first step. Fill out the attached Google form to express your interest and kick-start your journey towards a fulfilling career in remote sensing.

Rest assured that our dedicated admissions team will promptly reach out to you, providing further details and guidance throughout the application process. Stay tuned to our official website for updates and comprehensive information about the program.

Lovely Professional University India (LPU India) presents an incredible opportunity for aspiring remote sensing professionals to acquire the knowledge, skills, and industry exposure necessary to thrive in this dynamic field. With its prestigious ranking of 38 in the National Institutional Ranking Framework (NIRF) by the Government of India, LPU India stands as a testament to its commitment to excellence.

Don't miss out on this chance to pursue your passion for remote sensing and shape your future with LPU India. Fill out the Google form today and embark on an exciting educational journey that will set you on the path to success in the field of remote sensing.

Crop yield estimation using remote sensing and GIS video 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 is a powerful tool 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). In this article, we will explore the use of NDVI in conjunction with regression equations to estimate crop yields using remote sensing.

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. NDVI is highly correlated with crop growth and yield, and it can be used to estimate crop yields with a high degree of accuracy.

Regression equations can be used to create a relationship between NDVI and crop yield. These equations can be used to estimate crop yields based on NDVI values, making it possible to estimate crop yields using remote sensing data. The regression equation can be developed by collecting data on NDVI and crop yield from a specific crop and area, and then using that data to create a mathematical equation that describes the relationship between NDVI and crop yield.

Remote sensing data can be used to estimate crop yields by collecting NDVI images of the crop and then applying the regression equation to the NDVI values. The resulting estimates of crop yield can be used to make informed decisions about planting, fertilization, irrigation, and harvest timing. Additionally, remote sensing can be used to estimate crop yields across a large area, making it possible to identify areas with the highest crop yields and target resources and attention accordingly.

In conclusion, NDVI is a commonly used index in remote sensing for crop yield estimation. Regression equations can be used to create a relationship between NDVI and crop yield, which can be used to estimate crop yields using remote sensing data. Remote sensing can be used to estimate crop yields across a large area, making it possible to identify areas with the highest crop yields and target resources and attention accordingly. Crop yield estimation, NDVI, remote sensing, regression equations, crop growth, agricultural management, planting, fertilization, irrigation, harvest timing, precision agriculture.

The detailed step-by-step tutorial is available on this link

Click here to access 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