AgriUp | Agricultural Impacts of COVID-19

Agricultural Impacts of COVID-19

Remotely sensed data can provide information about conditions on the ground that may affect food supply chains and food security during pandemics. Your challenge is to demonstrate the agricultural socio-economic impacts from COVID-19.

AgriUp

Summary

Investigate the impact of climate change on the spatiotemporal agricultural production using satellite imagery and vegetation indices in GIS environment at Aswan Governorate; as well as simulating the different climate change scenarios on the long term for temperature and rainfall over Egypt.Comparing the performance of different vegetation indices (NDVI, SAVI) in detecting the vegetation intensity, as well as other types of indices (water and urban indices)Obtaining and analyzing the simulated meteorological information (temperatures and rainfall) over specific periods of time

How I Addressed This Challenge

In this study, we performed an in-season alfalfa yield prediction using Alfalfa is one of the most important and widespread perennial legumes, and it is considered as a valuable forage crop with relatively high yield and nutritional value.

The NDVI evolution follows the typical Alfalfa crop phenology in Tushka have been able to have been observed every year: starting the growing season in early June, reaching maximum values in mid-August, and ending the season in mid-September. Besides, some NDVI negative values are obtained in winter


the vegetation in 2019 is better than that earlier in almost all cases (area of 2019 is greater than any year)

How I Developed This Project

We have developed our project by collecting from information from

QGIS

How I Used Space Agency Data in This Project

Landsat LST datasets enable users to locally store the data involved, and thus the processing and analysis have also been computationally demanding. Making use of the computational resources of Google’s servers, the recent GEE online platform allows remote sensing data users to simply analyze large amounts of information.

the Sentinel-2 Surface Reflectance (SR) data to compute the NDVI values used for the year 2019 over a farm .

Project Demo

https://drive.google.com/file/d/1CqeAJvpaTZoZl6rcRibYa9SzkZeoGYRW/view?usp=sharing


Earth Observing Dashboard Integration

[1]               https://code.earthengine.google.com/da20a61ed1ada4ec9af4dfa8e95bcd4f

[2]               https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3Aannual%20ndvi

[3]               https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3ACalculate%20NDVI%20for%20a%20Single%20Month%20over%2036%20years

[4]               https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3A12%20-%20Chart%20NDVI%20over%20time

[5]               https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3Adevelop%20app%20to%20ndvi

[6]               https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3Amax%20and%20minimum%20ndvi%20value

[7]               https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3Athumb%20ndvi%20l8

[8]               https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3Ayearcoolection

[9]               https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3Avideo%20ndvi

[10]           https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3Acompare%20vegetation%20index

[11]           https://code.earthengine.google.com/?scriptPath=users%2Ffatenragabweb%2Faswan%3AEVI%20vegetation%20

[12]     https://code.earthengine.google.com/68b8880af2835fabc0b5bd5c9419d84e





Data & Resources

       Ermida, S. L., Soares, P., Mantas, V., Göttsche, F. M., & Trigo, I. F. (2020). Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. Remote Sensing, 12(9), 1471.


Judging

This project has been submitted for consideration during the Judging process.