Using Open Remote Sensing Data to build an Agriculture Big Data System

Ancy Stephen, Et Al

doi:10.17762/turcomat.v12i2.830

Abstract

Landsat, MODIS, and Sentinel satellites are continuously producing multispectral sensor data with different spatial, temporal, and radiometric resolutions. This raw sensor data is calibrated and processed further, and additional data products are derived, which greatly reduces the burden for downstream applications from preprocessing these data. These petabyte-scale datasets are available to anyone free of charge. Remote sensing plays a key role in modern Agriculture. We can extract information about Soil, Weather, Water, and vegetation from these datasets. By processing historical remote sensing data, we can build temporal profiles of soil, weather, water, and agricultural conditions of the land. Deep learning and Spatio-temporal data mining algorithms can be applied to this data to extract hidden information. Having access to all this information via an agriculture information system, farmers will understand their land better and they will be empowered to make better decisions on a day-to-day activity. Although it looks simple from the surface, collecting, analyzing, and deriving insights from these sensor data and other data products from a multitude of sources is a big data and high-performance computing challenge. In this paper, we discuss the current open datasets and how these datasets can be used to solve various problems in agriculture. Also, we discuss implementing a cloud-based scalable agricultural information system which provides actionable insights to farmers.

Highlights

The world population is increasing rapidly and reached 7 billion
The biggest advantage of remote sensing data is that we already have a good historical dataset collected by the Landsat, Terra/Aqua (MODIS), and Sentinel satellites
Apart from the default datasets, we can employ additional algorithms and processes to extract more features, perform analytics, and produce insights using data mining and machine learning techniques. This raises the need for a big data analytics platform, which operates on remote sensing big data that originates from a multitude of sources, processes the multispectral, multi-resolution datasets cohesively for the geospatial context, and produce meaningful insights and alerts, which can benefit Governments, policymakers, and farmers, to achieve sustainable agriculture

Summary

Introduction

The world population is increasing rapidly and reached 7 billion. It is estimated that it will reach 9 billion by the year 2050. The biggest advantage of remote sensing data is that we already have a good historical dataset collected by the Landsat, Terra/Aqua (MODIS), and Sentinel satellites. Apart from the default datasets, we can employ additional algorithms and processes to extract more features, perform analytics, and produce insights using data mining and machine learning techniques This raises the need for a big data analytics platform, which operates on remote sensing big data that originates from a multitude of sources, processes the multispectral, multi-resolution datasets cohesively for the geospatial context, and produce meaningful insights and alerts, which can benefit Governments, policymakers, and farmers, to achieve sustainable agriculture. Google Earth Engine Cloud Computing Platform, provides datasets, algorithm libraries, and computing power to analyze remote sensing data collected over a long period of time [11][12]. Azure [14] hosts MODIS and Harmonized Landsat Sentinel-2 data products [47]

Extracting Features from Remote Sensing Data

Soil Properties

Findings

Weather information