Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud

Itiya Aneece,Prasad S Thenkabail

doi:10.3390/rs13224704

Abstract

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and the new generation DLR Earth Sensing Imaging Spectrometer (DESIS) data to evaluate the performance of hyperspectral narrowbands in classifying major agricultural crops of the U.S. with machine learning (ML) on Google Earth Engine (GEE). EO-1 Hyperion images from the 2010–2013 growing seasons and DESIS images from the 2019 growing season were used to classify three world crops (corn, soybean, and winter wheat) along with other crops and non-crops near Ponca City, Oklahoma, USA. The supervised classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB), and the unsupervised clustering algorithm WekaXMeans (WXM) were run using selected optimal Hyperion and DESIS HS narrowbands (HNBs). RF and SVM returned the highest overall producer’s, and user’s accuracies, with the performances of NB and WXM being substantially lower. The best accuracies were achieved with two or three images throughout the growing season, especially a combination of an earlier month (June or July) and a later month (August or September). The narrow 2.55 nm bandwidth of DESIS provided numerous spectral features along the 400–1000 nm spectral range relative to smoother Hyperion spectral signatures with 10 nm bandwidth in the 400–2500 nm spectral range. Out of 235 DESIS HNBs, 29 were deemed optimal for agricultural study. Advances in ML and cloud-computing can greatly facilitate HS data analysis, especially as more HS datasets, tools, and algorithms become available on the Cloud.

Highlights

Classifying agricultural crops accurately is crucial for addressing the challenges of global food and water security [1]
This study provides a number of novelties that will advance our understanding of hyperspectral data by examining: how a narrow bandwidth of 2.55 nm can help improve crop classification and characterization; how a new generation hyperspectral sensor (DESIS) compares with an old generation hyperspectral sensor (Hyperion) in the study of agricultural crops; how spectral signatures of some of the major world crops compare between the two sensors; and how we can address the challenges of analyzing large datasets from hyperspectral sensors using machine learning on the Cloud
We focused on images over an area in Ponca City, Oklahoma, USA (Figure 1), selected because of the presence of study crop types and the availability of time-series images in the growing season from both Hyperion and Deutsches Zentrum für Luftund Raumfahrt (DLR) Earth Sensing Imaging Spectrometer (DESIS) sensors (Table 1)

Summary

Introduction

Classifying agricultural crops accurately is crucial for addressing the challenges of global food and water security [1]. Remote sensing (RS) allows us to non-destructively study crops at large spatial and temporal extents. Hyperspectral (HS) remote sensing captures data as hundreds of narrowbands, opening up possibilities for advancing the study and classification of agricultural crops [1,2,3,4,5]. There are challenges in using HS data [1,10,11,14,15,16], including finding ways to store and process large volumes of data [17], minimize data redundancy, and acquire high-quality training and validation data with high signal to noise ratio [1,5,18]. Recent research [2,3,4,5,11,12,17,19,20,21,22] has shown as much as 80% of HNBs can be redundant in Earth Observing (EO-1) Hyperion

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