Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Abstract

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

A New Crop Spectral Signatures Database Interactive Tool (CSSIT)

“…The spectral signatures are resampled again to fit the spectral resolution and wavelengths which are covered by a Hyperion hyperspectral imager on the EO-1 satellite platform [33,34]. A Hyperion image consists of 242 bands, has spectral resolution of 10 nanometers, and covers the entire reflected electromagnetic wavelength (350 to 2500 nanometers).…”

Improved Winter Wheat Spatial Distribution Extraction Using A Convolutional Neural Network and Partly Connected Conditional Random Field

“…When using low-and medium-resolution images as data sources, NDVI and other vegetation indices are typically used as the main features [71]. When higher-resolution remote sensing images are used as data sources, regression methods [72], support vector machines [73,74], random forests [75], linear discriminant analysis [76], and CNNs [77,78] are the more commonly used methods. There is a significant number of mis-segmented pixels at the edges of winter wheat planting areas, which are common problems that these methods must overcome.…”

Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

“…Therefore, lower flying platforms are an option capable of collecting data at very high spatial resolutions. Using remote sensing, such studies for crop separation have been conducted with unmanned aerial vehicles (UAVs) [12][13][14] or airborne imaging spectrometers (IS) [15,16].…”

“…Although IS sensors to be deployed on UAVs exist, more sophisticated sensors with very high signal-to-noise ratios are mainly operated from airborne platforms [19]. IS datasets are used in a range of applications in agriculture, i.e., biophysical properties [20,21], soil mapping [22], and, in particular, for crop separation [15,23]. Yet, the combined usage of IS and VHR datasets has so far been applied mainly in land cover studies in urban areas [24].…”