Classification of soybean varieties using different techniques: case study with Hyperion and sensor spectral resolution simulations

Abstract

Abstract. Next generation imaging spectrometers with higher signal-to-noise ratio and broader swath-width bring new perspectives for crop classification over large areas. Here, we used Hyperion/Earth Observing-One data collected over Brazilian soybean fields to evaluate the performance of four classification techniques (maximum likelihood -ML; spectral angle mapper -SAM; spectral information divergence -SID; support vector machine -SVM) to discriminate five soybean varieties. The spectral resolution influence on classifying them was analyzed by simulating the spectral bands of seven multispectral sensors using Hyperion data. Before classification, the Waikato environment for knowledge analysis was used for feature selection. Results showed the importance of the green, red-edge, near-infrared, and shortwave infrared to discriminate the soybean varieties. Because the soybean variety Monsoy 8411 was sensed by Hyperion in a later reproductive stage, it was more easily discriminated than the other varieties. The best classification techniques were ML and SVM with overall accuracy of 89.80% and 81.76%, respectively. The accuracy of spectral matching techniques was lower (70.84% for SAM and 72.20% for SID). When ML was applied to the simulated spectral resolution of the multispectral sensors, moderate resolution imaging spectroradiometer and enhanced thematic mapper plus presented the highest accuracy, whereas advanced very high resolution radiometer showed the lowest one. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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

“…Additionally, these data have been used to monitor mine waste [41], estimate foliar nutrition [42] and photosynthetic activity [43], and assess fire danger and post-fire effects [44,45]. Agricultural studies using Hyperion data include classification of crop residue [46,47], crops and crop varieties [48,49], crop planting area [50], and crop conditions after harvest [51]. These data have also been used to assess tillage intensity [47], and crop biophysical and biochemical characteristics [8,[52][53][54][55].…”

Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf-Based Hyperspectral Reflectance

“…Although few papers have been aimed at classifying soybean genotypes by their spectral response, the accuracy achieved in this paper was high when compared to other papers. Breunig et al [43] stated that the accuracy may be influenced by the classification method used, ranging from 44% to 100%. However, those authors performed the pixel classification of just five soybean genotypes using only a single Hyperion scene, in a single day, precluding the application of the obtained results to long-term analyses.…”

“…Ajayi et al [41] evaluated the spectral behavior of twenty wheat genotypes with wide genetic background under irrigated and dryland conditions using hyperspectral data collected at the canopy level; while Garriga et al [42] assessed the spectral response of 384 wheat genotypes at two phenological stages. Although much effort has been taken, there is a small number of research works addressed to discriminate and classify soybean genotypes by its spectral response [4,[43][44][45] and data analysis from multiple phenological stages, cropping seasons, and water availability were not addressed.…”

Research Progress on Remote Sensing Classification Methods for Farmland Vegetation

“…Among them, SVM and ML showed the best classification accuracy, 81.76% and 89.90%, respectively; the accuracy of the other two classification methods was less than 75%. However, because precipitation and irrigation increase the soil moisture content, which interferes with the extraction of soybeans, corn, rice, and wheat to a certain extent, the extraction of crops close to water sources and irrigation areas poses new challenges to classification by support vector machines [73]. Based on multi-temporal HJ-1A/B data, Jin et al used the support vector machine algorithm to study the classification and mapping of irrigation and dry-farming wheat.…”