A Simple and Robust Spectral Index for Identifying Lodged Maize Using Gaofen1 Satellite Data.

Yuanyuan Chen,Jiong You,Li Sun,Weijie Jiao,He Li,Zhiyuan Pei,Juanying Sun

doi:10.3390/s22030989

Abstract

Crop lodging is a major destructive factor for agricultural production. Developing a cost-efficient and accurate method to assess crop lodging is crucial for informing crop management decisions and reducing lodging losses. Satellite remote sensing can provide continuous data on a large scale; however, its utility in detecting lodging crops is limited due to the complexity of lodging events and the unavailability of high spatial and temporal resolution data. Gaofen1 satellite was launched in 2013. The short revisit cycle and wide orbit coverage of the Gaofen1 satellite make it suitable for lodging identification. However, few studies have explored lodging detection using Gaofen1 data, and the operational application of existing approaches over large spatial extents seems to be unrealistic. In this paper, we discuss the identification method of lodged maize and explore the potential of using Gaofen1 data. An analysis of the spectral features after maize lodging revealed that reflectance increased significantly in all bands, compared to non-lodged maize. A spectral sum index was proposed to distinguish lodged and non-lodged maize. Two study areas were considered: Zhaodong City in Heilongjiang Province and Ningjiang District in Jilin Province. The results of the identified lodged maize from the Gaofen1 data were validated based on three methods: first, ground sample points exhibited the overall accuracies of 92.86% and 88.24% for Zhaodong City and Ningjiang District, respectively; second, the cross-comparison differences of 1.01% for Zhaodong City and 1.13% for Ningjiang District were obtained, compared to the results acquired from the finer-resolution Planet data; and third, the identified results from Gaofen1 data and those from farmer survey questionnaires were found to be consistent. The validation results indicate that the proposed index is promising, and the Gaofen1 data have the potential for rapid lodging monitoring.

Highlights

Crop lodging, which is the bending of plant stems from their upright position or the destruction of the plant root–soil anchorage system, is usually caused by strong storms or heavy rains induced by typhoons [1,2]
To assess the robustness of the spectral sum index (SSI) method, the SSI index was applied to two study areas: Zhaodong City in Heilongjiang Province and Ningjiang District in Jilin Province
The validation based on the validating samples presented the overall accuracies of 92.86% in Zhaodong City and 88.24% in Ningjiang District for the lodged and non-lodged maize detection

Summary

Introduction

Crop lodging, which is the bending of plant stems from their upright position or the destruction of the plant root–soil anchorage system, is usually caused by strong storms or heavy rains induced by typhoons [1,2]. Crop lodging, which occurs after the passing of a strong typhoon, has been recognized as a major agricultural disaster. Lodging is detrimental to biomass accumulation; crop yield usually decreases and grain quality declines after lodging occurs [3,4,5]. Since they are relatively tall, cereal crops, especially maize, are more affected by lodging [6,7]. Timely, rapid, and quantitative identification of maize lodging areas is important and is the basis of loss assessments

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Conclusion