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Optimizing soybean harvest date using HJ-1 satellite imagery

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Abstract

Satellite imagery provides contiguous spatial coverage of a field and can be used to measure crop and soil attributes. In the last decade, remote sensing has been proved useful in guiding field management such as sowing, irrigation and fertilization, yet its potential in optimizing large-area crop harvest has not been explored. With a limited number of observations from Hongxing Farm in Northeast China, this paper first analyzes the influence of harvest date on yield. Optimal harvest date (OHD) was estimated for 41 sites in total based on maximum yield. Then the indicating ability of seven satellite-derived indices at different maturing stages for OHD was analyzed. These indices included NDVI (normalized difference vegetation index), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), visible atmospherically resistant index (VARI), chlorophyll vegetation index (CVI) and normalized difference water index (NDWI). The analysis showed that there was a continuous increase of yield before maximum yields were reached and then a rather fast decrease occurred. All seven indices acquired from HJ-1 satellite are capable of predicting soybean OHD; EVI and NDWI performed better than other indices with higher correlation coefficients. The highest correlation coefficients between remote sensing indices and observed optimal harvest date were 0.723 and 0.720 for EVI and NDWI respectively. The temporal variation of correlation coefficient between seven indices and observed OHD suggests that the best time for OHD prediction is the period 2–3 weeks earlier than the general harvest. Stepwise regression analysis was undertaken and optimum regression expressions were constructed, taking optimal soybean harvest date as the dependent variable and remote sensed indices (EVI, NDWI and NDVI at suitable dates) as independent variables. An optimal soybean harvest date map of Hongxing Farm was produced with average standard error of 1.15 days. This knowledge may be of use when determining the best times to harvest soybean to obtain a higher yield.

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Abbreviations

CCD:

Charge coupled device

CRESDA:

China Centre for Resources Satellite Data and Application

CROPGRO:

Crop growth

CVI:

Chlorophyll vegetation index

DAF:

Days after flowering

EVI:

Enhanced vegetation index

FLAASH:

Fast line-of-sight atmospheric analysis of spectral hypercubes

GNDVI:

Green normalized difference vegetation index

IRS:

Infrared Spectro-radiometer

NDVI:

Normalized difference vegetation index

NDWI:

Normalized difference water index

OHD:

Optimal harvest date

SAVI:

Soil adjusted vegetation index

SRF:

Spectral response function

STICS:

Simulateur multidiscplinaire pour les cultures standard

TOA:

Top of atmosphere

VARI:

Visible atmospherically resistant index

WOFOST:

World food study

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Acknowledgments

This work was funded by the National Natural Science Foundation of China (41171331, 41010118), the 863 Program of China (2013AA12A302, 2012AA12A307). We would like to thank Prof. Wu Bingfang for his valuable suggestions during the research, thank Dr. Zhang Miao and Dr. Dong Taifeng for sharing their experience in HJ-1 data pre-processing. Thanks go to Mr. Sun Hongjiang, Mr. Wang Qiang, Mr. Lv Jianzhao and Ms. Zhao Honglei for their extensive assistance in field survey. Additionally, we thank the CRESDA for providing the HJ-1 data.

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Authors and Affiliations

  1. Key Lab of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

    Jihua Meng, Jin Xu & Xingzhi You

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  1. Jihua Meng
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  2. Jin Xu
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Correspondence to Jihua Meng.

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Meng, J., Xu, J. & You, X. Optimizing soybean harvest date using HJ-1 satellite imagery. Precision Agric 16, 164–179 (2015). https://doi.org/10.1007/s11119-014-9368-3

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