Extracting Tempo-Spatial Features of Paddy Rice Using Time-Series MOD09A1-A Case Study in Hunan, China

Zhao, Jing; Huang, Liusheng; Zhang, Dongyan

doi:10.1007/978-3-662-49155-3_58

Cited by 1 publication

(2 citation statements)

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“…For each pixel, the MOD09A1 product selects a value from all the data collected in 8 days according to the high observation coverage, low viewing angle, cloudless or cloud shadow, and aerosol load. It provides the Land Surface Reflectance (LSR) in 1 to 7 bands with a spatial resolution of 500 m. Also, it corrects atmospheric conditions, such as gas, aerosol, and Rayleigh scattering, which can reflect the growth status and environment of winter wheat 39 . The MOD11A2 product provides the land surface temperature (LST) per pixel with a spatial resolution of 1 km every 8 days.…”

Section: Methodsmentioning

confidence: 99%

“…It provides the Land Surface Reflectance (LSR) in 1 to 7 bands with a spatial resolution of 500 m. Also, it corrects atmospheric conditions, such as gas, aerosol, and Rayleigh scattering, which can reflect the growth status and environment of winter wheat. 39 The MOD11A2 product provides the land surface temperature (LST) per pixel with a spatial resolution of 1 km every 8 days. It also includes relevant quality control evaluation, observation time, observation zenith angle, and clear sky coverage and emissivity for land cover types at bands 31 and 32.…”

Section: Modis Datamentioning

confidence: 99%

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CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction

Ye,

Ma,

Jin

et al. 2024

J. Appl. Rem. Sens.

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Timely and accurate prediction of winter wheat yield contributes to ensuring national food security. We propose a CNN-bidirectional gated recurrent unit method with triple attention for winter wheat yield prediction, named CBTA. This deep learning model uses convolutional neural networks to mine the spatial spectral information in hyperspectral remote sensing images. Furthermore, the bidirectional gated recurrent unit is used to adaptively learn the time dependence between the various stages of winter wheat growth. Data from Henan Province, China, is used in this study to train the model and also verify its prediction performance and stability. The results from our experiment show that our proposed model has an excellent effect on yield prediction in the county, with root-mean-square-error, mean absolute error, and R 2 of 0.469 t∕ha, 0.336 t∕ha, and 0.827, respectively. Moreover, our findings suggested that the precision of our model using the data from sowing to heading-flowering stage was very close to that from sowing to ripening stage, which proves that the CBTA model can accurately predict the yield of winter wheat 1 to 2 months in advance.

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Section: Methodsmentioning

confidence: 99%

Section: Modis Datamentioning

confidence: 99%