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The Optimal Image Date Selection for Evaluating Cultivated Land Quality Based on Gaofen-1 Images.
Xia, Ziqing; Peng, Yiping; Liu, Shanshan; Liu, Zhenhua; Wang, Guangxing; Zhu, A-Xing; Hu, Yueming.
Afiliação
  • Xia Z; College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
  • Peng Y; College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
  • Liu S; College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
  • Liu Z; College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
  • Wang G; College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
  • Zhu AX; Department of Geography and Environmental Resources, Southern Illinois University Carbondale (SIUC), Carbondale, IL 62901, USA.
  • Hu Y; College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
Sensors (Basel) ; 19(22)2019 Nov 13.
Article em En | MEDLINE | ID: mdl-31766165
This study proposes a method for determining the optimal image date to improve the evaluation of cultivated land quality (CLQ). Five vegetation indices: leaf area index (LAI), difference vegetation index (DVI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI) are first retrieved using the PROSAIL model and Gaofen-1 (GF-1) images. The indices are then introduced into four regression models at different growth stages for assessing CLQ. The optimal image date of CLQ evaluation is finally determined according to the root mean square error (RMSE). This method is tested and validated in a rice growth area of Southern China based on 115 sample plots and five GF-1 images acquired at the tillering, jointing, booting, heading to flowering, and milk ripe and maturity stage of rice in 2015, respectively. The results show that the RMSEs between the measured and estimated CLQ from four vegetation index-based regression models at the heading to flowering stage are smaller than those at the other growth stages, indicating that the image date corresponding with the heading to flowering stage is optimal for CLQ evaluation. Compared with other vegetation index-based models, the LAI-based logarithm model provides the most accurate estimates of CLQ. The optimal model is also driven using the GF-1 image at the heading to flowering stage to map CLQ of the study area, leading to a relative RMSE of 14.09% at the regional scale. This further implies that the heading to flowering stage is the optimal image time for evaluating CLQ. This study is the first effort to provide an applicable method of selecting the optimal image date to improve the estimation of CLQ and thus advanced the literature in this field.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China