The high-temporal-resolution monitoring of key management nodes in cotton management via agricultural remote sensing is vital forfield cotton macro-statistics,particularly for predicting cotton production and obtainin...The high-temporal-resolution monitoring of key management nodes in cotton management via agricultural remote sensing is vital forfield cotton macro-statistics,particularly for predicting cotton production and obtaining comprehensive data.This study examines Shihezi,Xinjiang as a case study,utilizing Sentinel-1 and Sentinel-2 data from 2019 to 2021.Three machine learning models(RF,SVM,and CART)were employed to extract annual crop classification area rasters,monitor weekly cultivation progress,and monitor abandoned cropland during the cultivation period.The results demonstrate that the random forest model has produced satisfactory results in gridded extraction for cotton classification areas,achieving the producer’s accuracy of the cotton category reached 98.5%,and the kappa coefficient is 0.947.Cotton cultivated in 2021 began is a week later than in 2020,yet exhibited a faster cultivate speed.The proportion of abandoned cottonfields in the study area rose in 2020 compared to 2019.The methodology presented in this study has a certain reference value for exploring the monitoring of continuous changes in crops over the years and macro-monitoring of important activities in the entire growth cycle.展开更多
基金supported by the Laboratory of Lingnan Modern Agriculture Project[grant number NT2021009]China Agriculture Research System[grant number CARS-15-22]+2 种基金Guangdong Technical System of Peanut and Soybean Industry[grant number 2019KJ136-05]Key-Area Research and Development Program of Guangdong Province[grant number 2019B020214003]the leading talents of Guangdong province program[grant number 2016LJ06G689].
文摘The high-temporal-resolution monitoring of key management nodes in cotton management via agricultural remote sensing is vital forfield cotton macro-statistics,particularly for predicting cotton production and obtaining comprehensive data.This study examines Shihezi,Xinjiang as a case study,utilizing Sentinel-1 and Sentinel-2 data from 2019 to 2021.Three machine learning models(RF,SVM,and CART)were employed to extract annual crop classification area rasters,monitor weekly cultivation progress,and monitor abandoned cropland during the cultivation period.The results demonstrate that the random forest model has produced satisfactory results in gridded extraction for cotton classification areas,achieving the producer’s accuracy of the cotton category reached 98.5%,and the kappa coefficient is 0.947.Cotton cultivated in 2021 began is a week later than in 2020,yet exhibited a faster cultivate speed.The proportion of abandoned cottonfields in the study area rose in 2020 compared to 2019.The methodology presented in this study has a certain reference value for exploring the monitoring of continuous changes in crops over the years and macro-monitoring of important activities in the entire growth cycle.
