Estimation of mangrove canopy chlorophyll content using hyperspectral image and stacking ensemble regression algorithm

نویسندگان

چکیده

çº¢æ ‘æž—æ˜¯ä¸–ç•Œä¸Šç”Ÿäº§åŠ›æœ€é«˜ã€ä»·å€¼æœ€é«˜çš„æ¹¿åœ°ç”Ÿæ€ç³»ç»Ÿä¹‹ä¸€ã€‚å† å±‚å¶ç»¿ç´ å«é‡CCC(Canopy Chlorophyll Contentï¼‰ä½œä¸ºçº¢æ ‘æž—é‡è¦çš„ç”Ÿç‰©ç‰©ç†å‚é‡ï¼Œæ˜¯ä¼°ç®—å ¶ç”Ÿäº§åŠ›å’Œè¯„ä»·å ¶å¥åº·çŠ¶å†µçš„é‡è¦æŒ‡æ ‡ã€‚æœ¬æ–‡åˆ©ç”¨ç æµ·ä¸€å·é«˜å ‰è°±å«æ˜Ÿï¼ˆOHS)影像与Sentinel-2Aå¤šå ‰è°±æ•°æ®è®¡ç®—ä¼ ç»Ÿæ¤è¢«æŒ‡æ•°ä¸Žç»„åˆæ¤è¢«æŒ‡æ•°å¹¶æž„å»ºäº†é«˜ç»´æ•°æ®é›†ï¼Œç»¼åˆåˆ©ç”¨æ­£æ€åˆ†å¸ƒæ£€éªŒã€æœ€å¤§ç›¸å ³ç³»æ•°æ³•ä¸Žå˜é‡é‡è¦æ€§è¯„ä»·è¿›è¡Œæ•°æ®é™ç»´å’Œå˜é‡ä¼˜é€‰ï¼›åˆ†åˆ«åŸºäºŽå•ä¸€çº¿æ€§å›žå½’ç®—æ³•ã€æœºå™¨å­¦ä¹ å›žå½’ç®—æ³•å’Œå †æ ˆé›†æˆå­¦ä¹ å›žå½’ç®—æ³•æž„å»ºäº†çº¢æ ‘æž—CCCé¥æ„Ÿåæ¼”æ¨¡åž‹ï¼ŒæŽ¢æ˜ŽåŒ—éƒ¨æ¹¾çº¢æ ‘æž—CCC的最佳遥感反演模型,验证OHSé«˜å ‰è°±å½±åƒä¸ŽSentinel-2Aæ•°æ®åæ¼”çº¢æ ‘æž—CCC的精度差异,评估SNAP-SL2Pç®—æ³•åæ¼”çº¢æ ‘æž—CCCçš„é€‚ç”¨æ€§ã€‚ç ”ç©¶ç»“æžœè¡¨æ˜Žï¼šï¼ˆ1)通过数据降维和变量选择处理,从高维度OHS数据集选取了8ä¸ªç‰¹å¾å˜é‡ï¼Œå ¶ä¸­RSI(12,17)、DSI(12,18)和NDSI(6,12ï¼‰ç»„åˆæ¤è¢«æŒ‡æ•°å¯¹çº¢æ ‘æž—CCC反演精度的贡献率较高;(2)联合OHSæ•°æ®å’Œæœ€ä¼˜å ˆGBRTé›†æˆå­¦ä¹ å›žå½’æ¨¡åž‹ï¼ˆScore=0.999,RMSE=0.963 μg/cm2)的训练精度优于最优RFæœºå™¨å­¦ä¹ å›žå½’æ¨¡åž‹ï¼ˆRMSE降低了7.531 μg/cm2),明显优于最优Lasso线性回归模型(RMSE降低了19.383 μg/cm2);(3)在最优å 回归模型下,OHSæ•°æ®åæ¼”çº¢æ ‘æž—CCC的精度(R2=0.761,RMSE=16.738 μg/cm2)高于Sentinel-2A影像(R2=0.615,RMSE=20.701 μg/cm2);(4)联合OHS和Sentinel-2A数据的最优å å›žå½’æ¨¡åž‹åæ¼”çº¢æ ‘æž—CCC的精度都明显优于SNAP-SL2P算法(R2=0.356,RMSE=49.419 μg/cm2ï¼‰ã€‚ç ”ç©¶ç»“æžœè®ºè¯äº†æ­£æ€åˆ†å¸ƒæ£€éªŒã€æœ€å¤§ç›¸å ³ç³»æ•°æ³•å’ŒåŸºäºŽXGBoost的特征选择方法有效降低了高维数据集的维度,并得到了最优特征变量;OHS数据的最优å å›žå½’æ¨¡åž‹è®­ç»ƒç²¾åº¦æœ€é«˜ï¼Œæ˜¯ä¼°ç®—çº¢æ ‘æž—CCC的最优反演模型;OHS和Sentinel-2Aæ•°æ®éƒ½èƒ½æœ‰æ•ˆåæ¼”çº¢æ ‘æž—CCC(R2均大于0.61),而OHS数据的估算精度更高(R2大于0.75);SNAP-SL2Pç®—æ³•ä¸èƒ½æœ‰æ•ˆåæ¼”çº¢æ ‘æž—CCC(R2小于0.4ï¼‰ï¼Œä¸”å¯¹çº¢æ ‘æž—CCC数值存在系统性低估。

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ژورنال

عنوان ژورنال: Journal of remote sensing

سال: 2022

ISSN: ['1007-4619', '2095-9494']

DOI: https://doi.org/10.11834/jrs.20211374