基于反射光譜和葉綠素熒光數據的作物病害遙感監測研究進展

競 霞 鄒 琴 白宗璠 黃文江

綜述

基于反射光譜和葉綠素熒光數據的作物病害遙感監測研究進展

競 霞1鄒 琴1白宗璠1黃文江2,*

1西安科技大學測繪科學與技術學院, 陜西西安 710054;2中國科學院空天信息創新研究院遙感科學國家重點實驗室, 北京 100101

作物病害是影響糧食產量和質量的生物災害, 病害的侵染消耗了作物營養和水分, 擾亂了其正常的生命過程, 引起了作物內部生理生化和外部表觀形態的改變。冠層反射光譜能夠較好地探測作物群體結構信息, 葉綠素熒光能敏感反映作物光合生理上的變化, 二者均能夠實現作物病害的遙感探測。本文從作物病害遙感探測的方法和尺度兩個方面綜述了基于反射率光譜的作物病害遙感監測現狀, 概括了主動熒光、被動熒光以及協同日光誘導葉綠素熒光和反射率光譜在作物病害遙感監測中的研究進展, 分析了反射率光譜和葉綠素熒光數據在作物病害遙感探測方面的優缺點, 探討了不同數據源、不同監測方法在作物病害遙感探測中可能存在的問題, 并在此基礎上展望了作物病害遙感監測的未來發展, 旨在為后續利用反射率光譜和葉綠素熒光數據探測作物病害提供重要的參考依據。

反射率; 葉綠素熒光; 作物病害; 遙感監測

受近年來極端天氣的影響, 作物病害出現來勢早、災情重和大面積爆發等特點[1], 嚴重影響了作物產量和質量, 快速、無損、高精度、大范圍的監測和預警是有效防控作物病害的關鍵[2]。傳統的作物病害監測主要由植保專家等通過田間調查的方法判斷病害嚴重度, 該方法費時費力, 時效性差, 且受觀測者的主觀因素影響較大[3], 難以適應大范圍病害實時監測和預報的需求[4]。遙感技術具有快速、大范圍和無破壞等顯著優點, 已被廣泛應用于作物長勢及病害脅迫監測中[5-6]。

作物受到病菌侵染后, 葉片色素及水分含量、光合生理狀態等均會發生變化, 病害的不同侵染階段其生理變化強度及其癥狀顯現程度均不相同[7]。在作物受到病害脅迫的早期階段, 主要是通過生理機制的調整使其快速適應外在脅迫的變化, 而葉綠素熒光能夠靈敏反映作物光合生理上的變化, 實現作物病害的早期探測[8-9]。當作物受到持續的病害脅迫后, 不但其細胞活性、生化組分等發生變化, 葉片形態、葉傾角分布及冠層結構、密度等均會隨之改變, 進而引起植物葉片、冠層反射光譜發生變化。因此, 利用反射率和葉綠素熒光光譜均能實現作物病害的遙感監測。

1 基于反射率光譜的作物病害遙感監測

作物受到病害脅迫后引起的葉片表面“可見–近紅外”波段光譜反射率的變化, 反映了植被物理生化組分的狀況, 是遙感探測病害的直接依據[10]。根據病害對作物生理生化及冠層結構的影響程度, 作物的反射率光譜會發生相應改變[11], 為受脅作物的生理脅迫提供豐富的信息[12], 被廣泛應用于作物病害的遙感監測研究。

1.1 基于反射率光譜的作物病害監測方法

受病害脅迫作物生理生化特性及表觀形態的改變會引起光譜特征的改變[13], 其光譜響應特性是由病害脅迫導致的植物損傷所引起的色素、水分、形態、結構等變化的函數[14]。作物不同, 病害種類及其發展階段不同, 導致了光譜特征的多樣性[15], 因此不同病害類型具有不同波段的光譜響應特性, 利用光譜響應的敏感波段及異常光譜的變化程度可實現作物病害的識別及發病程度的預測(表1)。目前主要采用過濾法(Filter)、包裹法(Wrapper)和嵌入法(Embedded)三類特征選擇算法挑選作物病害遙感探測的敏感因子[16]。Filter算法從數據特征的結構出發, 利用光譜特征參量與病情指數之間的相關性作為敏感因子的優選標準, 特征參量的選擇獨立于模型算法[17], 能夠快速實現作物病害的診斷, 但該方法忽略了各特征參量間的相關性, 難以挖掘出特征參量之間的組合效應, 影響了模型構建的精度[9]。為提高模型的泛化能力與預測精度[18], 結合特征選擇和模型構建方法的Wrapper算法誕生, Wrapper算法需要定義啟發策略, 復雜性高, 在作物病害監測的實現上具有一定的難度[19]。Embedded方法是基于Filter算法和Wrapper算法的折中方案, 能通過學習器自身主動選擇特征, 包括基于懲罰項的特征選擇法[20]和基于樹模型的特征選擇法[21-22]等, 具有良好的統計性質, 但參數設置需要深厚的背景知識[23]。

作物病害遙感探測精度除與所選特征因子有關外, 建模算法也是影響其精度的重要因素。作物病害遙感監測模型主要包括統計模型和人工智能模型(表2)。統計模型能夠綜合描述兩組變量之間典型的相關關系, 方法簡單且在樣本充足的情況下能達到較好的監測精度, 但由于數據獲取時外界條件的差異, 該方法在空間維和時間維上的普適性較差[36]。因此一些學者提出了能夠兼顧訓練誤差和泛化能力的模式識別和機器學習的作物病害監測模型[37], 該方法具有較好的非線性擬合能力, 能不斷訓練樣本數據使目標達到最優化[38], 解決了反射系數輕微變化而導致作物病害探測困難的問題[39], 但基于機器學習的作物病害遙感監測需要海量數據樣本, 且存在著過學習、局部極值點和維數災難等缺點[40]。

表1 特征選擇算法及敏感波段

表2 作物病害遙感監測算法

1.2 基于不同尺度的作物病害遙感監測

在葉片及冠層尺度上, 作物病害的遙感監測主要基于手持儀器[51]、塔基平臺[52-54]等近地平臺搭載的光譜儀獲取不同發病狀態下的作物光譜信息。葉片水平的光譜特征不受土壤和形態等因素影響, 適用于作物病害的機理研究, 受限于觀測范圍難以實現大區域的病害監測。1927年Taubenhaus等[55]利用飛機搭載的黑白相機實現了棉花根腐病判定, 首次實現了地塊尺度上的作物病害識別。隨著光譜成像技術的發展, 目前地塊尺度的作物病害遙感監測主要利用高光譜影像和多光譜影像等得到不同病情嚴重度下作物病害信息[56-61]。盡管基于航空的作物病害監測取得了較好的診斷效果, 但儀器昂貴限制了其廣泛使用[62], 區域尺度的病害監測以衛星影像作為數據源, 在觀測范圍和成本上具有一定優勢。利用衛星數據獲取作物病害脅迫信息要求使用的衛星傳感器具有較高的光譜分辨率以及可區分健康作物和受病害脅迫作物的光譜通道[63], 高分辨率和高精度的衛星遙感影像與其他多源異構信息結合形成時空序列數據集, 為區域內作物病害監測、大尺度病害預報和流行趨勢提供依據[64-65]。表3對葉片及冠層、地塊和區域尺度上的部分研究成果進行了總結和歸納。

表3 不同尺度的作物病害遙感監測應用案例

反射率光譜數據能夠有效反映冠層結構變化[11,74],為實現大面積作物病害的遙感探測奠定了重要基礎,然而病害對光譜的影響依賴其生理變化強度、病害發展階段和癥狀顯現程度[7]。病害早期, 作物主要通過調整自身生理機制以適應病害脅迫, 而生化組分則無顯著變化[61,65,75]。隨著病情嚴重度的增加, 病害脅迫導致的植被冠層結構變化在生物系統遭受嚴重傷害時才表現出來[76], 作物冠層結構對病害脅迫響應具有明顯的滯后性, 當作物病情指數低于20%時, 反射率光譜難以探測到作物病害脅迫信息[8]。葉綠素熒光與植物光合生理密切相關并參與了作物的能量分布, 在作物受到病害等脅迫時, 葉綠素熒光先于葉綠素含量發生變化, 因此葉綠素熒光能夠提供病害脅迫的早期探測信息[77], 更適于作物病害的早期監測[9]。

2 基于葉綠素熒光的作物病害遙感探測

不同于反射率光譜是葉片入射輻射與植物的生物物理和生化特性之間多次相互作用的結果[7], 葉綠素熒光是葉綠素分子吸收光子后, 被激發的葉綠素分子重新發射光子回到基態而產生的一種光信號[78]。葉片葉綠素熒光在紅光和遠紅光光譜區域中存在由光系統PS I和PS II引起的最大值(圖1), 且葉綠素對轉移到葉片表面的紅光波段熒光吸收作用更強, 因此健康的綠色葉片中紅光波段峰值通常低于遠紅光[79]。PS II對生物和非生物脅迫的敏感性致使光化學電子傳遞能力受到損害, 通常在熒光的發射變化中有明顯響應[80], 并能反映出葉綠素熒光和碳同化之間的復雜關系[81], 已有研究表明, 作物受水脅迫時,440/690和440/740熒光比值相對恒定, 而溫度脅迫則致使葉片葉綠素熒光參數和非光化學猝滅敏感性高、440/690和440/740比值減小[82], 受病害脅迫的作物的v/m比值減小, 病原菌入侵會破壞葉綠素分子合成和降解的動態平衡, 使得患病植株的PS II活性降低[83]。當作物同時處于生物脅迫(病害脅迫)和非生物脅迫(水、溫度、養分脅迫)時, 常引入熱紅外成像儀監測, 受生物脅迫時冠層溫度升高, 而受水、養分脅迫時, 冠層溫度不變甚至會下降[84], 對于病害脅迫和熱脅迫的區分, 則依賴于病癥表現與否進行判別。

圖1 穩態條件下葉片吸收光能后的釋放途徑及葉片熒光發射概念圖[79]

2.1 基于主動葉綠素熒光的作物病害探測

主動葉綠素熒光的探測主要包括葉綠素熒光動力學技術和激光誘導熒光技術2種方法。葉綠素熒光動力學技術多借助(非)調制式葉綠素熒光儀的葉片“點”式接觸方式測量葉綠素熒光參數[85]。而激光誘導熒光技術以紫外光作為激發光源, 測量單色光激發照明條件下熒光波長的發射熒光[86]。

通過主動方法探測的葉綠素熒光已被廣泛應用于作物病害監測以及病害識別和分類等研究中。如Atta等[87]在實驗條件下記錄了葉綠素熒光光譜隨病情嚴重度的變化, 基于同步熒光光譜特征實現了小麥條銹病的早期監測。周麗娜等[88]基于激光誘導的葉綠素熒光實現了稻瘟病發病等級預測; 隋媛媛等[89]利用葉綠素熒光光譜指數在顯癥前完成了黃瓜霜霉病預測。在脅迫的分類上, Belasque等[90]利用激光誘導熒光技術實現了人工損傷、養分脅迫和病害脅迫的準確分類; Wang等[91]利用PSII、v/m和550/510三個指標實現了氮、干旱和灰霉病脅迫的分類。上述研究主要是利用非成像熒光技術進行作物病害的遙感監測, 該方法具有成本低、數據量小、處理速度快的優勢, 然而葉片不同部位的組織結構和葉綠素含量存在差異, 導致葉片不同部位的光合作用具有橫向異質性, 而熒光成像技術能夠獲取植物的顏色紋理等特征信息和熒光強度信息, 揭示受生物或非生物因素脅迫的植物葉片或表面的時空異質性[92], 因此一些研究者利用熒光影像的這種特性實現了染病與健康植物的區分[93-94]、染病作物的早期診斷[95]和作物病害的實時檢測[96]等。

基于主動熒光的作物病害遙感監測對于揭示葉片光合狀態、解釋病害脅迫機理具有重要意義, 但該方式測定的葉綠素激發熒光與自然條件光合作用熒光的物理意義差別較大, 而且由于使用條件的限制(激光激發或葉片接觸式測量等), 難以推廣到大范圍的遙感應用[97-98]。

2.2 基于日光誘導葉綠素熒光探測作物病害

日光誘導葉綠素熒光(Sun-induced Chlorophyll Fluorescence, SIF)是植物在太陽光照條件下, 由光合中心發射出的光譜信號(650～800 nm), 具有紅光(685 nm左右)和遠紅外(740 nm左右)兩個波峰, 能直接反映植物實際光合作用的動態變化[99], 實現作物病害的遙感監測。

自然條件下, 遙感傳感器探測的冠層光譜信號中SIF信號與植被反射光譜信號混疊, 且冠層SIF信號很微弱、通常不足入射輻射的2%[100], 因此對于SIF的信息提取具有很大挑戰性[101]。隨著遙感技術的進步, 研究者發現SIF在Fraunhofer暗線處具有填充效應(圖2), 這使得SIF的直接遙感探測成為可能。學者們基于此原理提出了標準FLD (Fraunhofer Line Discriminator)[102]、3FLD (3-bands Fraunhofer Line Discriminator)[103]、iFLD (improved Fraunhofer Line Discriminator)[104]、pFLD (PCA-based FLD)[105]和光譜擬合法(Spectral Fitting Method, SFM)[106]等單波段SIF反演算法和全波段SFM[107]、FSR (Fluo rescence Spectral Reconstruction)[108]及F-SFM (Full-spectrum Spectral Fitting Method)[109]等全波段SIF反演算法。

圖2 狹窄的大氣吸收帶對太陽輻照度的影響(左)和熒光對吸收帶內的填充效應(右)[104]

標準FLD算法是在假設Fraunhofer吸收線內外的反射率和透過率相等的基礎上, 通過建立吸收谷內外的輻亮度光譜方程解求SIF[102]。為了克服標準FLD方法在吸收線內外波段的反射率和熒光值實際上存在差異的局限性[86], Maier等[103]提出了3FLD的SIF提取算法, 該方法假設反射率在很窄的Fraunhofer吸收線內外呈線性變化, 通過吸收線內外波段的加權平均值減弱SIF和反射率隨波長變化帶來的影響。Luis等[104]提出引入2個校正系數表示吸收線內外反射率和熒光關系的iFLD算法, 利用三次樣條函數插值獲得的表觀反射率代替真實反射率進行計算, 從而消除熒光和反射率對SIF反演算法的影響。Liu等[105]以主成分分析代替插值擬合吸收線處的反射率曲線, 以更精確的估算發射率及熒光校正系數。SFM則是假定Fraunhofer吸收線內外的熒光和發射率是變化的, 利用二次函數擬合SIF光譜和反射率光譜[106]。

全波段SIF光譜反演算法的精度取決于反射率和SIF光譜的估算精度, Mazzoni等[107]分別利用2個Voigt函數之和以及三次樣條函數代替二次函數擬合SIF光譜和反射率光譜, 基于SFM實現了675～ 770 nm波段范圍內的SIF光譜反演, 并用模擬數據進行了驗證。FSR算法利用SFM反演出5條吸收線處的SIF輻照度, 通過奇異值分解提取3個具有SIF光譜一般分布特征的基譜, 利用加權線性最小二乘和5個反演的SIF值擬合確定基譜系數, 重建全波段SIF光譜[108]。F-SFM算法則利用主成分分析提取反射率和SIF的特征波段, 根據不同權重的反射率和SIF主成分重建反射率和SIF光譜, 并引入迭代過程提高反射率的估算精度[109]。表4歸納了目前常用的單波段和全波度SIF反演方法, 為今后研究者選擇合適的SIF估測算法提供參考。

表4 單波段和全波段SIF的提取算法

SIF數據能夠快速、無損地探知植物光合生理及其脅迫狀況, 被廣泛應用于作物病害遙感監測。張永江等[110]基于FLD提取了O2-A (760 nm)和O2-B (688 nm)波段的SIF強度, 構建了用于反映作物受脅迫狀況的熒光比值指數688/760, 證實了利用FLD提取的SIF信息可以反映田間小麥條銹病的發病狀況。Hernández-Clemente等[111]利用不同分辨率下的SIF監測了受疫霉菌侵染的橡樹林, 基于提出的FluorFLIGHT模型實現了健康和發病橡樹林的分類。Raji等[112]利用O2-A和O2-B波段SIF數據構建了熒光比值687/760, 實現了木薯花葉病的早期探測。趙葉等[8]對比分析了反射率光譜和SIF數據對小麥條銹病不同發病狀態下的敏感性, 發現當病情指數低于20%, SIF數據對小麥條銹病害脅迫響應更為敏感, 冠層SIF數據比反射率光譜數據更適于作物病害的早期探測[9]。但葉綠素熒光光譜范圍有限, 無法探測到光譜響應位置不在此范圍內的病害類型, 且監測精度受SIF提取精度的影響。

3 SIF與反射率光譜協同的作物病害遙感探測

作物受到病菌侵染后, 其水分及葉綠素含量、光合速率和光能轉換率等一些生理生化指標均會發生變化[113], 反射光譜信號對作物群體生物量具有較穩定的敏感光譜特征, 能夠有效反映冠層幾何結構的變化[11], 但難以揭示植被光合生理狀態[12], 且受土壤顏色、陰影或者其他非綠色景觀成分等背景噪聲的影響較大[114]。葉綠素熒光與光合作用之間具有直接聯系[114], 能夠敏感反映作物光合生理上的變化[115],且熒光能探測到肉眼不可見的植物病害, 受濕度影響小[116]。但傳感器探測到的SIF信息同時受到作物脅迫狀況和冠層幾何結構等綜合因素的影響[117], 直接利用冠層SIF監測植物的脅迫狀況具有一定難度。綜合利用反射率光譜在作物生化參數探測方面的優勢[118-119]和SIF在光合生理診斷方面的優勢[114], 能夠更加客觀的映射作物受病害脅迫的真實狀況, 提高作物病害的遙感探測精度[4,21]。

陳思媛等[5]和競霞等[21]研究結果表明, 在反射率光譜指數及一階微分光譜指數中加入冠層SIF數據能夠改善小麥條銹病的遙感監測精度, 然而利用少量波段信息計算反射率光譜指數或一階微分光譜指數在一定程度上丟失了對作物病害遙感探測的有用信息。基于此, 白宗璠等[120]利用改進離散粒子群算法從全波段光譜數據中優選遙感探測小麥條銹病嚴重度的特征變量, 協同冠層SIF數據分別利用隨機森林和后向傳播神經網絡構建小麥條銹病遙感探測模型,改善了模型的收斂速度和尋優精度并提高了小麥條銹病遙感探測精度。上述研究是將SIF和反射率光譜特征直接拼接形成高維特征向量, 沒有考慮不同特征向量與作物病情嚴重度之間的最優映射關系,利用單一函數映射所有特征構建作物病害遙感監測模型, 不僅難以充分挖掘特征中包含的信息, 還會增加分類器訓練和預測時的計算代價。高媛等[20]基于核函數的特征融合法將SIF和反射率光譜特征用不同的核函數進行映射, 通過多核學習算法構建了反射率與SIF協同的小麥條銹病遙感監測模型。結果表明, 對SIF和反射光譜特征分別利用其最優核進行映射構建的小麥條銹病嚴重度估測精度優于直接拼接法。葉綠素熒光的發射和NPQ能量耗散都是植物碳固定機制中的重要組成部分[78,121], 均能夠敏感反映植物受脅狀況及其光合性能, 因此一些專家綜合利用反射率光譜、SIF和熱紅外信息進行作物病害的遙感監測。Poblete等[122]基于高光譜影像和熱影像提取的SIF和作物水分脅迫指數(CWSI), 實現了健康橄欖樹和受木糖桿菌脅迫橄欖樹的分類。Calderón等[123]利用連續3年的機載熱、多光譜和高光譜影像提取出SIF、溫度信息和窄帶光譜指數實現了橄欖黃萎病的早期監測。

4 討論與展望

極端氣候條件的變化導致大面積農作物病蟲害頻發, 對我國農業可持續發展和糧食安全產生嚴重的影響, 全世界每年由病蟲害導致的糧食減產約為總產量的1/4, 其中病害造成的損失為14%, 蟲害造成的損失為10%[15]。及時準確的探測到病害脅迫信息對作物病害的防控以及作物產量和品質的提高, 降低病害防治成本, 減少農藥對環境污染具有極為重要的意義。雖然基于反射率和葉綠素熒光數據的作物病害遙感探測取得了豐碩的成果, 但由于每種病害對作物侵染的方式都不相同, 病害的光譜響應具有多效性[1]。因此基于反射率和葉綠素熒光的作物病害遙感探測還存在一些問題和挑戰:

(1) 全波段SIF光譜的作物病害遙感監測精度及穩定性有待提高。全波段SIF光譜(650～850 nm)在紅光區(685～690 nm附近)和遠紅光區(730～740 nm附近)存在2個熒光峰值, 不僅包含病害脅迫下的SIF強度信息, 還能提供形狀信息, 與植被生理狀態存在顯著相關關系[78], 更適用于作物病害的識別與監測。然而遙感傳感器探測到的SIF信號微弱且與反射率信號混疊, 如何提高全波段SIF信息的提取精度, 是利用全波段SIF光譜進行作物病害遙感監測面臨的重要挑戰之一。

(2) 群體生物量影響了作物病害的SIF探測精度。作物在受病菌侵染初期即能通過調整光合速率的方式啟動光保護機制, 以發射葉綠素熒光消耗過剩的光能等生理機制對病害作出快速響應[11,62], 實現作物病害的早期探測。然而冠層SIF一方面隨能量耗散途徑的生理調節而改變, 另一方面也受到植物色素組成、葉面積、葉傾角等生化物理參數的影響, 如何消除群體生物量對冠層SIF的影響, 是基于SIF數據進行作物病害早期探測需要解決的關鍵問題。

(3) 病害微觀特性和宏觀遙感監測的結合不足。病菌生長、繁殖和侵染過程會消耗寄主養分、破壞其正常的生理過程, 如小麥條銹病夏孢子突破表皮破壞了大量的葉綠素, 從而使各個生育期的葉綠素含量降低, 導致了小麥葉片褪綠、發黃等癥狀[13], 這些變化在反射率和熒光光譜曲線上均有體現。研究不同病害脅迫下葉片的色素含量、細胞水含量、細胞間隙比等微觀特性以及葉面積指數群體參數與SIF和反射率光譜的作用機制, 在病害光譜響應特性分析的基礎上, 建立SIF和反射率光譜隨病情發展的動態響應規律和關鍵參數的估算模型, 對作物病害的遙感探測和科學防治具有重要意義。

(4) 作物病害逆向遙感識別問題沒有很好地解決。目前作物病害遙感探測主要側重于研究病害脅迫下反射率光譜和葉綠素熒光數據的響應特性, 并利用實驗數據中探測到的光譜差異分析作物是否受到病害脅迫及其病情嚴重度, 而極少有研究涉及作物病害類型的遙感識別問題, 即作物病害遙感逆向識別與診斷問題尚未得到很好地解決。農作物病害的逆向遙感識別是實現大范圍航空航天遙感監測的關鍵, 是利用遙感影像監測農作物病害不可回避的問題。建立基于大尺度范圍的作物病害逆向遙感識別技術方法和體系, 構建具有較強機理解釋和一定普適性的作物病害診斷模型, 還有待進一步研究。

(5) 不同尺度作物病害遙感探測之間沒能很好地結合。近地高光譜作物病害遙感監測具有航空航天遙感監測難以比擬的方便性、靈活性、經濟性等優勢, 而且受外界因素的影響較小, 能獲得相對比較理想的監測結果, 但在空間上具有一定隨機性, 只有結合航空航天遙感影像數據才能反映病害發生發展的空間特征, 從真正意義上實現作物病害的遙感監測[124]。利用航空航天遙感影像監測農作物病害時, 由于傳感器接收到的信號是地面分辨率范圍內像元目標物的總和, 受下墊面狀況、植株的形態結構、天氣狀況、栽培措施等因子的影響, 因此研究不同尺度作物病害遙感探測之間的關系, 對提高作物病害的探測精度, 實現大范圍作物病害的遙感監測具有重要意義。

(6) 作物病害遙感探測模型對植被病理機制和定量遙感機理結合不夠充分。將遙感探測機理與植被病理機制相結合, 構建具有一定生理機制的作物病害遙感探測模型對提高模型的實用性具有重要意義。已有研究主要側重于分析病害脅迫下反射率或熒光數據的響應特性, 忽略了病害發生的生理機制及其遙感探測機理。結合病害生理機制的遙感探測模型較單純基于光譜響應特性構建的模型更能提升對復雜農田環境的適應能力, 提高作物病害遙感監測精度和模型的普適性。

5 總結

隨著農業信息化的不斷深入, 利用遙感技術監測作物病害逐步從理論走向應用, 并且在彌補傳統病害監測時效性差和人力損耗大等缺陷上顯示出極大潛力。論文總結了利用反射率光譜數據進行作物病害遙感探測中常用的特征優選和模型構建方法, 概括了主動熒光、被動熒光以及協同SIF和反射率光譜在作物病害遙感監測中的研究進展, 分析了反射率數據和葉綠素熒光數據在作物病害遙感探測中的優勢和局限性, 探討了目前研究中可能存在的問題及未來的發展方向。盡管目前遙感監測技術與實際生產管理應用存在較大差距, 但在充分考慮病害生理機制和定量遙感機理的基礎上, 結合生境條件、農學背景深入挖掘多時相遙感數據所包含的病害信息, 可為現代農業大面積精準管理和植保提供實時動態監測信息, 使得作物病害遙感監測方法和技術在應用中不斷走向成熟。

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Research progress of crop diseases monitoring based on reflectance and chlorophyll fluorescence data

JING Xia1, ZOU Qin1, BAI Zong-Fan1, and HUANG Wen-Jiang2,*

1College of Geometrics, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China;2State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

Crop diseases are biological disasters that affect grain production and quality. The infestation of diseases consumes the nutrients and water, disrupts its normal life process, and causes changes in the internal physiological and biochemical state and external appearance of the crop. Canopy reflectance spectrum can detect crop population structure information well, and chlorophyll fluorescence data can sensitively reflect changes in crop photosynthetic physiology, both methods are capable of detecting crop diseases via remote sensing technology. This article outlined the current research status of crop diseases detection based on reflectance spectrum through remote sensing technology from the aspects of monitoring methods and monitoring scales, summarized the research progress of using active fluorescence, passive fluorescence and coordinated solar-induced chlorophyll fluorescence and reflectance spectroscopy to monitor crop diseases, analyzed the advantages and disadvantages of reflectance spectrum and chlorophyll fluorescence data in crop disease early warning detection, and discussed the possible problems in the remote sensing detection of crop diseases. On the basis, we made a prospect for the development of remote sensing monitoring crop diseases. This paper provides an important reference for the subsequent applications of crop diseases detection based on reflectance spectrum and chlorophyll fluorescence data.

reflectance; chlorophyll fluorescence; crop diseases; remote sensing monitoring

10.3724/SP.J.1006.2021.03057

本研究由國家自然科學基金項目(41601467, 52079103)資助。

This study was supported by the National Natural Science Foundation of China (41601467, 52079103).

黃文江, E-mail: huangwj@aircas.ac.cn

E-mail: jingxiaxust@163.com

2020-09-29;

2021-04-26;

2021-05-21.

URL: https://kns.cnki.net/kcms/detail/11.1809.S.20210521.1405.004.html