黃健熙，黃 海，馬鴻元，卓 文，黃 然，高欣然，劉峻明，蘇 偉，李 俐，張曉東，朱德海

?

遙感與作物生長(zhǎng)模型數(shù)據(jù)同化應(yīng)用綜述

黃健熙1,2，黃 海1，馬鴻元1，卓 文1，黃 然1，高欣然1，劉峻明1,2，蘇 偉1,2，李 俐1,2，張曉東1,2，朱德海1,2

（1. 中國(guó)農(nóng)業(yè)大學(xué)土地科學(xué)與技術(shù)學(xué)院，北京 100083；2. 農(nóng)業(yè)農(nóng)村部農(nóng)業(yè)災(zāi)害遙感重點(diǎn)實(shí)驗(yàn)室，北京 100083）

遙感是獲取大面積地表信息最有效的手段，在農(nóng)業(yè)資源監(jiān)測(cè)、作物產(chǎn)量預(yù)測(cè)中發(fā)揮著不可替代的重要作用；作物生長(zhǎng)模型能夠?qū)崿F(xiàn)單點(diǎn)尺度上作物生長(zhǎng)發(fā)育的動(dòng)態(tài)模擬，可對(duì)作物長(zhǎng)勢(shì)以及產(chǎn)量變化提供內(nèi)在機(jī)理解釋。遙感信息和作物生長(zhǎng)模型的數(shù)據(jù)同化有效結(jié)合二者優(yōu)勢(shì)，在大尺度農(nóng)業(yè)監(jiān)測(cè)與預(yù)報(bào)上具有巨大的應(yīng)用潛力。該文系統(tǒng)綜述了遙感與作物生長(zhǎng)模型的同化研究，概述了遙感與作物生長(zhǎng)模型數(shù)據(jù)同化系統(tǒng)的構(gòu)建，在歸納國(guó)內(nèi)外研究進(jìn)展的基礎(chǔ)上，總結(jié)了當(dāng)前主流同化方法的特點(diǎn)以及在不同條件下的同化效果。進(jìn)而具體分析影響同化精度的關(guān)鍵環(huán)節(jié)，明確了相關(guān)科學(xué)概念，并相應(yīng)指出改善精度的策略或者方向。最后從多參數(shù)協(xié)同、多數(shù)據(jù)融合、動(dòng)態(tài)預(yù)測(cè)、多模型耦合以及并行計(jì)算環(huán)境5個(gè)方面展望了遙感與作物生長(zhǎng)模型數(shù)據(jù)同化的未來研究重點(diǎn)和發(fā)展趨勢(shì)，同時(shí)結(jié)合農(nóng)業(yè)應(yīng)用現(xiàn)實(shí)需求，介紹一種數(shù)據(jù)同化與集合數(shù)值預(yù)報(bào)結(jié)合的應(yīng)用框架，為大區(qū)域、高精度同化研究提供新的思路與借鑒。

作物；遙感；模型；作物生長(zhǎng)模型；數(shù)據(jù)同化；農(nóng)業(yè)監(jiān)測(cè)；產(chǎn)量預(yù)報(bào)

0 引 言

遙感技術(shù)因其宏觀性、直觀性和大面積獲取能力等特點(diǎn)在各個(gè)領(lǐng)域中應(yīng)用廣泛。隨著遙感技術(shù)的長(zhǎng)足發(fā)展，特別是時(shí)間、空間、光譜分辨率的不斷提升，為全球陸地自然資源、大氣和海洋監(jiān)測(cè)等提供了重要的技術(shù)支撐。隨著定量遙感反演算法和產(chǎn)品的日益完善，如葉面積指數(shù)（leaf area index，LAI）、蒸散發(fā)（evapotranspiration, ET）、土壤水分（soil moisture，SM）、吸收性光合有效輻射（fraction of absorbed photosynthetically active radiation，F(xiàn)APAR）及地上生物量（aboveground biomass，AGB）等關(guān)鍵生物理化參數(shù)的定量產(chǎn)品，在區(qū)域尺度的農(nóng)作物監(jiān)測(cè)中發(fā)揮了重要作用。

作物生長(zhǎng)模型是根據(jù)作物品種特性、氣象條件、土壤條件以及作物管理措施，采用數(shù)學(xué)模型方法描述作物光合、呼吸、蒸騰、營(yíng)養(yǎng)等機(jī)理過程，能夠以特定時(shí)間步長(zhǎng)動(dòng)態(tài)模擬作物生長(zhǎng)和發(fā)育期間的生理生化參數(shù)、結(jié)構(gòu)參數(shù)以及作物產(chǎn)量，定量地描述光、溫、水、肥等因子以及田間栽培和管理措施對(duì)作物生長(zhǎng)和發(fā)育的影響[1-5]。在單點(diǎn)尺度上，基于作物光合、呼吸、蒸騰、營(yíng)養(yǎng)等機(jī)理過程的作物生長(zhǎng)模型依靠其內(nèi)在的物理過程和動(dòng)力學(xué)機(jī)制，可以準(zhǔn)確模擬作物在單點(diǎn)尺度上生長(zhǎng)發(fā)育的時(shí)間演進(jìn)以及產(chǎn)量的形成動(dòng)態(tài)過程。

利用數(shù)據(jù)同化技術(shù)把遙感反演參數(shù)信息融入到作物機(jī)理過程模型是當(dāng)前改進(jìn)區(qū)域作物生長(zhǎng)模擬精度的重要途徑[6-8]。當(dāng)作物生長(zhǎng)模型應(yīng)用到區(qū)域尺度時(shí)，地表、近地表環(huán)境非均勻性決定了作物模型中的初始條件、土壤參數(shù)、作物參數(shù)、氣象強(qiáng)迫因子空間分布的不確定性和獲取資料的困難性。衛(wèi)星遙感具有空間連續(xù)和時(shí)間動(dòng)態(tài)變化的優(yōu)勢(shì)，能夠有效解決作物模型中區(qū)域參數(shù)獲取困難這一瓶頸[9-10]。然而，由于受衛(wèi)星時(shí)空分辨率等因素的制約，遙感對(duì)地觀測(cè)還不能真正揭示作物生長(zhǎng)發(fā)育和產(chǎn)量形成的內(nèi)在過程機(jī)理、個(gè)體生長(zhǎng)發(fā)育狀況及其與環(huán)境氣象條件的關(guān)系，而這正是作物模型的優(yōu)勢(shì)所在。數(shù)據(jù)同化技術(shù)通過耦合遙感觀測(cè)和作物模型，能實(shí)現(xiàn)兩者的優(yōu)勢(shì)互補(bǔ)，提高區(qū)域作物生長(zhǎng)過程模擬能力。將遙感信息引入作物生長(zhǎng)模型，是促進(jìn)大面積作物長(zhǎng)勢(shì)監(jiān)測(cè)和產(chǎn)量預(yù)測(cè)向機(jī)理化和精確化方向發(fā)展的有效技術(shù)途徑。

本文從現(xiàn)有文獻(xiàn)中整理歸納，分析作物模型與遙感數(shù)據(jù)同化系統(tǒng)的基本構(gòu)架，總結(jié)當(dāng)前研究中模型、數(shù)據(jù)、方法等現(xiàn)狀，明確同化系統(tǒng)中影響精度的關(guān)鍵環(huán)節(jié)和重點(diǎn)內(nèi)容，討論未來研究的技術(shù)要點(diǎn)和發(fā)展趨勢(shì)。

1 遙感與作物生長(zhǎng)模型數(shù)據(jù)同化系統(tǒng)的構(gòu)建

數(shù)據(jù)同化的研究思想最早是由Charney等[11]在1969年提出，數(shù)據(jù)同化方法被逐漸應(yīng)用于大氣環(huán)流模式，例如數(shù)值天氣預(yù)報(bào)、海洋預(yù)報(bào)模式、陸面模式等地學(xué)模擬系統(tǒng)。數(shù)據(jù)同化是集成觀測(cè)和模式/模型這2種基本科學(xué)研究手段的重要方法，它能夠?qū)⒍嘣吹?、時(shí)間/空間不完整的觀測(cè)整合到一個(gè)演進(jìn)的作物生長(zhǎng)過程模型中，從而更加準(zhǔn)確一致地估計(jì)作物生長(zhǎng)過程的各個(gè)狀態(tài)變量。一般來說，一個(gè)數(shù)據(jù)同化系統(tǒng)均包含3個(gè)基本組成部分：動(dòng)態(tài)模型、觀測(cè)數(shù)據(jù)和同化算法。

圖1為一個(gè)典型遙感與作物模型數(shù)據(jù)同化系統(tǒng)的流程圖。作物生長(zhǎng)模型選擇WOFOST作物生長(zhǎng)模型，在單點(diǎn)樣本尺度（田間尺度）進(jìn)行充分觀測(cè)獲得模型輸入?yún)?shù)。在參數(shù)率定和模型本地化的基礎(chǔ)上，WOFOST能夠?qū)ψ魑锏纳L(zhǎng)發(fā)育過程以及LAI、SM、生物量、單產(chǎn)進(jìn)行較為準(zhǔn)確的模擬。同時(shí)基于貝葉斯理論的馬爾科夫鏈蒙特卡洛方法（Markov chain Monte Carlo, MCMC），獲得這些參數(shù)的后驗(yàn)分布，實(shí)現(xiàn)對(duì)參數(shù)的估計(jì)，同時(shí)參數(shù)的后驗(yàn)分布能夠定量表達(dá)在已有觀測(cè)條件下模型參數(shù)的不確定性。光學(xué)和雷達(dá)遙感能定量反演出關(guān)鍵的農(nóng)作物參數(shù)，例如生育期（development stage, DVS）、LAI、ET、SM、FAPAR、AGB等。因此，引入大區(qū)域的遙感參數(shù)、借助數(shù)據(jù)同化技術(shù)，在區(qū)域每個(gè)格網(wǎng)上對(duì)狀態(tài)變量進(jìn)行優(yōu)化或者經(jīng)過多次迭代優(yōu)化出一套模型參數(shù)，實(shí)現(xiàn)對(duì)區(qū)域作物模型的優(yōu)化，提高區(qū)域作物單產(chǎn)的模擬。特別通過耦合短臨天氣預(yù)報(bào)數(shù)據(jù)和作物生長(zhǎng)模型，可以實(shí)現(xiàn)對(duì)未來時(shí)段農(nóng)作物產(chǎn)量的預(yù)測(cè)。

注：MCMC、4Dvar、EnKF、SAR、DVS、LAI、ET、SM、FAPAR、AGB、CC分別表示馬爾科夫鏈蒙特卡洛方法、四維變分、集合卡爾曼濾波、合成孔徑雷達(dá)、生育期、葉面積指數(shù)、蒸散發(fā)、土壤水分、吸收性光合有效輻射、地上生物量、冠層覆蓋度。下同。

2 遙感與作物生長(zhǎng)模型數(shù)據(jù)同化研究進(jìn)展

數(shù)據(jù)同化研究中，將動(dòng)態(tài)模型（作物生長(zhǎng)模型）與觀測(cè)（遙感或地面試驗(yàn)）耦合的同化算法發(fā)揮著核心的作用，同化算法的性能直接影響著同化系統(tǒng)的運(yùn)行效率和精度。基于代價(jià)函數(shù)的參數(shù)優(yōu)化方法和基于估計(jì)理論的集合濾波方法是2類主要的現(xiàn)代數(shù)據(jù)同化方法。已有眾多學(xué)者對(duì)遙感與作物模型的同化策略進(jìn)行了系統(tǒng)性的綜述[6,12-14]，本文依據(jù)代表性文獻(xiàn)的歸納整理，進(jìn)一步分析2類同化方法的技術(shù)要點(diǎn)、研究進(jìn)展以及內(nèi)在差異等。

參數(shù)優(yōu)化方法迭代調(diào)整作物模型中與生長(zhǎng)發(fā)育和產(chǎn)量形成密切相關(guān)的、常規(guī)方式難以獲得的參數(shù)或初始條件，最小化遙感觀測(cè)值與模型模擬值之間的差異，以達(dá)到優(yōu)化作物模型的目的（如圖2所示）。參數(shù)優(yōu)化算法精度主要取決于同化變量、優(yōu)化算法以及目標(biāo)函數(shù)（或遺傳算法中的適應(yīng)度函數(shù)）形式。用于作物模型同化的優(yōu)化算法包括單純型搜索算法、最大似然法、復(fù)合型混合演化算法（shuffled complex evolution method developed at the University of Arizona, SCE-UA）、Powell 共軛方向法、粒子群算法（particle swarm optimization, PSO）、遺傳算法、模擬退火法等；代價(jià)函數(shù)的構(gòu)建有均方根誤差、最小二乘、三維變分（3DVar）、四維變分（4DVar）等形式。關(guān)于參數(shù)優(yōu)化方法的代表性研究如表1所示。

圖2 參數(shù)優(yōu)化法同化原理示意圖

順序數(shù)據(jù)同化算法又稱為濾波算法，其算法核心是在機(jī)理過程模型的動(dòng)力框架內(nèi)，融合來自于不同分辨率的遙感觀測(cè)信息，讓機(jī)理過程模型和各種觀測(cè)算子集成為不斷地依靠外部觀測(cè)而自動(dòng)調(diào)整模型軌跡，并且減小誤差的預(yù)報(bào)系統(tǒng)（如圖3所示）。順序?yàn)V波使得模型模擬的狀態(tài)變量不斷更新為最優(yōu)預(yù)報(bào)值，是一種時(shí)間連續(xù)且可應(yīng)用于實(shí)時(shí)模擬的數(shù)據(jù)同化方法。常用到的順序?yàn)V波算法有擴(kuò)展卡爾曼濾波（extended Kalman filter, EKF）、集合卡爾曼濾波（ensemble Kalman filter, EnKF）和粒子濾波（particle filter, PF）等順序同化算法。EnKF具有處理非線性觀測(cè)算子的能力，并解決了同化過程中預(yù)報(bào)誤差協(xié)方差求解困難的瓶頸，是順序同化方法中的重要方法。EnKF假定觀測(cè)和模型為高斯分布，而PF則可以假定觀測(cè)和模型為非高斯分布，已經(jīng)成為順序同化方法中最具潛力的方法。關(guān)于順序?yàn)V波同化的代表性研究如表2所示。

表1 參數(shù)優(yōu)化法同化的主要研究

注：“A+B”形式的作物模型名稱表示作物模型與輻射傳輸模型的耦合，其中A是作物模型名稱，B是輻射傳輸模型名稱； LNA、TSAVI、NDVI、EVI、、、CNA分別表示葉片氮積累量、轉(zhuǎn)換型土壤調(diào)整指數(shù)、歸一化植被指數(shù)、增強(qiáng)型植被指數(shù)、后向散射系數(shù)、波段反射率、冠層氮素累積量。下同。

Note: Crop growth model names in the form of “A+B” represent the coupling of crop growth model and the radiation transfer model, where A is the name of the crop model and B is the name of the radiation transfer model; LNA, TSAVI, NDVI, EVI,,and CNA respectively represent leaf nitrogen accumulation, transformed soil adjusted vegetation index, normalized vegetation index, enhanced vegetation index, backscatter coefficient, band reflectance, and canopy nitrogen accumulation. The same below.

圖3 順序?yàn)V波法同化原理示意

綜上所述，在遙感與作物生長(zhǎng)模型的數(shù)據(jù)同化研究中，大區(qū)域的同化應(yīng)用的衛(wèi)星遙感數(shù)據(jù)以MODIS為主，中等區(qū)域尺度上主要選擇Landsat TM, ETM+及OLI等遙感數(shù)據(jù)。作物模型以WOFOST、CERES等使用最為廣泛，作物對(duì)象主要選擇小麥、玉米和水稻等糧食作物。LAI是遙感與作物模型同化中最常用的同化變量。此外，也有研究通過耦合作物模型與輻射傳輸模型，直接同化遙感觀測(cè)和耦合模型模擬的反射率、植被指數(shù)或后向散射系數(shù)?？傮w上，產(chǎn)量的估算是最主要的應(yīng)用目標(biāo)。

表2 順序?yàn)V波法同化主要研究

注：VTCI表示條件溫度植被指數(shù)。

Note: VTCI represent vegetation temperature condition index.

方法上，參數(shù)優(yōu)化方法依據(jù)遙感觀測(cè)在同化單元上重新估計(jì)模型的一些參數(shù)或初始條件，從而實(shí)現(xiàn)模型在空間上的有效拓展。雖然代價(jià)函數(shù)形式和參數(shù)優(yōu)化方法各異，但參數(shù)優(yōu)化精度與遙感觀測(cè)的頻率和時(shí)間點(diǎn)密切相關(guān)[15,19,23]。而當(dāng)作物模型與輻射傳輸模型耦合時(shí)，參數(shù)優(yōu)化的效果很大程度依賴于輻射傳輸模型部分的參數(shù)精度，尤其在大區(qū)域尺度上，這些參數(shù)具有較大空間變異性[15]。4DVar是當(dāng)前參數(shù)優(yōu)化方法主流代表，其代價(jià)函數(shù)描述了模型初始參數(shù)的優(yōu)化值與初始值的距離以及同化變量的遙感觀測(cè)值和模擬值之間的距離。4DVar能夠更好地同化異步觀測(cè)數(shù)據(jù)，被廣泛認(rèn)為是一種有效而具有競(jìng)爭(zhēng)力的同化方法[30,66-68]；其主要缺點(diǎn)是在解析法來解時(shí)需要伴隨模式的計(jì)算，對(duì)于作物生長(zhǎng)模型這樣的復(fù)雜系統(tǒng)，往往帶來很大的計(jì)算難度和不確定性[30,32-33,66,69]。

以EnKF為代表的順序?yàn)V波算法通過將冠層的連續(xù)觀測(cè)信息納入模型模擬，以減少被同化的狀態(tài)變量的誤差，從而提高模型模擬的準(zhǔn)確性。EnKF的優(yōu)點(diǎn)在于假設(shè)觀測(cè)和模擬都存在誤差，同時(shí)相比于其他同化算法，EnKF公式簡(jiǎn)潔，計(jì)算高效，更容易表達(dá)出作物生長(zhǎng)模型非線性、高維度的特性[57,59,70]。總的來說，在具有高質(zhì)量觀測(cè)值進(jìn)行模型標(biāo)定的情況下，EnKF在相對(duì)較小的研究區(qū)域上能夠取得較高的產(chǎn)量模擬精度[27,46]；在中等研究區(qū)域，EnKF使用中低分辨率遙感數(shù)據(jù)（如MODIS LAI或者微波遙感反演的土壤水分等）進(jìn)行同化估產(chǎn)也能夠具有較好的表現(xiàn)[58,60]。但是當(dāng)同化的狀態(tài)變量（如LAI）與目標(biāo)模擬產(chǎn)物（如產(chǎn)量、土壤水等）相關(guān)性較弱時(shí)，EnKF并不一定會(huì)提高同化的準(zhǔn)確性[59,61]。尤其對(duì)于產(chǎn)量而言，其與氣候、土壤、品種、管理等因素密切相關(guān)，僅依靠一兩個(gè)狀態(tài)變量的更新，并不能有效實(shí)現(xiàn)對(duì)模型模擬偏差的糾正[55,57]。此外，EnKF同化表現(xiàn)也取決于遙感觀測(cè)質(zhì)量、模型及觀測(cè)不確定性的量化程度[71]。

參數(shù)優(yōu)化方法與順序?yàn)V波方法的區(qū)別在于，前者用整個(gè)同化窗口內(nèi)的觀測(cè)值來重新調(diào)整模型參數(shù)，而后者的觀測(cè)值是順序作用于模型，每一次后續(xù)的觀測(cè)值只會(huì)影響從當(dāng)前狀態(tài)之后的模型變化性質(zhì)。有研究表明，在短同化窗口內(nèi)，EnKF可以取得更高的分析精度，而在長(zhǎng)同化窗口，EnKF與4DVar的具有相似的準(zhǔn)確性[70]。但對(duì)于不同的模型，同化結(jié)果可能存在差異，比如在WOFOST模型的同化研究中，EnKF方法同化LAI會(huì)引起“物候漂移”現(xiàn)象進(jìn)而降低同化精度，而參數(shù)優(yōu)化方法則能夠取得較好結(jié)果[55]。

3 影響同化精度關(guān)鍵環(huán)節(jié)

3.1 同化單元大小

同化單元（最小分辨率）的大小選擇常常取決于要解決的應(yīng)用問題。例如，全球尺度的模擬一般需要10～50 km分辨率；國(guó)家尺度的模擬需要1～10 km分辨率；區(qū)域尺度模擬需要10 m～1 km分辨率。此外，數(shù)據(jù)同化單元大小也取決于作物模型輸入?yún)?shù)（氣象要素、作物和土壤以及田間管理）和遙感反演參數(shù)的時(shí)空分辨率。同化單元越小，空間差異性越顯著。但同化單元的減小不會(huì)一直提高同化精度，而是存在一個(gè)最優(yōu)同化單元，并與農(nóng)田地塊大小有緊密聯(lián)系。通常，更細(xì)的同化單元會(huì)帶來巨大的計(jì)算壓力。因此，需要研發(fā)更高效的同化策略和適合于高性能計(jì)算的組織架構(gòu)與模式。與格網(wǎng)同化單元不同的是，劃分均質(zhì)地塊單元，基于單元進(jìn)行同化，提高同化執(zhí)行效率的策略，是未來遙感與作物生長(zhǎng)模型數(shù)據(jù)同化值得探索的一個(gè)研究方向[22]。

3.2 遙感參數(shù)反演的不確定性

準(zhǔn)確評(píng)估遙感反演參數(shù)的不確定性，并將其考慮到數(shù)據(jù)同化之中，對(duì)于提高數(shù)據(jù)同化系統(tǒng)的精度具有重要作用。隨著定量遙感反演算法及產(chǎn)品不斷完善，反映作物生長(zhǎng)狀態(tài)的關(guān)鍵生物理化參數(shù)，如LAI、ET、SM、FAPAR和AGB等，其獲取途徑越來越豐富，精度越來越高，能夠不斷滿足作物生長(zhǎng)模型同化的需求。參數(shù)反演精度取決于遙感數(shù)據(jù)源和反演方法，前者可分為單一遙感數(shù)據(jù)和多源遙感數(shù)據(jù)，后者通常包含經(jīng)驗(yàn)?zāi)Ｐ?、輻射傳輸模型、神?jīng)網(wǎng)絡(luò)模型等。以LAI為例，MOD15數(shù)據(jù)集以MODIS為數(shù)據(jù)源，以三維輻射傳輸模型為主、經(jīng)驗(yàn)?zāi)Ｐ蜑檩o，生成覆蓋全球的時(shí)間分辨率8 d、空間分辨率1 km的LAI產(chǎn)品[72]，GEOV1數(shù)據(jù)集以VEGETATION為數(shù)據(jù)源，融合MOD15和CYCLOPES為訓(xùn)練數(shù)據(jù)，訓(xùn)練神經(jīng)網(wǎng)絡(luò)，生成覆蓋全球的時(shí)間分辨率為10 d、空間分辨率為1/112°的LAI產(chǎn)品[73]。由于植被結(jié)構(gòu)和生物物理特性的多樣性、冠層和大氣輻射傳輸過程的復(fù)雜性，植被參數(shù)和遙感觀測(cè)間的轉(zhuǎn)換仍存在較大的不確定性[74]。例如，有研究表明MODIS LAI產(chǎn)品對(duì)農(nóng)作物L(fēng)AI低估約33%～50%[75-76]；MODIS ET產(chǎn)品在森林地區(qū)與實(shí)測(cè)值較為一致，農(nóng)田地區(qū)的一致性較差[77]，在流域或站點(diǎn)尺度上，其與實(shí)測(cè)值的相關(guān)系數(shù)大約在0.7左右[78-79]。因而遙感科學(xué)領(lǐng)域的真實(shí)性檢驗(yàn)技術(shù)的發(fā)展，對(duì)遙感與作物模型數(shù)據(jù)同化系統(tǒng)中遙感反演參數(shù)的不確定性評(píng)估具有重要影響。

3.3 作物生長(zhǎng)模型的不確定性

作物生長(zhǎng)模型的不確定性主要來源于模型結(jié)構(gòu)、模型參數(shù)以及氣象驅(qū)動(dòng)數(shù)據(jù)。模型結(jié)構(gòu)的不確定性主要體現(xiàn)為模型對(duì)光合作用、水肥、營(yíng)養(yǎng)和土壤水平衡等過程難以定量和準(zhǔn)確描述，同時(shí)對(duì)諸如病蟲害、極端災(zāi)害天氣、漬災(zāi)等減產(chǎn)因素影響未在作物模型中考慮，也影響作物生長(zhǎng)發(fā)育和結(jié)果輸出的模擬效果。模型參數(shù)的不確定性主要反映在模型中部分初始田間管理?xiàng)l件和參數(shù)難以直接獲取，傳統(tǒng)的參數(shù)估計(jì)方法的目標(biāo)是在一些特定的模型結(jié)構(gòu)內(nèi)找到一組最優(yōu)參數(shù)[80]。例如，研究表明約一半的研究者采用“試錯(cuò)法”進(jìn)行模型標(biāo)定[81]，即依據(jù)一定數(shù)量的實(shí)測(cè)值，通過調(diào)整幾個(gè)特定參數(shù)，當(dāng)模型模擬與實(shí)測(cè)的誤差達(dá)到一定要求時(shí)，則以此時(shí)的參數(shù)作為標(biāo)定參數(shù)，此方法具有較大主觀性。一些學(xué)者通過構(gòu)建反映模型模擬值與實(shí)際觀測(cè)值差異的目標(biāo)函數(shù)，通過最小化其差異，從而獲得模型參數(shù)的估計(jì)值[82-83]。Liu[84]調(diào)整CERES-Maize模型的物候系數(shù)，直到模擬與觀測(cè)的物候日期相一致，以實(shí)現(xiàn)參數(shù)估計(jì)。Thorp等[85]計(jì)算不同生長(zhǎng)季內(nèi)CERES-Maize模型模擬產(chǎn)量與實(shí)際產(chǎn)量的均方根誤差（RMSE）作為目標(biāo)函數(shù)，采用模擬退火算法（simulated annealing optimization algorithm）實(shí)現(xiàn)對(duì)模型參數(shù)的自動(dòng)化估計(jì)。在復(fù)雜的過程模型中，不存在精確的反解，因此依靠觀測(cè)結(jié)果構(gòu)建的目標(biāo)函數(shù)或者適應(yīng)度函數(shù)，通過優(yōu)化算法只得到唯一的參數(shù)估計(jì)是不可行的[86]?；谪惾~斯理論的馬爾科夫鏈蒙特卡洛算法（MCMC）能夠求得模型參數(shù)的后驗(yàn)分布，因而得到越來越多的應(yīng)用[87-89]。氣象數(shù)據(jù)是模型中驅(qū)動(dòng)作物生長(zhǎng)發(fā)育的重要數(shù)據(jù)，為了獲得空間連續(xù)、時(shí)間連續(xù)的氣象驅(qū)動(dòng)數(shù)據(jù)集，往往需要使用插值方法得到區(qū)域范圍氣象數(shù)據(jù)。然而降水、風(fēng)速等非連續(xù)宏觀現(xiàn)象空間分布不均，使用插值方法的可靠性一直存在爭(zhēng)議。另一方面，在進(jìn)行作物產(chǎn)量預(yù)測(cè)預(yù)報(bào)時(shí)，氣象預(yù)報(bào)數(shù)據(jù)的不確定性也將直接影響作物生長(zhǎng)的模擬效果，是制約模型實(shí)際應(yīng)用的瓶頸之一。目前的研究主要是采用歷史天氣數(shù)據(jù)、天氣發(fā)生器或數(shù)值預(yù)報(bào)作為預(yù)報(bào)期驅(qū)動(dòng)數(shù)據(jù)[90-91]。由于大氣系統(tǒng)高度非線性、混沌的特征，氣候和天氣預(yù)報(bào)的不確定性是必然的[92-93]，而依賴于氣象驅(qū)動(dòng)的作物生長(zhǎng)模型也是如此。

3.4 數(shù)據(jù)同化策略及參數(shù)結(jié)合點(diǎn)

在數(shù)據(jù)同化策略方面，順序?yàn)V波同化效率高，但也存在不足之處。首先順序?yàn)V波同化直接修改狀態(tài)變量（例如：LAI或SM），而時(shí)間序列LAI的變化容易導(dǎo)致作物生育期改變，因此，同化LAI通常會(huì)引起一定程度的“物候漂移”，導(dǎo)致同化精度不如參數(shù)優(yōu)化方法[55]。其次諸如各種濾波算法如卡爾曼濾波等方法中對(duì)于模型和觀測(cè)的誤差隱含著預(yù)先假設(shè)，即模型和觀測(cè)都存在隨機(jī)誤差而不存在系統(tǒng)偏差，而實(shí)際上區(qū)域化的模型運(yùn)行難以滿足這一條件。因此，濾波同化往往會(huì)迅速收斂至模型或觀測(cè)，使得數(shù)據(jù)同化失去效果。更為普遍的是，作物模型和遙感觀測(cè)存在尺度差異，這種差異已經(jīng)不能夠作出沒有系統(tǒng)偏差的假設(shè)，因此在順序?yàn)V波中觀測(cè)信息只能選擇站點(diǎn)尺度[94]、或者利用其他手段進(jìn)行觀測(cè)數(shù)據(jù)的修 正[45,51]，這限制了順序?yàn)V波的區(qū)域化應(yīng)用。

通過構(gòu)建各種形式的代價(jià)函數(shù)來優(yōu)化參數(shù)的同化方法可以在一定程度上減小模型觀測(cè)尺度不一致帶來的系統(tǒng)偏差，而且沒有順序?yàn)V波引起物候漂移的缺點(diǎn)，因此，在作物模型同化領(lǐng)域應(yīng)用中得到了廣泛的應(yīng)用。然而，優(yōu)化方法也存在一些制約因素。首先，和順序?yàn)V波方法相比，優(yōu)化法的數(shù)據(jù)同化需要大量迭代計(jì)算搜索最優(yōu)參數(shù)集合，而由于模型非線性的特征無法應(yīng)用解析法，使得同化系統(tǒng)的運(yùn)行效率偏低，并且隨著參數(shù)的增加所需的搜索次數(shù)呈指數(shù)增長(zhǎng)；算法的選擇和代價(jià)函數(shù)的設(shè)計(jì)雖然能夠緩解該問題，但隨著區(qū)域尺度同化需求的增加，計(jì)算效率始終是優(yōu)化方法的主要瓶頸。其次，優(yōu)化算法和順序?yàn)V波的最主要的差異是同化的時(shí)間周期，順序?yàn)V波中同化的模擬是實(shí)時(shí)進(jìn)行的，隨著觀測(cè)值的輸入不斷向前分析更新，當(dāng)模擬結(jié)束時(shí)同化也隨之結(jié)束，而優(yōu)化方法是由算法在外部調(diào)用作物模型進(jìn)行多次循環(huán)模擬，每一次迭代都需要完整運(yùn)行整個(gè)時(shí)間周期，這使得優(yōu)化法在進(jìn)行實(shí)時(shí)模擬預(yù)報(bào)時(shí)不如順序?yàn)V波靈活高效。

3.5 尺度效應(yīng)及轉(zhuǎn)換模型

由于農(nóng)田表面存在復(fù)雜性，在某一尺度上觀測(cè)到的地物性質(zhì)、過程原理、形成規(guī)律，在另一尺度上可能一致、可能相似，也可能無效而需要進(jìn)行修正，加之遙感具有多空間分辨率的數(shù)據(jù)特點(diǎn)，從定量遙感出發(fā)的地學(xué)描述必然存在多尺度問題，即遙感的尺度效應(yīng)[95-98]。作物模型輸入?yún)?shù)的空間分辨率格網(wǎng)大小對(duì)模型輸出結(jié)果會(huì)有不同的精度，這是作物模型本身的尺度效應(yīng)。遙感與作物模型數(shù)據(jù)同化系統(tǒng)中的尺度效應(yīng)是指，遙感反演參數(shù)和作物模型模擬的參數(shù)（LAI、ET、SM以及AGB等）之間的差異以及不匹配。在遙感與作物模型數(shù)據(jù)同化方法的應(yīng)用過程中：一方面，由于作物生長(zhǎng)模型中不同參數(shù)的區(qū)域化方法不同，導(dǎo)致區(qū)域參數(shù)空間尺度不一致，解決不同空間尺度區(qū)域參數(shù)的空間匹配問題是實(shí)現(xiàn)局地模型空間尺度擴(kuò)展和區(qū)域應(yīng)用的前提；另一方面，由于地表空間異質(zhì)性以及作物生長(zhǎng)模型的非線性特點(diǎn)，導(dǎo)致尺度效應(yīng)明顯，遙感觀測(cè)數(shù)據(jù)與作物生長(zhǎng)模型變量之間的尺度不匹配仍是一個(gè)難題。因此，空間尺度轉(zhuǎn)換是遙感與作物模型數(shù)據(jù)同化系統(tǒng)應(yīng)用到區(qū)域尺度需解決的關(guān)鍵科學(xué)問題。

國(guó)內(nèi)外學(xué)者針對(duì)空間尺度轉(zhuǎn)換已開展了大量研究，思路主要有兩個(gè)：一是模型整體的尺度轉(zhuǎn)換，即對(duì)特定尺度下建立的物理定律、定理、模型以及概念進(jìn)行全面修正[99-102]；二是模型參數(shù)（或變量）的尺度轉(zhuǎn)換，即對(duì)不同觀測(cè)尺度（空間分辨率）下所獲取的地表生物物理參數(shù)進(jìn)行尺度差異校正[103-108]。尺度轉(zhuǎn)換過程可分為向上尺度變換（將高空間分辨率信息轉(zhuǎn)換成低分辨率的過程）和向下尺度變換（將低空間分辨率信息轉(zhuǎn)換成高分辨率的過程）。然而，通過向下尺度變換方法對(duì)空間異質(zhì)性進(jìn)行建模是一個(gè)極其復(fù)雜的過程，需要嚴(yán)格的近似和先驗(yàn)知識(shí)，不易于程序操作和實(shí)際應(yīng)用[109]。因此，面向遙感與作物模型數(shù)據(jù)同化，一種常用的尺度不匹配解決方案是模型參數(shù)的升尺度轉(zhuǎn)換，即將物候信息與低空間分辨率的遙感數(shù)據(jù)結(jié)合起來，并通過中高分辨率影像反演而得的相對(duì)精確值，調(diào)整作物生長(zhǎng)模型生成的同化參數(shù)軌跡，從而提高同化精度[27]。

4 遙感與作物生長(zhǎng)模型數(shù)據(jù)同化研究趨勢(shì)

4.1 單參數(shù)向多參數(shù)的轉(zhuǎn)變

LAI是遙感與作物生長(zhǎng)模型中最常用的同化變量，由于LAI是一個(gè)農(nóng)作物光溫水肥多要素交互作用后的綜合指標(biāo)，同化LAI難以定量描多要素對(duì)作物生長(zhǎng)發(fā)育的影響。LAI能刻畫冠層的生長(zhǎng)和發(fā)育過程，決定了葉片光的截獲和光合作用的大小。SM和ETa/ETp反映了土壤水的狀況和作物脅迫的程度。因此，在雨養(yǎng)區(qū)域，聯(lián)合同化LAI和SM，LAI和ETa/ETp能修正冠層生長(zhǎng)發(fā)育和土壤水平衡過程。包姍寧等研究表明，在水分脅迫模式下, 同化ET和LAI雙變量能取得比同化ET或LAI單變量更高的精度[28]，張樹譽(yù)等同化LAI和條件植被溫度指數(shù)（vegetation temperature condition index, VTCI）提高了模型的估產(chǎn)精度[65]。值得一提是，在同化變量的選擇方面，需要考慮不同變量之間的相關(guān)性，有些變量之間的相關(guān)性很強(qiáng)，選擇其中一個(gè)變量即可，例如LAI與FAPAR, SM和ETa/ETp。

4.2 單一遙感數(shù)據(jù)源向多數(shù)據(jù)源的轉(zhuǎn)變

光學(xué)數(shù)據(jù)易受云雨影響，常導(dǎo)致關(guān)鍵生育期監(jiān)測(cè)數(shù)據(jù)缺失，在時(shí)間和信息量上已不能完全滿足遙感與作物模型數(shù)據(jù)同化的需求。微波遙感具有全天候工作的特性，且具有一定的穿透性，能夠提供多云多雨天氣條件下的農(nóng)田地表信息，同時(shí)彌補(bǔ)了光學(xué)數(shù)據(jù)在時(shí)效性和全天候的不足。研究表明，LAI、生物量等農(nóng)學(xué)參數(shù)與SAR后向散射系數(shù)顯著相關(guān)[110-113]，這為SAR數(shù)據(jù)與作物生長(zhǎng)模型的同化奠定了理論基礎(chǔ)。未來，聯(lián)合光學(xué)數(shù)據(jù)和微波遙感數(shù)據(jù)進(jìn)行數(shù)據(jù)同化工作，將是遙感與作物模型數(shù)據(jù)同化的趨勢(shì)。目前歐洲航天局哥白尼計(jì)劃（GMES）中的地球觀測(cè)衛(wèi)星哨兵1號(hào)（Sentinel-1）和哨兵2號(hào)（Sentinel-2）為此提供了較為理想的數(shù)據(jù)來源。其中哨兵1號(hào)載有C波段合成孔徑雷達(dá)，不受光照與氣候條件限制，可提供全天時(shí)、全天候的連續(xù)影像；而哨兵2號(hào)是目前唯一具有三個(gè)紅邊波段的民用觀測(cè)衛(wèi)星，在大區(qū)域的農(nóng)作物監(jiān)測(cè)方面具有較大的應(yīng)用潛力。兩者的結(jié)合，能夠提供高重訪周期及分辨率的大區(qū)域監(jiān)測(cè)數(shù)據(jù)，很大程度上滿足遙感與作物模型數(shù)據(jù)同化系統(tǒng)的需求。

4.3 監(jiān)測(cè)向預(yù)測(cè)的轉(zhuǎn)變

現(xiàn)有的作物模型與遙感數(shù)據(jù)的同化研究絕大部分是在“重現(xiàn)”歷史作物生長(zhǎng)過程，即利用往年實(shí)測(cè)的整個(gè)作物生育期氣象數(shù)據(jù)進(jìn)行驅(qū)動(dòng)模型。這種往往屬于事后的產(chǎn)量監(jiān)測(cè)（估測(cè)），因此，在作物生育期內(nèi)已有的氣象觀測(cè)數(shù)據(jù)以及年型的分析上，通過引入未來時(shí)段的天氣預(yù)報(bào)數(shù)據(jù)，使得作物生長(zhǎng)模型具有預(yù)測(cè)能力，實(shí)現(xiàn)提前半個(gè)月到1個(gè)月的產(chǎn)量預(yù)測(cè)。

傳統(tǒng)的作物模型運(yùn)行過程只是輸出數(shù)據(jù)到輸出數(shù)據(jù)的單一映射，無法提供模型模擬的不確定性信息。作物模型本身以及輸入?yún)?shù)、氣象驅(qū)動(dòng)存在著不確定性，如果以集合的思想將作物模型放入一個(gè)外部框架內(nèi)，用不同集合預(yù)報(bào)數(shù)據(jù)驅(qū)動(dòng)模擬的結(jié)果作為集合成員，可以使模擬結(jié)果的集合代表其概率分布，最終將單一數(shù)值的模擬輸出轉(zhuǎn)換為概率分布，實(shí)現(xiàn)作物產(chǎn)量的概率預(yù)測(cè)。

作物產(chǎn)量的同化預(yù)估也是農(nóng)業(yè)應(yīng)用的現(xiàn)實(shí)需求，本文基于已有研究[27,114-115]，介紹一種數(shù)據(jù)同化與集合數(shù)值預(yù)報(bào)結(jié)合的應(yīng)用框架。以WOFOST模型為例，通過與PROSAIL模型耦合直接實(shí)現(xiàn)與中高分辨率遙感影像（例如Sentinel-2影像）進(jìn)行同化。為降低高分辨率遙感同化引起的海量計(jì)算壓力，引入一種“分塊聚類”策略，即：1）在作物生長(zhǎng)季節(jié)將遙感影像按時(shí)間序列疊加在一起，并將其裁剪為數(shù)個(gè)網(wǎng)格單元（比如10 km′10 km）；2）對(duì)分塊后的全生長(zhǎng)季時(shí)間序列反射率進(jìn)行聚類分析，每個(gè)網(wǎng)格單元被聚類為指定數(shù)目類別（比如40個(gè)），然后為每個(gè)聚類類別分配此類別中所有像素的平均反射率值作為該類別的反射率值；3）對(duì)網(wǎng)格單元中的所有聚類運(yùn)行數(shù)據(jù)同化算法，從而為網(wǎng)格單元中每個(gè)聚類類別獲得同化的產(chǎn)量；4）使用同化后每個(gè)聚類的產(chǎn)量和聚類分析圖恢復(fù)出空間上的產(chǎn)量分布圖，從而獲得了原高空間分辨率下的產(chǎn)量空間分布。氣象數(shù)據(jù)由播種日至預(yù)報(bào)時(shí)刻的氣象數(shù)據(jù)觀測(cè)、TIGGE集合預(yù)報(bào)[116-117]、與集合預(yù)報(bào)成員最相似歷史年份氣象資料3部分拼接而成，在模型標(biāo)定的基礎(chǔ)上，驅(qū)動(dòng)整個(gè)生育期的模型運(yùn)行，并實(shí)現(xiàn)由集合預(yù)報(bào)數(shù)據(jù)得到作物產(chǎn)量的概率預(yù)報(bào)。

4.4 單一作物模型向多作物模型耦合的轉(zhuǎn)變

目前主流的作物生長(zhǎng)模型有CERES、WOFOST、APSIM、AquaCrop、STICS等，由于作物實(shí)際生長(zhǎng)的機(jī)理過程的復(fù)雜性，單一的作物生長(zhǎng)模型往往根據(jù)不同應(yīng)用需求目的，對(duì)作物生長(zhǎng)多過程的模擬各有側(cè)重。例如CERES模型以光能因子為主要驅(qū)動(dòng)，通過不同模塊集成對(duì)不同作物進(jìn)行模擬[3]；WOFOST模型以CO2同化作為關(guān)鍵模擬，側(cè)重于產(chǎn)量的形成和定量描述[2]；在WOFOST基礎(chǔ)上開發(fā)的SWAP模型則更細(xì)致對(duì)土壤水分進(jìn)行模擬，在土壤水平衡過程模擬上具有優(yōu)勢(shì)[118]；APSIM模型以土壤水、鹽作為主要驅(qū)動(dòng)，在模擬土壤要素對(duì)作物生長(zhǎng)的影響上得到了廣泛驗(yàn)證[119]；AquaCrop是典型的土壤水分驅(qū)動(dòng)模型，強(qiáng)調(diào)于水分脅迫對(duì)作物產(chǎn)量形成的影響[4]；STICS模型[120]則較好地考慮了管理因素對(duì)作物生長(zhǎng)的影響。因此，集成與整合多個(gè)作物模型，實(shí)現(xiàn)不同作物模型在多過程模擬中的優(yōu)勢(shì)互補(bǔ)，能更為準(zhǔn)確模擬作物生長(zhǎng)與土壤、氣象、水肥以及管理措施等的交互影響。國(guó)際上，多作物模型集合研究已經(jīng)取得了一些進(jìn)展[121-122]。特別是將集合預(yù)報(bào)的技術(shù)引入到多作物模型中，有望能進(jìn)一步提高作物生長(zhǎng)模型在不確定性條件下的預(yù)測(cè)模擬精度。

4.5 單機(jī)同化模式向高性能并行計(jì)算模式的轉(zhuǎn)變

傳統(tǒng)的遙感與作物模型數(shù)據(jù)同化系統(tǒng)中所有的輸入?yún)?shù)，是以文件系統(tǒng)的形式進(jìn)行存儲(chǔ)和管理。對(duì)輸入輸出的讀寫效率較低，對(duì)于大區(qū)域的海量計(jì)算具有局限性。GPU在浮點(diǎn)運(yùn)算和并行計(jì)算方面相對(duì)于CPU有著巨大的優(yōu)勢(shì)，采用GPU模式可以大幅提高計(jì)算速度[123]。GPU加速能提高計(jì)算速度，卻對(duì)于復(fù)雜邏輯計(jì)算效率不高，且代碼編寫調(diào)試復(fù)雜。基于Hadoop MapReduce實(shí)現(xiàn)的分布式算法能夠在批量處理數(shù)據(jù)的任務(wù)中相對(duì)于單機(jī)實(shí)現(xiàn)的算法有極大的性能優(yōu)勢(shì)[124]。然而，在許多迭代計(jì)算的任務(wù)中，每一次迭代過程的中間數(shù)據(jù)集都需要從硬盤中加載，如此頻繁的IO操作會(huì)消耗大量時(shí)間，限制了基于MapReduce在多次迭代算法上的速度性能優(yōu)勢(shì)。將作物生長(zhǎng)模型輸入?yún)?shù)以柵格數(shù)據(jù)的形式存儲(chǔ)到大數(shù)據(jù)環(huán)境下（例如HBase），在同化過程中動(dòng)態(tài)生成作物模型的輸入?yún)?shù)，同時(shí)采用SPARK的內(nèi)存計(jì)算模式實(shí)現(xiàn)同化過程中需要的多次迭代過程，能極大提高計(jì)算效率[125]，滿足全國(guó)尺度遙感與作物模型數(shù)據(jù)同化系統(tǒng)的快速計(jì)算要求。值得一提的是，基于谷歌地球引擎（Google Earth Engine, GEE）強(qiáng)大的計(jì)算資源和豐富的遙感數(shù)據(jù)[126]，在GEE平臺(tái)下實(shí)現(xiàn)遙感與作物模型數(shù)據(jù)同化系統(tǒng)，能極大提升在10~30 m中等分辨率尺度的同化模型計(jì)算效率，為大區(qū)域范圍的地塊尺度的遙感同化產(chǎn)量預(yù)測(cè)提供了有效的技術(shù)途徑，也將是遙感與作物模型同化應(yīng)用的重要發(fā)展方向。

5 結(jié) 論

當(dāng)前遙感與作物生長(zhǎng)模型的同化研究在模型、數(shù)據(jù)和算法上已經(jīng)相對(duì)成熟，但隨著近年來對(duì)地觀測(cè)技術(shù)不斷發(fā)展，以及考慮到農(nóng)業(yè)應(yīng)用在時(shí)間、空間上的現(xiàn)實(shí)需求，同化更豐富類型、更高分辨率的遙感數(shù)據(jù)對(duì)現(xiàn)有策略存在較大挑戰(zhàn)。因而未來在完善和創(chuàng)新同化系統(tǒng)架構(gòu)的同時(shí)，也需要進(jìn)一步提高性能與效率。

1）農(nóng)業(yè)系統(tǒng)的復(fù)雜性決定了遙感與作物生長(zhǎng)模型同化的較大不確定性，需要通過模型參數(shù)優(yōu)化、遙感定量反演、數(shù)據(jù)同化算法等關(guān)鍵技術(shù)的共同發(fā)展，才能進(jìn)一步提高同化系統(tǒng)精度。

2）為更精確描述大區(qū)域作物生長(zhǎng)狀態(tài)、滿足服務(wù)農(nóng)業(yè)生產(chǎn)的實(shí)際需求，多參數(shù)協(xié)同、多數(shù)據(jù)融合、動(dòng)態(tài)預(yù)測(cè)以及多模型耦合成為遙感與作物生長(zhǎng)模型數(shù)據(jù)同化系統(tǒng)的必然發(fā)展趨勢(shì)。

3）密集的計(jì)算是限制遙感與作物生長(zhǎng)模型數(shù)據(jù)同化大區(qū)域應(yīng)用精度的主要因素之一，基于大數(shù)據(jù)環(huán)境的同化系統(tǒng)構(gòu)建為提高同化效率提供了可行方案。此外， GEE平臺(tái)的出現(xiàn)與發(fā)展將進(jìn)一步滿足數(shù)據(jù)同化系統(tǒng)的并行計(jì)算要求，將是未來同化研究的重點(diǎn)方向。

[1] Williams J R. The EPIC crop growth model[J]. Transactions of the ASAE, 1989, 32(2): 497－511.

[2] Diepen C A, Wolf J, Keulen H, et al. WOFOST: A simulation model of crop production[J]. Soil Use & Management, 2010, 5(1): 16－24.

[3] Jones J W, Hoogenboom G, Porter C H, et al. The DSSAT cropping system model[J]. European Journal of Agronomy, 2003, 18(3): 235－265.

[4] Theodorec H, Lee H, Pasquale S, et al. AquaCrop: The FAO crop model to simulate yield response to water[J]. Agronomy Journal, 2009, 101(3): 426－459.

[5] Moulin S, Bondeau A, Delecolle R. Combining agricultural crop models and satellite observations: From field to regional scales[J]. International Journal of Remote Sensing, 1998, 19(6): 1021－1036.

[6] Dorigo W A, Zurita-Milla R, de Wit A J W, et al. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling[J]. International Journal of Applied Earth Observation and Geoinformation, 2007, 9(2): 165－193.

[7] 趙艷霞，周秀驥，梁順林. 遙感信息與作物生長(zhǎng)模式的結(jié)合方法和應(yīng)用：研究進(jìn)展[J]. 自然災(zāi)害學(xué)報(bào)，2005，14(1)：103－109. Zhao Yanxia, Zhou Xiuji, Liang Shunlin. Methods and application of coupling remote sensing data and crop growthmodels: Advance in research[J]. Journal of Natural Disaster, 2005, 14(1): 103－109.(in Chinese with English abstract)

[8] 邢雅娟，劉東升，王鵬新. 遙感信息與作物生長(zhǎng)模型的耦合應(yīng)用研究進(jìn)展[J]. 地球科學(xué)進(jìn)展，2009，24(4)：444－451. Xing Yajuan, Liu Dongsheng, Wang Pengxin. Advances of the coupling application of remote sensing information and crop growth model[J]. Advanced Earth Sciences, 2009, 24(4): 444－451. (in Chinese with English abstract)

[9] 吳蕾，柏軍華，肖青，等. 作物生長(zhǎng)模型與定量遙感參數(shù)結(jié)合研究進(jìn)展與展望[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2017，33(9)：155－166. Wu Lei, Bai Junhua, Xiao Qing, et al. Research progress and prospect on combining crop growth models with parameters derived from quantitative remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(9): 155－166. (in Chinese with English abstract)

[10] Plummer S E. Perspectives on combining ecological process models and remotely sensed data[J]. Ecological Modelling, 2000, 129(2/3): 169－186.

[11] Charney J G, Halem M, Jastrow R. Use of incomplete historical data to infer the present state of the atmosphere (Incomplete historical data to infer state of atmosphere based on global circulation model, noting tradeoff of temperature for wind and time for space)[J]. Journal of the Atmospheric Sciences, 1969, 26: 1160－1163.

[12] Liang S. Quantitative Remote Sensing of Land Surfaces[M]. New York: John Wiley & Sons, 2004.

[13] Lewis P, Gómez-Dans J, Kaminski T, et al. An earth observation land data assimilation system (EO-LDAS)[J]. Remote Sensing of Environment, 2012, 120(120): 219－235.

[14] Jin X, Kumar L, Li Z, et al. A review of data assimilation of remote sensing and crop models[J]. European Journal of Agronomy, 2018, 92: 141?152.

[15] Guérif M, Duke C L. Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation[J]. Agriculture Ecosystems & Environment, 2000, 81(1): 57－69.

[16] Dong T, Liu J, Qian B, et al. Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data[J]. International Journal of Applied Earth Observations & Geoinformation, 2016, 49: 63－74.

[17] Jin M, Liu X, Wu L, et al. An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice[J]. International Journal of Applied Earth Observations & Geoinformation, 2015, 41: 118－129.

[18] Zhou G, Liu X, Zhao S, et al. Estimating FAPAR of rice growth period using radiation transfer model coupled with the WOFOST model for analyzing heavy metal stress[J]. Remote Sensing, 2017, 9(5): 424－438.

[19] Jégo G, Pattey E, Liu J. Using leaf area index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops[J]. Field Crops Research, 2012, 131(2): 63－74.

[20] Dente L, Satalino G, Mattia F, et al. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield[J]. Remote Sensing of Environment, 2008, 112(4): 1395－1407.

[21] 王航，朱艷，馬孟莉，等. 基于更新和同化策略相結(jié)合的遙感信息與水稻生長(zhǎng)模型耦合技術(shù)的研究[J]. 生態(tài)學(xué)報(bào)，2012，32(14)：4505－4515. Wang Hang, Zhu Yan, Ma Mengli, et al. Coupling remotely sensed information with a rice growth model by combining updating and assimilation strategies[J]. Acta Ecologica Sinica, 2012, 32(14): 4505－4515. (in Chinese with English abstract)

[22] Launay M, Guerif M. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications[J]. Agriculture Ecosystems & Environment, 2005, 111(1/2/3/4): 321－339.

[23] Guérif M, Duke C L. Calibration of the SUCROS emergence and early growth module for sugar beet using optical remote sensing data assimilation[J]. European Journal of Agronomy, 1998, 9(2/3): 127－136.

[24] 陳勁松，黃健熙，林琿，等. 基于遙感信息和作物生長(zhǎng)模型同化的水稻估產(chǎn)方法研究[J]. 中國(guó)科學(xué)：信息科學(xué)，2010(S1)：173－183. Chen Jinsong, Huang Jianxi, Lin Hui, et al. Rice yield estimation by assimilation remote sensing into crop growth model[J]. Science China Information Sciences, 2010(S1)：173－183. (in Chinese with English abstract)

[25] Ma G, Huang J, Wu W, et al. Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield[J]. Mathematical & Computer Modelling, 2013, 58(3/4): 634－643.

[26] Shen S H, Yang S B, Li B B, et al. A scheme for regional rice yield estimation using ENVISAT ASAR data[J]. Science in China, 2009, 52(8): 1183－1194.

[27] Huang J, Tian L, Liang S, et al. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model[J]. Agricultural and Forest Meteorology, 2015, 204: 106－121.

[28] 包姍寧，曹春香，黃健熙，等. 同化葉面積指數(shù)和蒸散發(fā)雙變量的冬小麥產(chǎn)量估測(cè)方法[J]. 地球信息科學(xué)學(xué)報(bào)，2015，17(7)：871－882. Bao Shanning, Cao Chunxiang, Huang Jianxi, et al. Research on winter wheat yield estimation based on assimilation of leaf area index and evapotranspiration data[J]. Journal of Geo-Information Science, 2015, 17(7): 871－882.(in Chinese with English abstract)

[29] Zhou H, Wu J, Li X, et al. Improving soil moisture estimation by assimilating remotely sensed data into crop growth model for agricultural drought monitoring[C]// Geoscience and Remote Sensing Symposium. Piscataway： IEEE, 2016: 4229?4232.

[30] He B, Li X, Quan X, et al. Estimating the aboveground dry biomass of grass by assimilation of retrieved LAI into a crop growth model[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(2): 550－561.

[31] Fang H, Liang S, Hoogenboom G. Integration of MODIS LAI and vegetation index products with the CSM-CERES- Maize model for corn yield estimation[J]. International Journal of Remote Sensing, 2011, 32(4): 1039－1065.

[32] Jin H, Li A, Wang J, et al. Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data[J]. European Journal of Agronomy, 2016, 78: 1－12.

[33] Dong Y, Wang J, Li C, et al. Comparison and analysis of data assimilation algorithms for predicting the leaf area index of crop canopies[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2013, 6(1): 188－201.

[34] Huang J, Ma H, Su W, et al. Jointly Assimilating MODIS LAI and ET products into the SWAP model for winter wheat yield estimation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 4060－4071.

[35] 朱元?jiǎng)?lì)，朱艷，黃彥，等. 應(yīng)用粒子群算法的遙感信息與水稻生長(zhǎng)模型同化技術(shù)[J]. 遙感學(xué)報(bào)，2010，14(6)：1226－1240. Zhu Yuanli, Zhu Yan, Huang Yan, et al. Assimilation technique of remote sensing information and rice growth model based on particle swarm optimization[J]. Journal of Remote Sensing, 2010, 14(6): 1226－1240. (in Chinese with English abstract)

[36] Fang H, Liang S, Hoogenboom G, et al. Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model[J]. International Journal of Remote Sensing, 2008, 29(10): 3011－3032.

[37] Liu F, Liu X, Ding C, et al. The dynamic simulation of rice growth parameters under cadmium stress with the assimilation of multi-period spectral indices and crop model[J]. Field Crops Research, 2015, 183: 225－234.

[38] Jin X, Kumar L, Li Z, et al. Estimation of winter wheat biomass and yield by combining the AquaCrop model and field hyperspectral data[J]. Remote Sensing, 2016, 8(12): 1－15.

[39] Jin X, Li Z, Yang G, et al. Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 126: 24－37.

[40] Silvestro P, Pignatti S, Pascucci S, et al. Estimating wheat yield in China at the field and district scale from the assimilation of satellite data into the Aquacrop and simple algorithm for yield (SAFY) models[J]. Remote Sensing, 2017, 9(6): 509－532.

[41] Li Z, Wang J, Xu X, et al. Assimilation of two variables derived from hyperspectral data into the DSSAT-CERES model for grain yield and quality estimation[J]. Remote Sensing, 2015, 7(9): 12400－12418.

[42] Ines A V M, Honda K, Gupta A D, et al. Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture[J]. Agricultural Water Management, 2006, 83(3): 221－232.

[43] Vazifedoust M, van Dam J C, Bastiaanssen W, et al. Assimilation of satellite data into agrohydrological models to improve crop yield forecasts[J]. International Journal of Remote Sensing, 2009, 30(10): 2523－2545.

[44] Wu S, J. Huang, X. Liu, et al. Assimilating MODIS-LAI into crop growth model with EnKF to predict regional crop yield[C]//International Conference on Computer and Computing Technologies in Agriculture. Springer, Berlin, Heidelberg, 2011:410?418.

[45] 黃健熙，武思杰，劉興權(quán)，等. 基于遙感信息與作物模型集合卡爾曼濾波同化的區(qū)域冬小麥產(chǎn)量預(yù)測(cè)[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2012，28(4)：142－148. Huang Jianxi, Wu Sijie, Liu Xingquan, et al. Regional winter wheat yield forecasting based on assimilation of remote sensing data and crop growth model with Ensemble Kalman method[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(4): 142－148. (in Chinese with English abstract)

[46] Zhao Y, Chen S, Shen S. Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation[J]. Ecological Modelling, 2013, 270(2): 30－42.

[47] Zhu X, Zhao Y, Feng X. A methodology for estimating leaf area index by assimilating remote sensing data into crop model based on temporal and spatial knowledge[J]. Chinese Geographical Science, 2013, 23(5): 550－561.

[48] Liu J, Huang J, Tian L, et al. Regional winter wheat yield prediction by integrating MODIS LAI into the WOFOST model with sequential assimilation technique[J]. Journal of Food, Agriculture & Environment, 2014, 12(1): 180－187.

[49] 陳思寧，趙艷霞，申雙和，等. 基于PyWOFOST作物模型的東北玉米估產(chǎn)及精度評(píng)估[J]. 中國(guó)農(nóng)業(yè)科學(xué)，2013，46(14)：2880－2893. Chen Sining, Zhao Yanxia, Shen Shuanghe, et al. Study on maize yield estimation and accuracy assessment based on PyWOFOST crop model in northeast China[J]. Scientia Agricultura Sinica, 2013, 46(14): 2880－2893.(in Chinese with English abstract)

[50] Li Y, Zhou Q, Zhou J, et al. Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions[J]. Ecological Modelling, 2014, 291: 15－27.

[51] Huang J, Sedano F, Huang Y, et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation[J]. Agricultural and Forest Meteorology, 2016, 216: 188－202.

[52] Ma H, Huang J, Zhu D, et al. Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST–ACRM model with Ensemble Kalman Filter[J]. Mathematical and Computer Modelling, 2013, 58(3/4): 759－770.

[53] Cheng Z, Meng J, Qiao Y, et al. Preliminary study of soil available nutrient simulation using a modified WOFOST model and time-series remote sensing observations[J]. Remote Sensing, 2018, 10(1): 64－84.

[54] Wang J, Li X, Lu L, et al. Estimating near future regional corn yields by integrating multi-source observations into a crop growth model[J]. European Journal of Agronomy, 2013, 49(49): 126－140.

[55] Curnel Y, de Wit A J W, Duveiller G, et al. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment[J]. Agricultural and Forest Meteorology, 2011, 151(12): 1843－1855.

[56] 解毅，王鵬新，劉峻明，等. 基于四維變分和集合卡爾曼濾波同化方法的冬小麥單產(chǎn)估測(cè)[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2015，31(1)：187－195. Xie Yi, Wang Pengxin, Liu Junming, et al. Winter wheat yield estimation based on assimilation method combined with 4DVAR and EnKF[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(1): 187－195. (in Chinese with English abstract)

[57] de Wit A J W, van Diepen C A. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts[J]. Agricultural And Forest Meteorology, 2007, 146(1/2): 38－56.

[58] Chakrabarti S, Bongiovanni T E, Judge J, et al. Assimilation of downscaled SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions in Brazil[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(9): 3867－3879.

[59] Pauwels V, Verhoest N E, De Lannoy G J, et al. Optimization of a coupled hydrology–crop growth model through the assimilation of observed soil moisture and leaf area index values using an ensemble Kalman filter[J]. Water Resources Research, 2007, 43(4): 244－247.

[60] Ines A V M, Das N N, Hansen J W, et al. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction[J]. Remote Sensing of Environment, 2013, 138: 149－164.

[61] Nearing G S, Crow W T, Thorp K R, et al. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment[J]. Water Resources Research, 2012, 48(5): 213－223.

[62] Jiang Z, Chen Z, Chen J, et al. Application of crop model data assimilation with a particle filter for estimating regional winter wheat yields[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(11): 4422－4431.

[63] Liu J, Huang J, Tian L, et al. Particle filter-based assimilation algorithm for improving regional winter wheat yield estimation[J]. Sensor Letters, 2014, 12(3): 763－769.

[64] Machwitz M, Giustarini L, Bossung C, et al. Enhanced biomass prediction by assimilating satellite data into a crop growth model[J]. Environmental Modelling & Software, 2014, 62: 437－453.

[65] 張樹譽(yù)，孫輝濤，王鵬新，等. 基于同化葉面積指數(shù)和條件植被溫度指數(shù)的冬小麥單產(chǎn)估測(cè)[J]. 干旱地區(qū)農(nóng)業(yè)研究，2017，35(6)：266－271. Zhang Shuyu, Sun Huitao, Wang Pengxin, et al. Winter wheat yield estimation based on the assimilated leaf area index and vegetation temperature condition index[J]. Agricultural Research in the Arid Areas, 2017, 35(6): 266－271. (in Chinese with English abstract)

[66] Gustafsson N. Discussion on ‘4D‐Var or EnKF?’[J]. Tellus Series A-dynamic Meteorology & Oceanography, 2007, 59(5): 774－777.

[67] Huang X, Xiao Q, Barker D M, et al. Four-dimensional variational data assimilation for WRF: Formulation and preliminary results[J]. Monthly Weather Review, 2009, 137(1): 299－314.

[68] Gauthier P, Tanguay M, Laroche S, et al. Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the meteorological service of canada[J]. Monthly Weather Review, 2007, 135(6): 2339－2354.

[69] Qin J, Liang S, Liu R, et al. A weak-constraint-based data assimilation scheme for estimating surface turbulent fluxes[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(4): 649－653.

[70] Kalnay E, Hong L I, Miyoshi T, et al. 4-D-Var or ensemble Kalman filter?[J]. Tellus Series A-dynamic Meteorology & Oceanography, 2007, 59(5): 758－773.

[71] Casa R, Varella H, Buis S, et al. Forcing a wheat crop model with LAI data to access agronomic variables: Evaluation of the impact of model and LAI uncertainties and comparison with an empirical approach[J]. European Journal of Agronomy, 2012, 37(1): 1－10.

[72] Myneni R B, Hoffman S, Knyazikhin Y, et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data[J]. Remote Sensing of Environment, 2002, 83(1/2): 214－231.

[73] Baret F, Weiss M, Lacaze R, et al. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production[J]. Remote Sensing of Environment, 2013, 137: 299－309.

[74] 劉洋，劉榮高，陳鏡明，等. 葉面積指數(shù)遙感反演研究進(jìn)展與展望[J]. 地球信息科學(xué)學(xué)報(bào)，2013，15(5)： 734－743. Liu Yang, Liu Ronggao, Chen Jingming, et al. Current status and perspectives of leaf area index retrieval from optical remote sensing data[J]. Journal of Geo-Information Science, 2013, 15(5): 734－743.(in Chinese with English abstract)

[75] 楊飛，孫九林，張柏，等. 基于PROSAIL模型及TM與實(shí)測(cè)數(shù)據(jù)的MODIS LAI精度評(píng)價(jià)[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2010，26(4)：192－197. Yang Fei, Sun Jiulin, Zhang Bai, et al. Assessment of MODIS LAI product accuracy based on the PROSAIL model, TM and field measurements[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(4): 192－197. (in Chinese with English abstract)

[76] 付立哲，屈永華，王錦地. MODIS LAI產(chǎn)品真實(shí)性檢驗(yàn)與偏差分析[J]. 遙感學(xué)報(bào)，2017，21(2)：206－217.

Fu Lizhe, Qu Yonghua, Wang Jindi. Bias analysis and validation method of the MODIS LAI product[J]. Journal of Remote Sensing, 2017, 21(2): 206－217. (in Chinese with English abstract)

[77] Kim H W. Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia[J]. Ksce Journal of Civil Engineering, 2012, 16(2): 229－238.

[78] Velpuri N M, Senay G B, Singh R K, et al. A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET[J]. Remote Sensing of Environment, 2013, 139: 35－49.

[79] Xue B, Wang L, Li X, et al. Evaluation of evapotranspiration estimates for two river basins on the Tibetan Plateau by a water balance method[J]. Journal of Hydrology, 2013, 492: 290－297.

[80] Mertens J, Madsen H, Feyen L, et al. Including prior information in the estimation of effective soil parameters in unsaturated zone modelling[J]. Journal of Hydrology, 2004, 294(4): 251－269.

[81] Seidel S J, Palosuo T, Thorburn P, et al. Towards improved calibration of crop models: Where are we now and where should we go?[J]. European Journal of Agronomy, 2018, 94: 25－35.

[82] Irmak A, Jones J W, Batchelor W D. Estimating spatially variable soil properties for application of crop models in precision agriculture[J]. Transactions of the ASAE, 2001, 44(5): 1343－1353.

[83] Gauch H G, Hwang J T G, Fick G W. Model evaluation by comparison of model-based predictions and measured values[J]. Agronomy Journal, 2003, 95(6): 1442－1446.

[84] Liu W T H. Application of CERES-Maize Model to yield prediction of a Brazilian maize hybrid[J]. Agricultural & Forest Meteorology, 1989, 45(3): 299－312.

[85] Thorp K R, Batchelor W D, Paz J O, et al. Using cross-validation to evaluate CERES-Maize yield simulationswithin a decision support system for precision agriculture[J]. Transactions of the ASABE, 2007, 50(4): 1467－1479.

[86] Romanowicz R J, Beven K J. Comments on generalised likelihood uncertainty estimation[J]. Reliability Engineering & System Safety, 2006, 91(10): 1315－1321.

[87] Toshichika I, Masayuki Y, Motoki N. Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach[J]. Agricultural & Forest Meteorology, 2009, 149(2): 333－348.

[88] Marin F, Jones J W, Boote K J. A stochastic method for crop models: Including uncertainty in a sugarcane model[J]. Agronomy Journal, 2017, 109(2) : 483－495.

[89] 何亮，侯英雨，趙剛，等. 基于全局敏感性分析和貝葉斯方法的WOFOST作物模型參數(shù)優(yōu)化[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2016，32(2)：169－179. He Liang, Hou Yingyu, Zhao Gang, et al. Parameters optimization of WOFOST model by integration of global sensitivity analysis and Bayesian calibration method[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(2): 169－179. (in Chinese with English abstract)

[90] Baigorria G A, Jones J W, O Brien J J. Potential predictability

of crop yield using an ensemble climate forecast by a regional circulation model[J]. Agricultural and Forest Meteorology, 2008, 148(8/9): 1353－1361.

[91] Hoogenboom G. Contribution of agrometeorology to the simulation of crop production and its applications[J]. Agricultural & Forest Meteorology, 2000, 103(1/2): 137－157.

[92] Lorenz E N, Hilborn R C. The essence of chaos[J]. Physics Today, 1993, 63(48):862－863.

[93] 丑紀(jì)范. 大氣科學(xué)中非線性與復(fù)雜性研究的進(jìn)展[J]. 中國(guó)科學(xué)院院刊，1997，12(5)：325－329. Chou Jifan. Progress in nonlinearity and complexity research in atmospheric sciences[J]. Proceedings of the Chinese Academy of Sciences, 1997,12(5):325－329.(in Chinese with English abstract)

[94] 姜志偉. 區(qū)域冬小麥估產(chǎn)的遙感數(shù)據(jù)同化技術(shù)研究[D]. 北京：中國(guó)農(nóng)業(yè)科學(xué)院，2012. Jiang Zhiwei. Study of the Remote Sensing Data Assimilation Technology for Regional Winter Wheat Yield Estimation[D]. Beijing: Chinese Academy of Agricultural Sciences, 2012. (in Chinese with English abstract)

[95] 李小文. 地球表面時(shí)空多變要素的定量遙感項(xiàng)目綜述[J].地球科學(xué)進(jìn)展，2006，21(8)：771－780. Li Xiaowen. Review of the project of quantitative remote sensing of major factors for spatial-temporal heterogeneity on the land surface[J]. Advances in Earth Science, 2006, 21(8):771－780. (in Chinese with English abstract)

[96] 李小文，曹春香，張顥. 尺度問題研究進(jìn)展[J]. 遙感學(xué)報(bào)，2009，13(s1)：12－20. Li Xiaowen, Cao Chunxiang, Zhang Hao. Development of research in scale effects[J]. Journal of Remote Sensing, 2009, 13(s1): 12－20. (in Chinese with English abstract)

[97] 李小文，王祎婷. 定量遙感尺度效應(yīng)芻議[J]. 地理學(xué)報(bào)，2013，68(9)：1163－1169.Li Xiaowen, Wang Yiting. Prospects on future developments of quantitative remote sensing[J]. Acta Geographica Sinica, 2013, 68(9):1163－1169. (in Chinese with English abstract)

[98] 李小文，趙紅蕊，張顥，等. 全球變化與地表參數(shù)的定量遙感[J]. 地學(xué)前緣，2002，9(2)：365－370. Li Xiaowen, Zhao Hongrui, Zhang Hao, et al. Global change study and quant itat ive remote sensing for land surface parameters[J]. Earth Science Frontiers, 2002, 9(2): 365－370. (in Chinese with English abstract)

[99] Raffy M, Gregoire C. Semi-empirical models and scaling: A least square method for remote sensing experiments[J]. International Journal of Remote Sensing, 1998, 19(13): 2527－2541.

[100] 李小文，王錦地，Strahler A H. 尺度效應(yīng)及幾何光學(xué)模型用于尺度糾正[J]. 中國(guó)科學(xué)：技術(shù)科學(xué)，2000，30(s1)：12－17. Li Xiaowen, Wang Jindi, Strahler A H. Scale effect and geometric optics model for scale correction[J]. Science in China: Series E, 2000, 30(s1): 12－17. (in Chinese with English abstract)

[101] Li X, Wang J, Strahler A H. Scale effect of Planck’s law over nonisothermal blackbody surface[J]. Science in China, 1999, 42(6): 652－656.

[102] Gao Q, Yu M, Yang X, et al. Scaling simulation models for spatially heterogeneous ecosystems with diffusive transportation[J]. Landscape Ecology, 2001, 16(4): 289－300.

[103] Martínez B, García-Haro F J, Coca C D. Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site[J]. Agricultural & Forest Meteorology, 2009, 149(1): 130－145.

[104] Miller J R, Chen J M, Rubinstein I G. Evaluating image-based estimates of leaf area index in boreal conifer stands over a range of scales using high-resolution CASI imagery[J]. Remote Sensing of Environment, 2004, 89(2): 200－216.

[105] Garrigues S, Allard D, Baret F, et al. Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data[J]. Remote Sensing of Environment, 2006, 105(4): 286－298.

[106] 徐希孺，范聞捷，陶欣. 遙感反演連續(xù)植被葉面積指數(shù)的空間尺度效應(yīng)[J]. 中國(guó)科學(xué):地球科學(xué)，2009，39(1)：79－87. Xu Xiru, Fan Wenjie, Tao Xin. Spatial scale effects of remote sensing inversion of continuous vegetation leaf area index[J]. Scientia Sinica Terrae, 2009, 39(1): 79－87. (in Chinese with English abstract)

[107] Raffy M. Change of scale in models of remote sensing: A general method for spatialization of models[J]. Remote Sensing of Environment, 1992, 40(2): 101－112.

[108] Chen J M. Spatial scaling of a remotely sensed surface parameter by contexture-three land-atmospheric modeling experiments[J]. Remote Sensing of Environment, 1999, 69(1): 30－42.

[109] Duveiller G, Baret F, Defourny P. Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy[J]. Remote Sensing of Environment, 2011, 115(10): 2686－2701.

[110] Inoue Y, Kurosu T, Maeno H, et al. Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables[J]. Remote Sensing of Environment, 2002, 81(2/3): 194－204.

[111] Toan T L, Laur H, Mougin E, et al. Multitemporal and dual-polarization observations of agricultural vegetation covers by X-band SAR images[J]. European Journal of Nutrition, 2017, 56(3): 1339－1346.

[112] Shao Y, Fan X, Liu H, et al. Rice monitoring and production estimation using multitemporal RADARSAT[J]. Remote Sensing of Environment, 2001, 76(3): 310－325.

[113] Molijn R A, Iannini L, Mousivand A, et al. Analyzing C-band SAR polarimetric information for LAI and crop yield estimations[J]. Proceedings of SPIE-The International Society for Optical Engineering, 2014, 9239: 4－15.

[114] 馬鴻元. 遙感與作物模型數(shù)據(jù)同化的冬小麥產(chǎn)量集合預(yù)報(bào)方法研究[D]. 北京：中國(guó)農(nóng)業(yè)大學(xué)，2018. Ma Hongyuan. Research on Winter Wheat Yield Ensemble Forecasting Based on Data Assimilation of Remote Sensing and Crop Model[D]. Beijing: China Agricultural University, 2018.(in Chinese with English abstract)

[115] 陳上，竇子荷，蔣騰聰，等. 基于聚類法篩選歷史相似氣象數(shù)據(jù)的玉米產(chǎn)量DSSAT-CERES-Maize預(yù)測(cè)[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2017，33(19)：147－155. Chen Shang, Dou Zihe, Jiang Tengcong, et al. Maize yield forecast with DSSAT-CERES-Maize model driven by historical meteorological data of analogue years by clustering algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(19): 147－155. (in Chinese with English abstract)

[116] Park Y Y, Buizza R, Leutbecher M. TIGGE: Preliminary results on comparing and combining ensembles[J]. Quarterly Journal of the Royal Meteorological Society, 2008, 134(637):

2029－2050.

[117] Swinbank R, Kyouda M, Buchanan P, et al. The TIGGE project and its achievements[J]. Bulletin of the American Meteorological Society, 2016, 97(1): 49－67.

[118] Eitzinger J, Trnka M, H?sch J, et al. Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions[J]. Ecological Modelling, 2004, 171(3): 223－246.

[119] Keating B A, Carberry P S, Hammer G L, et al. An overview of APSIM, a model designed for farming systems simulation[J]. European Journal of Agronomy, 2003, 18(3): 267－288.

[120] Brisson N, Gary C, Justes E, et al. An overview of the crop model STICS[J]. European Journal of Agronomy, 2011, 18(3): 309－332.

[121] Martre P, Wallach D, Asseng S, et al. Multimodel

ensembles of wheat growth: many models are better than one[J]. Global Change Biology, 2015, 21(2): 911－925.

[122] Ruane A C, Hudson N I, Asseng S, et al. Multi-wheat- model ensemble responses to interannual climate variability[J].Environmental Modelling & Software, 2016, 81: 86－101.

[123] Nickolls J, Dally W J. The GPU Computing Era[J].IEEE Micro, 2010, 30(2): 56－69.

[124] Lee K H, Lee Y J, Choi H, et al. Parallel data processing with MapReduce: A survey[J]. Acm Sigmod Record, 2012, 40(4): 11－20.

[125] Zaharia M, Chowdhury M, Franklin M J, et al. Spark: cluster computing with working sets[C]//Usenix Conference on Hot Topics in Cloud Computing. Berkeley: USENIX Association, 2010: 95－101.

[126] Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202: 18－27.

Review on data assimilation of remote sensing and crop growth models

Huang Jianxi1,2, Huang Hai1, Ma Hongyuan1, Zhuo Wen1, Huang Ran1, Gao Xinran1, Liu Junming1,2, Su Wei1,2, Li Li1,2, Zhang Xiaodong1,2, Zhu Dehai1,2

(1.100083,2.100083,)

Data assimilation technology, which can combine the advantages of remote sensing and crop growth models, has great potential in large-scale application of agricultural monitoring and yield forecasting. This review included 5 parts. And the first part introduced the framework of data assimilation system of crop growth model and remote sensing. The data assimilation system contained 3 basic components: dynamic model, observation data and assimilation algorithm. Taking the WOFOST model as an example, a schematic representation of assimilating remotely sensed data into a crop model was shown. The second part summarized the progress of data assimilation of crop growth model and remote sensing. The parameter optimization methods based on cost function and the sequential filtering methods based on estimation theory were two major groups of modern data assimilation strategies. The main difference between the two groups was that each subsequent observation for sequential filtering assimilation would only influence the change nature of the model from the current state; in contrast, parameter optimization methods adjusting the estimation using all of the available observations throughout the assimilation window. In general, MODIS data was the most commonly used remotely sensed data for large regional assimilation research, and data of Landsat TM, ETM+ and OLI were the major remotely sensed data used at regional scale. General models, like WOFOST, CERES, etc. were most widely used in agricultural data assimilation researches. The main object of these researches was food crops such as wheat, corn and rice. LAI (leaf area index) was most commonly used as the assimilation variable linking remote sensing and crop models. In addition, a number of studies found that time series of reflectance, vegetation index or backscattering coefficient could be directly assimilated into a coupled crop growth–radiative-transfer model to avoid the process of regional LAI retrieval. In general, yield estimation and forecast was the most important application. The third part discussed some key aspects affecting the assimilation accuracy, including 5 parts: 1) The pixel size for assimilation, which depended mainly on the specific application. However, heterogeneous, smallholder farming environments presented significant challenges for remotely sensed data assimilation for crop yield forecasting, as field size within these highly fragmented landscapes was often smaller than the pixel size of remote sensing products that were freely available. 2) The uncertainty of remote sensed parameter inversion, which needed to be quantitatively evaluated to ensure the accuracy of data assimilation. 3) The uncertainty of crop growth models, which caused by model structure, model parameters and weather driven data. 4) Data assimilation strategies and linking parameters. Two main data assimilation strategies, parameter optimization and sequential filtering methods, both had pros and cons. Therefore, more effective assimilation algorithms still needed to be developed. 5) The scale effect. Due to the variability of land cover and the complexity of the crop planting pattern in agricultural landscapes, the scale mismatch between the remotely sensed observations and the state variables of crop growth models remained a difficult challenge. The fourth part summarized the research trend of data assimilation for crop growth model and remote sensing. It included 5 directions: 1) from single assimilated parameter to multiple ones. 2) from single remotely sensed data to multiple ones, especially the combination of optical remote sensing and SAR(synthetic aperture radar). 3) from monitoring to forecasting. Based on former researches, an application framework combining data assimilation and numerical prediction was proposed. 4) from single crop growth model to multi-crop model coupling. 5) from single machine system to the high-performance parallel computing system, especially considering the recent advances in Google Earth Engine.

crops; remote sensing; models; crop growth models; data assimilation; agricultural monitoring; yield forecasting

10.11975/j.issn.1002-6819.2018.21.018

TP79; S31

A

1002-6819(2018)-21-0144-13

2018-06-14

2018-08-19

國(guó)家自然科學(xué)基金資助（41671418，41471342）

黃健煕，副教授，博士，博士生導(dǎo)師，主要從事農(nóng)業(yè)定量遙感等研究。Email：jxhuang@cau.edu.cn

黃健熙，黃 海，馬鴻元，卓 文，黃 然，高欣然，劉峻明，蘇 偉，李 俐，張曉東，朱德海.遙感與作物生長(zhǎng)模型數(shù)據(jù)同化應(yīng)用綜述[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2018，34(21)：144－156. doi：10.11975/j.issn.1002-6819.2018.21.018 http://www.tcsae.org

Huang Jianxi, Huang Hai, Ma Hongyuan, Zhuo Wen, Huang Ran, Gao Xinran, Liu Junming, Su Wei, Li Li, Zhang Xiaodong, Zhu Dehai. Review on data assimilation of remote sensing and crop growth models[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(21): 144－156. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2018.21.018 http://www.tcsae.org