Inversion of Rice Biophysical Parameters Using Simulated Compact Polarimetric SAR C-Band Data

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1 sensors Article Inversion Rice Biophysical Parameters Using Simulated Compact Polarimetric SAR C-Band Data Xianyu Guo 1,, Kun Li,3, *, Yun Shao,3, Zhiyong Wang 1, *, Hongyu Li, Zhi Yang 5, Long Liu,3 and Shuli Wang 1 1 College Geomatics, Shandong University Science and Technology, Qingdao 59, China; guoxianyu1@1.com (X.G.; shuli.wang1@hotmail.com (S.W. Laborary Radar Remote Sensing Application Technology, Institute Remote Sensing and Digital Earth (RADI, Chinese Academy Sciences, Datun Road, Chaoyang District, Beijing 111, China; shaoyun@radi.ac.cn (Y.S.; liulong@radi.ac.cn (L.L. 3 Laborary Target Microwave Properties (LAMP, Zhongke Academy Satellite Application in Deqing (DASA, Deqing 313, China School Earth Science and Resources, China University Geosciences (Beijing, Beijing 183, China; lhy1993@cugb.edu.cn 5 Institute Transmission and Transmation Engineering, China Electric Power Research Institute, Beijing 155, China; yangzhi@radi.ac.cn * Correspondence: likun@radi.ac.cn (K.L.; wzywlp@13.com (Z.W.; Tel.: (K.L.; (Z.W. Received: May 18; Accepted: 11 July 18; Published: 13 July 18 Abstract: Timely and accurate estimation plays a significant role in moniring and yield ecasting ensuring food security. Compact-polarimetric ( syntic aperture radar (SAR, a good compromise between dual- and quad-polarized SARs, is an important part new generation Earth observation systems. In this paper, ability SAR data retrieve biophysical was explored using a modified water cloud model. The showed that S 1 was superior or variables in height inversion with a coefficient determination (R.9 and a root-mean-square error (RMSE 5.81 cm. RL was most suitable inverting volumetric water content canopy, with an R.95 and a RMSE.31 kg/m 3. The m-χ decomposition produced highest accuracies ear biomass: R was.89 and RMSE was.17 kg/m. The highest accuracy leaf area index (LAI retrieval was obtained RH (right circular transmit and horizontal linear receive with an R.79 and a RMSE.33. This study illustrated capability SAR data with respect retrieval biophysical, especially height, volumetric water content canopy, and ear biomass, and this mode may fer best option -moniring applications because swath coverage. Keywords: compact-polarimetric SAR; biophysical retrieval; m-χ/m-δ decomposition; modified water cloud model 1. Introduction Rice, one world s leading food crops, occupies more than 11 percent world s tal arable land and provides food more than half world s population [1]. Rice biophysical, such as biomass, crop height, leaf area index (LAI, and water content, are vital indicars growth condition. For example, dry biomass ears is a direct manifestation yield. Rice crop height, an important index phenology, is a comprehensive reflection environmental facrs, such as climate, hydrology, and soil. The LAI is a key parameter in plant population and community growth analysis, and volumetric water content Sensors 18, 18, 71; doi:1.339/s

2 Sensors 18, 18, 71 (VWC can fully characterize any vegetation water shortage, both which are closely related vegetation growth. Theree, timely and accurate estimation biophysical properties is great significance and consequently plays a role in ecasting productivity ensuring regional or national food security. Syntic aperture radar (SAR has been proven have a great potential moniring [ ] and inversion biophysical [5,] because its all-wear day night imaging capability, its ability penetrate microwave signals, and sensitivity its signals target properties. Erten et al. [5] dealt with retrieval agricultural height from space by using multi-polarization SAR images, and coherent and incoherent crop height estimation methods discussed first time. Tan et al. [] built a biomass inversion algorithm and inverted biomass, leaf area index, canopy height and related eco-physiological canopy variables, and inversion showed that multitemporal SAR images at C-band can be used monir growth. Although application effect is very good, swath coverage is small, and it is difficult meet a wide range application requirements. Compact-polarimetric ( SAR transmits on only one polarization and receives on two orthogonal polarizations, retaining ir relative phase. The major motivation compact polarimetry is strive quantitative applications same level as those from a fully polarized system, while avoiding principal disadvantages (mass, power, and limited coverage associated with a quad-polarized (quad-pol SAR [7]. As a good compromise between dual-polarized and quad-pol SARs, SAR is an important part new generation Earth observation systems (EOS. RISAT-1, first radar satellite with measurement capability, was successfully launched in April 1 [8]. ALOS-, launched in 1, also includes as in experimental imaging model in 1 [9]. Moreover, in near future RADARSAT Constellation Mission (RCM Canada will launch a C-band SAR satellite as well [1]. With emergence SARs EOS, several studies have reported on applications SAR in moniring such as mapping [11 13] and phenology [1 1]. However, potential SAR inversion biophysical has not been fully explored. Kumar et al. [17] found that are sensitive growth changes wheat and corn, but did not look at relationship between and biophysical. Zhang et al. [18] not only studied response rape C-band SAR, y also carried out inversion rape biophysical based on random est method, thus demonstrating potential SAR inversion crop biophysical. However, physical mechanism crop was not taken in account by inversion strategy. H. Yang et al. [19] devised a scattering index based on m-χ decomposition components simulated SAR data and constructed a regression model obtain inversion oilseed rape biomass. The above studies indicated capability SAR in crop biophysical inversion. However, potential SAR inversion biophysical has not been fully explored. Theree, in this study, five temporal RADARSAT- fully polarimetric single look complex (SLC C-band datasets used simulate SAR data, and 1, such as backscattering coefficients, Skes vecrs, and decomposition, extracted in each acquisition. Then, capability SAR data inversion biophysical was fully investigated by introducing a classic semi-empirical model, water cloud model (WCM [], and a modified WCM (MWCM in which heterogeneity canopy and its phenological changes, as well as double-bounce scattering between canopy and underlying surface considered.. Study Site, Data Source, and Ground Measurement Campaign The study site was located at Jinhu, Xuyi, and Hongze, Jiangsu province ( ~ E, ~ N. Lacustrine plain is main terrain type re, and so, terrain is flat with an altitude 5 9 m. The climate type is humid subtropical monsoon with abundant sunshine and rainfall. The site is one major grain-producing areas in east China, with being main crop. The farmland plots in this area are large and produce one crop per year. The is

3 Sensors 18, 18, 71 3 Sensors 18, 18, x FOR PEER REVIEW 3 5 at growing stage from mid-june late Ocber or early November. The main types in region region are hybrid are hybrid and and japonica, japonica, and and planting planting method method used used is is transplanting and and sowing. sowing. In this In this study, study, hybrid hybrid in a in a transplanted paddy paddy was was used. used. The The phenological changes changes indica indica are are shown shown in Figure in Figure Figure 1. Phographs showing eight phenological stages indica in field. As As variations variations biophysical biophysical change change with with development development plants, plants, five temporal five temporal RADARSAT- RADARSAT- fully fully polarimetric polarimetric single single look look complex complex (SLC (SLC C-band C-band datasets datasets (Table (Table 1, 1, selected selected from from 1 1 July July Ocber Ocber 1, 1, used used simulate simulate SAR SAR data. data. Table Table Details Details full full polarimetric polarimetric RADARSAT- RADARSAT- SAR SAR data. data. Phenology Date Mode Product Incidence Angle ( Pixel Spacing (A R, m Date Mode Product Incidence Angle ( Phenology Pixel Spacing (A R, m Stage Stage 1 July 1 FQW 1 SLC Elongation 1 July 1 FQW 1 SLC Elongation August 1 1 FQ9W FQ9W SLC Booting 8 8 August 1 1 FQ9W FQ9W SLC Heading 1 September 1 FQ9W SLC Dough 1 15 Ocber September 1 1 FQ9W FQ9W SLC SLC Mature Dough 15 1 Ocber 1 FQ9W SLC Mature FQW = fine quad-polarimetry wide, or 9 is number beam position, which is related incidence 1 angles; FQW = SLC fine = quad-polarimetry single look complex wide, [1]. or 9 is number beam position, which is related incidence angles; SLC = single look complex [1]. During passage RADARSAT- satellite, field work was carried out. Thirty transplanted hybrid During fields passage selected as RADARSAT- sample plots satellite, measuring field work was. carried out. Each Thirty transplanted was recordedhybrid with an accurate fields global selected positioning as system sample (GPS. plots Secondly, measuring. Each measured from was three recorded plants, with an which accurate randomly global positioning selected insystem each (GPS. field. The Secondly, sizes plants (such as plant measured height, from stemthree length and plants, diameter, which leaf length randomly width, selected ear length in each and width, field. The etc., sizes planting density, plants and (such or as plant structural height, stem length involved and diameter, in MWCM leaf length measured and width, with ear a length steel ruler and and width, a Vernier etc., caliper. planting The LAI density, was measured and or using structural a SunScan Canopyinvolved analysis system in MWCM []. The biomass measured andwith watera content steel ruler and obtained a Vernier by destructive caliper. The measurements LAI was measured sampled using a SunScan plants Canopy based onanalysis normal gravimetric system []. processes. The biomass and water content obtained by destructive measurements If volume scattering sampled is predominant plants based on mechanism normal gravimetric responsible processes. backscatter from vegetation, If volume it seems scattering appropriate is predominant model a vegetation mechanism canopy responsible as a cloud containing backscatter a volumetric from vegetation, water content it seems (VWC appropriate m model a vegetation canopy as a cloud containing a volumetric v (kg/m 3, which is defined as water content per unit volume water content (VWC mv canopy [3]. Here, considering (kg/m 3, which heterogeneity is defined as water canopy, content per VWC unit volume each layer in canopy [3]. Here, considering heterogeneity canopy, VWC each layer in canopy, such as ear, leaf, stem, and so on, proposed and calculated according definition VWC:

4 Sensors 18, 18, 71 canopy, Sensors such 18, as18, ear, x FOR leaf, PEER stem, REVIEW and so on, proposed and calculated according definition 5 VWC: m v_e = e D e N/h e m m v_s = (F F s - D s N/h N / h v _ e e e e s (1 m v_l m = (F l F - D l N/h N / h v _ s s s s l v = (F DN/h (1 m F - D N / h v _ l l l l where N (plants m is density m plants; F- DmN v_e / h, m v v_s, m v_l, and m v are VWC (ear, stem, leaf, and tal, respectively; F (kg is fresh weight tal plant, and F e, F s, and F l (kg are where N (plants m fresh weight is density plants; mv_e, mv_s, mv_l, and mv are VWC (ear, stem, ear, stem, and leaf each plant, respectively; D (kg is dry weight leaf, and tal, respectively; F (kg is fresh weight tal plant, and Fe, Fs, and Fl (kg are tal plant, and D e, D s, and D l (kg are dry weight ear, stem, and leaf each plant, fresh weight ear, stem, and leaf each plant, respectively; D (kg is dry weight respectively; difference between F i and D i (i = e, s, l indicates water content corresponding tal plant, and De, Ds, and Dl (kg are dry weight ear, stem, and leaf each plant, each part respectively; a plant, difference and h e between, h s, h l, and h (m are height (ear, stem, leaf, and tal. Fi and Di (i = e, s, l indicates water content corresponding each part a plant, and he, hs, hl, and h (m are height (ear, stem, leaf, and tal. 3. Methodology The 3. Methodology flow chart methodology adopted in this study is shown in Figure. This methodology consistedthe mainly flow chart methodology SAR data simulation, adopted in this study parameter is shown extraction, in Figure. This data methodology preprocessing, inversion consisted mainly model building SAR using data simulation, WCM/MWCM parameter model, extraction, genetic data preprocessing, algorithm (GA, and finally, inversion validation model andbuilding analysis. using WCM/MWCM model, genetic algorithm (GA, and finally, validation and analysis. Data Simulation Multi-Temporal FP SAR SLC Images SAR Simulation SAR Simulation Images [Skes Vecr] Extraction SAR Parameters Skes Parameters Backscatter Coefficients (RH/RV/RR/RL m-χ Decomposition Parameters (Pd/PV/PS m-δ Decomposition Parameters (Pd/PV/PS Data preprocessing Radiometric Calibration Geometrical Correction Speckle Filtering Study Site Mask Backscatter Coefficients /Skes Parameters m-χ/m-δ Decomposition Parameters Water Cloud Model (WCM Modified Water Cloud Model (MWCM Training Data ( Ground Truth data Genetic Algorithm Inversion Model Rice Biophysical Parameters Verification Data ( Ground Truth data Inversion Results Rice Biophysical Parameters (h, LAI, m v /m v_s, De Verification and Compare Inversion Results Figure. Methodology flow chart inversion biophysical with water Figure. Methodology flow chart inversion biophysical with water cloud model (WCM and modified WCM (MWCM and compact-polarimetric ( syntic aperture cloud model (WCM and modified WCM (MWCM and compact-polarimetric ( syntic aperture radar (SAR. radar (SAR.

5 Sensors 18, 18, SAR Data Simulation The SAR data we used simulated using RADARSAT- full polarimetric (FP SAR data as follows []: (1 The sensor emits right-hand circularly polarized microwave radiation, denoted R. Based on Sinclair matrix FP SAR data [Γ], electric vecr, E B, target scattering field is constructed as follows: ( 1 E B = [Γ]R, R = [1, j] T, ( E B = [Γ]R = ( 1 [S HH js HV, S HV js VV ] T, (3 where S HH, S HV, and S VV are elements Sinclair matrix FP SAR data. ( The E H and E V components are calculated in case where antenna sends and receives linear polarization (H and V signal as follows: [ E H = 1 [ E V = 1 ] E B = ( 1 ] E B = ( 1 (3 The four elements coherence matrix J are calculated as follows: (S HH js HV, ( (S HV js VV. (5 J HH = S HH + S HV + j S HH S HV j S HVS HH, ( J HV = S HH S HV S HVSVV j S HV j S HH SVV, (7 J VH = J HV, (8 J VV = S VV + S HV j S VV S HV + j S HVSVV. (9 ( Based on elements matrix J, Skes vecr, S, is calculated as follows: S 1 = J HH + J VV, (1 S = J HH J VV, (11 S 3 = Re{ S HH S HV + S HVSVV } Im S HHSVV, (1 S = Im{ S HH S HV S HVS VV } Re S HHS VV + S HV, (13 where S 1, S, S 3, and S are four elements Skes vecr. (5 According relation between Sinclair matrix [Γ] and covariance matrix, C 3, a relation between matrix elements FP data and Skes vecr mode is established as follows: ( S 1 = 1/C /C + 1/C / ( ImC 1 + 1/ ImC 3, (1 ( S = 1/C 11 1/C / ( ImC 1 1/ ImC 3, (15 S 3 = ( 1/ ( ReC 1 + 1/ ReC 3 ImC 13, (1 ( S = ReC 13 1/C 1/ ( ImC 1 + 1/ ReC 3. (17

6 S 1/ ReC 1/ ReC ImC, ( Sensors 18, 18, 71 S ReC13 1/ C 1/ ImC1 1/ ReC. 3 (17 The simulated SAR data transmitted as right-circular polarization (R and received as horizontal (H and vertical (V polarizations, with a resolution 3 m and a noise floor 5 db. Figure 3 shows color composite images backscattering coefficients generated from simulated SAR data. Figure 3. The simulated SAR data 1 July 1 color composite images backscattering Figure 3. The simulated SAR data 1 July 1 color composite images backscattering coefficients (R = RR (right circular transmit and right circular receive, G = RV (right circular transmit coefficients (R = RR (right circular transmit and right circular receive, G = RV (right circular transmit and horizontal linear receive, and B = RH (right circular transmit and horizontal linear receive. The and horizontal linear receive, and B = RH (right circular transmit and horizontal linear receive. red circles in figure represents 3 typical fields as experimental area. The red circles in figure represents 3 typical fields as experimental area. 3.. Extraction SAR Parameters and Data Preprocessing 3.. Extraction SAR Parameters and Data Preprocessing Following process described in Section 3.1, simulated SAR data was sred in Skes Following process described in Section 3.1, simulated SAR data was sred in Skes vecr S = {S1, S, S3, S}. A tal 1, including Skes vecrs, four backscattering vecr S = {S coefficients, three 1, S, S m-δ 3, S decomposition }. A tal 1, including Skes vecrs, four backscattering, and three m χ decomposition, coefficients, three m-δ decomposition, and three m χ decomposition, extracted in each acquisition, as described in Section. A 5 5 frost filter was applied all, extracted in and each acquisition, resulting as images described in georectified. Section. A Then, 5 5 image frost filter subsets was applied taken within all, and resulting images georectified. Then, image subsets taken within boundaries study site, and areas designated as urban, water, road, est, and or land cover boundaries types removed. study site, Only and areas areas classified designated as as hybrid urban, water, road, extracted est, and or inversion land cover types biophysical removed.. Only areas classified as hybrid extracted inversion biophysical. The four Skes represent intensity and polarization state radar echoes. S 1 and S are closely related overall backscatter. Figure shows temporal behaviors four Skes from elongation stage mature stage. It can be seen that energy is mainly concentrated in S 1 and S components, whereas contributions S 3 and S are very small. Although re significant changes in biophysical during growth, energy values S 3 and S close zero and did not change much during each phenology stage. It can be seen that S 3 and S are not sensitive changes in growth and biophysical. Theree, S 3 and S are not suitable inversion biophysical. For S 1 and S, from elongation stage booting stage, height increased, and water content also increased. In addition, leaves widened and enlarged. Moreover, values S 1 and S also closely reached maximum values. However, from booting stage heading stage, dough stage, and mature stage, appearance ear and change in leaf

7 . Theree, S3 and S are not suitable inversion biophysical. For S1 and S, from elongation stage booting stage, height increased, and water content also increased. In addition, leaves widened and enlarged. Moreover, values S1 and S also closely reached maximum values. However, from booting stage heading Sensors stage, 18, 18, dough 71 stage, and mature stage, appearance ear and change in 7 leaf color and underlay surface resulted in decrease value S1 and S. Theree, S1 color and S and deserve underlay be surface used invert resulted biophysical in decrease. value S 1 and S. Theree, S 1 and S deserve be used invert biophysical. The value parameter S 1 S S 3 S -.5 7/1 8/ 1/15 Rice phenology stages Figure.. The temporal behaviors four Skes from elongation stage mature stage. backscattering have been proven have a great potential in crop backscattering have been proven have a great potential in crop inversion [18,19]. Four backscattering coefficients simulated respectively in RH (right inversion [18,19]. Four backscattering coefficients simulated respectively in RH (right circular transmit and horizontal linear receive, RV (right circular transmit and horizontal linear circular transmit and horizontal linear receive, RV (right circular transmit and horizontal linear receive, RR (right circular transmit and right circular receive, and RL (right circular transmit and receive, RR (right circular transmit and right circular receive, and RL (right circular transmit and left circular receive from fully-polarized Skes vecr S. The calculation details are described by left circular receive from fully-polarized Skes vecr S. The calculation details are described by Equations (18 (1 as follows: Equations (18 (1 as follows: = [1, 1,, ] S, (18 σrh, (18 RH 1,1,, S = [1, 1,, ] S, (19 σrv RV 1, 1,, S, (19 σrr = [1,,, 1] S, ( = [1,,, 1] S. (1 σrl, ( Figure 5 shows temporal behaviors backscattering coefficients in RH, RV, RR, and RL polarizations from elongation stage mature stage. All backscatter powers high values at elongation stage. The backscattering coefficients RV dropped sharply in booting RR 1,,, 1 S. (1 RL 1,,,1 S stage, and or three backscatter values showed little difference. The increase in height resulted in high attenuation vertically-polarized return. From booting stage heading, dough, and mature stages, backscattering coefficients RV had declined slowly. This was because had grown a certain height so that degree attenuation vertically-polarized return had not changed much. From booting stage heading and dough stages, backscattering coefficients RH\RR\RV declined dramatically, and increase in leave layer and appearance ear layer resulted in high attenuation horizontal-polarized return. Moreover, as leaves wired and turned yellow gradually, volume scattering and double-bounce scattering decreased, and backscattering power RH\RR\RV decreased gradually as well. With leaves continuing fall f and wir, although volume scattering decreased, double-bounce scattering surface scattering from underlying surface increased. Theree, from dough mature stage, all backscatter powers increased slowly with a gentle slope. All backscattering

8 backscattering coefficients RH\RR\RV declined dramatically, and increase in leave layer and appearance ear layer resulted in high attenuation horizontal-polarized return. Moreover, as leaves wired and turned yellow gradually, volume scattering and double-bounce scattering decreased, and backscattering power RH\RR\RV decreased Sensors gradually 18, 18, as 71 well. With leaves continuing fall f and wir, although volume scattering 8 decreased, double-bounce scattering surface scattering from underlying surface increased. Theree, from dough mature stage, all backscatter powers increased slowly with a coefficients gentle slope. All excellent backscattering indicars coefficients different phenological excellent indicars stages different and phenological also had great potential stages and inversion also had great potential biophysical inversion. biophysical. Backscattering coefficient(db RH RR RV RL 7/1 8/ 1/15 Rice phenology stages Figure Figure The The temporal behaviors backscattering coefficients in in RH, RH, RV, RV, RR RR and and RL RL polarizations from from elongation stage mature stage. The three scattering components m-χ and m-δ decomposition, physical meanings The three scattering components m-χ and m-δ decomposition, physical meanings which similar that Freeman Durden decomposition full polarimetric SAR, which generated from similar simulated that Freeman Durden SAR data as described decomposition by Equations ( ( full polarimetric [5] as follows: SAR, generated from simulated SAR data as described by Equations ( ( [5] as follows: m (S S S S / S, 3 1 ( m = + S 3 + S /S1, ( 1 sin χ = S S //(ms 1,, (3 (3 δ = arg(s 3 + js, ( where m is degree polarization, δ is arg( relative S js 3 phase,, and χ is ellipticity angle( Poincaré sphere. They are all child Skes vecr. The degree polarization m is negatively where m correlated is degree with polarization, scattering δ entropy; is relative greater phase, and scattering χ is entropy, ellipticity angle smaller m. The Poincaré phase angle sphere. δ, They whichare isall relative child phase angle between Skes vecr. RH and The RVdegree polarizations polarization in m SAR data, is negatively includes correlated phase inmation with scattering backscatter entropy; greater targets. scattering The sign entropy, ellipticity smaller angle χ is an unambiguous indicar even versus odd bounce backscatter, and sinχ is known mally as degree circularity []. The Skes parameter S 1, which is tal backscattered energy received by systems, in combination with m, δ, and χ used generate m-δ (as shown in Equation (5 and m-χ decompositions (as shown in Equation (, respectively. P d, P v, and P s correspond double-bounce, randomly polarized constituent (volume, and single-bounce (and Bragg backscattering. P d P v p s P d P v p s m δ m χ = = ms1 (1 + sin δ/ S1 (1 m ms1 (1 sin δ/ ms1 (1 sin χ/ S1 (1 m ms1 (1 + sin χ/, (5. (

9 Sensors 18, 18, 71 9 Figure shows temporal behaviors three scattering components m-χ and m-δ decomposition from elongation stage mature stage. It can be seen that main scattering mechanism reflected radar signal is multiple scattering (volume scattering, which is much higher than even scattering (double-bounce scattering and single scattering (surface scattering. As crop was in elongation stage on 1 July, it was short, and underlay surface was mostly water, which prompted three kinds scattering be weak. With growth, biophysical, such as height, biomass, and water content, increased on August (booting stage, which resulted in increase three kinds scattering. However, from heading stage dough and mature stages, with emergence, growth, and maturity ear, leaves turned yellow gradually, water content decreased, and biomass increased, which made three kinds scattering energy decline. It can be seen that with growth and change biophysical, three scattering energies also changed varying degrees. Theree, scattering are very sensitive biophysical in growth, which provides a possibility building a model relationship between biophysical and decomposition. Sensors 18, 18, x FOR PEER REVIEW 1 5 The value parameter (db m- -db m- -vol m- -s m- -db m- -vol m- -s -18 7/1 8/ 1/15 Rice phenology stages Figure.. The The temporal temporal behaviors behaviors three scattering three scattering components components m-χ and m-δ decomposition m-χ and m-δ from decomposition elongation from stage elongation mature stage stage, db mature = double stage, bounce, db = vol double = volume, bounce, and vol s = volume, surface. and s = surface The Inversion Model Rice Parameters Building with SAR 3.3. The Inversion Model Rice Parameters Building with SAR The, including backscattering coefficients, Skes, and three scattering The components, including m-χ and m-δ backscattering decomposition, coefficients, was investigated Skes, inversionand three biophysical scattering components by introducing m-χ and m-δ a classic decomposition, semi-empirical was investigated model, water cloud inversion model (WCM [,3], biophysical and a modified by WCM introducing (MWCM a classic in which semi-empirical heterogeneity model, water canopy cloud model and its (WCM phenological [,3], and changes, a modified as well WCM as (MWCM double-bounce in which scattering heterogeneity between canopy canopy and and its underlying phenological surface, changes, as considered well as [7]. double-bounce scattering between canopy and underlying surface, considered [7] The Inversion Model Rice Parameters Using Backscatter and WCM The Inversion Model Rice Parameters Using Backscatter and WCM The WCM is one most popular semi-empirical models. It was first proposed by Attema The [3] WCM andis Ulaby one et al. most [] and popular was extensively semi-empirical applied models. It was inversion first proposed problems by Attema radar remote [3] and sensing Ulaby [8 3]. et al. [] It treats and was extensively canopy as aapplied water cloud, consisting inversion problems a collection radar identical remote water sensing particles, [8 3]. characterized It treats canopy by a unim as a water scattering cloud, phase consisting function a [3]. collection Ignoring identical second-order water particles, characterized by a unim scattering phase function [3]. Ignoring second-order contributions resulting from multiple scattering between canopy particles and soil surface, backscattering coefficient canopy is given by following equation: ( ( (, (7 can veg s

10 Sensors 18, 18, 71 1 contributions resulting from multiple scattering between canopy particles and soil surface, backscattering coefficient canopy is given by following equation: σ can (θ = σ veg (θ + σ s (θ, (7 where σ veg (θ is contribution vegetation volume, σ s (θ is backscattering contribution soil surface in presence vegetation cover, and θ is angle incidence relative nadir []. With respect canopy, as re is a large difference volumetric water content between leaf and stem layer in canopy, it was assumed consist two layers: an upper layer height h 1, dominated by leaves, and a lower layer height h, dominated by stem. We established scattering energy from each layer canopy in framework WCM express biophysical : σ can (θ = σ l (θ + σ st (θ + σ s (θ, (8 σl (θ = A l (1 exp( B l L/h 1 cos(θ (1 γl (θ, (9 σ st (θ = A st (θm v h γl (θ, (3 σs (θ = C S (θm s γst(θγ l (θ, (31 γ l (θ = exp( α ll sec(θ, (3 γ st(θ = exp( α st m v h, (33 where θ is incidence angle SAR data. The with subscript l are related leaves layer, and with subscript st are related stem layer. Moreover, with subscript s are related soil layer. Because h 1 and h are proportional tal plant height h, and h 1 and h replaced with h in Equations (9, (3, and (33. L is LAI, and γ is transmission coefficient. However, WCM treats canopy as a water cloud, consisting a collection identical water particles, characterized by a unim scattering phase function. So, VWC canopy (m v is identical that stem layer (m v_s. It can be seen that Equation (8 contains six model coefficients (A st, A l, B l C s, α st, and α l and four biophysical (L, h, m s and m v. As backscattering coefficients and Skes are power backscatter wave, y can replace σ can and be expressed as a function biophysical by substituting Equations (8 (33 in Equation (7. For each equation σ can(θ, re is an ill-posed problem. Theree, it is obviously impossible solve four biophysical in one equation. Theree, solve one, values or three must be assumed ensure that one equation can be used calculate value a biophysical parameter. Taking seedling stage heading stage as an example, we take h as parameter be solved, and or three must be assumed. From seedling stage heading stage, underlying surface is calm water, so m s is assumed be 1. The quantity m v h 1 p layer is related wet and dry biomasses leaves, which in turn are related green LAI []. So, we can substitute m v h 1 LAI in Equations (9 and (3. In each phenological stage, we measured m v sample plots, and entire image was interpolated by nearest neighbor interpolation algorithm using measured m v so that each pixel had a value m v. So, when every pixel in image was used Equation (8 calculate h, m v was replaced by corresponding value. Theree, an unknown parameter (h has only one equation solve h. When solving or, such as LAI, we assume that or three are specific values according same method so as effectively solve LAI.

11 Sensors 18, 18, Construction MWCM In order furr investigate capability SAR data in inversion, especially decomposition, such as surface, volume, and double-bounce scattering, a modified WCM (MWCM was introduced in which heterogeneity canopy and its phenological changes, as well as double-bounce scattering between canopy and underlying surface considered [7]. In MWCM, canopy was assumed consist many scattering cells in horizontal direction, containing and space part. A volume fraction coefficient F and a water content fraction coefficient n are defined quantitatively describe heterogeneity canopy in horizontal direction. While in vertical direction, canopy was divided in leaves, stems, and ears. The multi-layers, parts, and ir volumetric water content changes with phenological stages. In frame work MWCM, ten kinds scattering mechanisms canopy considered during whole growing season, in which four periods included (Table. In first period, seedling stage, because stem is short and leaves are small, part can be regarded as a whole. However, no leaves distributed in space part, and water content in space part is negligible. Theree, re are four kinds scattering, including underlying surface scattering through part (S g_r, underlying surface scattering through space part (S g_s, volume scattering from part (V f_r, and double-bounce scattering between underlying surface and part (D g_f. From tillering stage booting stage, leaves and stems grew longer, water content space part increased, and water contents stem layer and leaf layer different. So, re are three additional scattering mechanisms, including volume scattering from leaf layer in space part (V f_s, surface scattering from stem layer (S t, and double-bounce scattering between underlying surface and stem (D g_t. From heading stage flowering stage, due growth ear, growth reached its peak and underlying surface was covered by plant, and so, double-bounce scattering between leaves layer and underlying surface is negligible. Moreover, volume scattering from ear layer in part (V e_r and double-bounce scattering between underlying surface and ear layer (D e_s is considered. From dough stage mature stage, some ear moves space part due increase biomass ear. Theree, it is n necessary increase volume scattering (V e_s space part. Table shows scattering mechanisms different phenological stages. All 1 scattering mechanisms can be expressed as a function biophysical with 1 model coefficients, including F, n 1, n, A e1 (θ, A e (θ, A f1, B f1, A f, B f, A t1, A t, C g1 (θ, C g (θ, α f, α t, and α e, physical meanings which described in [7]. Table. Rice scattering mechanisms different phenological stages. Phenological Stages Underlying Surface Rice Part Scattering Space Part Scattering Seedling Water surface S g_r V f_r S g_s D g_f Tillering Booting Water surface S g_r V f_r S t D g_t S g_s D g_f V f_s Heading Flowering Moist soil S g_r V f_r S t D g_t V e_r S g_s V f_s D g_e Dough Mature Moist soil S g_r V f_r S t D g_t V e_r S g_s V f_s D g_e V e_s Coupling Parameters from m-χ and m-δ Decomposition with MWCM With decomposition, surface scattering P s, double-bounce scattering P d, and volume scattering P v are calculated. Based on MWCM, three equations relating three scattering components variables could be built as follows: P d p v P s m χ = D g_e + D g_t + D g_ f V e_r + V e_s + V f _r + V f _s S g_r + S g_s + S t, (3

12 Sensors 18, 18, 71 1 P d p v P s m δ = D g_e + D g_t + D g_ f V e_r + V e_s + V f _r + V f _s S g_r + S g_s + S t, (35 where V, D, and S with subscripts can be expressed as a function (i.e., D e, LAI, h, m v_s, and m s by introducing 1 model coefficients [7]. However, only three variables could be estimated simultaneously from Equation (3 or (35, which leads ill-posed problem inversion. Theree, as MWCM, different phenological stages assumed. Bee heading stage, no grains have been med, and so, biomass D e is. The underlying surface throughout se stages is water, and so, moisture content underlying surface, m s, is 1. Theree, re are only three, that LAI, h, and m v_s, which could be calculated easily. From heading mature stage, particles have been presented, and underlying surface field is wet soil. Two hyposes applied as follows: (1 assume that backscattering from underlying surface is constant so that m s can be eliminated; and ( stem layer is ignored considering that attenuation from ear and leaf layers are large after heading stage, which in variable m v being eliminated. Three unknown ( LAI, h, and D e can thus be calculated using Equations (3 or ( Calculating Model Parameters Based on GA Rice Characteristics A genetic algorithm [31] was used solve Equations (8, (33, and (3 estimating biophysical in five phenological stages. A tal 1 fields, distributed unimly in study area, used as training data model training, and remaining twenty fields used validation. The specific steps as follows. (1 Initialization and Genetic Representation As mentioned in Sections and 3.3., re are six and 1 model coefficients in inversion models WCM (Equation (8 and MWCM (Equations (33 or (3, respectively. In genetic representation GA, each coefficient Equations (8 and (33/(3 was encoded by using binary coding as gene substring. In WCM, initial values and ranges four model coefficients initialized by solving WCM with a set training data. For example, attenuation coefficients leaf layer and stem layer, α l and α st, respectively, initialized [, ] and [, ], respectively. As MWCM, initial values and ranges volume fraction coefficient F and VWC fraction coefficient n i determined by ground data measurements and empirical assumption. In terms or coefficients, ir initial values and ranges initialized by solving MWCM with a set training data. For example, attenuation coefficients α f and α e initialized [, 1] and [, ], respectively. Based on ir ranges, gene substring lengths coefficients determined when desired solution accuracy four decimal places was considered. Then, length chromosome (tal gene substring was determined (i.e., inversion model WCM, from elongation (1 July booting stage ( August, 51 in heading stage (8 August, and 55 from dough (1 September mature stages (15 Ocber; In inversion model using MWCM, 175 from elongation (1 July booting stage ( August, 135 in heading stage (8 August, and 19 from dough (1 September mature stages (15 Ocber. The population coefficients was generated using a random generar, and population size 1 was initialized. ( Reproduction The chromosomes generated in initial population n chosen participation in reproduction process based on ir fitness values [3]. In this study, fitness value was calculated

13 Sensors 18, 18, as Equations (3 (38 and proportionate fitness selection was used. A chromosome with a higher fitness value had a higher probability being copied in cross-over pool. For MWCM, SSE = For WCM, SSE = m i=1 m i=1 f = SSE, (3 [ (S i,est S i,obs ] or m i=1 [ (σi,est σ i,obs ], (37 [ (P v,obs,i P v,est,i + (P d,obs,i P d,est,i + (P s,obs,i P s,est,i ] m χ or m δ where σ i,est is estimated backscattering coefficient ith training sample, σ i,obs is backscattering coefficient observed value ith training sample, S i,est is estimated Skes scattering ith training sample, and S i,obs is observed value Skes ith training sample. P v,est,i, P d,est,i, and P s,est,i represent decomposition three scattering components ith training sample estimates scattering component values; P v,obs,i, P d,obs,i and P s,obs,i represent decomposition three scattering components observe scattering ith training sample component values. m is number training samples. (3 Cross-over Cross-over is a recombinant operar that selects two chromosomes from cross-over pool at random and cuts m in bits at a randomly chosen position [31]. The number strings participating in mating depended on cross-over probability. In this study, cross-over probability was assumed.9, and one-point cross-over was selected. ( Mutation Mutation is an important process that permits new genetic material be introduced a population. A mutation probability is specified that permits random mutations be made individual genes (e.g., changing 1, and vice versa, binary GAs. The mutation probability.1 was selected in this study. (5 Termination Criteria Finally, termination criterion GA process was determined. The GA process could be spped when fitness criterion was satisfied or maximum number generations was exceeded. In this study, maximum number generations was.. Results and Discussions Based on schemes Section 3, biophysical, including height (h, LAI, VWC canopy or stem layer (m v /m v_s, and biomass ear (D e, estimated using simulated SAR data. A tal 1 fields used as training data GA, and remaining twenty fields used validation. The estimated biophysical in different phenological stages shown in Figures Inversion Rice Biophysical Parameters Using Backscattering Coefficients Figure 7 shows estimated biophysical using backscattering coefficients and validation with ground truth data. As WCM treats canopy as a water cloud, consisting a collection identical water particles, characterized by a unim scattering phase function, VWC canopy (m v is identical that stem layer (m v_s. In general, inversion h and m v superior those D e and LAI, when four (38

14 Sensors 18, 18, 71 1 backscattering coefficients (RH/RV/RR/RL used. The coefficient determinations (R h and m v both above.81, and root-mean-square errors (RMSEs less than 1 cm and.39 kg/m 3, respectively. This indicated that backscattering coefficients four compact polarizations more sensitive height and VWC canopy compared with LAI and D e. Among four compact polarizations (RH/RV/RR/RL, RV had best permance in inversion h and m v. The preferable with RV polarization mainly due its sensitivity volume scattering canopy. For inversion D e, RH and RR got accuracies R above.85 and a RMSE less than.19 kg/m 3, a little higher than that RV but much better than that RL. This suggested that RL was not as sensitive ear biomass as or compact polarizations. The reason is that volume scattering was dominant in ear layer, while Sensors 18, 18, x FOR PEER REVIEW 15 5 RL was more related surface scattering. Compared with h, m v, and D e, LAI had poorest inversion RL was when more related using backscattering surface scattering. coefficients Compared with h, mv, four and compact De, LAI polarizations, had poorest which inversion when using backscattering coefficients RH was best retrieval LAI, with a R four compact polarizations,.79 and an RMSE.33. which RH was best retrieval LAI, with a R.79 and an RMSE.33. RH estimates h(cm /1 8/ 115 h RV estimates h(cm /1 8/ 1/15 h RR estimates h(cm R =.815 RMSE= Ground-truth data h(cm /1 8/ 1/15 h RL estimates h(cm R =.9199 RMSE= Ground-truth data h(cm 8 7/1 8/ 1/15 h RH estimates LAI R =.83 RMSE= Ground-truth data h(cm LAI 7/1 8/ 5 1/15 3 RV estimates LAI R =.91 RMSE= Ground-truth data h(cm 5 3 7/1 8/ 1/15 LAI RR estimates LAI 1 R =.79 RMSE= Ground-truth data LAI LAI 7/1 8/ 5 1/ R =. RMSE= Ground-truth data LAI RL estimates LAI 1 R =.19 RMSE= Ground-truth data LAI 5 3 7/1 8/ 1/15 LAI 1 R =.738 RMSE= Ground-truth data LAI Figure 7. Cont.

15 Sensors 18, 18, Sensors 18, 18, x FOR PEER REVIEW 1 5 RH estimates mv(kg/m 3 8 7/1 8/ 1/15 mv RV estimates mv(kg/m 3 8 7/1 8/ 1/15 mv RR estimates mv(kg/m 3 8 R =.99 RMSE=.39 Ground-truth data mv(kg/m 3 8 7/1 8/ 1/15 mv RL estimates mv(kg/m 3 8 R =.991 RMSE=.5 8 7/1 8/ 1/15 Ground-truth data mv (kg/m 3 mv RH estimates D e (kg/m RR estimates De(kg/m R =.913 RMSE=.3 Ground-truth data mv(kg/m 3 8 D 3. e.5 1/ R =.857 RMSE= Ground-truth data De(kg/m.5 3. De / R =.853 RMSE= Ground-truth data De(kg/m.5 3. R =.953 RMSE=.31 Ground-truth data mv(kg/m 3 8 De /15 RV estimates De(kg/m RL estimates De(kg/m R =.838 RMSE= Ground-truth data D e (kg/m.5 3. De / R =.593 RMSE= Ground-truth data De(kg/m.5 3. Figure 7. Results biophysical using backscattering coefficients Figure 7. Results different phenological stages biophysical. using backscattering coefficients different phenological stages... Inversion Rice Biophysical Parameters Using Skes Parameters.. Inversion Figure Rice Biophysical 8 shows Parameters estimated Using Skes Parameters biophysical using Skes (S1 and S and validation with ground truth data. In general, similar Figurebackscattering 8 shows coefficients, estimated inversion h and mv biophysical superior those De and using LAI, Skes when (S Skes (S1 and S used. The R h and mv both above.85, and 1 and S and validation with ground truth data. In general, similar RMSEs less than 8 cm and.9 kg/m 3, respectively. This indicated that Skes backscattering coefficients, inversion h and m v superior those D e and LAI, when Skes (S 1 and S used. The R h and m v both above.85, and RMSEs less than 8 cm and.9 kg/m 3, respectively. This indicated that Skes (S 1 and S more sensitive height and VWC canopy compared with LAI and D e. In addition, based on equation water cloud model and idea establishing water cloud model, h and m v contributed a lot model, which also led high precision inversion h and m v. Among Skes (S 1 and S, S 1 had best permance in inversion h and m v. As can be seen from above Equations (1 and (15 and Figure, S 1 contained multi-polarization characteristics, which contained more energy than S. The preferable with

16 (S1 and S more sensitive height and VWC canopy compared with LAI and De. In addition, based on equation water cloud model and idea establishing water cloud model, h and mv contributed a lot model, which also led high precision inversion h and mv. Among Skes (S1 and S, S1 had best permance Sensors 18, 18, 71 1 in inversion h and mv. As can be seen from above Equations (1 and (15 and Figure, S1 contained multi-polarization characteristics, which contained more energy than S. The preferable S 1 with mainly S1 due mainly its sensitivity due its sensitivity tal scattering tal scattering canopy. For inversion canopy. For D e, Sinversion 1 got accuracies De, S1 got R accuracies.79 and RMSE R.79.1 and kg/m RMSE 3, much.1 better kg/mthan 3, much that better S with than athat R.1 S with and a RMSER.1.7. and The a RMSE main scattering.7. The energy main scattering ear energy came from its volume ear came scattering, from its indicating volume scattering, that S was indicating not as good that S aswas S 1 in not as characterization good as S1 in characterization volume scattering volume characteristics. scattering Especially characteristics. on 1Especially Septemberon and 115 September Ocber, and 15 inversion Ocber, D e, inversion points inversion De, points deviated inversion from deviated line y = from x significantly, line y = x which significantly, meant that which meant accuracy that S accuracy was far less S than was that far less Sthan 1 in that dough S1 in stage dough and stage mature and stage. mature Compared stage. Compared with h, m v, with andh, Dmv, e, and LAI De, had LAI poorest had inversion poorest inversion when using when using Skes Skes (S 1 and S (S1, and which S, S 1 which was S1 best was best retrieval retrieval LAI, with LAI, a R with.75 a Rand.75 a RMSE and a RMSE S1 estimates h(cm /1 8/ 1/15 h S estimates h(cm /1 8/ 9/7 1/15 h S1 estimates LAI 5 3 R =.95 RMSE= Ground-truth data h(cm LAI 7/1 8/ 1/15 S estimates LAI 5 3 R =.8591 RMSE= Ground-truth data h(cm 7/1 8/ 1/15 LAI S1 estimates mv(kg/m 3 1 R =.758 RMSE= Ground-truth data LAI 8 7/1 8/ 1/15 mv R =.9397 RMSE=.3 Ground-truth data m v (kg/m 3 8 S estimates mv(kg/m 3 1 R =.51 RMSE= Ground-truth data LAI 8 7/1 8/ 1/15 mv R =.893 RMSE=.9 Ground-truth data m v (kg/m 3 8 Figure 8. Cont.

17 Sensors 18, 18, Sensors 18, 18, x FOR PEER REVIEW 18 5 S1 estimates D e (kg/m /15 De.5 R =.799 RMSE= Ground-truth data D e (kg/m.5 3. S estimates D e (kg/m /15 De.5 R =.19 RMSE= Ground-truth data De(kg/m.5 3. Figure Figure Results Results biophysical biophysical using using Skes Skes different different phenological phenological stages stages...3. Inversion Rice Biophysical Parameters Using m-χ and m-δ Decomposition Parameters.3. Inversion Rice Biophysical Parameters Using m-χ and m-δ Decomposition Parameters Figure shows estimated biophysical using m-χ and m-δ Figure 9 shows estimated biophysical using m-χ and m-δ decomposition and validation with ground truth data. In MWCM, decomposition and validation with ground truth data. In MWCM, heterogeneity heterogeneity canopy and its phenological changes, as well as double-bounce scattering canopy and its phenological changes, as well as double-bounce scattering between between canopy and underlying surface considered. Theree, VWC canopy and underlying surface considered. Theree, VWC canopy canopy (mv is not identical that stem layer (mv_s. In general, inversion h and (m v is not identical that stem layer (m v_s. In general, inversion h and D e De superior those mv_s and LAI, when m-χ and m-δ decomposition superior those m v_s and LAI, when m-χ and m-δ decomposition used. used. The R h and De both above.81, and RMSEs less than 1 cm and. kg/m, The R h and D e both above.81, and RMSEs less than 1 cm and. kg/m, respectively. This indicated that m-χ and m-δ decomposition more sensitive respectively. This indicated that m-χ and m-δ decomposition more sensitive height and De compared with LAI and VWC canopy. Moreover, re height and D e compared with LAI and VWC canopy. Moreover, re close close relations between h and Vf_r, Vf_s, St, Sg_r, Dg_f, and Dg_t in MWCM. The three scattering types relations between h and V f_r, V f_s, S t, S g_r, D g_f, and D g_t in MWCM. The three scattering types are are all related h and so make a significant contribution model, producing highly accurate all related h and so make a significant contribution model, producing highly accurate inversion height. Comparing inversion h based on two inversion height. Comparing inversion h based on two kinds kinds decomposition, inversion accuracy based on m-χ decomposition was higher than that decomposition, inversion accuracy based on m-χ decomposition was higher than that based on based on m-δ decomposition R was.7 larger, and RMSE was 1.7 cm smaller. m-δ decomposition R was.7 larger, and RMSE was 1.7 cm smaller. According Figure, dominant scattering type both m-χ and m-δ decomposition is According Figure, dominant scattering type both m-χ and m-δ decomposition is volume scattering. Moreover, scattering characteristics ear mainly reflected volume scattering. Moreover, scattering characteristics ear mainly reflected in volume scattering. Theree, De inversion using decomposition had in volume scattering. Theree, D e inversion using decomposition had a higher accuracy, with an R above.8 and a RMSE below. kg/m. Comparing inversion higher accuracy, with an R above.8 and a RMSE below. kg/m. Comparing inversion De based on two kinds decomposition, inversion accuracy based on m-χ D e based on two kinds decomposition, inversion accuracy based on m-χ decomposition was higher than that m-δ decomposition: R was.59 bigger, and decomposition was higher than that m-δ decomposition: R was.59 bigger, and RMSE RMSE was.5 kg/m smaller. From 8 August 15 Ocber, m-χ-db was bigger than m-δ-db (see was.5 kg/m smaller. From 8 August 15 Ocber, m-χ-db was bigger than m-δ-db (see Figure Figure which is related effect Dg_e in MWCM and indicates that m-χ-db is more which is related effect D g_e in MWCM and indicates that m-χ-db is more sensitive than sensitive than m-δ-db De inversion. m-δ-db D e inversion. For inversion mv_s, m-δ decomposition got accuracies about.83 For inversion m v_s, m-δ decomposition got accuracies R about.83 and RMSE.3 kg/m 3, much better than that m-χ decomposition. From Figure 9, and RMSE.3 kg/m 3, much better than that m-χ decomposition. From Figure 9, it can be seen that both sets inversion produced overestimates on 1 July, with it can be seen that both sets inversion produced overestimates on 1 July, with overestimation by m-χ decomposition being more obvious. This may be related three kinds overestimation by m-χ decomposition being more obvious. This may be related three scattering components; also, at this stage, double-bounce scattering and surface scattering kinds scattering components; also, at this stage, double-bounce scattering and surface scattering values m-χ higher than those m-χ decomposition. values m-χ higher than those m-χ decomposition. Compared with h, mv_s, and De, LAI had poorest inversion when using m-χ Compared with h, m v_s, and D e, LAI had poorest inversion when using m-χ and m-δ decomposition. The m-δ decomposition better than m-χ and m-δ decomposition. The m-δ decomposition better than m-χ decomposition retrieval LAI, with.7 and RMSE.8, but decomposition retrieval LAI, with a R.7 and a RMSE.8, but difference in accuracy was small: was.5 higher, and RMSE was.1 smaller. difference in accuracy was small: R was.5 higher, and RMSE was.1 smaller.

18 Sensors 18, 18, Sensors 18, 18, x FOR PEER REVIEW 19 5 m- estimates h(cm /1 8/ 1/15 h m- estimates h(cm /1 8/ 1/15 h m- estimates LAI R =.88 RMSE= Ground-truh data h(cm LAI m- estimates mv_s(kg/m 3 m- estimates D e (kg/m 7/1 8/ 1/15 R =.53 RMSE= Ground-truth data LAI 8 7/1 8/ 1/15 mv_s R =.78 RMSE=.8 Ground-truth data m v_s (kg/m 3 8 D 3. e.5 1/ R =.8995 RMSE= Ground-truth data D e (kg/m.5 3. m- estimates LAI m- estimates mv_s(kg/m 3 m- estimates De(kg/m R =.8119 RMSE= Ground-truh data h(cm LAI 7/1 8/ 5 1/ R =.731 RMSE= Ground-truth data LAI 8 7/1 8/ 1/15 mv_s R =.8355 RMSE=.3 Ground-truth data m v_s (kg/m 3 8 De / R =.8 RMSE= Ground-truth data D e (kg/m.5 3. Figure Figure Results Results biophysical biophysical using using m-χ and m-χ m-δand decomposition m-δ decomposition different phenological different phenological stages. stages. Figure 1 shows inversion biophysical h, mv_s, and LAI Figure 1 shows inversion biophysical h, m v_s, and LAI using m-χ decomposition. It can be seen from Figure 1 that inversion using m-χ decomposition. It can be seen from Figure 1 that inversion h, h, mv_s, and LAI have low values in elongation stage (1 July, and inversion increase m v_s, and LAI have low values in elongation stage (1 July, and inversion increase from elongation stage (1 July booting stage ( August. The height was about 8 from elongation stage (1 July booting stage ( August. The height was about 8 cm, cm, LAI is around 3, and value mv_s is about.5 3 in elongation stage and booting stage. During heading stage (8 August and dough stage (1 September, three biophysical reached ir peak values. The peak height was n about 1 cm,

19 Sensors 18, 18, LAI is around 3, and value mv_s is about.5 3 in elongation stage and booting stage.18, During stage (8 August and dough stage (1 September, three biophysical Sensors 18, x FORheading PEER REVIEW 5 Sensors 18, 18, x FOR PEER REVIEW 5 reached ir peak values. The peak height was n about 1 cm, peak LAI 3. These peak LAI isis3 5, around 3 5,peak and peak peak value mv_s v_s 3 about 3 kg/m33.values. These Thesen values n decreased decreased is around and 3 5, value m about kg/m decreased slightly v_s is peak LAI around and value m isis about 3 kg/m values n slightly during mature stage (15 Ocber. This was because as mature, plant turned during mature stage (15 Ocber. This was because as mature, plant turned yellow, slightly during mature stage (15 Ocber. This was because as mature, plant turned yellow, stemcontained contained less water, size layer ear ear layerincreased, increased, andgot shorter. got gotthe shorter. The stem contained less water, size ear increased, and inversion yellow, stem less water, size layer and shorter. The inversion or show similar patterns those seen in Figure 1. or show similar patterns those seen in Figure 1. inversion or show similar patterns those seen in Figure 1. Figure 11 shows inversion Deeeusing using m-χ decomposition. Because Figure11 11shows shows inversion inversion D using m-χ m-χdecomposition decomposition..because Because Figure ears began m during heading stage (8 August, D ee n is low. D ee reached its peak ears began m during heading stage (8 August, D n is low. D reached itspeak peak ears began m during heading stage (8 August, De n is low. De reached its during dough stage (1 September. The ears on September 1 nearly mature, and so, during dough stage (1 September. The ears on September 1 nearly mature, and so, during dough stage (1 September. The ears on September 1 nearly mature, and so, value D e remained basically unchanged on 15 Ocber. The inverting D e using or value valuedde remained basicallyunchanged unchangedon on15 15Ocber. Ocber.The The inverting invertingdde eusing using or or e remainedbasically show similar patterns those seen in Figure 11. show similar patterns those seen in Figure 11. show similar patterns those seen in Figure 11. Figure 1. The inversion inversion biophysical at five five different different phenological Figure1. 1. The The biophysical at five different phenological stages Figure biophysical at phenological stages using m-χ decomposition. (The five images in first row are inversion using using m-χ. (The five images in first rowfirst are stages decomposition m-χ decomposition. (The five images in rowinversion are inversion h, h, five five images in second second row are leaf leaf area and index (LAI, and h, five images in second row are row leaf area index (LAI, five images in images in are area index (LAI, and five images in third row are m v_s. third row are m. v_s five images in third row are mv_s. Figure 11. The inversion biophysical parameter D at three phenological stages Figure Figure The Theinversion inversion biophysical biophysicalparameter parameterd Deeeat atthree threephenological phenologicalstages stages using m-χ decomposition. using using m-χ m-χdecomposition decomposition.. 5.Conclusions Conclusions Conclusions The objective objective this study study was investigate investigate capability capability SAR SAR data data in in inversion inversion The The objectivethis this studywas was investigate capability SAR data in inversion biophysical biophysical during during growing growing season. Five Five temporal RADARSAT- RADARSAT- fully biophysical during growingseason. season. Fivetemporal temporal RADARSAT-fully fully polarimetric SAR datasets used simulate SAR data, and, such such as as polarimetric SAR datasets used simulate SAR data, and, polarimetric SAR datasets used simulate SAR data, and, such as backscattering coefficients, coefficients, Skes vecrs vecrs and decomposition decomposition extracted in in each backscattering backscattering coefficients,skes Skes vecrsand and decomposition extracted extracted ineach each acquisition. Then, inversion models biophysical with SAR data acquisition. acquisition.then, Then, inversion inversionmodels models biophysical biophysical with with SAR SARdata data developedby by introducingaaclassic classic semi-empiricalmodel, model,water watercloud cloudmodel, model,and and amodified modifiedwcm WCM developed developed byintroducing introducing a classic semi-empirical semi-empirical model, water cloud model, and aamodified WCM in in which which heterogeneity heterogeneity canopy canopy and and its its phenological phenological changes changes as as well well as as doubledoublein bounce scattering between canopy and underlying surface considered. Finally, bounce scattering between canopy and underlying surface considered. Finally, biophysical, including height (h, LAI, VWC canopy or stem layer biophysical, including height (h, LAI, VWC canopy or stem layer (mv/v/m mv_s v_s, and biomass ear (De, obtained, and validation was conducted using (m, and biomass ear (De, obtained, and validation was conducted using ground truthdata. data.the Thedetailed detailedconclusions conclusionsare areas asfollows: follows: ground truth

20 Sensors 18, 18, 71 which heterogeneity canopy and its phenological changes as well as double-bounce scattering between canopy and underlying surface considered. Finally, biophysical, including height (h, LAI, VWC canopy or stem layer (m v/ m v_s, and biomass ear (D e, obtained, and validation was conducted using ground truth data. The detailed conclusions are as follows: (1 Using four backscattering coefficients (RH/RV/RR/RL, inversion h and m v_s superior those LAI and D e, with R above.81 and RMSE less than 1 cm and.39 kg/m 3, respectively. RV had best permance in inversion h and m v_s. For D e, RH and RR permed better than RV and RL, giving an R above.85 and an RMSE less than.18 kg/m. Again, inversion LAI poorest, with RV and RL giving better than RH and RR. For RL, R was.73, and RMSE was less than.55. ( Using Skes, inversion four biophysical good. For h, R had a value about.9, and RMSE was less than 5.8 cm. For m v_s, R was about.93, and RMSE was less than.3 kg/m 3. For D e, R was about.8, and RMSE was less than.1 kg/m. The inversion LAI poorest, with an R.75 and an RMSE up.5. (3 Using m-χ and m-δ decomposition, inversion accuracy h and D e was higher than m v_s and LAI. For h, coefficient determination, R, was about.88, and RMSE was less than 8. cm. For D e, R was.89, and RMSE was less than.17 kg/m. For m v_s, coefficient determination was.83, and RMSE was less than. kg/m 3. As bee, inversion LAI had lowest accuracy, with an R.73 and an RMSE up.55. Overall, h and D e inversion obtained using m-χ decomposition better than those obtained using m-δ decomposition. For LAI and m v_s, accuracy obtained using m-δ decomposition was slightly higher than that found using m-χ decomposition. In early stages growth, m v_s was slightly overestimated; in case m-χ decomposition, this overestimation was even greater. ( In general, S 1, RV, RL, and m-χ decomposition proved be more suitable inversion h, and high inversion accuracy was obtained. S 1, RH, RV, RR, and RL are more suitable inverting m v. RH, RR, and decomposition are more suitable inverting D e. In this paper, capability SAR data in inversion biophysical has been proven, especially inversion h, D e, and m v_s /m v, with an R higher than.89 and an RMSE less than 1 cm,.8 kg/m 3, and.7 kg/m. Furr validation and improvement will be conducted with subsequent dataset this site and dataset or regions. Author Contributions: X.G. and K.L. proposed and permed experiments and wrote paper; Y.S. and Z.W. checked paper and provided suggestions. H.L., Z.Y., L.L., and S.W. contributed data analysis, making charts and paper modification, respectively. The order authors reflects ir level contribution. Acknowledgments: This research was supported by funding from National Natural Science Foundation China (171358, 13117, YSJ1CX, and GEO international cooperation project (Joint Experiment Crop Assessment and Moniring, JECAM. Conflicts Interest: The authors declare no conflict interest. References 1. Maclean, J.L.; Dawe, D.C.; Hardy, B.; Patel, G.P. Rice Almanac: Source Book Most Important Economic Activity on Earth; International Rice Research Institute: Los Banos, Philippines, ; p. 53, ISBN Toan, T.L.; Ribbes, F.; Wang, L.F.; Floury, N.; Ding, K.H.; Kong, J.A.; Fujita, M.; Kurosu, T. Rice crop mapping and moniring using ERS-1 data based on experiment and modeling. IEEE Trans. Geosci. Remote Sens. 1997, 35, 1 5. [CrossRef]

21 Sensors 18, 18, Bouvet, A.; Toan, T.L.; Lam-Dao, N. Moniring Rice Cropping System in Mekong Delta Using ENVISAT/ASAR Dual Polarization Data. IEEE Trans. Geosci. Remote Sens. 9, 7, [CrossRef]. Shao, Y.; Fan, X.; Liu, H.; Xiao, J.; Ross, S.; Brisco, B.; Brown, R.; Staples, G. Rice moniring and production estimation using multitemporal RADARSAT. Remote Sens. Environ. 1, 7, [CrossRef] 5. Erten, E.; Lopez-Sanchez, J.M.; Yuzugullu, O.; Hajnsek, I. Retrieval agricultural crop height from space: A comparison SARtechniques. Remote Sens. Environ. 1, 187, [CrossRef]. Tan, L.; Chen, Y.; Jia, M.; Tong, L.; Li, X.; He, L. Rice biomass retrieval from advanced syntic aperture radar image based on radar backscattering measurement. J. Appl. Remote Sens. 15, 9, [CrossRef] 7. Raney, R.K.; Spudis, P.D.; Bussey, B.; Crusan, J.; Jensen, J.R.; Marinelli, W.; Mckerracher, P.; Neish, C.; Palsetia, M.; Schulze, R. The Lunar Mini-RF Radars: Hybrid Polarimetric Architecture and Initial Results. Proc. IEEE 11, 99, [CrossRef] 8. Kunhikrishnan, P.; Sowmianarayanan, L.; Nampoothiri, M.V. PSLV-C19/RISAT-1 mission: The launcher aspects. Curr. Sci. 13, 1, Arikawa, Y.; Suzuki, S. ALOS- launch and initial checkout result. In Proceedings Sensors, Systems, and Next-Generation Satellites XVIII, Amsterdam, The Nerlands, 11 November Sanden, J.J.V.D.; Geldsetzer, T. Compact Polarimetry in Support Lake Ice Breakup Moniring: Anticipating RADARSAT Constellation Mission. Can. J. Remote Sens. 15, 1, 57. [CrossRef] 11. Brisco, B.; Li, K.; Tedd, B.; Charbonneau, F.; Yun, S.; Murnaghan, K. Compact polarimetry assessment and wetland mapping. Int. J. Remote Sens. 13, 3, [CrossRef] 1. Uppala, D.; Kothapalli, R.V.; Poloju, S.; Mullapudi, S.S.V.R.; Dadhwal, V.K. Rice Crop Discrimination Using Single Date RISAT1 Hybrid (RH, RV Polarimetric Data. Phogramm. Eng. Remote Sens. 15, 81, [CrossRef] 13. Li, K.; Brisco, B.; Shao, Y.; Touzi, R. Polarimetric decomposition with RADARSAT- mapping and moniring. Can. J. Remote Sens. 1, 38, [CrossRef] 1. Watanabe, T.; Izumi, Y.; Tetuko, J.; Sumantyo, S. Comparison three compact-sar mode through phenology moniring. In Proceedings International Symposium on Antennas and Propagation (ISAP, Phuket, Thailand, 3 Ocber November Yang, Z.; Li, K.; Liu, L.; Shao, Y.; Brisco, B.; Li, W. Rice growth moniring using simulated compact polarimetric C band SAR. Radio Sci. 1, 9, [CrossRef] 1. Lopez-Sanchez, J.M.; Vicente-Guijalba, F.; Ballester-Berman, J.D.; Cloude, S.R. Polarimetric Response Rice Fields at C-Band: Analysis and Phenology Retrieval. IEEE Trans. Geosci. Remote Sens. 1, 5, [CrossRef] 17. Kumar, V.; Mcnairn, H.; Bhattacharya, A.; Rao, Y.S. Temporal Response Scattering From Crops Transmitted Ellipticity Variation in Simulated Compact-Pol SAR Data. IEEE. Sel. Top. Appl. Earth Obs. Remote Sens. 17, 1, [CrossRef] 18. Zhang, W.; Li, Z.; Chen, E.; Zhang, Y.; Yang, H.; Zhao, L.; Ji, Y. Compact Polarimetric Response Rape (Brassica napus L. at C-Band: Analysis and Growth Parameters Inversion. Remote Sens. 17, 9, Yang, H.; Xie, L.; Chen, E.X.; Zhang, H.; Yang, G.J.; Li, Z.H.; Gu, X.H. Biomass estimation oilseed rape using simulated compact polarimtric SAR imagery. In Proceedings 1 IEEE International Geoscience and Remote Sensing Symposium (IGARSS, Beijing, China, 1 15 July 1.. Ulaby, F.; Allen, C.; Eger, G.; Kanemasu, E. Relating microwave backscattering coefficient leaf area index. Remote Sens. Environ. 198, 1, [CrossRef] 1. Thompson, A.A.; Mcleod, I.H. The RADARSAT- SAR processor. Can. J. Remote Sens., 3, [CrossRef]. Shi, Z.Y.; Gao, X.F.; Xie, Y. The application SUNSCAN canopy analysis system in measurement field ecosystem. Agric. Res. Arid Areas 5, 3, Attema, E.; Ulaby, F.T. Vegetation modeled as a water cloud. Radio Sci. 1978, 13, [CrossRef]. Raney, R.K. Hybrid-Polarity SAR Architecture. IEEE Trans. Geosci. Remote Sens. 7, 5, [CrossRef] 5. Yin, J.; Yang, J. Ship detection by using M-Chi and M-Delta decompositions. In Proceedings 1 IEEE International Geoscience and Remote Sensing Symposium (IGARSS, Quebec, QC, Canada, July 1.

22 Sensors 18, 18, 71. Raney, R.K.; Cahill, J.S.T.S.; Patterson, G.W.; Bussey, D.B.J. The m-chi decomposition hybrid dual-polarimetric radar data. In Proceedings 1 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 7 July Yang, Z.; Li, K.; Shao, Y.; Brisco, B.; Liu, L. Estimation Paddy Rice Variables with a Modified Water Cloud Model and Improved Polarimetric Decomposition Using Multi-Temporal RADARSAT- Images. Remote Sens. 1, 8, 878. [CrossRef] 8. Beriaux, E.; Lucau-Danila, C.; Auquiere, E.; Defourny, P. Multiyear independent validation water cloud model retrieving maize leaf area index from SAR time series. Int. J. Remote Sens. 13, 3, [CrossRef] 9. Lievens, H.; Verhoest, N.E.C. On Retrieval Soil Moisture in Wheat Fields from L-Band SAR Based on Water Cloud Modeling, IEM, and Effective Roughness Parameters. IEEE Geosci. Remote Sens. Lett. 11, 8, 7 7. [CrossRef] 3. Bériaux, E.; Waldner, F.; Collienne, F.; Bogaert, P.; Defourny, P. Maize Leaf Area Index Retrieval from Syntic Quad Pol SAR Time Series Using Water Cloud Model. Remote Sens. 15, 7, [CrossRef] 31. Golberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison Wesley: Bosn, MA, USA, Kumar, K.; Hari Prasad, K.S.; Arora, M.K. Estimation water cloud model vegetation using a genetic algorithm. Hydrol. Sci. J. 1, 57, [CrossRef] 18 by authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under terms and conditions Creative Commons Attribution (CC BY license (