Rice biomass retrieval frommultitemporal ground-basedscatterometer data and RADARSAT-2images using neural networks

Mingquan JiaLing TongYan ChenYong WangYuanzhi Zhang

Rice biomass retrieval from multitemporalground-based scatterometer data and RADARSAT-2

images using neural networks

Mingquan Jia,a Ling Tong,a Yan Chen,a Yong Wang,b,c andYuanzhi Zhangd

aUniversity of Electronic Science and Technology of China (UESTC), School of AutomationEngineering, Chengdu, Sichuan 611731, China

jiamingquan@163.combUniversity of Electronic Science and Technology of China (UESTC), School of Resources and

Environment, Chengdu, Sichuan 611731, ChinacEast Carolina University, Department of Geography, Greenville, North Carolina 27858

dChinese University of Hong Kong, Yuen Yuen Research Centre for Satellite Remote Sensing,Institute of Space and Earth Information Science, Shatin, Hong Kong, China

Abstract. A neural network (NN) algorithm to invert biomass of rice plants using quad-polari-zation radar datasets of ground-based scatterometer and spaceborne RADARSAT-2 has beenstudied. The NN is trained with pairs of multipolarization radar backscattering and biomassdata. The backscattering data are simulated from a Monte Carlo backscatter model that usesthe outputs from a growth model of the rice plant. The growth model is developed from theplant data collected in growing cycles of several years. In addition to producing parametersneeded by the backscatter model, the growth model outputs the biomass value of the plant.Multipolarization data collected by a ground-based scatterometer at eight stages during the2012 growing cycle are input to the NN to invert biomass. Satisfactory results are obtaineddue to a small root mean squared error (RMSE) of 0.477 kg∕m2 and a high correlation coef-ficient of 0.989 when the inverted and measured biomass values are compared. Finally,RADARSAT-2 synthetic aperture radar images acquired on four different dates during the2012 growth period are analyzed to delineate rice paddies within the study area and to invertbiomass using the NN. Inversion results from the delineated rice paddies are encouragingbecause the RMSE is 0.582 kg∕m2 and correlation coefficient is 0.983. © 2013 Society ofPhoto-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.7.073509]

Keywords: backscattering coefficient; biomass of rice plant; Monte Carlo simulation model;neural network; RADARSAT-2 imagery; rice paddy mapping.

Paper 13175 received May 19, 2013; revised manuscript received Jul. 26, 2013; accepted forpublication Aug. 12, 2013; published online Sep. 6, 2013.

1 Introduction

Rice is a staple food around the globe, and >90% rice is produced in Asia.1 With a growingglobal population, adequate food is considered as national security.2 In the assessment that coun-tries or regions have a steady supply of rice, it is important to delineate rice paddies from nonricefields, to monitor rice growth and to estimate its biomass in an effective and efficient manner.3

Remotely sensed satellite data constitute a unique means that can in a timely manner consistentlyprovide spatial and temporal coverage needed at regional to global scales.4 Rice is often plantedin tropical and subtropical areas where there is adequate rainfall and typically with cloud cover aswell as mild spring, hot summer, and warm fall seasons. Microwave energy from an active sensoris able to penetrate cloud and rainfall to some extent. Thus, the active sensor can work under allweather conditions and all day. Intuitively, the sensor should be used to study the biomass varia-tion of rice paddy through the entire growth period.5

0091-3286/2013/$25.00 © 2013 SPIE

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Studies with ground-based active microwave sensors have been conducted, and insight andunderstanding in the estimation of rice growth parameters obtained.6–9 In particular, Inoue et al.9

carried out comprehensive ground-based measurements that consist of daily microwave back-scattering coefficients using combinations of all five frequencies, quad-polarization, and fourincident angles for one entire rice growth period. They found that the coefficients were stronglycorrelated with the height, stem density, leaf area index (LAI), and biomass of plant. In additionto understand interactions between electromagnetic waves and paddy canopies better, research-ers have developed backscattering models.6,10,11 One of the models was based on Monte Carlosimulation using a coherent scattering model.6 Modeled outcome indicated that the co-polarizedbackscatter from inundated rice fields was dominantly influenced by the double bounce stem-ground interactions.11

Regardless whether studies are observation-based studies and/or modeling activities, one ofthe ultimate goals is to map the spatial extent of rice paddies and to monitor the rice growththrough biomass inversion. Data collected by active microwave sensors, especially from space-borne ERS-1/2,12,13 JERS-1,14 ENVISATASAR,15,16 RADARSAT-1/2,17,18 and TerraSAR-X19,20

synthetic aperture radar (SAR), have been used to monitor and assess the rice plant biomass. Dueto the complex scattering mechanisms from rice paddies, the retrieved information of the plantstatus during the growing season from the SAR data is largely statistical or empirical.Furthermore, variable levels of plant biomass could produce the same or similar backscattering.Thus, the retrieval of rice parameters (such as plant height, stem density, and LAI) is an ill-posedproblem. Alternatively, a neural network (NN)21–24 has been explored for the parameter inversionbecause assumptions about the analytic expression or expressions between input and output pairsare not needed in the NN. Therefore, the NN is particularly applicable for the inversion frommultidimensional inputs and outputs.21,23 The NNs trained with theoretical scattering modelshave been applied to the inversion of soil properties24–26 and vegetation parameters.22,27–29

Levels of accuracy from the retrieved parameters are satisfactory at certain conditions.Although multiple temporal observations from scatterometer and satellite are useful in

assessing the variations of backscattering due to the growth of rice plant, simultaneous obser-vations using both types of datasets during one rice-growing season are rarely reported becauseof the lack of acquisition opportunity. Thus, one could not assess the usefulness in studying andcomparing the relationship between rice plant and ground-based instrument and the relationshipbetween rice plant and spaceborne sensor. Fortunately, four simultaneous ground-based scatter-ometer and RADARSAT-2 observations were achieved during the growing season of 2012.Therefore, the objectives are to study the biomass from rice paddies through analyses of themultitemporal simultaneous datasets and to invert biomass of rice plant from rice paddies inthe study area. In particular, the scatterometer data are used in the development and validationof a backscatter model and an NN inversion algorithm. Since numerous inputs are needed in theparameterization of the backscatter model, but some of the inputs are difficult to collect in thefield representatively, a rice growth model is developed to simulate the representative parametersas inputs to the backscatter model. To compensate for limited scatterometer observations spa-tially, we map the rice paddies in the study area using SAR imagery. Then, SAR backscatteringcoefficients from individual rice paddies are extracted and input to the NN to invert the biomassof rice plant in the rice-growing season of 2012. The details are given below.

2 Experiment and Dataset

2.1 Study Area and Rice Phenological Cycle

The site (103°32'24"E, 30°24'11"N) is located in Qionglai County, southwest of Chengdu.Chengdu is the capital city of Sichuan Province, China. Qionglai is near the southwestern cornerof Chengdu Plain (Fig. 1). (A grayscale RADARSAT-2 HH image is overlaid on theGoogleEarth© data to show the study area.) Rice is one of the major crops in the area. Theentire rice growth period is typically from the middle of April to early September, whereasrice transplanting usually occurs in late May or early June. Due to cool or cold weather inearly spring or late fall, rice is grown only once per year. F You 498, Gangyou 188, and II

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You 498 are common rice species or varieties that have a growth cycle∼150 days. The study areabelongs to the Agricultural Development and Extension Centre of Sichuan AgriculturalUniversity and researchers from the center study growing processes of rice annually.

2.2 Field Observation and Measurement in 2012

The flooded site was transplanted with young rice plants on May 30, 2012. After the transplant-ing, the rice would grow ∼105 days to be mature. The grow period could be further divided intoeight stages: seedling, tillering, jointing, booting, heading, flowering, milking, and ripening[Figs. 2(a)–2(h)]. Also, the site was inundated until the middle of August and then drainedfor harvest occurring in September.

During the growth cycle, total height, canopy coverage, biomass (fresh and dry weight of riceplant per squared meters), LAI, and plant structure were collected. The dry weights of stem, leaf,and ear were scaled after drying them in an oven for 48 h and with the oven temperature of 95°C.The LAI was measured with LI-COR/LAI-2000 (http://www.licor.com/). Data for the canopystructure include stem density, length, radius, leaf length, width, thickness, and gravimetric watercontent of stem and leaf. Table 1 summarizes main parameters that have been averaged fromsamples at each stage. In each case, at least 20 sample locations in the rice field were randomlyselected.

Fig. 1 The study area. A gray-scaled RADARSAT-2 HH image was overlaid on the GoogleEarth©image.

Fig. 2 Eight stages of the rice growth period in 2012: (a) seedling (06/11), (b) tillering (06/23),(c) jointing (07/05), (d) booting (07/19), (e) heading (07/29), (f) flowering (08/10), (g) milking(08/22), and (h) ripening (09/12).

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2.3 Scatterometer Measurement

The ground-based radar scatterometer (GBRS) (Fig. 3) is of frequency-modulated continuous-wave (FM-CW) and quad-polarization.30 The center frequency for C-band is 5.3 GHz, which isthe same frequency of RADARSAT-2 SAR. The GBRS antennas are mounted on extendablerods. The antennas have a tilt and azimuth scanning capability, and are able to operate and scanautomatically with incidence angles from 0 to 90 deg and azimuth angles between 0 and 360 deg.During the rice-growing season of 2012, the scatterometer was set up at the same location andthe antennas were 12.5 m vertically above ground. After extending the rod outwards, the anten-nas were over the rice fields. The measurements were at quad-polarization with incidence anglesfrom 0 to 80 deg. Once the incidence angle was beyond 80 deg, the footprint of antenna beamwas outside the rice field of interest. From June 11 to September 12, eight sets of measurementscorresponding to eight growth stages were acquired (Table 2). The plants were harvested onSeptember 10, 2012.

Table 1 Measured ground data.

DateDays after

transplanting

Totalheight(cm)

Fresh weightbiomass(kg∕m2)

Dry weightbiomass(kg∕m2) LAI

Stemdensity(∕m2)

Leaf watercontent(%)

Stem watercontent(%)

June 11 13 34.0 0.129 0.021 0.37 141 77.1 90.2

June 23 25 38.7 0.466 0.070 2.20 320 78.6 91.5

July 05 37 78.8 2.020 0.350 3.26 417 76.6 88.7

July 19 51 99.2 4.269 0.615 4.48 503 83.9 87.3

July 29 61 108.9 4.566 1.128 4.98 402 70.5 80.1

August 10 73 145.3 5.314 1.275 5.11 393 68.0 84.0

August 22 85 125.9 5.620 1.377 4.46 414 67.4 83.6

September 12 106 50.0 5.486 1.407 3.66 380 65.6 83.1

Fig. 3 The ground view when measurement was made at stage 2 or tillering (Table 2).

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2.4 RADARSAT-2 Data and Data Processing

The return period of the RADARSAT-2 is 24 days. The first flight over the study area occurredon June 11, 2012, and the harvest date for the rice was September 10, 2012. Thus, with thecoordination of dates in fieldworks and RADARSAT-2 overflights, four simultaneous observa-tions have been achieved corresponding to seeding, jointing, heading, and milking (Table 2).SAR data were in a single look complex (SLC) format and with a nominal slant range resolutionof 5.2 m and an azimuth resolution of 7.6 m. The provided SLC data were resampled with a pixelsize of 4.7 mðslant rangeÞ × 5.1 mðazimuthÞ. After conversion from slant range to ground range,the image coverage was about 25 × 25 km2 (Fig. 1). The Next European Space Agency (ESA)SAR Toolbox software (http://nest.array.ca/web/nest) was used to process multitemporal SARimages. The procedures include radiometric calibration, geocorrection, co-registration, specklereduction, and data export. In geocorrection, each SAR image was re-projected using theUniversal Transverse Mercator projection and World Geodetic System 1984 as datum and sphe-roid. In co-registration of the multitemporal images, a set of 200 ground control points wasspecified to the registration routine and used to achieve a root mean squared error (RMSE)of 0.6 pixels or less. The multitemporal speckle filter18 was adopted in a speckle reduction.The filter window was set at 11 × 11. Color composites were created using multitemporalRADARSAT-2 HH images. As an example, a small area, about 4 × 3 km2, image coveringthe study site is shown (Fig. 4). Green and dark green areas represent rice fields.

3 Analytical Approach

3.1 Models

3.1.1 Rice growth model and biomass

A rice paddy with 3 × 4 bunches of rice plants is illustrated. Multiple stems per bunch are clearlyshown. In addition to dielectric constants of leaf, stem and water, and azimuth and zenith anglesof leaves/stems, the following inputs to the backscattering model from the rice paddy are needed.They are the distance between adjacent lines, DLin; spacing of adjacent rows, DRow; radius of abunch, Rb; number of stems per bunch, Nstem; averaged stem length per bunch, Ls; averagedstem radius per bunch, Rs; averaged leaf length per stem, ll; averaged leaf width per stem, wl;averaged leaf thickness per stem, dl; and number of leaves per stem, Nl. To make the plantparameters be more representative and more applicable to other locations where rice paddies

Table 2 Acquisition dates of scatterometer and synthetic aperture radar (SAR) data in 2012.

DateRice phenological

stage Scatterometer acquisition RADARSAT-2 image

June 11 Seedling 5.3 GHz, 1 to 80 deg, Quad-pol. 5.3 GHz, 43 deg,Quad-pol.

June 23 Tillering 5.3 GHz, 1 to 80 deg, Quad-pol.

July 05 Jointing 5.3 GHz, 1 to 80 deg, Quad-pol. 5.3 GHz, 43 deg,Quad-pol.

July 19 Booting 5.3 GHz, 1 to 80 deg, Quad-pol.

July 29 Heading 5.3 GHz, 1 to 80 deg, Quad-pol. 5.3 GHz, 43 deg,Quad-pol.

August 10 Flowering 5.3 GHz, 1 to 80 deg, Quad-pol.

August 22 Milking 5.3 GHz, 1 to 80 deg, Quad-pol. 5.3 GHz, 43 deg,Quad-pol.

September 12 Ripening 5.3 GHz, 1 to 80 deg, Quad-pol.

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exist than the data randomly collected at a limited number of measurements at the study site, wehave developed a rice growth model using collected field data during the rice growth period in2010. The model is the function of time after transplanting and initial line and row spacing of ricebunches, and can be expressed as

ðRb; Nstem; Ls; Rs; ll; wl; dl; NlÞ ¼ FRðDaT; DLin; DRowÞ; (1)

where DaT is the number of growing days after transplanting. FR represents the generation rules.Assuming each growing parameter [within the parentheses on the left side of Eq. (1)] follows anindividual normal distribution function Nðμ; σÞ with the mean value of μ and one standarddeviation of σ. ðμ; σÞ are individually calculated according to the sample data. (Table 1shows some of the data.)

Using simulated outputs from the growth model, we can derive the rice biomass (kg∕m2) as

WBiomass ¼ Nb × Nstem × ðgs þ glÞ; (2)

where Nb is the number of bunches per squared meters. gs is the fresh weight of one stem, and glis the fresh weight of leaves per stem. Since the stem is modeled as a cylinder, then

gs ¼ π × R2s ×Hs × ρs;

where ρs is the specific density of the stem. Also, an ellipsoid is used to model one leaf. Thus,

gl ¼4

3πll2

wl

2

dl2× ρl × Nl ¼

1

6πllwldl × ρl × Nl;

where ρl is the leaf specific density.In order to extend applicability of the rice growth model that is based on the field data of

2010, we introduce two additional correction factors, the biomass factor, FB, and the rice growthcycle factor, FC. Both FB and FC are determined according to the field data collected in 2012.FB is the ratio of the fresh weight biomass of a given field of interest, WBiomass−i to that ofreferenced field, WBiomass−r. Thus, FB ¼ WBiomass−i∕WBiomass−r. FB allows simulations ofrice growth parameters with different fresh weights of biomass as compared to the weight atthe site where measurement is made. Due to variable rice varieties or different timing in trans-planting at individual areas, the growth cycle can differ from rice species to species or fromlocation to location. Thus, using FC ¼ DaTc−i∕DaTc−r, one can simulate rice growth parameterswhere the growth cycle DaTc−i is different from the growth cycle DaTc−r at the sample site.

Fig. 4 The study site extracted from a color composite of multitemporal RADARSAT-2 HH images(R=June 11, G=July 05, and B=August 22).

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Thus, with the incorporation of the factors, the rice growth model at the paddy field of interest atDaTi of transplanting can be expressed as

FRðDaTi; DLin−i; DRow−iÞ ¼ FB × FRðFc × DaT; DLin−i; DRow−iÞ

¼ WBiomass−i

WBiomass−r× FR

�DaTc−i

DaTc−r× DaT; DLin−i; DRow−i

�:

(3)

In this study, DaTc−r is 106 days. Because parameters ðRb; Nstem; Ls; Rs; ll; wl; dl; NlÞ andWBiomass−r have been collected at eight growing stages (e.g., Fig. 2) in the fieldwork, the validityof the rice growth model has been tested from seedling stage to milking stage. The result ispresented in Sec. 4.1 (Fig. 8).

3.1.2 Scattering model

With multiple runs of Monte Carlo simulation from a rice paddy, a scattering model is developedto calculate the backscattering coefficient. Scatterers of the model including dielectric cylindersand elliptical discs are over a dielectric half-space using the Foldy–Lax multiple scattering equa-tions.31 Given the configuration of a paddy field (Fig. 5) and an incident wave Ei in the directionof ðθi;φiÞ (Fig. 6), the first-order solution of the backscattered electric field, Es, can be solved inMonte Carlo simulation as

Esp;qðrÞ ¼

eikr

rðσa þ σb þ σc þ σdÞEi

p;q; (4)

Fig. 5 A simplified rice paddy with 3 × 4 bunches.

Fig. 6 Modeled scattering components of one rice bunch.

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where the solution has been expressed as the summation of four major types of scattering mecha-nism in the rice field (Fig. 6). σa is the direct backscattering from the canopy, σb is asingle scattering from the scattering followed by a reflection off the water surface, σc is σbin the reverse direction, and σd describes a reflection by the water surface followed by a singlebackscattering from the canopy and then by a reflection off the water surface (Fig. 6). p orq (H or V) is polarization component of the incident and scattered waves. r is the distancebetween the antenna and the individual scattering component. The backscattering coefficient,σap;q, is

σap;q ¼4πr2

A¼ hjEs

p;qj2ijEi

p;qj2; (5)

where A is the illuminated area, and hi denotes average.In Monte Carlo simulation for the backscattering, individual rice bunches are positioned

according to the initial distance between adjacent lines and spacing of adjacent rows. Then,in each simulation run, stem positions within each bunch are located using a random numbergenerator with a uniform distribution. Positions of stems are checked so that there is no overlapamong each other. (Different location is assigned for each of the overlapped stems.) The posi-tions and orientations of leaves of each stem are also generated randomly based on the prob-ability density function measured in the field. Other model inputs such as the radius of a bunch,number of stems per bunch, averaged stem length per bunch, averaged stem radius per bunch,averaged leaf length per stem, averaged leaf width per stem, averaged leaf thickness per stem,and number of leaves per stem are outputs from the rice growth model. Because quad-polari-zation backscattering coefficients have been collected using the scatterometer at eight stages ofthe growth period in 2012, the validity of the backscattering model has been investigated. Theresult is shown in Sec. 4.1.

3.2 Inversion Algorithm

3.2.1 Neural network

An NN is composed of many nonlinear computational elements (called neurons) operating inparallel and being linked with each other through multiplying weighting factors. An NNmodel isable to learn patterns through training using input–output pairs. Once trained, the NN acting as “ablack box” is ready to produce outputs based on the inputs. Thus, the NN is inherently suitablefor addressing a problem or relationship for which an analytic solution might not be available.Here, an NN is developed in the biomass inversion over paddy from backscatter coefficient. TheNN is a standard feed-forward multilayer perceptron (MLP) trained using the back-propagation(BP) learning rule. The NN consists of one input, two hidden, and one output layers. Three nodesof the input layer are related to HH, HV, and VV backscattering coefficients, respectively. Thenumber of neurons associated with each hidden layer is determined through training differentnetworks. Here, 11 neurons or nodes of the first hidden layer and nine of the second hidden layerare able to estimate biomass with the acceptable level of accuracy while keeping the computa-tional load manageable. The NN outputs the biomass. The NN is implemented with the NNToolbox of matlab software (http://www.mathworks.com/).

3.2.2 Inversion procedure

Major steps are simulation and training, inversion and validation, and biomass mapping (Fig. 7).In this figure, input and output parameters are represented as parallelograms with the inputscoded in yellow color and outputs in green color. In the simulation and training, the objectiveis to train an NN that is able to output rice plant biomass using backscattering coefficients asinputs (to the trained NN). To do so, Q sets of backscattering coefficients, ½σHHi

; σHVi; σVVi

�;(i ¼ 1; 2; : : : ; Q) as inputs andQ biomass values, ½WBiomassi

� (i ¼ 1; 2; : : : ; Q) as outputs are firstneeded. The details are

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• Input–output pairs for training.• Using the rice growth model of Eq. (1) for the growth periods, we derived Q sets of rice

growth parameters, ðRbi ; Nstemi; Lsi ; Rsi ; lli ; wli ; dli ; NliÞ.

• With each set of growth parameters, Q biomass values, WBiomassiof Eq. (2) are then

derived. Modification using Eq. (3) is needed when biomass from the nonreferencesite is computed.

• With each set of growth parameters plus other needed model inputs as discussed previ-ously, Q sets of backscattering coefficients ðσHHi

; σHVi; σVVi

Þ are modeled using the back-scattering model of Eq. (5).

• We train the NN with fðσHHi; σHVi

; σVViÞ;WBiomassi

g until the RMSE of biomass from theNN is <0.5 kg∕m2. The RMSE is defined as

RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

Q

XQi¼1

ðWbiomassi−OiÞ2

vuut ; (6)

where Wbiomassiis the biomass for the i’th input pair and Oi is the i’th biomass output from the

NN. In addition, the correlation coefficient, r

r ¼XQi¼1

ðWbiomassi− WbiomassÞðOi − OÞ∕

� ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXQi¼1

ðWbiomassi− WbiomassÞ2

vuutffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXQi¼1

ðOi − OÞ2vuut �

;

(7)

Fig. 7 Rice biomass retrieval using the neural network (NN). The procedure consists of (a) sim-ulation and training, (b) inversion and validation, and (c) biomass mapping.

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where Wbiomass is the average of biomass inputs to the NN and O is the average of the biomassoutputs of the NN. After training, the NN is ready for the biomass inversion of the rice plants.

In the inversion and validation, the objective is to assess the validity of inverted biomass ofthe trained NN. The validation consists of two aspects:

a. The first verification is conducted at the study site. The scatterometer measured data asinputs to the NN are used to derive biomass values. Then, the derived biomass data areassessed with those measured in the field when and where the scatterometer measurementsare made.

b. After the validation at the study site, further verification is conducted using another set ofsimulated data. Thus, the NN is potentially usable in other rice paddies. With the set ofinputs, biomass values are derived from the growth mode and HH, HV, VV backscattercoefficients from the backscattering model. Then, the modeled backscattering coefficientsare applied to the NN to output the biomass that is compared with the biomass from thegrowth model.

In the biomass mapping, the objective is to delineate rice paddies and then to derive rice plantbiomass using multitemporal SAR images (e.g., Table 2). The delineation is done after SARimage preprocessing as discussed in Sec. 2, and image classification using the support vectormachine (SVM) algorithm of ENVI software. Finally, the biomass map is created after applyingthe extracted SAR backscattering coefficients to the NN.

4 Results and Discussion

4.1 Model Validation

4.1.1 Rice growth model

After the transplanting and into the growing cycle, the trend of biomass variation had beenobserved (Fig. 8). The value was small, but increased slowly for the first 25 days and then rapidlybetween 25 and 60 days. After 60 days or so, the biomass increased slowly and finally reached itsmaximum value. The trend of the simulated biomass values was similar to the observed one.However, the values fluctuated because each parameter of the growth model was generated basedon an individual random distribution function that reflects the variability of the actual samplingdata. In comparison of both observed and simulated values, there was a general agreementbetween them until late in the season (Fig. 8). In that period of time, the model might under-estimate the plant biomass slightly. The underestimation could be attributed to the difference in

Fig. 8 Observed and simulated biomass values after transplanting. Eight open circles represent-ing measurements made at eight stages of the growth period in 2012.

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plant biomass measured in 2012 and 2010. The 2010 biomass data were used to develop thegrowth model.

4.1.2 Monte Carlo simulation

With satisfactory validation result of the rice growth model, the model was used to simulate riceplant parameters from 10 to 107 days after transplanting. Then, the simulated parameters wereinputs to the backscatter model Eq. (5), and backscattering coefficients were modeled for thatperiod of time. The backscatter model was set at 5.3 GHz of frequency, quad-polarization, and43 deg of an incidence angle. They were the same central frequency, polarizations, and incidenceangle as RADARSAR-2 SAR data. Simulated HH, HV, and VV backscattering coefficientsincreased ∼40 days after the transplanting, and then were steady (Fig. 9). Due to the primarilyvertical orientation of individual stems of each rice bunch, the backscattering from interactions ofthe stem and water surface or the stem and wet ground surface could dominate. The dominancecould be the cause for the highest HH backscattering as compared to that at VVor HV (Fig. 9). Itshould be noted that because the HVand VH backscattering coefficients were the same, only HVcoefficients were shown.

Measured HH, HV, and VV backscattering coefficients at eight stages were also plotted inFig. 9. At HH, the modeled and observed backscattering coefficients were close to each otheruntil ripening stage. After that stage, the model overestimated the backscattering. One possiblereason was that the Monte Carlo simulation did not consider bending of the rice ear that couldincrease the attenuation to the backscattered signal. At VV, the modeled and observed backscat-tering coefficients were also close to each other except for the tillering stage, at which the mea-sured value was larger than the modeled one. One possible reason was that at the tillering stagethe inclination of the rice stem was larger than others [cf. Fig. 2(b)], which resulted in theincrease of the backscattered signal. Finally, at HV, patterns of the modeled and observed back-scattering coefficients were similar to those at HH. In summary, after transplanting the modeledand observed quad-polarization backscattering coefficients increased and reached maximum atjointing stage and then decreased until heading stage. This decrease is consistent with the find-ings of Inoue et al. at C-band, 45-deg case.9 There is also another peak at flowering stage for eachpolarization of the measured data and then the backscattering coefficients continue to decrease atlast two stages. This dual peak has also been pointed out by Oza and Parihar from scatterometerdata.32 With the validation of the rice growth model and the backscatter model, we were ready togenerate input–output pairs for the training of an NN.

Fig. 9 Measured and modeled backscattering coefficients versus rice age.

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4.2 NN Training and Inversion Validation

4.2.1 NN training and accuracy assessment

With the rice growth and backscattering models, Q sets of biomass and backscattering coeffi-cients of HH, HV, and VV were then simulated. Using Q pairs of coefficients and biomass, theNN discussed previously was trained. Once the NN outputs met the error requirement that is<0.5 kg∕m2, the NN was considered as being trained and ready for the biomass retrieval. Also,another set of verification data were independently simulated using the growth model and thescattering model, and the verification datasets were not used in the NN training. The backscatter-ing coefficients of the verification data were applied to the trained NN to retrieve the rice bio-mass, and the results of the trained NN were compared with the biomass of the verification data(Fig. 10). The horizontal axis was the biomass simulated by the growth model, and the verticalaxis was the retrieved biomass. Except for small number points, the majority of them were in thevicinity of the 1:1 line (Fig. 10). The RMSE was 0.476 kg∕m2 and the correlation coefficientwas 0.966. Thus, the discrepancy in biomass parameters should be acceptable and the retrievedbiomass values resulted satisfactorily.

4.2.2 Inversion of biomass using the scatterometer data and trained NN

With the completion of the NN training, measured HH, HV, and VV backscattering coefficientsusing the scatterometer are first applied in the biomass inversion. Figure 11 shows trends of theretrieved and measured biomass values at eight stages. Satisfactory results are obtained becausethe RMSE is 0.477 kg∕m2 and the correlation coefficient is 0.989. Inversion results are veryconsistent with the measured values at the early and middle of the rice growth. The retrievedbiomass is slightly underestimated in the late period of rice growth. It is consistent with thebiomasses generated by the rice growth model; the modeled value is smaller than the measuredone (cf. Fig. 8). Consequently, the retrieval result shows that the NN inversion model is able toinvert the biomass with the high level of accuracy.

4.3 Comparison of Multitemporal Scatterometer and RADARSAT-2 Datasets

With the concurrent scatterometer and SAR observations, minimum (Min), average (Mean), andmaximum (Max) values of the same rice field were extracted and tabulated (Table 3), and thereare 87 pixels in the rice field. The average errors of four simultaneous observations were −0.41,−0.64, and 0.87 for HH, VV, and HV, respectively, and the correlation coefficients were 0.93,0.87, and 0.99. Therefore, the backscattering coefficients agreed with each polarization in therice-growing season, although the spatial scale of observation is different. The footprint area of

Fig. 10 Comparison of retrieved biomass by the NN and simulated biomass by the growth model.

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antenna beam of the scatterometer was about 10 m2 and the pixel area of RADARSAT-2 wasabout 40 m2 according to the resolution (cf. Sec. 2.4)

4.4 Delineation of Rice Paddy and Biomass Inversion of Rice Plants UsingSAR Imagery

4.4.1 Delineation of rice fields

Visual examination (e.g., Fig. 4) indicates that among the RADARSAT-2 HH, HV, and VVpolarization data, multitemporal HH images are the most sensitive in separating rice paddiesand nonrice paddies and in delineating rice fields due to the time-variation value, which is>8 dB at the early and middle rice growing stages (cf., Fig. 8). In the SVM, a supervised clas-sification method has been initially used for the classification of the rice paddies and nonricepaddies with the multitemporal HH images. Figure 12 shows the classification image of ricepaddies and nonrice paddies and the extracted backscattering coefficients in the rice paddiesfrom HH-polarization image on June 11. The classification result of the rice area is 15.04%of the total area, and it is about 518 acres in the test area. The entire classification is processedby NEVI 4.8 software. As can be seen from the color bar, the backscattering coefficients of the ricefields are < − 10 dB because the rice fields are flooded in the early growth stage of rice, and thebiomass is approximately 0.1 to 0.5 kg∕m2 in this period. The backscattering coefficients ofother RADARSAT-2 images were also extracted for biomass inversion in the following.

Fig. 11 Multitemporal rice biomass data measured and NN retrieved.

Table 3 Backscattering coefficients from the same rice field.

Scatterometer RADARSAT-2

HH(dB)

VV(dB)

HV(dB)

HH (dB) VV (dB) HV (dB)

Mean Max Min Mean Max Min Mean Max Min

June 11 −15.78 −16.04 −26.15 −12.81 −10.84 −15.36 −15.18 −11.22 −16.68 −23.79 −20.96 −26.75

July 05 −6.18 −10.33 −13.95 −8.620 −8.120 −10.79 −11.45 −10.40 −13.62 −16.86 −16.12 −18.26

July 29 −9.28 −13.99 −17.54 −9.120 −7.420 −11.52 −12.83 −11.54 −15.52 −18.44 −16.83 −21.02

August 22 −9.50 −13.46 −15.59 −8.54 −7.50 −11.10 −11.81 −10.32 −14.81 −17.62 −16.22 −19.79

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4.4.2 Biomass mapping

Figure 13 shows the retrieved biomass versus measured biomass value in the test site at fourgrowing stages. Most of the inverted results are very consistent with the measured values.Especially in the late rice growth stage, the dynamic ranges are very consistent between theinversion values and the actual sampled values. In the early stage of rice growth, there are afew higher inversed biomass values than the average; the reason may be the inconsistencyof some of the rice planting and growing times in these periods, and it may also be specklenoise of SAR image that leads to some inaccurate classification. Fortunately, such a smallpart of the error inversion does not affect the total inversion accuracy. The RMSE is0.582 kg∕m2 and the correlation coefficient is 0.983. Overall, the results show that a goodretrieval performance was obtained for these data by using the NN inversion model, and themultitemporal RADARSAT-2 images can be used to monitor the growth of rice very well.

With the rice fields delineated previously, biomass mapping of rice plants of the study area iscarried out using HH, HV, and VV backscattering coefficients of the multitemporal

Fig. 12 Results of backscattering coefficients from rice paddies of RADARSAT-2 HH imageacquired on 11 June 2012 in the test site: rice paddy delineation using support vector machine(SVM) classification result of multitemporal HH-polarization RADARSAT-2 image by SVM, coloredareas ¼ rice paddies and white areas ¼ nonrice paddies.

Fig. 13 Comparison of inverted and measured biomass of rice plants at four dates. The inversionwas done using HH, HV, and VV backscattering coefficients from four RADARSAT-2 images asinputs to the NN.

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RADARSAT-2 images. First, the coefficients are extracted in each rice field (e.g., Fig. 12). Then,the coefficients are input to the NN to derived biomass output. Figure 14 displays the spatialapplication of the NN biomass inversion method at different periods according to the fourRADARSAT-2 images. Refer to the color bar; the biomass maps show the values of the biomassare very uniform for each image. In Fig. 14(a), most of the rice was at seedling and tilleringstages (cf. Table 2), and the inverted average value was 0.974 kg∕m3, so the main color isdark blue and a small part of the color is light blue due to earlier transplanting times. InFig. 14(b), the rice was mainly at jointing and booting stages, and the inverted averagevalue was 2.255 kg∕m3. The blue fields have small biomass since the age after transplantingis shorter than other fields. In Fig. 14(c), most of the rice was at heading and flowering stages,and the inverted average value was 3.444 kg∕m3. The biomass at earlier transplanting fields hadbeen greater than 4 kg∕m3. In Fig. 14(d), the rice was mainly at milking and ripening stages, andthe inverted average value was 4.273 kg∕m3. Some red fields showed that the rice growth wasparticularly lush. In short, earlier transplanting times had larger biomass at each figure and viceversa. It showed that the multitemporal biomass mapping can monitor the rice growth.

5 Conclusion

Rice plant parameters have been collected in several rice-growing seasons. In addition to thenumber of bunches per square meters, data per bunch included plant height, biomass, LAI,and canopy structure that consists of number of stems, stem length, and radius; number of leavesper stem; and leaf length, width, and thickness. Then, with the measured parameters, a growthmodel for rice plant has been developed. The model as the function of time after the transplantingand initial line and row spacing of rice bunches was able to simulate more representative andapplicable (to other rice plant fields) plant parameters than those collected randomly with a smallnumber of samples at limited locations of the study site.

Fig. 14 Rice plant biomass (kg∕m2) of rice paddies. The biomass was derived using RADARSAT-2 images acquired on June 11 (a), July 05 (b), (c) July 29 (c), and August 22 (d).

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In order to study radar backscattering, we collected water content of stem and leaf in field-work as well. With multiple runs of Monte Carlo simulation from a rice paddy, a scatteringmodel was developed to simulate backscattering coefficient. Scatterers of the model includingdielectric cylinders for stems and elliptical discs for leaves were over a dielectric half-space usingthe Foldy–Lax multiple scattering equations.

After the validation of the growth model and backscattering model using collected field andscatterometer datasets, both models were run to simulate numerous pairs of the rice plant bio-mass and HH, HV, and VV backscattering coefficients after the transplanting of the rice plantsinto rice paddy. Using the data pairs, the NN that is a standard feed-forward MLP was trainedusing the BP learning rule. The NN consists of one input, two hidden, and one output layers.Three nodes of the input layer were related to HH, HV, and VV backscattering coefficients,respectively. There were 11 neurons or nodes on the first hidden layer and nine on the secondhidden layer. The NN output the plant biomass. After training, the NN was used to invert thebiomass using HH, HV, and VV backscattering coefficients. The NN-retrieved biomass wasassessed with measured biomass. The RMSE was 0.476 kg∕m2 and correlation coefficientwas 0.966.

With satisfactory biomass output from the NN, the scatterometer datasets collected in thegrowing season of 2012 were next used in biomass inversion. In comparison of collectedand inverted biomass data, the RMSE was 0.477 kg∕m2 and correlation coefficient was0.989. RADARSAT-2 SAR images acquired on four different dates during the 2012 growthperiod were analyzed to delineate rice paddies within the study area. Then, HH, HV, andVV backscattering coefficients extracted from the multitemporal images were input to theNN model for the final biomass inversion. The inversion result was satisfactory as well becausethe RMSE was 0.582 kg∕m2 and correlation coefficient was 0.983 when the NN-retrieved andmeasured biomass data were evaluated. In summary, using the trained NN, successful retrieval ofrice plant biomass with the acquired multitemporal ground-based scatterometer data and space-borne RADARSAT-2 images has been achieved.

Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China (Grant#41071222 to UESTC) and the Advance Research Program of Civil Aerospace Technology ofthe 12th Five-Year Period, China.

References

1. C. Kuenzer and K. Knauer, “Remote sensing of rice crop areas—a review,” Int. J. Rem.Sens. 34(6), 2101–2139 (2013), http://dx.doi.org/10.1080/01431161.2012.738946.

2. International Rice Research Institute (IRRI), Bringing Hope, Improving Lives, StrategicPlan 2007–2015, 61 p, IRRI, Manila (2006).

3. C. Eisfelder, C. Kuenzer, and S. Dech, “Derivation of biomass information for semi-aridareas using remote-sensing data,” Int. J. Rem. Sens. 33(9), 2937–2984 (2012), http://dx.doi.org/10.1080/01431161.2011.620034.

4. A. Bouvet, T. Le Toan, and L.-D. Nguyen, “Monitoring of the rice cropping system in theMekong Delta using ENVISAT/ASAR dual polarization data,” IEEE Trans. Geosci. Rem.Sens. 47(2), 517–526 (2009), http://dx.doi.org/10.1109/TGRS.2008.2007963.

5. C. Yonezawa et al., “Growth monitoring and classification of rice fields using multitemporalRADARSAT-2 full-polarimetric data,” Int. J. Rem. Sens. 33(18), 5696–5711 (2012), http://dx.doi.org/10.1080/01431161.2012.665194.

6. T. Le Toan et al., “Rice crop mapping and monitoring using ERS-1 data based on experi-ment and modeling results,” IEEE Trans. Geosci. Rem. Sens. 35(1), 41–56 (1997), http://dx.doi.org/10.1109/36.551933.

7. Y. Oh et al., “Polarimetric backscattering coefficients of flooded rice fields at L- andC-bands measurements, modeling, and data analysis,” IEEE Trans. Geosci. Rem. Sens.47(8), 2714–2721 (2009), http://dx.doi.org/10.1109/TGRS.2009.2014053.

Jia et al.: Rice biomass retrieval from multitemporal ground-based scatterometer data. . .

Journal of Applied Remote Sensing 073509-16 Vol. 7, 2013

8. K.-S. Lim et al., “High angular resolution measurements of the monostatic backscatteringcoefficient of rice fields,” J. Electromagn. Waves Appl. 23(1), 1–10 (2009), http://dx.doi.org/10.1163/156939309787604689.

9. Y. Inoue 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 withbiological variables,” Rem. Sens. Environ. 81(2), 194–204 (2002), http://dx.doi.org/10.1016/S0034-4257(01)00343-1.

10. M. Bracaglia, P. Ferrazzoli, and L. Guerriero, “A fully polarimetric multiple scatteringmodel for crops,” Rem. Sens. Environ. 54(3), 170–179 (1995), http://dx.doi.org/10.1016/0034-4257(95)00151-4.

11. L. Wang et al., “Electromagnetic scattering model for rice canopy based on Monte Carlosimulation,” Prog. Electromagn. Res. 52(1), 153–171 (2005), http://dx.doi.org/10.2528/PIER04080601.

12. T. Kurosu, M. Fujita, and K. Chiba, “Monitoring of rice crop growth from space using theERS-1 C-band SAR,” IEEE Trans. Geosci. Rem. Sens. 33(4), 1092–1096 (1995), http://dx.doi.org/10.1109/36.406698.

13. S. C. Liew et al., “Application of multitemporal ERS-2 synthetic aperture radar in delin-eating rice cropping systems in the Mekong River Delta, Vietnam,” IEEE Trans. Geosci.Rem. Sens. 36(5), 1412–1420 (1998), http://dx.doi.org/10.1109/36.718845.

14. A. Rosenqvist, “Temporal and spatial characteristics of irrigated rice in JERS-1 L-band SARdata,”Int. J. Rem. Sens. 20(8), 1567–1587 (1999), http://dx.doi.org/10.1080/014311699212614.

15. J. Chen, H. Lin, and Z. Pei, “Application of ENVISAT ASAR data in mapping rice cropgrowth in Southern China,” IEEE Geosci. Rem. Sens. Lett. 4(3), 431–435 (2007), http://dx.doi.org/10.1109/LGRS.2007.896996.

16. L.-D. Nguyen et al., “Estimation of the rice yield in the Mekong Delta using SAR dualpolarisation data,” in Asian Conference for Remote Sensing, pp. 1047–1052, AsianAssociation on Remote Sensing, Taiwan (2011).

17. J.-Y. Koay et al., “Paddy fields as electrically dense media: theoretical modeling and meas-urement comparisons,” IEEE Trans. Geosci. Rem. Sens. 45(9), 2837–2849 (2007), http://dx.doi.org/10.1109/TGRS.2007.902291.

18. S. Yang et al., “Interpreting RADARSAT-2 quad-polarization SAR signatures from ricepaddy based on experiments,” IEEE Geosci. Rem. Sens. Lett. 9(1), 65–69 (2012), http://dx.doi.org/10.1109/LGRS.2011.2160613.

19. J. M. Lopez-Sanchez, S. R. Cloude, and J. D. Ballester, “Rice phenology monitoring bymeans of SAR polarimetry at X-band,” IEEE Trans. Geosci. Rem. Sens. 50(7), 2695–2709(2012), http://dx.doi.org/10.1109/TGRS.2011.2176740.

20. S. Gebhardt et al., “A comparison of TerraSAR-X Quadpol backscattering with RapidEyemultispectral vegetation indices over rice fields in the Mekong Delta, Vietnam,” Int. J.Rem. Sens. 33(24), 7644–7661 (2012), http://dx.doi.org/10.1080/01431161.2012.702233.

21. Y. Q. Jin and C. Liu, “Biomass retrieval from high-dimensional active/passive remote sens-ing data by using artificial NN,” Int. J. Rem. Sens. 18(4), 971–979 (1997), http://dx.doi.org/10.1080/014311697218863.

22. Y. Wang and D. Dong, “Retrieving forest stand parameters from SAR backscatter data usinga neural network trained by a canopy backscatter model,” Int. J. Rem. Sens. 18(4), 981–989(1997), http://dx.doi.org/10.1080/014311697218872.

23. F. Del Frate and L. F. Wang, “Sunflower biomass estimation using a scattering model and aneural network algorithm,” Int. J. Rem. Sens. 22(7), 1235–1244 (2001), http://dx.doi.org/10.1080/01431160151144323.

24. M. Q. Jia and L. Tong, “Ground-based scatterometer measurements and inversion of surfaceparameters by using NN,” Int. J. Rem. Sens. Appl. 2(1), 29–35 (2011).

25. C. Notarnicola et al., “Neural network adaptive algorithm applied to high resolution C-bandSAR images for soil moisture retrieval in bare and vegetated areas,” Proc. SPIE 7829(1),1–9 (2010).http://dx.doi.org/10.1117/12.865988

26. N. Baghdadi et al., “Estimation of soil parameters over bare agriculture areas from C-bandpolarimetric SAR data using NN,” Hydrol. Earth Syst. Sci. 16(6), 1607–1621 (2012), http://dx.doi.org/10.5194/hess-16-1607-2012.

Jia et al.: Rice biomass retrieval from multitemporal ground-based scatterometer data. . .

Journal of Applied Remote Sensing 073509-17 Vol. 7, 2013

27. F. Del Frate et al., “Wheat cycle monitoring using radar data and a neural network trained bya model,” IEEE Trans. Geosci. Rem. Sens. 42(1), 35–44 (2004), http://dx.doi.org/10.1109/TGRS.2003.817200.

28. F. Del Frate and D. Solimini, “On neural network algorithms for retrieving forest biomassfrom SAR data,” IEEE Trans. Geosci. Rem. Sens. 42(1), 24–34 (2004), http://dx.doi.org/10.1109/TGRS.2003.817220.

29. S. Englhart, V. Keuck, and F. Siegert, “Modeling aboveground biomass in tropical forestsusing multi-frequency SAR data-A comparison of methods,” IEEE J. Sel. Topics Appl.Earth Obs. Rem. Sens. 5(1), 298–306 (2012), http://dx.doi.org/10.1109/JSTARS.2011.2176720.

30. M. Q. Jia, L. Tong, and Y. Chen, “Multifrequency and multitemporal ground-based scatter-ometers measurements on rice fields,” in 32nd IEEE Int. Geoscience and Remote SensingSymposium (IGARSS), pp. 642–645, Institute of Electrical and Electronics Engineers,Munich, Germany (2012).

31. L. Tsang et al., “Backscattering enhancement and clustering effects of randomly distributeddielectric cylinders overlying a dielectric half space based on Monte-Carlo simulations,”IEEE Trans. Antennas Propag. 43(5), 488–499 (1995), http://dx.doi.org/10.1109/8.384193.

32. S. Oza and J. Parihar, “Evaluation of Ku-band QuikSCAT scatterometer data for rice cropgrowth stage assessment,” Int. J. Rem. Sens. 28(16), 3447–3456 (2007), http://dx.doi.org/10.1080/01431160601034860.

Mingquan Jia received the B.A. degrees in automation from XihuaUniversity, in 2005 and PhD degrees in detection technology and automa-tion devices from the University of Electronic Science and Technology ofChina (UESTC), Chengdu, China, in 2013. Between 2007 and 2008, heworked on the model simulation and parameter inversion of soil and veg-etation using microwave scattering measurements data. Between 2009 and2010, his research included establish electromagnetic scattering model ofsoil, rice and wheat, and develop inversion method for geo- and biophysicalparameter, and study polarization calibration techniques for scattering

measurement system. Now, his main involvement currently is in development scatteringmodel and studying algorithms of information extraction from SAR image, and monitoring dis-aster based on INSAR technology.

Ling Tong Automation Engineering, University of Electronic Science andTechnology Associate Dean, Professor. In 1985 graduated from theChengdu Institute of Telecommunication Engineering Department ofPhysics, a bachelor's degree. In 1988 graduated from the University ofElectronic Science and Technology Institute of High EnergyElectronics, received a master's degree. From 1988 to 1995 engaged inthe Institute of Measurement and Testing Technology of microwave andantenna measurements. 1995-present, University of Electronic Scienceand Technology in Remote Sensing in the mechanism of microwave remote

sensing, microwave millimeter-wave measurement, measurement data processing and measure-ment aspects of information theory. Mid-2003 to 2004 the University of Birmingham Institute ofElectrical Engineering and Computer Science Department was a visiting research fellow.

Biographies and photographs of the authors are not available.

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