Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons

International Journal of Applied Earth Observation and Geoinformation 44 (2016) 104–112

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International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag

Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons Xinchuan Li a , Youjing Zhang a,∗ , Juhua Luo b , Xiuliang Jin c , Ying Xu a , Wenzhi Yang a a b c

School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Jilin 130102, China

a r t i c l e

i n f o

Article history: Received 13 April 2015 Received in revised form 8 August 2015 Accepted 10 August 2015 Keywords: LAI HJ-1CCD image Remote sensing feature Winter wheat Partial least squares regression

a b s t r a c t Remote sensing images are widely used to map leaf area index (LAI) continuously over landscape. The objective of this study is to explore the ideal image features from Chinese HJ-1 A/B CCD images for estimating winter wheat LAI in Beijing. Image features were extracted from such images over four seasons of winter wheat growth, including five vegetation indices (VIs), principal components (PC), tasseled cap transformations (TCT) and texture parameters. The LAI was significantly correlated with the nearinfrared reflectance band, five VIs [normalized difference vegetation index, enhanced vegetation index (EVI), modified nonlinear vegetation index (MNLI), optimization of soil-adjusted vegetation index, and ratio vegetation index], the first principal component (PC1) and the second TCT component (TCT2). However, these image features cannot significantly improve the estimation accuracy of winter wheat LAI in conjunction with eight texture measures. To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values. Four remote sensing features (TCT2, PC1, MNLI and EVI) were chosen based on VIP values. The result of leave-one-out cross-validation demonstrated that the PLSR model based on these four features produced better result than the ten features’ model, throughout the whole growing season. The results of this study suggest that selecting a few ideal image features is sufficient for LAI estimation. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Remote sensing is a reliable method for estimating the spatial and temporal variations in biophysical parameters (Cho et al., 2007; Croft et al., 2014). Leaf area index (LAI), defined as one-half of the total leaf surface area per unit horizontal ground surface area (Chen and Black, 1992), is a critical biophysical parameter and widely used in climate change (Yuan et al., 2011), ecology (Barr et al., 2004), evapotranspiration (Mo et al., 2004), light-use efficiency (Scott Green et al., 2003) and crop yield (Fang et al., 2011). However, traditional LAI sampling measurements are expensive, time-consuming and difficult to extrapolate spatially. Alternative approaches based on the use of remote sensing technologies reduce the cost and time of data collection and obtain high-resolution data,

∗ Corresponding author. Fax: +86 25 8378 7234. E-mail addresses: rs [email protected] (X. Li), [email protected] (Y. Zhang), [email protected] (J. Luo), [email protected] (X. Jin), [email protected] (Y. Xu), [email protected] (W. Yang). http://dx.doi.org/10.1016/j.jag.2015.08.004 0303-2434/© 2015 Elsevier B.V. All rights reserved.

which have been deemed as the only feasible option to obtain a continuous LAI surface over large areas (Song and Dickinson, 2008). A number of publications have described the application of remote-sensing techniques in the retrieval of LAI using optical methods (Mirzaie et al., 2014; Propastin and Panferov, 2013), synthetic aperture radar (Inoue et al., 2014), and light detection and ranging (LiDAR) (Jensen et al., 2011). Satellite optical bands exhibit the spatial and temporal variations in spectral vegetation characteristics. Vegetation indices (VIs) are usually calculated based on an analysis of the ratio of red and near-infrared light. Such indices are widely used in conjunction with optical remote sensing methods for measuring LAI (Haboudane et al., 2004; Kross et al., 2015). May VIs have been developed and are well-correlated with vegetation parameters (Darvishzadeh et al., 2009; Gong et al., 2003; Tian et al., 2011). However, many VIs tend to saturate under conditions of moderate-to-high LAI values (e.g., >3–5) (Davi et al., 2006; Haboudane et al., 2004). In addition, image transform methods, such as principal components analysis (PCA) and tasseled cap transformation (TCT), can extract and strengthen image information. Principal component analysis (PCA) is an attractive means of incorporating spectral data

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from numerous dates into a small set of axes that often contain the most “important” aspects of the data (Pagola et al., 2009). TCT is a useful tool for compressing spectral data into a few bands that are associated with physical scene characteristics (Crist and Cicone, 1984). TCT is composed of three indices: brightness, greenness, and wetness. Of these three components, greenness is defined in the direction of vegetation signatures on an axis that is orthogonal to the brightness vector and strongly correlates with variation in the vigor of green vegetation (Mather and Koch, 2010). Moreover, image texture analysis involves measuring heterogeneity in the tonal values of pixels within a defined area of an image (Wood et al., 2012), and has been used to estimate vegetation structure parameters (Beguet et al., 2014; Nichol and Sarker, 2011). Most empirical approaches consider site-specific, empirical relationships in mapping LAI for the spatial extent over which the model was developed. However, these image features have not been thoroughly investigated for LAI retrieval. The use of full spectral subsets or of the greatest available amount of spectral information is not likely to improve retrieval performance but simply increases computation time (Darvishzadeh et al., 2008). Therefore, the application of only a few features is sufficient to extract and discriminate essential information and characteristics (Li et al., 2014; Mutanga et al., 2015; Oumar et al., 2013; Thenkabail et al., 2004). Winter wheat is a main crop in the North China Plain. The prediction of LAI for this crop is important for agricultural production and management in this region. China HJ-1 A/B satellites provide ground surface spectral information at 30 m spatial resolution with a four-day revisit frequency, and offer an opportunity to monitor winter wheat efficiently and objectively over large areas. Thus, the objectives of the current study are to: (1) fully exploit the image features derived from Chinese HJ-1 A/B CCD images using VIs, PC, TCT, and their eight texture features; (2) evaluate the potential of every feature for LAI estimation; and (3) identify optimal features for LAI estimation via partial least squares regression (PLSR).

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2.3. Remote sensing data and pre-processing Two small environment- and disaster-reduction satellites (HJ1A/B) were launched by the China Center for Resources Satellite Data and Applications (CRESDA) on September 6, 2008. The charge-coupled device (CCD) cameras of these satellites have four bands, namely, blue (430–520 nm), green (520–600 nm), red (630–690 nm), and near-infrared (NIR) spectra (760–900 nm). The spatial resolution (30 m) and the setting of these bands are similar to the first four bands of Landsat-5 TM. Nonetheless, the revisit period of HJ-1CCD (two days) is significantly shorter than that of TM. This short period facilitates temporal analysis at key growth stages for crop monitoring. Corresponding cloud-free images were acquired on April 1, April 16, April 30, and May 20, 2009 (Table 1). The Landsat-5 TM image on April 15, 2009 was also selected to compare the performance for the calibration of HJ-1CCD bands sensitive to wheat LAI. Remote sensing images were processed with ENVI 4.5 software through radiometric calibration, atmospheric correction, and geometric correction. Radiometric calibrations were performed with coefficients provided by CRESDA. Land surface reflectance was then retrieved using Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), which incorporates the MODTRAN4 radiation transfer code Scene and sensor information including the scene center location, the average ground elevation of the scene, the sensor type, the sensor altitude, the flight date and time and the geometry parameters, provided in each image metadata. Atmospheric model and atmospheric visibility relied on weather conditions. A further description of atmospheric correction module is provided by the user manual (ENVI User’s Guide, 2009). Each HJ-1CCD image was co-registered with a geo-referenced Landsat TM image at a geometric accuracy of <0.5 pixel. 3. Method 3.1. Remote sensing features

2. Material 2.1. Study site The study was conducted in a suburban area in Beijing, China (Fig. 1). This area is under a northern temperate monsoon climate. The annual mean temperature is about 11.8 ◦ C. The coldest month is January with an average temperature of −4.6 ◦ C and the hottest month is July at an average temperature of 26.1 ◦ C. The annual precipitation averages around 650 mm, and the frost-free period is 180 days. The experimental fields were located on flat terrain in Tongzhou District and Shunyi District. The predominant soil texture is a fine clay loam. The types of winter wheat analyzed were Nongda 211 (erectophile), Zhongyou 206 (middle), Jingdong 8 (middle), and Jing 9428 (planophile) (Li et al., 2014). Winter wheat was planted on late September 2008 and harvested on late June 2009.

2.2. Field measurements of LAI Field experiments were conducted in the 2009 growth season on the following dates: April 1 (tillering), April 15 (jointing), April 30 (heading), and May 17 (anthesis). During each experimental point, all winter wheat was harvested from 0.5 m × 0.5 m plots that were randomly selected from the central 30 m × 30 m field. The leaf areas of winter wheat were measured by a leaf area meter (Li-Cor 3100, LICOR, Inc., Lincoln, NE, USA)(Li et al., 2014). The LAI measurements of each experiment are listed in Table 1’.

3.1.1. Vegetation indices In addition to the four original HJ-1CCD bands, five commonly used VIs were acquired. These VIs are enhanced vegetation index (EVI), ratio difference vegetation index (RVI), modified nonlinear vegetation index (MNLI), normalized difference vegetation index (NDVI) and optimization of soil-adjusted vegetation index (OSAVI) (Table 2). 3.1.2. Principal component analysis PCA is used to identify data patterns and to highlight the similarities and differences in data. The main advantage of PCA is that once data patterns have been identified and data have been compressed (i.e., by reducing the number of dimensions), information loss is limited (Zheng et al., 2014). The main objective of the PCA method is to determine a lower-dimensional representation that can account for most of the variance of the original dataset (Fei et al., 2012). PCA is used to extract features for comparison. In the current study, image pixels of winter wheat alone were used to form a covariance matrix in the original spectral space. Fig. 2 shows that the first three PCs explain more than 98% of the four reflectance bands. These components were marked as PC1, PC2, and PC3. 3.1.3. Tasseled cap transformation TCT originally evolved from the Landsat multi-spectral scanner launched in 1972. This transformation method is widely adapted to modern sensors such as MODIS, QuickBird, IKONOS, and Landsat 8 (Baig et al., 2014). Chen et al. (2012) studied and evaluated TCT consistencies in HJ-1A/B CCD data. We chose the first three bands generated from TCT and marked them as brightness (TCT1),

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Fig. 1. Location of samples in the study area. The distribution of winter wheat was derived in Section 3.2.

Table 1 Summary of LAI measurements and satellite images. Growth stages

Stage1 (tillering) Stage2 (jointing) Stage3 (heading) Stage4 (anthesis)

LAI measurements

Matching images

Data

Numbers

Mean value

April 1 April 16 April 30 May 17

29 29 13 29

1.211 1.620 3.389 3.171

Value range 0.19–3.18 0.40–3.04 1.35–5.36 1.21–5.22

Sensors

Image date

HJ-1A/CCD2 HJ-1A/CCD2 HJ-1B/CCD2 HJ-1B/CCD1

April 1 April 16 April 30 May 20

Table 2 References and equations of VIs used in this study. Vegetation index

Formulas

Reference

Enhanced vegetation index (EVI)

NIR−R EVI = 2.5 NIR+6R−7.5B+1

Hui and Huete (1995)

Ratio difference vegetation index (RVI)

RVI =

NIR R

Jordan (1969)

1.5(NIR2 −R)

Modified nonlinear vegetation index (MNLI)

MNLI =

Normalized difference vegetation index (NDVI)

NDVI =

Optimization of soil-adjusted vegetation index (OSAVI)

NIR−R OSAVI = (1 + 0.16) NIR+R+0.16

Gong et al. (2003)

NIR2 +R+0.5 NIR−R NIR+R

Rouse et al. (1973) Rondeaux et al. (1996)

Table 3 Tasseled cap coefficients of HJ-1A/B CCD sensors. Index

Band1

Band2

Band3

Band4

TCT1 TCT2 TCT3

0.3024 −0.1350 0.7562

0.4010 −0.3317 0.3587

0.5031 −0.6246 −0.5251

0.7033 0.6940 −0.1541

greenness (TCT2), and wetness (TCT3). The derived transformation parameters applied to HJ-1CCD images are presented in Table 3.

3.1.4. Texture Texture analysis is the process of extracting texture features through image processing techniques and analyzing the texture

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Fig. 3. Comparison between Landsat-5 TM and HJ-1A/CCD2 data for LAI estimation.

model representing the relationship between a dependent variable (Y) and independent variables (X), and can be calculated as follows: Fig. 2. Cumulated percentage variance in spectral data as a number of PCs.

Y = b0 + b1 X1 + b2 X2 + · · · + bp Xp either quantitatively or qualitatively. Image texture metrics were generated statistically with a Gray Level Co-occurrence Matrix (GLCM). Many such metrics can be derived from this matrix; we focus on the eight metrics of mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation (Wood et al., 2012). We derived each HJ-1CCD image feature through an analysis of eight textures at a window size of 3 pixels × 3 pixels to match the scale at which our LAI data were collected from the ground, as well as small field areas of winter wheat. 3.2. Extraction of the winter wheat planting area

(1)

where b0 is the regression coefficient for the intercept, the bi values are regression coefficients, and p is the number of independent variables. Studies have shown that the variable importance in projection (VIP) scores serves as an apt measure for evaluating the relative importance of variables in the PLSR model (Lazraq et al., 2003; Li et al., 2014). VIP is used to measure the contribution of independent variables to the contribution dependent variable, with the most influential predictors in the model selected according to the magnitude of their values (Chong and Jun, 2005). 3.4. Statistical analyses

Both PCA and texture features are calculated on the basis of the entire image and on the neighborhood of pixels, respectively, unlike NDVI, which is calculated on a pixel-by-pixel basis. To eliminate the influence of other ground objects, we used the decision tree method to extract the winter wheat planting area in the HJ-1CCD scene acquired on May 20, 2009. The threshold of each node was first determined according to 200 field survey points with known land cover types and then modified slightly using a quantitative stepwise approximation method. The NDVI threshold of 0.48 was used to differentiate the vegetated and non-vegetated areas. The pixels with NIR <0.52 were classified as grasslands in the remaining vegetated area that consisted of farmlands, grasses and forests. Then, the vegetated pixels with an elevation of over 100 m were classified as forests, whereas those with an elevation of 100 m were farmlands (Zhang et al., 2014). Finally, a majority/minority filter with a kernel size of 3 × 3 was utilized to remove small farmland pixels. As per comparison with the field survey points, overall accuracy reached 96% for the extraction of the winter wheat planting area using the decision tree method. This accuracy satisfied the requirement for image features extraction.

where Pi and Qi are the predicted and observed values, respectively. Q¯ is the observed mean value, and n is the number of samples. The number of each experiment database is low, thus, we do not set the evaluation subset. The leave-one-out CV method was used to test the prediction capability of the model. This method sets a single observation from the original sample as validation data, and the remaining observations as training data. Then, the models 2 ) and crossare compared in terms of the cross-validated R2 (Rcv validated RRMSE (RRMSECV ) values. All analyses were conducted using MATLAB software (The Math Works, Inc., Natick, MA, USA).

3.3. Partial least squares regression

4. Results

The PLSR technique generalizes and combines the features of principal component regression (PCR) and multiple linear regression. The method is a powerful modeling tool that reduces the large number of measured collinear spectral variables to a few non-correlated latent variables or factors (Abdi, 2003; Hansen and Schjoerring, 2003). The noise and collinearity in the original spectra are thus significantly eliminated from the condensed components. The optimal number of factors in PLSR analysis is determined by minimizing the prediction residual error sum of squares statistic. This statistic is calculated via cross-validation (CV) prediction for each model (Pu, 2012; Sheng et al., 2004). The PLSR model is a linear

4.1. Comparison between Landsat-5 TM and HJ-1CCD data

The performances of the PLSR models were compared on the basic of coefficient of determination (R2 ) and relative root mean square error (RRMSE). RRMSE facilitates comparison of the accuracy of each estimation model and is calculated with the following equation:

  n 1  100 2 RRMSE =  (Pi − Q i ) × n

i=1

Q¯ i

(2)

One matching image was used to compare the similarity between Landsat-5 TM (acquired in April 15, 2009) and HJ-1 A/CCD2 image (acquired in April 16, 2009) in both study areas (Fig. 3). Relevant field LAI measurements (n = 29) were used to quantify two sensors. The performance of HJ-1 A/CCD2 sensitive to LAI had a high degree of consistency with Landsat-5 TM data. First three bands had negative correlation with LAI, whereas band 4 and five VIs had positive correlation with LAI. Their five VIs performed better than the single spectral reflectance. As for four bands,

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Fig. 5. Normalized VIP values of image features in different growth stages. VIP nor = (VIP − VIPmin )/(VIPmax − VIPmin ), where VIPmin is the minimum value for variable VIP and VIPmax is the maximum value for variable VIP. Fig. 4. Spectral response profiles of Landsat TM and HJ-1A/CCD2.

4.4. Principal least square regression analysis

band 2 and band 3 had higher correlation between two sensors. HJ-1 A/CCD2 data were more sensitive to LAI than Landsat-5 TM data in band 4. The relative differences among these sensors are more importantly dependent on the mismatch of the relative sensor response function. Especially comparing with Landsat-5 TM for band 4 (the NIR band), the spectral response function of HJ1A/CCD2 decreases rapidly beyond a wavelength of 830 nm, and the total reflectance tends to the red-edge region (Fig. 4), which is more sensitive to LAI (Herrmann et al., 2011).

4.2. Relationships between image features and LAI To quantify the effect of each feature on winter wheat LAI, we extracted the value of remote sensing features, including the four reflectance bands, VIs, PCA, TCT, and texture features. Upon analyzing the correlation between LAI and each feature at four different growth stages, we listed the 10 most important features and their ideal texture parameters. All features were significantly correlated with LAI (Table 4). Among the four individual bands, the best results were obtained with the NIR band (Band 4), with positive correlations. The performance of the five VIs was generally better than that of the single-band variables. Furthermore, PC1 and TCT2 were significantly correlated with LAI. The mean metric alone yielded the most accurate LAI estimates from among the eight texture metrics of each image feature. Nonetheless, the combination of each image feature with the mean did not improve the accuracy of the estimated LAI values. Moreover, most features displayed a similar change tendency in each growth stage. For each image feature, the correlation coefficients of different growth stages were averaged and ordered from highest to lowest. The first four features were TCT2 (r = 0.758), MNLI (r = 0.757), PC1 (r = 0.753), and EVI (r = 0.752).

The four optimal features’ PLSR model equations for estimation of LAI were described (Table 5). Regression coefficients of these image features were positive values, which indicted that these four features had positive correlation with LAI. The values of each feature fall into different range. In order to quality the sensitive of each feature in the PLSR models, feature normalization was used. Regression coefficients of their normalized features were similar, which indicted that each feature had the same effect in the PLSR model. Table 6 presents a comparative analysis of the performance levels of PLSR models based on the 10 features and the 4 optimal features in each growth season. Models based on the 4 optimal features generated generally better (or similar) results than (or to) those based on the 10 features did. Specifically, the result predicted on April 30 by the PLSR model based on the 4 ideal features was much better than that estimated with the model based on the 10 features. In addition, the models based on the four ideal features over the entire vegetation period yielded R2 cv and RRMSEcv values of 0.773 and 29.15% through CV, respectively (Fig. 6a). Thus, these models were more accurate than the 10-feature dataset in terms of 2 = 0.759, RRMSE = 30.04% Fig. 6b). LAI prediction (Rcv cv , 4.5. LAI mapping The spatial distribution and temporal dynamics of LAI were determined using PLSR models. Fig. 7 provides a close and detailed view of LAI in the local region. LAI was low in early April (mean LAI of 1.28 and 1.70 on April 1 and April 16, respectively), increased rapidly in late April (mean LAI of 3.28 on April 30), and decreased in mid-May (mean LAI of 3.09 on May 20). These findings agreed with our field observations. The cropland of the study area is dispersed and small; in fact, the largest cropland area measured 1 ha. Moreover, the growth of winter wheat varied in the field; this crop is generally greenest in the center region. 5. Discussion

4.3. VIPs All VIP values of the 10 important features were calculated on the basis of the dataset for the four growth seasons. To eliminate the differences in various datasets, we normalized the VIP value (VIP nor) in each dataset (Fig. 5). A high VIP value indicates that the feature is significant in LAI estimation. The four features with highest VIP values were TCT2, PC1, MNLI, and EVI, thereby suggesting their potential for inclusion into the model for LAI estimation. Therefore, these four features were used to develop the multivariate LAI estimation model using PLSR.

The current study focused on retrieving winter wheat LAI using best HJ-1CCD image features obtained by PLSR. Field experiment data and relevant images were derived during the four winter wheat growth seasons. HJ-1CCD image information was derived and tested extensively through image features, including the four reflectance bands, VIs, PCA, TCT, and texture features. HJ-1CCD image features were obtained during four winter wheat growth seasons to explore relationships with LAI (Fig. 8). Increases in NIR reflectance with the increase in LAI were more prominent than the reduction in blue and red reflectance. Red

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Table 4 Correlation coefficients (r) between image features and LAI at different growth stages. Image features April 1 (n = 29) April 16 (n = 29) April 30 (n = 13) May 20 (n = 29) Image features April 1 (n = 29) April 16 (n = 29) April 30 (n = 13) May 20 (n = 29) Band2 Band3 Band4 NDVI EVI MNLI OSAVI RVI TCT2 PC1

−0.696b −0.651b 0.740b 0.749b 0.808b 0.781b 0.780b 0.683b 0.797b 0.748b

−0.732b −0.728b 0.756b 0.726b 0.765b 0.766b 0.748b 0.708b 0.777b 0.767b

−0.552a −0.725b 0.801b 0.795b 0.801b 0.833b 0.814b 0.811b 0.833b 0.850b

−0.479b −0.507b 0.617b 0.627b 0.633b 0.648b 0.646b 0.619b 0.625b 0.648b

−0.536b −0.639b 0.600b 0.564b 0.638b 0.645b 0.578b 0.682b 0.784b 0.754b

Band2mean Band3mean Band4mean NDVImean EVImean MNLImean OSAVImean RVImean TCT2mean PC1mean

−0.685b −0.636b 0.690b 0.697b 0.673b 0.748b 0.737b 0.736b 0.786b 0.816b

−0.572a −0.618a 0.807b 0.687b 0.751b 0.816b 0.713b 0.788b 0.847b 0.811b

−0.065 −0.199 0.617b 0.513b 0.555b 0.587b 0.549b 0.517b 0.609b 0.625b

Band2mean represents the mean texture of Band2. a Denotes correlations that are significant at the 0.05 level. b Indicates correlations significant at the 0.01 level. Table 5 Four optimal features’ PLSR models before and after data normalization. Data

PLSR model equation before data normalization

PLSR model equation after data normalization

April 1 April 16 April 30 May 20

2.0 × EVI + 0.00053 × TCT2 + 0.00082 × PC1 + 2.34 × MNLI + 0.132 1.53 × EVI + 0.00043 × TC2 + 0.00040 × PC1 + 2.01 × MNLI + 0.070 3.70 × EVI + 0.00090 × TCT2 + 0.00086 × PC1 + 4.10 × MNLI − 0.625 4.86 × EVI + 0.00089 × TCT2 + 0.00086 × PC1 + 4.9233 × MNLI − 0.853

0.224 × EVI + 0.221 × TCT2 + 0.207 × PC1 + 0.216 × MNLI 0.194 × EVI + 0.197 × TCT2 + 0.195 × PC1 + 0.194 × MNLI 0.202 × EVI + 0.210 × TCT2 + 0.214 × PC1 + 0.210 × MNLI 0.165 × EVI + 0.163 × TCT2 + 0.169 × PC1 + 0.169 × MNLI

EVI , TCT2 , PC1 , and MNLI are the normalized value. Normalization formula: X = (X − )/,  and  are the mean value and the standard deviation calculated directly from the original data (X). X is the normalized value.

Table 6 PLS regression based on the ten features and four optimal features. Data

April 1 April16 April 30 May 20

Samples

29 29 13 29

Ten features

Four optimal features

2 Rcv

RRMSEcv

2 Rcv

RRMSEcv

0.643 0.629 0.650 0.392

34.7 27.2 25.6 27.7

0.646 0.628 0.679 0.398

34.5 27.8. 24.5 27.6

Fig. 6. Comparison of field LAI with estimated LAI acquired on (a) the ten features’ PLSR model and (b) the four optimal features’ PLSR model.

reflectance decreases with increases in LAI as light is absorbed by leaf pigments (such as chlorophylls). The NIR signal increases as additional leaf layers scatter radiation upward (Gong et al., 2003). The advantage of VIs is that they can be used to obtain relevant information rapidly and easily. Some VIs have ability to reduce some effects, such as soil effect, atmosphere perturbation and scan angles. These indices mostly perform better than a single spectral reflectance. Among the five tested VIs, EVI, MNLI, and OSAVI were superior to NDVI and RVI in this respect. EVI, MNLI, and OSAVI optimize the vegetation or soil signal with better sensitivity than NDVI and RVI do in high LAI. (Gong et al., 2003; Hui and Huete, 1995; Rondeaux et al., 1996).

One match image was selected to compare the performance for the calibration of HJ-1CCD bands sensitive to wheat LAI. The acquired time difference between two images was one day, the blue band (band1) had low correlation than other bands, which coincides with previous research and indicates that atmospheric effects are much larger in the blue band images (Hu et al., 2014). Landsat5 TM and HJ-1CCD data not only have a strong linear relationship, but also have same relationship with LAI. PC1 concentrates the features common to all original image bands. In each dataset, PC1 was highly correlated with LAI. However, the prediction performance of PC1 was low throughout the entire growth stage (Fig. 8j). PCA components are based on statis-

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Fig. 7. Spatial variability of LAI across four growth stages on: (a) April 1; (b) April 16; (c) April 30; and (d) May 20, 2009. Red plus signs denote LAI sample plots.

tical relationships that are difficult to interpret. These components for a single landscape are variable between different landscapes and dates (Collins and Woodcock, 1994). Tasseled cap components are based on the physical characteristics in an image. Therefore, these components are ecologically interpretable and comparable among image dates. In this study, the second component (greenness) was the best predictor in both signals and the dataset for the entire growth season. High positive loadings are observed in NIR, whereas high negative loadings are observed in red spectra (Baig et al., 2014). Most previous studies showed that image texture improves the accuracy of analysis and feature extraction (Berberoglu et al., 2000). Image texture is also used to map LAI effectively and to increase stock volume and aboveground biomass in dense forests (Lu, 2006; Zheng et al., 2014; Zhou et al., 2014). In the current study, we extracted texture parameters from the HJ-1CCD images of a winter wheat planting area. The mean texture was higher than all other texture parameters. This result is consistent with those of previous studies (Kelsey and Neff, 2014; Wood et al., 2012). However, the accuracy of LAI estimates cannot be improved by combining textural information with image features in the present study. The field scales and growing conditions of winter wheat are small and nonuniform in this study, unlike in large, dense, and heterogeneous forests. Texture is a measure of variability in pixel values among neighboring pixels for a defined analysis window (Kelsey and Neff, 2014). Window size is small at 3 × 3; as a result, the difference within the moving windows is often exaggerated. Thus, noise content increases in the texture image (Lu, 2006). HJ-1CCD images with a resolution of 30 m complicate the provision of textural information more than high-resolution data do. As determined with PCA,

textures cannot reflect plant vitality during different phonological stages. The calculated VIP scores provide insight into the usefulness of each variable in the PLSR model given the many important image features. The best features were TCT2, PC1, MNLI, and EVI, as determined based on VIP scores from different growth datasets. Leave-one-out CV was performed to validate the PLSR models. Furthermore, the PLSR method is a powerful tool that can model several response variables simultaneously while effectively addressing strong collinear variables and noisy independent variables. This method is important in information extraction. The extraction process is in turn significant in accurately predicting vegetation parameters, such as LAI, water content, and biomass (Fei et al., 2012; Mirzaie et al., 2014). The PLSR models based on the four best features produced generally similar (or better) results to (or than) those based on the 10 features, both in the dataset for a single growth season and in that for all four growth seasons. The four optimal features PLSR model could hand best vegetation spectral information (MNLI and EVI), the vegetation physical characteristic (TCT2) and the compression of total spectral information (PC1) to work together for improving model accuracy, which take advantage of different features to reduce external factor and are highly sensitive to LAI. Each feature had similar contribution in the PLSR model. For the remaining features, they provide less superfluous information to improve LAI estimation accuracy. Instead, less important features would add noise and negatively influence prediction accuracy. This study indicated that appropriate features must be selected and determined for vegetation parameter estimation given different types of useful information. The presented results demonstrate

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seasons. VIP and PLSR results were applied to determine the best features and to build a suitable model. The following conclusions can be drawn: (1) Landsat-5 TM and HJ-1CCD data not only have a strong linear relationship, but also have same relationship with LAI. (2) As per a comprehensive analysis of the correlation between winter wheat LAI and the image information extracted from HJ-1CCD images. TCT2 and PC1 were highly correlated with LAI. However, their texture parameters could not improve the prediction of winter wheat LAI. (3) The best image features were TCT2, PC1, MNLI, and EVI, as per VIP values. (4) The PLSR model based on the four best features estimated LAI better than that based on all 10 features. We conclude that the best feature model is sufficient for LAI estimation. Acknowledgments The research was supported by Open Fund of State Key Laboratory of Remote Sensing Science (Grant No.OFSLRSS201306), National Natural Science Foundation of China (Grant No.41301375) and the Fundamental Research Funds for the Central Universities (Grant No. 2014B38514). We acknowledge the support given by the National Engineering Research Center for Information Technology in Agriculture, Beijing. The authors also thank to reviewers for providing critical comments and suggestions that have improved the original manuscript. References

Fig. 8. Remote sensing features vs. winter wheat LAI relationships.

the potential of PLSR and VIP techniques in identifying important variables for LAI estimation. This finding agrees with that of the study conducted by Oumar et al. (2013). 6. Conclusion The current study investigated the applicability of remote sensing image features (including the four reflectance bands, VIs, PCA, TCT, and texture features) as calculated from HJ-1CCD data to estimate winter wheat LAI in a suburban area during different growth

Abdi, H., 2003. Partial Least Square Regression (PLS Regression). Sage, Thousand Oaks, CA, pp. 792–795. Baig, M.H.A., Zhang, L., Shuai, T., Tong, Q., 2014. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sens. Lett. 5, 423–431. Barr, A.G., Black, T.A., Hogg, E.H., Kljun, N., Morgenstern, K., Nesic, Z., 2004. Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agric. Forest Meteorol. 126, 237–255. Beguet, B., Guyon, D., Boukir, S., Chehata, N., 2014. Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery. ISPRS J. Photogramm. Remote Sens. 96, 164–178. Berberoglu, S., Lloyd, C.D., Atkinson, P.M., Curran, P.J., 2000. The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Comput. Geosci. 26, 385–396. Chen, C., Tang, P., Bian, Z., 2012. Tasseled cap transformation for HJ-1A/B charge coupled device images. J. Appl. Remote Sens. 6, 63575–63581. Chen, J.M., Black, T.A., 1992. Defining leaf area index for non-flat leaves. Plant Cell Environ. 15, 421–429. Cho, M.A., Skidmore, A., Corsi, F., van Wieren, S.E., Sobhan, I., 2007. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int. J. Appl. Earth Obs. Geoinf. 9, 414–424. Chong, I., Jun, C., 2005. Performance of some variable selection methods when multicollinearity is present. Chemometr. Intell. Lab. 78, 103–112. Collins, J.B., Woodcock, C.E., 1994. Change detection using the Gramm-Schmidt transformation applied to mapping forest mortality. Remote Sens. Environ. 50, 267–279. Crist, E.P., Cicone, R.C., 1984. A physically-based transformation of thematic mapper data—the TM tasseled cap. IEEE Trans. Geosci. Remote Sens. GE-22, 256–263. Croft, H., Chen, J.M., Zhang, Y., 2014. Temporal disparity in leaf chlorophyll content and leaf area index across a growing season in a temperate deciduous forest. Int. J. Appl. Earth Obs. 33, 312–320. Darvishzadeh, R., Atzberger, C., Skidmore, A.K., Abkar, A.A., 2009. Leaf area index derivation from hyperspectral vegetation indicesand the red edge position. Int. J. Remote Sens. 30, 6199–6218. Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C., Corsi, F., Cho, M., 2008. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J. Photogramm. Remote Sens. 63, 409–426. Davi, H., Soudani, K., Deckx, T., Dufrene, E., Le Dantec, V., FranC¸ois, C., 2006. Estimation of forest leaf area index from SPOT imagery using NDVI distribution over forest stands. Int. J. Remote Sens 27, 885–902. ENVI User’s Guide, 2009. Atmospheric Correction Module: QUAC and FLAASH User’s Guide. Available online: .

112

X. Li et al. / International Journal of Applied Earth Observation and Geoinformation 44 (2016) 104–112

Fang, H., Liang, S., Hoogenboom, G., 2011. Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation. Int. J. Remote Sens. 32, 1039–1065. Fei, Y., Jiulin, S., Hongliang, F., Zuofang, Y., Jiahua, Z., Yunqiang, Z., Kaishan, S., Zongming, W., Maogui, H., 2012. Comparison of different methods for corn LAI estimation over northeastern China. Int. J. Appl. Earth Obs. 18, 462–471. Gong, P., Pu, R., Biging, G.S., Larrieu, M.R., 2003. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Trans. Geosci. Remote Sens. 41, 1355–1362. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I.B., 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens. Environ. 90, 337–352. Hansen, P.M., Schjoerring, J.K., 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 86, 542–553. Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., Bonfil, D.J., 2011. LAI assessment of wheat and potato crops by VEN␮S and Sentinel-2 bands. Remote Sens. Environ. 115, 2141–2151. Hu, Y., Liu, L., Liu, L., Peng, D., Jiao, Q., Zhang, H., 2014. A Landsat-5 atmospheric correction based on MODIS atmosphere products and 6S model. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 7, 1609–1615. Hui, Q.L., Huete, A., 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens. 33, 457–465. Inoue, Y., Sakaiya, E., Wang, C., 2014. Capability of C-band backscattering coefficients from high-resolution satellite SAR sensors to assess biophysical variables in paddy rice. Remote Sens. Environ. 140, 257–266. Jensen, J.L.R., Humes, K.S., Hudak, A.T., Vierling, L.A., Delmelle, E., 2011. Evaluation of the MODIS LAI product using independent lidar-derived LAI: a case study in mixed conifer forest. Remote Sens. Environ. 115, 3625–3639. Jordan, C.F., 1969. Derivation of leaf area index from quality of light on the forest floor. Ecology 50, 663–666. Kelsey, K., Neff, J., 2014. Estimates of aboveground biomass from texture analysis of landsat imagery. Remote Sens. 6, 6407–6422. Kross, A., McNairn, H., Lapen, D., Sunohara, M., Champagne, C., 2015. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. 34, 235–248. Lazraq, A., Cléroux, R., Gauchi, J., 2003. Selecting both latent and explanatory variables in the PLS1 regression model. Chemom. Intell. Lab. 66, 117–126. Li, X., Zhang, Y., Bao, Y., Luo, J., Jin, X., Xu, X., Song, X., Yang, G., 2014. Exploring the best hyperspectral features for LAI estimation using partial least squares regression. Remote Sens. 6, 6221–6241. Lu, D., 2006. The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 27, 1297–1328. Mather, P., Koch, M., 2010. Computer Processing of Remotely-Sensed Images: An Introduction, 4th edition. John Wiley and Sons, London. Mirzaie, M., Darvishzadeh, R., Shakiba, A., Matkan, A.A., Atzberger, C., Skidmore, A., 2014. Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements. Int. J. Appl. Earth Obs. 26, 1–11. Mo, X., Liu, S., Lin, Z., Zhao, W., 2004. Simulating temporal and spatial variation of evapotranspiration over the Lushi basin. J. Hydrol. 285, 125–142. Mutanga, O., Adam, E., Adjorlolo, C., Abdel-Rahman, E.M., 2015. Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset. Int. J. Appl. Earth Obs. 34, 178–187.

Nichol, J.E., Sarker, M.L.R., 2011. Improved biomass estimation using the texture parameters of two high-resolution optical sensors. IEEE Trans. Geosci. Remote Sens. 49, 930–948. Oumar, Z., Mutanga, O., Ismail, R., 2013. Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor. Int. J. Appl. Earth Obs. 21, 113–121. Pagola, M., Ortiz, R., Irigoyen, I., Bustince, H., Barrenechea, E., Aparicio-Tejo, P., Lamsfus, C., Lasa, B., 2009. New method to assess barley nitrogen nutrition status based on image colour analysis. Comput. Electron. Agric. 65, 213–218. Propastin, P., Panferov, O., 2013. Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: implication of leaf inclination angle. Int. J. Appl. Earth Obs. 25, 38–46. Pu, R., 2012. Comparing canonical correlation analysis with partial least squares regression in estimating forest leaf area index with multitemporal Landsat TM imagery. GISci. Remote Sens. 49, 92–116. Rondeaux, G., Steven, M., Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 55, 95–107. Rouse, J.W., Hass, R.H., Shell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resour. Technol. Satell. Symp. 1, 309. Scott Green, D., Erickson, J.E., Kruger, E.L., 2003. Foliar morphology and canopy nitrogen as predictors of light-use efficiency in terrestrial vegetation. Agric. Forest Meteorol. 115, 163–171. Sheng, C., Xia, H., Harris, C.J., Sharkey, P.M., 2004. Sparse modeling using orthogonal forward regression with PRESS statistic and regularization. Syst. Man Cybern. Part B: Cybern. IEEE Trans. 34, 898–911. Song, C., Dickinson, M.B., 2008. Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index. Int. J. Remote Sens. 29, 5605–5622. Thenkabail, P.S., Enclona, E.A., Ashton, M.S., Van Der Meer, B., 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens. Environ. 91, 354–376. Tian, Y.C., Yao, X., Yang, J., Cao, W.X., Hannaway, D.B., Zhu, Y., 2011. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crop. Res. 120, 299–310. Wood, E.M., Pidgeon, A.M., Radeloff, V.C., Keuler, N.S., 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sens. Environ. 121, 516–526. Yuan, H., Dai, Y., Xiao, Z., Ji, D., Shangguan, W., 2011. Reprocessing the MODIS leaf area index products for land surface and climate modelling. Remote Sens. Environ. 115, 1171–1187. Zhang, J., Pu, R., Yuan, L., Wang, J., Huang, W., Yang, G., 2014. Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery. PLoS One 9, e93107. Zheng, S., Cao, C., Dang, Y., Xiang, H., Zhao, J., Zhang, Y., Wang, X., Guo, H., 2014. Retrieval of forest growing stock volume by two different methods using Landsat TM images. Int. J. Remote Sens. 35, 29–43. Zhou, J., Zhao, Z., Zhao, J., Zhao, Q., Wang, F., Wang, H., 2014. A comparison of three methods for estimating the LAI of black locust. Int. J. Remote Sens. 35, 171–188.