Evaluating Sugarcane Growth and Maturity Using ... - ThaiScience

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Thai Journal of Agricultural Science 2012, 45(1): 17-28

Evaluating Sugarcane Growth and Maturity Using Ground-Based Measurements and Remote Sensing Data W. Gunnula1, M. Kosittrakun1,*, T. L. Righetti2, P. Weerathaworn3 and M. Prabpan3 1

Applied Taxonomic Research Center, Department of Biology, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand 2 Department of Biology, University of Guam, Mangilao, Guam 9692,USA 3 Mitr Phol Sugarcane Research Center, Chumpae-Phukiao Road, Khoksa-at Phukiao, Chaiyaphum 36110, Thailand *

Corresponding author. Email: [email protected]

Abstract Most sugar mills would like to make preharvest predictions of either cane weight or sugar yield for individual fields. Sugarcane growth, maturity and yield were measured at Mitr Phol Sugarcane Research Center farmers’ fields from September 2008 to March 2009. The major sugarcane cultivars (K84-200, K88-92 and LK92-11) planted in the Mitr Phukhieo Sugar Mill area were selected for this study. The soil textures of these fields were grouped into coarse (sand) and fine (loam and light clay) categories. Both high-resolution (20 meter) SPOT and moderate-resolution (250 meter) MODIS satellite images were evaluated to find out whether or not a combination of ground-based measurements and remote sensing data can predict growth and sugar content. Earlyseason yield and stalks per hectare were strongly correlated to final cane yield (r2= 0.89 and 0.87, respectively). Remote assessments and simple cane measurements (degree brix, height, diameter, number of nodes, weight) in themselves were not strongly associated with final yield or sugar content. Neither the SPOT nor MODIS NDVI could detect yield differences associated with different soil textures or sugarcane cultivars. The simple use of early-season cane number per 10 m of row and row spacing to estimate canes per hectare provides a usable approach to predict final yield. The tedious and expensive measurement of early-season yield provides a little more prediction strength than simply counting canes. Combining early-season canes per hectare with an evaluation of early-season brix at the base of the canes provides a usable approach to predicting final sugar yield (r2 = 0.82). Adding other ground-based measurements and SPOT or MODIS image-derived values to predictive equations did not improve cane yield or sugar yield prediction. Keywords: cane yield, prediction, soil texture, sugar yield, sugarcane cultivar

Introduction The cane and sugar industry in Thailand generates an income of about 50,000 million baht (US$ 1,600 million) per year. The cane planting area covers about one million hectares. The 10-year average cane yield in Thailand is about 50 tons/ha and the average total cane production is around 5560 million tons per year (Prasertsak, 2005). Production is dispersed to almost all parts of

Thailand except in the South. Most sugarcane is grown in the west and northeast of the country. The harvest season extends from December to March, when the percentage of extractable sugar (commercial cane sugar, CCS) is highest (Chiadamrong and Kawtummachai, 2008). Different areas have different soil properties and fertility. Therefore, cultural practices and final yield vary tremendously. Early and accurate crop forecasts offer substantial benefits to the sugarcane

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industry through better logistical management, increased profitability and improved customer satisfaction (Everingham et al., 2009). Predicting, mapping and tracking yield in sugarcane-producing areas are important to manage production and maximize milling efficiency. Companies also can focus on improving growing practices in areas where the yield is low. Many crop prediction techniques, from simple empirical equations to complicated physiological models have been reported. Visual field estimates (Agraval and Jian, 1996), regression models (Subbaramayya and Kumar, 1980; Ilyas and Khan, 2010), cropsimulation models (Bezuidenhout and Singels, 2001; Promburom et al., 2001; Everingham et al., 2002; Potgieter et al., 2003), a combination of crop modeling and seasonal climate forecasts (Lumsden et al., 2000), and climate-based predictions (Bezuidenhout and Schulze, 2006) have been used. However, most of these prediction systems are limited to specific regions and time periods due to significant spatial-temporal variations. Further more, the limited network of weather stations and incomplete climate data make crop monitoring and yield assessment a daunting task (Kogan, 1997; Zhang et al., 2005). Another approach to yield prediction involves accessing crop vigor through remotely sensed data. Remote sensing, owing to its synoptic, timely and repetitive coverage, has been recognized as a valuable tool for yield and production forecasting (Manjunath et al., 2002; Prasad et al., 2006). Agricultural monitoring from space, in particular, preharvest assessments of crop yield and production, has been a topic of research since the early 1970s (Wall et al., 2007). Vegetation indices (VIs) derived from the spectral bands in multispectral imagery have long been used to estimate crop yields (Tucker et al., 1980; Wiegand et al., 1991; Plant et al., 2000; Yang and Everitt, 2002). These VIs are usually formed from the combinations of visible and near-infrared (NIR) wavebands. Two of the earliest and most widely used VIs are the simple NIR/Red ratio (Jordan, 1969) and the normalized difference vegetation index (NDVI) (Rouse et al., 1973). The NDVI data have been used extensively in vegetation monitoring, crop yield assessment and forecasting (Benedetti and Rossinni, 1993; Quarmby et al.,

Thai Journal of Agricultural Science

1993; Prasad et al., 2006). Using remotely sensed techniques to predict sugarcane yield has been reported in Brazil (Rudorff and Batista, 1990; Almeida et al., 2006; Simões et al., 2005; Fernandes et al., 2011), Australia (Lee-Lovick and Kirchner, 1991), South Africa (Agricultural Research Council, 2000), Mozambique (Ha, 2005) and Japan (Ueno et al., 2005). Previous research suggested that remote NDVI assessments are confounded during the dry season in northeastern Thailand (Gunnula et al., 2011). Maturing canes have decreasing NDVI while larger biomass for individual fields is associated with increasing NDVI. Remote assessments by themselves cannot be used for yield prediction in northeastern Thailand. In this region, the final sugarcane yield is currently predicted from early-season yield measurements on farmers’ fields. A less expensive alternative is desired. The objective of this study was to evaluate the possibility of using a combination of easy-to-collect, ground-based data and remote-sensing imagery to predict yield and sugar content for individual fields. We also evaluated whether different sugarcane cultivars or canes grown on a coarse-textured rather than finetextured soil, produce different remote assessments. Materials and Methods Field Measurement Data Thirty-nine selected farmers’ fields that were situated within a 5 km radius of Mitr Phol weather stations (Figure 1) were evaluated in this study. The boundary and position of all fields were determined using a hand-held global positioning system (Garmin GPS 76) with an accuracy of < 15 m. The sampled fields were grouped by planting type, cultivar and soil texture (Table 1). Planting types were 1) rainfall-dependent, dry-season planted 2) irrigated canes planted at the same time 3) rainfall-dependent, rainy-season planted and 4) ratoon crops that were planted in previous seasons. The sugarcane cultivars used in this study included K84-200, K88-92 and LK92-11. The soil textures were grouped into coarse (sand) and fine (loam and light clay) categories. Each field was divided into three subplots for data collection (Figure 1). Six canes from each subplot were

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Evaluating sugarcane growth and maturity

Table 1 The number of fields for three cultivars (most popular in the region) and four planting types for the 43 farmers’ fields.

Cultivar K 84-200 K 88-92 LK 92-11

Fine texture (clay & loam) Dry 3 5

w

Irrigated 10 3 7

x

Rainy 1 -

y

Ratoon 1 -

Coarse texture (sand) z

Dry 2 1

Irrigated 2 4 4

w Dry refers to rainfall-dependent dry-season (Oct-Nov) planted canes x Irrigated refers to dry-season planted (Jan-Mar) irrigated canes y Rainy refers to rainfall-dependent rainy-season (April-May) planted canes z Ratoon refers to canes that were established in previous years.

Figure 1 Study site and details on the collection of ground-based data for individual fields. Data on growth, weight, stalk diameter, internode number and height were collected from three 10-m subplots containing two rows of canes. Maturity, degrees brix of sugarcane juice sampled from the base, middle and top portions of the same stalk were measured with a hand refractometer.

evaluated each month. For growth assessments, weight, diameter, internode number and height were measured monthly. The height was measured from the stalk base to the top portion where the topmost dew lap is visible. Degree brix of sugarcane juice sampled from the bottom, middle and top portions of the same stalk was measured with a hand refractometer. Row spacing and stalk count per 10 meter were combined with cane dry weight and sugar content to calculate estimated dry weight and sugar yield.

Remote Sensing Data Remote sensing data were also collected. Images from SPOT (Satellite Pour l’Observation de la Terre) and MODIS (Moderate Resolution Imaging Spectroradiometer) on the Terra satellites were selected for this study. The SPOT 2 and SPOT 4 imageries were purchased from GISTDA (Geo-Informatics and Space Technology Development Agency). These images have 20meter spatial resolution. For the 8 SPOT scenes that were purchased, their acquisition dates and

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ancillary data are listed in Table 2. The first two images (07/11/2007 and 19/12/2007) were selected to represent the beginning of the planting period for the non-irrigated, dry-season cane plantings and the beginning of the growing season for ratoon crops. The third (08/02/2008) image represents the beginning of planting for irrigated canes, whereas the fourth (05/04/2008) represents the start of rainy-season cane planting. Geometric correction was carried out by using image-to-image registration method. The SPOT images were geometrically registered with IRS-1D LISS-III data (14 December 2005) which were corrected with ground control points collected through GPS using second polynomial order and nearest neighborhood sampling method in ERDAS imagine (ver. 8.4). All SPOT images were co-registered to the UTM coordinate system (Zone 48 North) with a root mean square error less than 0.5 pixels per image. Due to the appearance of haze in some images, haze reduction was conducted using simple dark object subtraction (DOS) method. This method assumes that any radiance received by a sensor for a dark object pixel is due to atmospheric path radiance (Chavez, 1988). Therefore, the lowest digital number (DN) values were selected from dark object (deep lake) that were assumed to have near zero reflectance. This DN value was subtracted from all pixels in a particular spectral band. Relative radiometric correction method was used to detect spectral reflectance from multi-date satellite images. This method was selected because no in situ atmospheric data at the time of satellite overpasses are required. It involves normalizing or rectifying the intensities or DN of multi-date image band by band to a reference image (Yang and Lo, 2000). Relative radiometric normalization was done based on linear regression normalization from pseudo-invariant features (PIFs) method (Schott et al., 1988). PIFs are objects with nearly invariant reflectivity from the reference image scene to another. The brightness variation of these invariant elements was assumed to be a linear function. These objects are typically man-made objects whose reflectance is independent of seasonal or biological cycle. The image having the clearest atmosphere and high dynamic DN range (2 December 2008) was selected as the reference image for normalization of other seven images.


Main road, roof tops and other man-made buildings were manually masked as PIFs. Histogram statistics [mean and standard deviation (SD)] of PIFs in each band were recorded to create band by band transformation equations. Accordingly, a linear equation can be used to perform the following normalization equation: S´k = mkSk + Bk where Sk is the DN of band k in image S on date 1, S´k is the normalized DN of band k on date 1, mk is the slope or gain, and bk is the interception or offset. According to Schott et al. (1988), band-to-band radiometric normalization was conducted based on standard deviation and mean of PIFs from each band. The transformation coefficients are: mk= SD from PIFs of reference image / SD from PIFs of subject image Bk= mean from PIFs of reference image - mk* mean from PIFs of subject image In order to obtain physical measurement independent of radiometer characteristics, the unsigned 8 bit (0-255) DN was converted to atsatellite radiance for each band by the following equation: Lsat = (DN/G) + Bias where Lsat is at-satellite radiance (Wm-2sr-1µm-1), DN is digital number, G and Bias are gain parameters and offset parameters obtained from the header files of the SPOT images (Table 2). After conversion to at-satellite radiance, each image was converted to at-satellite reflectance using the following equation: ρsat = (π*Lsat)/(Es*cosθs*d2) where ρsat is at-satellite reflectance, Es is exoatmospheric solar constant (Wm-2µm-1), θs is solar zenith angle, and d is the ratio of the SunEarth distance at the acquisition date to the mean Sun-Earth. Es and θs (180 minus solar elevation angle) obtained from the header files of the SPOT images (Table 2) while d was retrieved from the solar system live website: http://www.fourmilab.ch /cgi-bin/uncgi/Solar/action?sys=-Si. The NDVI calculation (Rouse et al., 1973) was derived by using reflectance data from XS3 (near infrared) and XS2 (red) as follows: NDVI =

(NIR-RED) (NIR+RED)

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Table 2 The acquisition dates and ancillary data from the SPOT images utilized in this study. No.

Satellite

Date/time

Sun angle (degrees)

1

SPOT 4

2007-11-17 03:46:56

Azimuth: 153.3 Elevation: 50.8

2

SPOT 2

2007-12-19 03:56:11


L17.9

3

SPOT 4

2008-02-08 03:49:24


L2.5

4

SPOT 4

2008-04-05 03:52:41


L9.6

5

SPOT 2

2008-09-05 03:29:01


R20.6

6

SPOT 2

2008-10-11 03:35:26


R5.3

7

SPOT 2

2008-11-21 03:45:57


L18.6

8

SPOT 2

2008-12-02 03:34:16


R5.0

where NIR is near infrared spectral reflectance and RED is red spectral reflectance. The MODIS sensor provides daily 250-meter resolution for the NDVI product computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. The 16-day composite MODIS NDVI (MOD13Q1.5) images analyzed in this study were downloaded via the LP DAAC Data Pool (https://lpdaac.usgs.gov /lpdaac/get_data/data_pool). Converting the original sinusoidal projection of the MODIS scene to World Geographic System coordinate was carried out by using the MODIS reprojection tool (https://lpdaac.usgs.gov/lpdaac/tools/modis_reproje ction_tool). All farmers’ fields were converted from polygons to points (10 m space). SPOT and MODIS NDVI values were then assigned to each of these points. Overlaying and joining the SPOT and MODIS NDVI values from satellite imagery

Incidence angle (degrees) R5.9

Absolute calibration gains (1/W*m2*sr*µm) XS1= 2.98034 XS2= 2.70203 XS3= 2.02410 XS4= 12.1645 XS1=1.67365 XS2=1.35024 XS3=1.62634 XS1=2.12386 XS2=1.85482 XS3=1.96345 XS4=6.11650 XS1=1.32840 XS2=1.80695 XS3=1.35180 XS4=5.41610 XS1= 1.45196 XS2= 1.34939 XS3= 1.62855 XS1= 1.67365 XS2= 1.35024 XS3= 1.62634 XS1= 1.67365 XS2= 1.35024 XS3= 1.62634 XS1= 1.45196 XS2= 1.34939 XS3= 1.62855

Physical bias

Solar irradiance

None

XS1=1851 XS2=1586 XS3=1054 XS4=240 XS1=1865 XS2=1615 XS3=1090 XS1=1843 XS2=1568 XS3=1052 XS4=233 XS1=1851 XS2=1586 XS3=1054 XS4=240 XS1=1865 XS2=1620 XS3=1085 XS1=1865 XS2=1615 XS3=1090 XS1=1865 XS2=1615 XS3=1090 XS1=1865 XS2=1620 XS3=1085

None None

None

None None None None

with farmers’ fields data were accomplished by using ArcGIS 9.2 and Hawth’s tool analysis add-in (Bayer, 2004). Nonparametric tests were performed to determine the differences in cane yield, sucrose yield and NDVI values from different soil types and different cultivars. Best subsets regression in Minitab 15 was used to identify important predictor variables from both ground-measured and remotely sensed data that were subsequently used for creating predictive models for cane yield and sugar yield. Results and Discussion Figure 2 shows NDVI changes over time for the MODIS and SPOT imageries. Commercial preharvest yield predictors would likely need to be made before the end of the rainy season (midOctober) and mid-January. Both the MODIS and

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0.9

MODIS NDVI

0.8

Max SPOT NDVI

NDVI value

0.7

Mean SPOT NDVI

0.6

Sampling time

0.5 0.4 0.3 0.2 0.1

22_Mar

18 Feb

17 Jan 2009

18_Dec

16_Nov

15_Oct

13 Aug

12_Aug

11_Jul

9_Jun

22 Apr

21_Mar

18_Feb

17_Jan_2008

3 Dec

7_Nov_2007

0

Figure 2 The NDVI pattern derived from the MODIS and SPOT images during the 2008-2009 planting season. The maximum SPOT NDVI is the highest NDVI value for each field while mean SPOT NDVI values represent the average of all NDVI pixels for individual fields on a given date. Six monthly, ground-based assessment sampling times are also indicated.

SPOT NDVI appear to decrease from midNovember to March although we do not have the SPOT imagery late in the growing season for the second year. Unfortunately, NDVI is a confounded measurement during the most likely evaluation period (Gunnula et al., 2011) since it is associated with the differences in both plant biomass and cane maturity. Once the rainy season ends, NDVI declines (Figure 1) while yield increases (Figure 3). When all the fields are averaged, there is no cane yield or sucrose yield difference for coarse-textured and fine-textured soils (Figure 3). However, there are cane yield and sucrose yield differences among the three cultivars (Figure 3). When only irrigated fields are evaluated, coarse-textured fields have higher cane yield and sucrose yield (Figure 4). Coarse-textured fields are likely susceptible to drought under rainfed conditions. However, when irrigated, coarse-textured fields are likely both better drained and well watered. Cultivar yield differences under irrigated conditions (Figure 4) were similar to the overall average for all fields (Figure 3). The LK 92-11 cultivar is clearly superior to the K 84-200 cultivar for both cane yield and sucrose yield. In all cases there was no clear difference among cultivars or soil textures for either the SPOT or MODIS NDVI for farmers’ fields. The SPOT and MODIS NDVI values for farmers’ fields that

differed in texture or cultivar planted, appear in Figure 5. At no time did either SPOT or NDVI explain more than 10% of the variability in cane or sucrose yield (data not shown). Our results are similar to those reported by ARC (2000) where there was no significant correlation between either estimated or recorded sugarcane yield and median NDVI derived from fine-resolution DMSV. Gers (2004), who used a moderate spatial resolution sensor (Landsat ETM+) and principal component analysis to relate cane yield and at-satellite reflectance values also found no significant relationship between yield and spectral characteristics of sugarcane. Ueno et al. (2005) found relatively low prediction accuracy when using Landsat TM to predict yields. Even though, many of the conditions that favorably or adversely affect plant development and ultimately yield (e.g. fertilization treatment, drought, or precipitation events) result in a corresponding increase or reduction of the crop's photosynthetically active biomass and this response can often be captured through spectral measures such as NDVI (Tucker, 1979). Yield estimation based on empirical NDVI approaches are often inaccurate when photosynthetic capacity at the time of measurement is not the main determinant of the final yield (BeckerReshef et al., 2010).

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A

50 45 40 35 30

Fine texture Coarse texture

25

Cane yield (tons/ha)

20

70 65 60 55 50 45 40 35 30 25 20

Sucrose yield (tons/ha)

60 55

B

8 7 6 5 4 3 2

Fine texture

1 0

Coarse texture Oct_08(1) Nov_08(2) Dec_08(3) Jan_09(4) Feb_09(5) Mar_09(6)

Oct_08(1) Nov_08(2) Dec_08(3) Jan_09(4) Feb_09(5) Mar_09(6)

C

K 84-200 K 88-92 Lk 92-11



65

23


Oct_08(1) Nov_08(2) Dec_08(3) Ja n_09(4) Feb_09(5) Mar_09(6)

10 9 8 7 6 5 4 3 2 1 0

D

K 84-200 K 88-92 LK 92-11 Oct_08(1) Nov_08(2) Dec_08(3) Jan_09(4) Feb_09(5) Mar_09(6)

70 65 60 55 50 45 40 35 30 25 20

A

Fine texture Coarse texture



Figure 3 Cane yield and sucrose yield for different soil texture categories (A, B) and three different cultivars (C, D). All 43 farmers’ fields were interpreted irrespective of planting types for the data shown. Vertical bars represent the standard errors of means (n =32). When pooled across sampling times, cane yield and sucrose yield do not significantly differ for texture (Figures 3A and 3B; Mann-Whitney test P = 0.4987 and 0.127, respectively), but all three cultivars significantly differ (Figures 3C and 3D; Kruskal-Wallis test P < 0.05).

20

K 84-200 K 88-92 Lk 92-11 Oct_08(1) Nov_08(2) Dec_08(3) Jan_09(4) Feb_09(5) Mar_09(6)



50

30

Fine texture Coarse texture Oct_08(1) Nov_08(2) Dec_08(3) Jan_09(4) Feb_09(5) Ma r_09(6)

12

C

60

40

B

Oct_08(1) Nov_08(2) Dec_08(3) Jan_09(4) Feb_09(5) Mar_09(6)

80 70

10 9 8 7 6 5 4 3 2 1 0

10

D

8 6 4

K 84-200

2

K 88-92

0

LK 92-11 Oct_08(1) Nov_08(2) Dec_08(3) Jan_09(4) Feb_09(5) Mar_09(6)

Figure 4 Cane yield and sucrose yield for different soil texture categories (A, B) and three different cultivars (C, D). Only 19 irrigated fields (from a total of 43) were included for this analysis. Vertical bars represent the standard errors of means (n =19). When pooled across sampling times, cane yield and sucrose yield do not significantly differ for texture (Figures 4A and 4B; Mann-Whitney test P = 0.1543 and 0.0994, respectively), but all three cultivars significantly differ (Figures 4C and 4D; Kruskal-Wallis test P