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Disaster Advances
Vol. 7 (12) December 2014
Monitoring and forecasting of winter wheat freeze injury
and yield from Multi-temporal remotely-sensed data
Wang Hui-fang1,2*, Guo wei 2, Wang Ji-hua1, 2*, HUANG Wen-jiang2, Gu Xiao-he2, Dong Ying-ying1,2
and Xu Xin-gang2
1. Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310012, CHINA
2. Beijing Research Centre for Information Technology in Agriculture, Beijing 100097, CHINA
*whf428@126.com
meteorological
sensing model.
Abstract
Remote sensing techniques provide crop growth
information economically, rapidly and objectively on
a large scale. It has been widely used in monitoring
crop growth and forecasting yield. In this study, HJCCDs images were acquired in Gaocheng areas at
four growth-wintering stages of winter wheat (Dec. 2,
2009, pre-freeze injury), regreen stage (Apr. 2, 2010,
post-freeze injury), jointing stage (Apr. 23, 2010)
corresponding with field investigations data.
According to the change characteristics of normalized
difference vegetation index (NDVI) of field samplings
of post-freeze injury, a mutil-line-progress model
(MLR) was built between the NDIV difference
(ΔNDVI) and field samplings.
models,
agronomy
model
remote
Because the influence of crop yield is complicated,
meteorology and agronomy model have fluctuated quite
a bit in crop yield forecast accuracy in the large-scale
monitoring. Remote sensing model to forecast crop
yield is built based on the growing indicators and
measured spectral information. It integrated agronomic
parameters and meteorological factors which have
unique advantages in predicting crop yield in largescale 6. With the development of remote sensing
technology, advantages of suatle wide, short revisit
cycle and multi-source remote sensing technology have
been widely used for crop disaster monitoring and yield
forecasting.
Previous studies have shown that use of remote sensing
data to monitor winter wheat freeze injury is reliable
and effective 7-9 . But, the use of research has not been
reported on the crop yield of winter wheat freeze injury
severity. Therefore, the purpose of this study is to use
multi-temporal HJ-CCD images to predict winter wheat
yield after freeze disaster.
The damage levels (uninjured, light, moderate and
serious) and the growth levels (better, good, bad and
worse) were also specified. As a result, the coefficient
of determination (R2) of this model reached 0.6001;
twenty sampling points were used to validate the
model and R2 reached 0.5255. This study not only
proved the feasibility of using early growth stage
model to predict yield but also provided a tentative
prediction of the yield in Hebei area using HJ-CCD
images of China.
Method
Description of the study area: Gaocheng is located
between 37°51′18″N－38°18′44″N and 114°38′45″E
－114°58′47″E, belonging to Hebei province, China (Fig.
1). This area is dominated by the characteristics of a
continental monsoon climate. The average temperature is
12.50C and the average rainfall is 494mm. On 3rd Nov.
2009 the temperature suddenly droped from 70C to 40C and
then the low temperature lasted a long time because the
accumulatively temperature was not enough and winter
wheat lacked the experiences in anti-freeze which enter in
wintering phase earlier than before. Furthermore, it makes
the winter wheat in Gaocheng suffered by degrees of the
freeze injury from wintering stage to jointing stage.
Keywords: Winter wheat, Freeze injury levels, Change
detection, Yield.
Introduction
The North China Plain is the main production zone of
winter wheat (Triticum aestivum L.), where wheat is
affected seriously by freeze injury1. It occurs in the winter
period and early spring period 2. Severe global climate
change, has greatly caused production losses and
quality reduction3,4. So, on a regional scale monitoring
the disaster and predict crop yield are urgent needs of
the government. It is not only very important to
monitor the degrees of the disaster and the yield timely
and accurately but also to keep the food supply and
demand balance5 . There are some ways to forecast the
crop yield, mainly three models carried out forecasts:
Field investigation: Field samplings were carried out
during winter wheat wintering stage, re-green stage and
jointing stage in Gaocheng, Hebei Province from 2009 to
2010. 46 winter wheat planting areas of Gaocheng were
selected as shown in fig.1. The geo-references wheat plants
samples were collected and threshed manually for wheat
population and yield. For each field, there was five 1m *1m
plot cropped manually which was randomly distributed in a
*Author for Correspondence
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Disaster Advances
Vol. 7 (12) December 2014
30m*30m pixel that was located in the center of the field.
The GPS location of the five plots was recorded and
averaged.
Methodological process: There are in total four steps to
determine the freeze injury winter wheat, specify damage
levels and forecast yield (Fig. 3):
1. The time at which the freeze damage occurred should be
determined, then the corresponding remotely sensed data
and plant growth stage can be identified.
2. Assuring the growth stage and selecting available HJCCD remotely sensed images, NDVI difference (Δ NDVI)
calculation and damage/growth level specification.
3. The MLR were built between Δ NDVI and measured
yield.
4. Verification results.
Pre-progress: On September 6th, 2008, two optical
satellites [Huan Jing (HJ)-1 A and B] were successfully
launched by China for environmental and disaster
monitoring and forecasting. Since then, images with a
spatial resolution of 30 m, a wide swath of 360 km and a
revisiting period of two days have been received which
have been widely applied in environmental monitoring,
land cover/use data updates, land resource investigations
etc.10-12 There are 3 HJ-CCDs images available on 2nd Dec
2009, 2nd Apr 2010, 23rd Apr 2010 that corresponded to the
wintering stage, re-green stage and jointing stage of winter
wheat.
In this study, the freeze damage occurred at the wintering
stage of winter wheat and green vegetation dominated most
of the winter wheat fields. After the winter wheat was hit
by the low temperature, leaves were broken and vegetation
cover accordingly decreased. Therefore, NDVI which is
very sensitive to changes in canopy structure and biomass13
from HJ-1 CCD satellite images was used to identify the
freeze-damaged winter wheat area. Considering field
samples and photographs of different damage levels, the
analysis was performed in terms of damaged acreage,
damage levels and potential yield. In order to avoid the
phenomenon of the fake growth strains of winter wheat
after freeze disaster, assuring the jointing stage of wheat
growing is necessary, it will acquire aquires the model with
high significance to forcecast the winter wheat yield after
freeze disaster.
Three remote sensing images pre-progress are: (1)
Radiation correction: utilize trance Digital Number into
radiance; (2) Atmosphere corrections was carried out in the
fast line-of-sight atmospheric analysis of spectral
hypercubes(FLASSH) module integrated in the ENVI 4.7
image processing software and (3) Geometric correction
was performed in the IMAGINE Auto-Sync add-on of
ERDAS Image. The registration accuracy was required to
be less than 0.5 pixels. This module automatically produces
generating thousands of tie points which can align and or
rectify the HJ-1 CCD images combined with the georeferenced Landsat TM dataset (path/row: 124/034,
124/033). Additionally, image enhancement and haze
removal were also performed to improve the interpretation
quality of HJ-1 CCD images in ENVI software.
Calculation of NDVI and ΔNDVI: To better detect
changes in vegetation, a ΔNDVI image was generated by
simply subtracting the values between the earlier image and
the later image. NDVI and ΔNDVI were calculated using
the following formulae:
Winter wheat planting area extraction: Winter wheat
planting area was extracted from LandSat-TM5 data
obtained on December 2, 2009. According to phonological
characteristics of the study area, except winter wheat, the
other green vegetation (including grasslands, woodlands,
other crops etc.) is in a withered state in winter which is
suitable for extracting winter wheat growing area. After
image preprocessing, through spectral signature analysis,
the decision tree classification method was adopted for
obtaining information of vegetation, use the mask to get
spatial distribution scope of winter wheat planting area in
the study areas.
NDVI
( RRED RNIR ) / ( RRED
RNIR )
where NIR (0.76-0.90 µm) and RED (0.63-0.69 µm)
represent the two spectral bands of HJ-1-CCD imagery.
NDVI1
NDVI pre
NDVI post
NDVI 2
NDVI re cov er
NDVI post
Table 1
Acquired remotely sensed images of HJ-1A/1B CCD and their technical specification
Sensors
HJ-1A-CCD-1
457/70
HJ-1A-CCD-1
457/70
HJ-1B-CCD-2
2/68
Acquisition
date
2009-12-02
2010-04-02
Spectral
range(μm)
band1:0.43-0.52
band2:0.52-0.60
band3:0.63-0.69
band4:0.76-0.90
Spectral
resolution
30m
2010-04-23
2
Revisiting
cycle
4(days)
Swath(km)/width
360(single CCD)
700 (dual CCDs)

Disaster Advances
Vol. 7 (12) December 2014
Fig. 1: The description of the study area.
Fig. 2: Schematic chart integrating HJ-CCD and other ancillary data
where NDVIpre and NDVIpost are the NDVI images pre
and post-freeze-damage, NDVIrecover is the NDVI image
growth respectively.
spectrum, seriously freeze-damaged wheat shows increased
defoliation and results in higher spectral reflectance while
in the near infrared bands, lower radiance is observed.
Therefore, the before and after freeze injury NDVI images
can describe the degrees of the freeze injury and the growth
(Fig. 4a). In order to avoid the false-growing appearances,
the growing recovery situation after freeze injury was
monitored in jointing stage (Fig. 4b).
Results and Discussion
Characterization and Identification of freeze-damaged
winter wheat: Freeze injury primarily affects yields by
reducing the number of tiller number, so the spectral
responses of wheat are different at different freezedamaged levels (Fig. 3). Healthy wheat illustrates the
expected typical vegetation patterns of low reflectance in
the visible wavelengths and high reflectance in the near
infrared regions. Comparatively, within the visible
The identified results are shown in figures 4a and 4b, it can
be found that the freeze-damage occurrence in north to
south direction affected was greater in the north than in
south, the hutuo river is the nature boundary. The spatial
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Disaster Advances
Vol. 7 (12) December 2014
distribution of affected winter wheat was non-uniform due
to different temperature and soil cultivation condition. But
it always concentrated in a certain region of the soil
condition under a certain temperatures. Winter wheat freeze
damage is a complicate progress, its occurrence is by many
factors, the main reason is low temperature, in Gaocheng
another reason is the soil property. It is mostly sandy soil in
the north of Hutuo river soil and the southern is loamy.
jointing stage and it used to build a multi-line-regression
model between field investigations. The coefficient of
determination (R2) reached 0.6011. Fig.5a indicates that the
ΔNDVI is significantly positive correlated with measured
yield at 99% level. Fig. 4c showed the distribution of the
yields of winter wheat in Gaocheng. The distribution of
yield was similar at the degrees of the freeze injury.
According to statistics yearbook of Gaocheng, the yield
decreased by 11% over last year. Because of the freeze
injury affect the biomass decreased. Additionally, twentyfive sampling points were used to validate the model. As
shown in fig. 5b, R2 reached 0.5255 at 99% level.
Forecasting yield based on growth in jointing stage:
Many studies have shown that the NDVI can present the
vegetation coverage and it has a positively correlation with
yield14-17. It is possible to forecast yield by ΔNDVI at
jointing stage of winter wheat. For forecasting yield, 21
sampling points were extracted from the ΔNDVI image in
Yield = 458.9* ΔNDVIjointing+259.3 (R2=0.6001)
Fig. 3: The characters of winter wheat freeze injury in HJ-CCD images.
(a)
(b)
(c)
Fig.4: The affected winter wheat area and related severity: (a)The distribution and freeze-danage levels of winter
wheat in Gancheng, (b) the final danage levels map of winter wheat growth in Gancheng, (c)the distribution of wheat
yield in Gaocheng.
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Disaster Advances
Vol. 7 (12) December 2014
(a)
(b)
Fig.5: Relations: (a) Relation between ΔNDVI and measured yield at jointing stage (n=21) (b) Relation between
measured yield and estimate yield
monitoring the freeze injury yield loss reliably.
Discussion and Conclusion
This study is based on the analysis of physiological and
biochemical characteristics of winter wheat after the freeze
injury, first it clarified the changes in spectral
characteristics and its variation after freezing stress;
secondly utilized multi-temporal HJ images to monitoring
the severity of the winter wheat freeze injury and then the
relationship between the degree of freeze injury and the
measured output of winter wheat was analyzed. It indicated
that utilize multi-temporal satellite data to monitor the
distribution and degree of winter wheat freeze injury which
can provide freezing damage evaluation results of yield
loss. However, to produce a practical remote sensing
monitoring system of freeze injury yield losses, it also
needs to consider the following factors:
Acknowledgement
This work is subsidized by National Science Foundation of
China (41001199, 41101395 and 41101397), Beijing New
Star Project on Science & Technology (2010B024).
National science and technology support of China
(2012BAH29B04).
References
1. Li Z. C. et al, Hyperspecral feature if winter wheat after frost
stress at jointing stage, Acta Agronomica Sinica, 34(5), 831-837
(2008)
2. Feng M. C. et al, Monitoring winter wheat freezing injury
using multi-temporal MODIS data, Agricultural Science in China,
8(9), 1053-1062 (2009)
1) The multi-temporal remote sensing technology is the
basis of remote sensing to identify freeze injury. Freeze
injury can lead to physiological changes, mainly chlorosis,
dehydration, decrease in biomass, so the common spectral
characteristics were: near-infrared reflectance reduced, the
visible reflection and shortwave infrared reflectance
increased, so based on the single temporal remote sensing
data, to identify the degree of the freeze injury is very
difficult.
3. Mirza M. M. Q., Climate change and extreme weather events:
can developing countries adapt? Climate Policy, 3(3), 233–248
(2003)
4. Tai F. J., Yuan Z. L., Wu X. L., Zhao P. F., Hu X. L. and Wang
W., Identification of membrane proteins in maize leaves, altered
in expression under drought stress through polyethylene glycol
treatment, Plantomics, 4(5), 250-256 (2011)
5. Wang C. Y., Lou X. R. and Wang J. L., Influence of agricultural
meteorological disaster on output of crop in China, Journal of
natural disaster, 16(5), 37-43 (2007)
2) Because affecting factors of wheat yield also include the
development impact of the latter part of the pests and
diseases, so to evaluate the freeze injury affecting yield loss
should consider the impact of pests and diseases of late
stages. And the plots of farmland micro-environment also
can affect the growth of wheat, thus affecting the yield.
6. Jiao X. F. et al, Monitoring crop yield using NOAA/AVHRR
based on vegetation indices, Transactions of the CSAE, 21(4),
104-108 (2005)
7. Kerdiles H., Grondona M., Rodriguez R. and Seguin B., Frost
mapping using NOAA AVHRR data in the pampean region,
Agintina Agricultural and Forest Meteorology, 79(3), 157-182
(1996)
3) The premise of the freeze injury yield loss assessment is
multi-temporal remote sensing technology. The spatial
difference of crop growth and yield could lead to
production management differences. The early predict yield
model was built by using the remote sensing data after
freeze injury combined with remote sensing data after
freeze injury recover data, it can reduce the influence of the
remote sensing evaluation of freeze injury yield losses
caused by crop different management. So it achieves
8. Zhang X. F. et al, Spatial-temporal distribution and multipletemporal scale variation analyses of winter wheat late freezing
injury, Acta Meteorologica Sinica, 67(2), 321-330 (2009)
5
Disaster Advances
Vol. 7 (12) December 2014
9. Wang H. F. et al, Monitoring freeze injury and growth recovery
of winter wheat based on change vector analysis, Transactions of
the CSAE, 27(11), 145-150 (2011)
14. Labus M. P., Nielsen G. A., Lawrence R. L., Engel R. and
Long D. S., Wheat yield estimates using muti-temporal NDVI
satellite imagery, International journal of remote sensing, 23(20),
4169-4180 (2002)
10. Chen S. R., Sun H. and Zhang B. J., Application research and
implementation of drought monitoring by HJ-1A/1B satellites,
Spacecraft Engineering, 18(6), 138–141 (2009)
15. Kastens J. H., Kastens T. L., Kastens D. L. A., Price K. P.,
Martinko E. A. and Lee R. Y., Image masking for crop yield
forecasting using AVHRR NDVI time series imagery, Remote
Sensing of Environment, 99(3), 341-356 (2005)
11. Wang Q., Wu C. Q. and Li Q., Environment Satellite 1 and its
application in environmental monitoring, Journal of Remote
Sensing, 1(14),102–112 (2010)
16. Moriondo M., Maselli F. and Bindi M., A simple model of
regional wheat yield based on NDVI data, European Journal of
Agronomy, 26(3), 266-274 (2007)
12. Wang Z. T., Li Q., Tao J. H., Li S. S., Wang Q. and Chen L. F.,
Monitoring of aerosol optical depth over land surface using CCD
camera on HJ-1 satellite, China Environmental Science, 29(9),
902–907 (2009)
17. Ren J. Q., Chen Z. X., Zhou Q. B. and Tang H. J., Regional
yield estimation for winter wheat with MODIS-NDVI data in
Shandong, China, International Journal of Applied Earth
Observation and Geo-information, 10(4), 403-413 (2007).
13. Rouse J. W., Haas R. H., Schell J. A. and Deering D. W.,
Monitoring vegetation systems in the Great Plains with ERTS,
Paper presented at the proceedings of the third earth resources
technology satellite-1 symposium, Goddard Space Flight Center,
Washington, D.C., December 1973 (1974)
(Received 20th February 2014, accepted 10th April 2014)
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