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 *[email protected] 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 1 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 3 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. 4 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. 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