Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data

Abstract

Abstract:The winter oilseed rape (Brassica napus L.) accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010-2011 growing season. The normalized difference vegetation index (NDVI) was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury: terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree (P<0.01, two-tailed). The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south-and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature (LST) of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

“…Combined with indicators of a potential agricultural disaster such as extreme temperature, these data can improve the ability to predict the development and spatial distribution of damage caused by cold [5,46], freezing [7] or high temperatures [6]. In addition, the growth degree days (GDD), an important indicator for the cropping system in a region, can be calculated with high spatial-temporal daily mean air temperature data.…”

“…Air temperature is an important parameter of the climate system and useful for a wide range of agriculture applications, including crop growth simulation [1,2], yield prediction [3,4], estimation of heat accumulation during the growing season [5], assessment of high-temperature damage [6], evaluation of crop freeze injury [7,8], and crop insect development prediction [9]. Currently, near-surface temperature data is collected by meteorological stations, and although such measurements offer the advantage of high accuracy and temporal resolution, their spatial resolution may be low and they may not adequately represent surface temperatures in areas with rugged or heterogeneous surfaces [10].These limitations can bias estimates of the spatial distribution of air temperature, even when researchers use advanced spatial interpolation methods [11].With the development of remote sensing technology, it has become possible to use thermal images from satellites to obtain land surface temperatures (LSTs) over wide areas, and this data can be used to instantaneously estimate spatially contiguous air temperatures [12][13][14][15].…”

A Regional Mapping Method for Oilseed Rape Based on HSV Transformation and Spectral Features

“…In addition to the improvement of accuracy and robustness, the unsupervised CSRA method greatly improved the mapping efficiency due to its automatic ability. OR is one of the most important winter crops of China, especially in the Yangtze River Basin [11,16,17,48]. Compared with traditional methods, monitoring the spatial and temporal distributions of OR at the large-regional scale by satellite remote sensing can provide detailed spatial distributions and near real-time information, which is significant for agricultural agencies to monitor crop planting patterns, maintaining cropland utilization and soil fertility, gaining sufficient food, etc.…”

“…She et al [10] found the special movement of the red edge of OR from its original position towards the blue band direction during the flowering period to the pods period and applied to extract OR based on Hyperion imagery. However, the shortages of small spatial coverage, as well as expensive cost of hyperspectral images, limit its application on regional OR mapping.The second category mainly applies supervised classification methods on multispectral images during the flowering period to identify and extract OR at the local scale, since the flowering period is the best phenology stage of identifying OR from other crops [11]. As a member of the Brassicaceae family, OR appears as bright-yellow flowers lasting 30 days (approximately a quarter of its entire growing season) [3,12], which leads to a large difference on the reflectance at green, red, and near-infrared bands when compared with other crop species during the same period because of the radiation reflected by the flower petals [13][14][15].…”

“…As a member of the Brassicaceae family, OR appears as bright-yellow flowers lasting 30 days (approximately a quarter of its entire growing season) [3,12], which leads to a large difference on the reflectance at green, red, and near-infrared bands when compared with other crop species during the same period because of the radiation reflected by the flower petals [13][14][15]. She et al [11] introduced the effectiveness of identifying OR from other crops during its flowering phase and the difficulty in other growing stages. Li et al [16] found the best phase for OR identification in Shou County, Anhui Province, China is early April, which is the full blooming stage (FBS) of OR in Anhui Province.…”