Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-25T04:43:09.114Z Has data issue: false hasContentIssue false

Study on the monitoring and classification of winter wheat freezing injury in spring based on 3S technology

Published online by Cambridge University Press:  08 March 2022

Z. G. Wang
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
H. Q. Wang
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
M. C. Feng*
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
M. X. Qin
Affiliation:
College of Resources and Environment, Shanxi Agricultural University, Taigu 030801, China
X. R. Zhang
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
Y. K. Xie
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
C. Wang
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
W. D. Yang
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
L. J. Xiao
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
M. J. Zhang
Affiliation:
Dryland Farming Engineer Institute, Shanxi Agricultural University, Taigu 030801, China
*
Author for correspondence: M. C. Feng, E-mail: fmc101@163.com

Abstract

Frequent freezing injury greatly influences winter wheat production; thus, effective prevention and a command of agricultural production are vital. The freezing injury monitoring method integrated with ‘3S’ (geographic information systems (GIS), global positioning system (GPS) and remote sensing (RS)) technology has an unparalleled advantage. Using HuanJing (HJ)-1A/1B satellite images of a winter wheat field in Shanxi Province, China plus a field survey, crop types and winter wheat planting area were identified through repeated visual interpretations of image information and spatial analyses conducted in GIS. Six vegetation indices were extracted from processed HJ-1A/1B satellite images to determine whether the winter wheat suffered from freezing injury and its degree of severity and recovery, using change vector analysis (CVA), the freeze injury representative vegetation index and the combination of the two methods, respectively. Accuracy of the freezing damage classification results was verified by determining the impact of freezing damage on yield and quantitative analysis. The CVA and the change of normalized difference vegetation index (ΔNDVI) monitoring results were different so a comprehensive analysis of the combination of CVA and ΔNDVI was performed. The area with serious freezing injury covered 0.9% of the total study area, followed by the area of no freezing injury (3.5%), moderate freezing injury (10.2%) and light freezing injury (85.4%). Of the moderate and serious freezing injury areas, 0.2% did not recover; 1.2% of the no freezing injury and light freezing injury areas showed optimal recovery, 15.6% of the light freezing injury and moderate freezing injury areas showed poor recovery, and the remaining areas exhibited general recovery.

Type
Climate Change and Agriculture Research Paper
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, LG, Hanson, DJ and Haas, HR (1993) Evaluating land sat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Elsevier 45, 165175. http://dx.doi.org/10.1016/0034-4257(93)90040-5.Google Scholar
Bascietto, M, Cinti, BD, Matteucci, G and Cescatti, A (2012) Biometric assessment of aboveground carbon pools and fluxes in three European forests by Randomized Branch Sampling. Forest Ecology & Management 267, 172181.CrossRefGoogle Scholar
Cao, G, Zhao, Y, Duan, X and Cao, X (2018) Spatial distribution of throughfall of evergreen broad-leaved forest in Mopan Mountain based on Kriging interpolation method. Journal of Northwest Forestry University 33, 1925.Google Scholar
Chen, C (2012) Resampling Method in Geometric Correction. University of Electronic Science and Technology. http://dx.doi.org/10.7666/d.D763938.CrossRefGoogle Scholar
Crimp, SJ, Zheng, BY, Khimashia, N, Gobbett, DL and Nicholls, N (2016) Recent changes in southern Australian frost occurrence: implications for wheat production risk. Crop and Pasture Science 67, 801811.CrossRefGoogle Scholar
Cuomo, V, Lanfredi, M, Lasaponara, R, Macchiato, MF and Simoniello, T (2001) Detection of interannual variation of vegetation in middle and southern Italy during 1985–1999 with 1 Km NOAA AVHRR NDVI data. Journal of Geophysical Research 106, 1786317876.CrossRefGoogle Scholar
Dong, YS, Chen, HP, Wang, HF, Gu, XH and Wang, JH (2012) Evaluation of winter wheat freezing injury based on multi temporal environmental disaster reduction satellite data. Journal of Agricultural Engineering 28, 172179.Google Scholar
Feng, MC and Yang, WD (2010) Extraction of winter wheat planting area based on RS and selection of the best time phase. Journal of Shanxi Agricultural University (Natural Science Edition) 30, 487490.Google Scholar
Feng, MC, Yang, WD, Cao, LL and Ding, GW (2009) Monitoring winter wheat freeze injury using multi-temporal MODIS data. Agricultural Sciences in China 8, 10531062.CrossRefGoogle Scholar
Frederiks, TM, Christopher, JT, Sutherland, MW and Borrell, AK (2015) Post-head-emergence frost in wheat and barley: defining the problem, assessing the damage, and identifying resistance. Journal of Experimental Botany 66, 3487–3498. http://dx.doi.org/10.1093/jxb/erv088.CrossRefGoogle ScholarPubMed
Gitelson, AA, Kaufman, YJ and Merzlyak, MN (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58, 289298.CrossRefGoogle Scholar
Gupta, RK, Prasad, S, Sesha Sai, MVR and Viswanadham, TS (1997) The estimation of surface temperature over an agricultural area in the state of Haryana and Panjab, India, and its relationship with the Normalized Difference Vegetation Index (NDVI), using NOAA-AVHRR data. International Journal of Remote Sensing 18, 37293741.CrossRefGoogle Scholar
Huang, J, Wang, A and Zhai, S (2015) Spatial interpolation of negative skew distribution data based on Kriging method. Surveying and Mapping Engineering 24, 1619.Google Scholar
Jurgens, C (1997) The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data. International Journal of Remote Sensing 18, 35833594.CrossRefGoogle Scholar
Kerdiles, H, Grondona, M, Rodriguez, R and Seguin, B (1996) Frost mapping using NOAA AVHRR data in the Pompeian region, Argentina. Agricultural & Forest Meteorology 79, 157182.CrossRefGoogle Scholar
Kuang, ZM, Li, Q, Yao, YM and Ding, MH (2009) Application of EOS/MODIS data in monitoring and evaluation of sugarcane cold damage. Journal of Applied Meteorology 20, 360364.Google Scholar
Lacoste, C, Nansen, C, Thompson, S, Moir-Barnetson, L, Mian, A, Mcnee, M and Flower, KC (2015) Increased susceptibility to aphids of flowering wheat plants exposed to low temperatures. Environmental Entomology 44, 610618.CrossRefGoogle ScholarPubMed
Lanfredi, M (2003) Multiresolution spatial characterization of land degradation phenomena in southern Italy from 1985 to 1999 using NOAA-AVHRR NDVI data. Geophysical Research Letters 30, 10691069.CrossRefGoogle Scholar
Lindkvist, L, Gustavsson, T and Bogren, J (2000) A frost assessment method for mountainous areas. Agricultural & Forest Meteorology 102, 5167.CrossRefGoogle Scholar
Liu, HQ and Huete, A (1995) A feedback-based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience & Remote Sensing 33, 457465.CrossRefGoogle Scholar
Myneni, RB and Williams, DL (1994) On the relationship between FAPAR and NDVI. Remote Sensing of Environment 49, 200211.CrossRefGoogle Scholar
Penuelas, J, Baret, F and Filella, I (1995) Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance. Photosynthetic 31, 221230.Google Scholar
Pepin, NC, Maeda, EE and Williams, R (2016) Use of remotely-sensed land surface temperature as a proxy for air temperatures at high elevations: findings from a 5000-meter elevational transect across Kilimanjaro. Journal of Geophysical Research Atmospheres 121, 999810015.CrossRefGoogle Scholar
Ren, P, Feng, MC, Yang, WD, Wang, C, Liu, TT and Wang, HQ (2014) Response of winter wheat (Triticum aestivum L.) hyperspectral characteristics to low temperature stress. Spectroscopy & Spectral Analysis 34, 24902494.Google ScholarPubMed
Richardson, AJ and Wiegand, CL (1977) Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing 43, 15411552.Google Scholar
Rouse, JW, Haas, RW, Schell, JA and Deering, DW (1974) Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. Greenbelt, MD: NASA/GSFC Type III, Final Report. Nasa/gsfct Type Final Report. Available at https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19730009608.pdf.Google Scholar
Sun, PJ, Yang, JW, Zhang, JS, Pan, YJ and Yun, Y (2015) Remote Sensing Extraction of autumn crops based on pattern and change vector analysis. Journal of Beijing Normal University (Natural Science Edition) 51, 8994.Google Scholar
Tait, A and Zheng, XG (2003) Mapping frost occurrence using satellite data. Journal of Applied Meteorology 42, 193203.2.0.CO;2>CrossRefGoogle Scholar
Tan, ZK, Ding, MH, Wang, LH, Xin, Y and Ou, ZR (2008) Monitoring freeze injury and evaluating losing to sugar-cane using RS and GPS. Computer and Computing Technologies in Agriculture II 293, 307316.Google Scholar
Wang, CY, Wang, SL, Huo, ZG, Guo, JP and Li, J (2005) Research progress in monitoring, early warning and assessment of Major Agrometeorological Disasters in China in the past 10 years. Journal of Meteorology 63, 121133.Google Scholar
Wang, HF, Gu, XH, Dong, YY, Wang, JH, Huang, WJ, Guo, W, Wang, DC and Wang, K (2011) Vector analysis of winter wheat freezing disaster and its growth recovery. Journal of Agricultural Engineering 27, 154159.Google Scholar
Wang, HF, Gu, XH, Wang, JH and Dong, YY (2012) Monitoring winter wheat freeze injury based on multi-temporal data. Intelligent Automation & Soft Computing 18, 10351042. http://dx.doi.org/10.1080/10798587.2008.10643308.CrossRefGoogle Scholar
Wang, HF, Guo, W, Wang, JH, Huang, WJ, Gu, XH, Dong, YY and Xu, XG (2013). Journal of Integrative Agriculture 12, 11621172. http://dx.doi.org/10.1016/S2095-3119(13)60445-1.CrossRefGoogle Scholar
Wang, HF, Wang, JH, Dong, YY, Gu, XH and Huo, ZG (2014) Hyperspectral analysis of winter wheat freezing stress and inversion of freezing severity. Spectroscopy and Spectral Analysis 34, 13571361.Google Scholar
Xie, X and Chen, XY (2008) An analysis on the freezing damage caused by the low temperature in Hejing in winter, 2006. Xinjiang Agricultural Sciences 45, 269270.Google Scholar
Yang, BJ, Wang, MX and Pei, ZY (2002) Remote sensing monitoring of winter wheat freezing injury. Journal of Agricultural Engineering 18, 136140.Google Scholar
Yang, JH, Wei, JU and Yang, XJ (2011) Cause of freeze injury, cold resistance mechanism and countermeasures of winter wheat. Journal of Hebei Agricultural Sciences 15, 3336+40.Google Scholar
Zeng, LL, Wardlow, B, Tadesse, T, Shan, J, Hayes, M, Li, D and Xiang, DX (2015) Estimation of daily air temperature based on MODIS land surface temperature products over the corn belt in the US. Remote Sensing 7, 951970. http://dx.doi.org/10.3390/rs70100951.CrossRefGoogle Scholar
Zhao, LC, Li, QZ, Zhang, Y, Wang, HY and Du, X (2020) Normalized NDVI valley area index (NNVAI)-based framework for quantitative and timely monitoring of winter wheat frost damage on the Huang-Huai-Hai Plain, China. Agriculture Ecosystems & Environment 292, 106793.CrossRefGoogle Scholar
Zhu, RJ (2017) Remote Sensing Monitoring of Wheat Total Erosion Based on Change Vector Analysis. Henan Agricultural University. http://cdmd.cnki.com.cn/Article/CDMD-10466-1017281251.htm.Google Scholar