农作物种植结构遥感提取研究进展

doi:10.3864/j.issn.0578-1752.2015.10.004

Abstract

Abstract: Mapping crop patterns with remote sensing is of great implications for agricultural production, food security and agricultural sustainability. In this paper, the theoretical basis behind the mapping was summarized, mapping methods were classified into several categories, characteristics and applicabilities of different mapping methods in the latest decade were discussed intensively, and some important directions and priorities for future studies were proposed. Currently, spectral, temporal and spatial features are the major theoretical bases for crop pattern mapping. The mapping method based on single imagery is characterized by its simple implementation, but with difficulty of capturing imagery at the best time for distinguishing different crops. Instead, the mapping method based on time-series of imagery can make full use of temporal features and is thus widely used for crop mapping, among which the methods using multiple features are more suitable than the ones using a single feature for regions with complicated planting structure. To some extent, feature-oriented statistical modeling method can resolve the mixed-pixel problem but its robustness needs to be improved. Furthermore, large-scale crop pattern mapping can be done by combining the remote sensing and agriculture statistics. However, due to coarse resolution, the derived maps show poor region suitability. Future crop pattern mapping should target at developing “a map of crops”, the emphasis must be put on covering more crop types, enlarging the mapping areas, utilizing the superiority of blending multi-source data, strengthening the data preprocessing, optimizing the feature extraction and classifier selection, and improving the temporal and spatial scales of crop pattern mapping so as to better meet the needs of multi-faceted agricultural applications.

Key words: crop pattern mapping, remote sensing, classification, methods

HU Qiong, WU Wen-bin, SONG Qian, YU Qiang-yi, YANG Peng, TANG Hua-jun. Recent Progresses in Research of Crop Patterns Mapping by Using Remote Sensing[J].Scientia Agricultura Sinica, 2015, 48(10): 1900-1914.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2015.10.004

https://www.chinaagrisci.com/EN/Y2015/V48/I10/1900

References

[1] 唐华俊, 吴文斌, 杨鹏, 周清波, 陈仲新. 农作物空间格局遥感监测研究进展. 中国农业科学, 2010, 43(14): 2879-2888.

Tang H J, Wu W B, Yang P, Zhou Q B, Chen Z X. Recent progresses in monitoring crop spatial patterns by using remote sensing technologies. Scientia Agricultura Sinica,2010, 43(14): 2879-2888. (in Chinese)

[2] Ozdogan M. The spatial distribution of crop types from MODIS data: temporal unmixing using independent component analysis. Remote Sensing of Environment, 2010, 114(6): 1190-1204.

[3] 马丽, 顾晓鹤, 徐新刚, 黄文江, 贾建华. 地块数据支持下的玉米种植面积遥感测量方法. 农业工程学报, 2009, 25(8): 147-151.

Ma L, Gu X H, Xu X G, Huang W J, Jia J H. Remote sensing measurement of corn planting area based on field-data. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(8): 147-151. (in Chinese)

[4] Wardlow B D, Egbert S L, Kastens J H. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 2007, 108(3): 290-310.

[5] Wardlow B D, Egbert S L. Large-area crop mapping using time-series MODIS 250 m NDVI data: an assessment for the US Central Great Plains. Remote Sensing of Environment, 2008, 112(3): 1096-1116.

[6] Wu W, Shibasaki R, Yang P, Tan G, Matsumura K, Sugimoto K. Global-scale modelling of future changes in sown areas of major crops. Ecological Modelling, 2007, 208(2): 378-390.

[7] Vintrou E, Desbrosse A, Bégué A, Traoré S, Baron C, Lo Seen D. Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products. International Journal of Applied Earth Observation and Geoinformation, 2012, 14(1): 83-93.

[8] 马丽. 多源信息复合的SVM混合地块分解法提取玉米种植面积[D]. 西安: 西安科技大学, 2009.

Ma L. Extracting corn planting area by multi-source data with SVM mixed-field decomposed method[D]. Xian: Xian University of Technology, 2009. (in Chinese)

[9] Xiao X, Boles S, Frolking S, Li C, Babu J Y, Salas W, Moore Iii B. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 2006, 100(1): 95-113.

[10] 孙华生. 利用多时相MODIS数据提取中国水稻种植面积和长势信息[D]. 杭州: 浙江大学, 2009.

Sun H S. Extracting planting area and growth information of paddy rice using multi-temporal MODIS data in China [D]. Hangzhou: Zhejiang University, 2009. (in Chinese)

[11] 黄青, 唐华俊, 周清波, 吴文斌, 王利民, 张莉. 东北地区主要作物种植结构遥感提取及长势监测. 农业工程学报, 2010, 26(09): 218-223.

Huang Q, Tang H J, Zhou Q B, Wu W B, Wang L M, Zhang L. Remote-sensing based monitoring of planting structure and growth condition of major crops in Northeast China. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(9): 218-223. (in Chinese)

[12] 陈水森, 柳钦火, 陈良富, 李静, 刘强. 粮食作物播种面积遥感监测研究进展. 农业工程学报, 2005, 21(6): 166-171.

Cheng S S, Liu Q H, Chen L F, Li J, Liu Q. Review of research advances in remote sensing monitoring of grain crop area. Transactions of the Chinese Society of Agricultural Engineering, 2005, 21(6): 166-171. (in Chinese)

[13] Mariotto I, Thenkabail P S, Huete A, Slonecker E T, Platonov A. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sensing of Environment, 2013, 139: 291-305.

[14] Turker M, Ozdarici A. Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study. International Journal of Remote Sensing, 2011, 32(24): 9735-9768.

[15] Blaes X, Holecz F, Van Leeuwen H, Defourny P. Regional crop monitoring and discrimination based on simulated ENVISAT ASAR wide swath mode images. International Journal of Remote Sensing, 2007, 28(2): 371-393.

[16] Boryan C, Craig M. Multiresolution Landsat TM and AWiFS sensor assessment for crop area estimation in Nebraska. Proceedings from Pecora, 2005, 16: 22-27.

[17] 李静, 柳钦火, 刘强, 陈良富, 柏军华, 李少昆. 基于波谱知识的CBERS-02卫星遥感图像棉花像元识别方法研究. 中国科学E辑: 信息科学, 2005, 35(增刊): 141-155.

Li J, Liu Q H, Liu Q, Chen L F, Bai J H, Li S K. Method research of pixel discrimination about CBERS-02 satellite image based on spectral knowledge. Science in China Series E: Information Science, 2005, 35(Suppl.): 141-155. (in Chinese)

[18] Van Niel T G, Mcvicar T R. Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia. Computers and Electronics in Agriculture, 2004, 45(1): 91-108.

[19] Hartfield K A, Marsh S E, Kirk C D, Carrière Y. Contemporary and historical classification of crop types in Arizona. International Journal of Remote Sensing, 2013, 34(17): 6024-6036.

[20] Peña-Barragán J M, Ngugi M K, Plant R E, Six J. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 2011, 115(6): 1301-1316.

[21] Arvor D, Jonathan M, Meirelles M S P, Dubreuil V, Durieux L. Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil. International Journal of Remote Sensing, 2011, 32(22): 7847-7871.

[22] Yu Q, Gong P, Clinton N, Biging G, Kelly M, Schirokauer D. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing, 2006, 72(7): 799-811.

[23] 贾坤, 李强子. 农作物遥感分类特征变量选择研究现状与展望. 资源科学, 2013, 35(12): 2507-2516.

Jia K, Li Q Z. Review of features selection in crop classification using remote sensing data. Resource Science, 2013, 35(12): 2507-2516. (in Chinese)

[24] Patil A, Ishwarappa R K. Classification of crops using FCM segmentation and texture, color feature. World Journal of Science and Technology, 2013, 2(10): 371-376.

[25] 郑长春, 王秀珍, 黄敬峰. 基于特征波段的SPOT-5卫星影像水稻面积信息自动提取的方法研究. 遥感技术与应用, 2008, 23(3): 294-299.

Zheng C C, Wang X Z, Huang J F. Decision tree algorithm of automatically extracting paddy rice information from SPOT-5 images based on characteristic bands. Remote Sensing Technology and Application, 2008, 23(3): 294-299. (in Chinese)

[26] Mathur A, Foody G M. Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 2008, 29(8): 2227-2240.

[27] Jia K, Wu B, Li Q. Crop classification using HJ satellite multispectral data in the North China Plain. Journal of Applied Remote Sensing, 2013, 7(1): 73576.

[28] 贾坤, 李强子, 田亦陈, 吴炳方, 张飞飞, 蒙继华. 微波后向散射数据改进农作物光谱分类精度研究. 光谱学与光谱分析, 2011, 31(2): 483-487.

Jia K, Li Q Z, Tian Y C, Wu B F, Zhang F F, Meng J H. Accuracy improvement of spectral classification of crop using microwave backscatter data. Spectroscopy and Spectral Analysis, 2011, 31(2): 483-487. (in Chinese)

[29] 彭光雄, 宫阿都, 崔伟宏, 明涛, 陈锋锐. 多时相影像的典型区农作物识别分类方法对比研究. 地球信息科学学报, 2009, 11(2): 225-230.

Peng G X, Gong A D, Cui W H, Ming T, Chen F R. Study on methods comparison of typical remote sensing classification based on multi-temporal images. Journal of Geo-Information Science, 2009, 11(2): 225-230. (in Chinese)

[30] Yang C, Everitt J H, Murden D. Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 2011, 75(2): 347-354.

[31] 张喜旺, 秦耀辰, 秦奋. 综合季相节律和特征光谱的冬小麦种植面积遥感估算. 农业工程学报, 2013, 29(8): 154-163.

Zhang X W, Qin Y C, Qin F. Remote sensing estimation of planting area for winter wheat by integrating seasonal rhythms and spectral characteristics. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(8): 154-163. (in Chinese)

[32] Simonneaux V, Duchemin B, Helson D, Er-Raki S, Olioso A, Chehbouni A G. The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco. International Journal of Remote Sensing, 2008, 29(1): 95-116.

[33] Xiao X, Boles S, Liu J, Zhuang D, Frolking S, Li C, Salas W, Moore III B. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 2005, 95(4): 480-492.

[34] 张霞, 焦全军, 张兵, 陈正超. 利用MODIS_EVI图像时间序列提取作物种植模式初探. 农业工程学报, 2008(05): 161-165.

Zhang X, Jiao Q J, Zhang B, Chen Z C. Preliminary study on cropping pattern mapping using MODIS_EVI image time series. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(5): 161-165. (in Chinese)

[35] 张健康, 程彦培, 张发旺, 岳德鹏, 郭晓晓, 董华, 王计平, 唐宏方. 基于多时相遥感影像的作物种植信息提取. 农业工程学报, 2012, 28(2): 134-141.

Zhang J K, Cheng Y P, Zhang F W, Yue D P, Guo X X, Dong H, Wang J P, Tang H F. Crops planting information extraction based on multi-temporal remote sensing images. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(2): 134-141. (in Chinese)

[36] Foerster S, Kaden K, Foerster M, Itzerott S. Crop type mapping using spectral-temporal profiles and phenological information. Computers and Electronics in Agriculture, 2012, 89: 30-40.

[37] 何馨. 基于多源数据融合的玉米种植面积遥感提取研究[D]. 南京: 南京信息工程大学, 2010.

He X. Study of extracting corn planting area using the fusion technologies of multi-source remote sensing data[D]. Nanjing: Nanjing University of Information Science & Technology, 2010. (in Chinese)

[38] 蔡学良, 崔远来. 基于异源多时相遥感数据提取灌区作物种植结构. 农业工程学报, 2009, 25(8): 124-130.

Cai X L, Cui Y L. Crop planting structure extraction in irrigated areas from multi-sensor and multi-temporal remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(8): 124-130. (in Chinese)

[39] Mingwei Z, Qingbo Z, Zhongxin C, Jia L, Yong Z, Chongfa C. Crop discrimination in northern China with double cropping systems using Fourier analysis of time-series MODIS data. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(4): 476-485.

[40] 熊勤学, 黄敬峰. 利用NDVI指数时序特征监测秋收作物种植面积. 农业工程学报, 2009, 25(1): 144-148.

Xiong Q X, Huang J F. Estimation of autumn harvest crop planting area based on NDVI sequential characteristics. Transactions of the Chinese Society of Agricultural Engineering, 2009, 25(1): 144-148. (in Chinese)

[41] 郝卫平, 梅旭荣, 蔡学良, 杜建涛, 刘勤. 基于多时相遥感影像的东北三省作物分布信息提取. 农业工程学报, 2011, 27(1): 201-207.

Hao W P, Mei X R, Cai X L, Du J T, Liu Q. Crop planting extraction based on multi-temporal remote sensing data in Northeast China. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(1): 201-207. (in Chinese)

[42] 李鑫川, 徐新刚, 王纪华, 武洪峰, 金秀良, 李存军, 鲍艳松. 基于时间序列环境卫星影像的作物分类识别. 农业工程学报, 2013, 29(2): 169-176.

Li X C, Xu X G, Wang J H, Wu H F, Jin X L, Li C J, Bao Y S. Crop classification recognition based on time-series images from HJ satellite. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(2): 169-176. (in Chinese )

[43] 林文鹏, 王长耀, 储德平, 牛铮, 钱永兰. 基于光谱特征分析的主要秋季作物类型提取研究. 农业工程学报, 2006, 22(9): 128-132.

Lin W P, Wang C Y, Chu D P, Niu Z, Qian Y L. Extraction of fall crop types based on spectral analysis. Transactions of the Chinese Society of Agricultural Engineering, 2006, 22(9): 128-132. (in Chinese )

[44] 郝鹏宇, 牛铮, 王力, 王秀兰, 王长耀. 基于历史时序植被指数库的多源数据作物面积自动提取方法. 农业工程学报, 2012, 28(23): 123-131.

Hao P Y, Niu Z, Wang L，Wang X L, Wang C Y. Multi-source automatic crop pattern mapping based on historical vegetation index profiles. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(23): 123-131. (in Chinese )

[45] Boryan C, Yang Z, Mueller R, Craig M. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto International, 2011, 26(5): 341-358.

[46] Zhong L, Gong P, Biging G S. Efficient corn and soybean mapping with temporal extendability: a multi-year experiment using Landsat imagery. Remote Sensing of Environment, 2013, 140: 1-13.

[47] Chang J, Hansen M C, Pittman K, Carroll M, Dimiceli C. Corn and soybean mapping in the United States using MODIS time-series data sets. Agronomy Journal, 2007, 99(6): 1654-1664.

[48] Biradar C M, Thenkabail P S, Noojipady P, Li Y, Dheeravath V, Turral H, Velpuri M, Gumma M K, Gangalakunta O R P, Cai X L, Xiao X, Schull M A, Alankara R D, Gunasinghe S, Mohideen S. A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. International Journal of Applied Earth Observation and Geoinformation, 2009, 11(2): 114-129.

[49] Thenkabail P S, Biradar C M, Noojipady P, Dheeravath V, Li Y, Velpuri M, Gumma M, Gangalakunta O R P, Turral H, Cai X, Vithanage J, Schull M A, Dutta R. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. International Journal of Remote Sensing, 2009, 30(14): 3679-3733.

[50] 王久玲, 黄进良, 王立辉, 胡砚霞, 韩鹏鹏, 黄维. 面向对象的多时相HJ星影像甘蔗识别方法. 农业工程学报, 2014,30(11): 145-151.

Wang J L, Huang J L, Wang L H, Hu Y X, Han P P, Huang W. Identification of sugarcane based on object-oriented analysis using time-series HJ CCD data. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(11): 145-151. (in Chinese)

[51] Pan Y, Li L, Zhang J, Liang S, Zhu X, Sulla-Menashe D. Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sensing of Environment, 2012, 119: 232-242.

[52] Lobell D B, Asner G P. Cropland distributions from temporal unmixing of MODIS data. Remote Sensing of Environment, 2004, 93(3): 412-422.

[53] Atzberger C, Rembold F. Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR NDVI time series and neural nets. Remote Sensing, 2013, 5(3): 1335-1354.

[54] Verbeiren S, Eerens H, Piccard I, Bauwens I, Van Orshoven J. Sub-pixel classification of SPOT-VEGETATION time series for the assessment of regional crop areas in Belgium. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(4): 486-497.

[55] 唐鹏钦, 杨鹏, 陈仲新, 李正国, 游良志, 刘珍环, 吴文斌, 姚艳敏. 利用交叉信息熵模拟东北地区水稻种植面积空间分布. 农业工程学报, 2013, 29(17): 96-104.

Tang P Q, Yang P, Chen Z X, Li Z G, You L Z, Liu Z H, Wu W B, Yao Y M. Using cross-entropy method simulate spatial distribution of rice in Northeast China. Transactions of the Chinese Society of Agricultural Engineering,2013, 29(17): 96-104. (in Chinese )

[56] You L, Wood S. An entropy approach to spatial disaggregation of agricultural production. Agricultural Systems, 2006, 90(13): 329-347.

[57] Leff B, Ramankutty N, Foley J A. Geographic distribution of major crops across the world. Gobal Biogeochemical Cycles, 2004, 18: 1-27.

[58] Ramankutty N, Foley J A. Characterizing patterns of global land use: an analysis of global croplands data. Global Biogeochemical Cycles, 1998, 12(4): 667-685.

[59] Monfreda C, Ramankutty N, Foley J A. Farming the planet: 2. geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles, 2008, 22(1), doi: 10.1029/2007GB002947.

[60] Portmann F T, Siebert S, Döll P. MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: a new high- resolution data set for agricultural and hydrological modeling. Global Biogeochemical Cycles, 2010, 24(1), doi:10.1029/2008GB003435.

[61] You L, Wood S, Wood-Sichra U. Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agricultural Systems, 2009, 99(2/3): 126-140.

[62] Tan J, Yang P, Liu Z, Wu W, Zhang L, Li Z, You L, Tang H, Li Z. Spatio-temporal dynamics of maize cropping system in Northeast China between 1980 and 2010 by using spatial production allocation model. Journal of Geographical Sciences, 2014, 24(3): 397-410.

[63] Fischer G, Nachtergaele F, Prieler S, Teixeira E, Toth G, van Velthuizen H, Verelst L, Wiberg D. Global Agro-Ecological Zones (GAEZ v3.0) Model Documentation. International Institute for Applied systems Analysis (IIASA), Laxenburg. Rome, Italy: Austria and the Food and Agriculture Organization of the United Nations (FAO), 2012.

[64] Wu B, Li Q. Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes. International Journal of Applied Earth Observation and Geoinformation, 2012, 16: 101-112.

[65] Carrão H, Gonçalves P, Caetano M. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sensing of Environment, 2008, 112(3): 986-997.

[66] 杨鹏, 吴文斌, 周清波, 查燕. 基于光谱反射信息的作物单产估测模型研究进展. 农业工程学报, 2008, 24(10): 262-268.

Yang P, Wu W B, Zhou Q B, Zha Y. Research progress in crop yield estimation models based on spectral reflectance data. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(10): 262-268. (in Chinese )

[67] Low F, Michel U, Dech S, Conrad C. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 85: 102-119.

[68] Somers B, Asner G P. Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests. Remote Sensing of Environment, 2013, 136: 14-27.

[69] Du P, Xia J, Zhang W, Tan K, Liu Y, Liu S. Multiple classifier system for remote sensing image classification: a review. Sensors, 2012, 12(4): 4764.

[70] Shao Y, Lunetta R S. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 70: 78-87.

[71] 余强毅, 吴文斌, 唐华俊, 杨鹏, 陈仲新, 陈佑启. 复杂系统理论与Agent模型在土地变化科学中的研究进展. 地理学报, 2011, 66(11): 1518-1530.

Yu Q Y, Wu W B, Tang H J, Yang P, Chen Z X, Chen Y Q. Complex system theory and agent-based modeling: progresses in land change science. Acta Geographic Sinica, 2011, 66(11): 1518-1530. (in Chinese)

[72] Zhong L, Hawkins T, Biging G, Gong P. A phenology-based approach to map crop types in the San Joaquin Valley, California. International Journal of Remote Sensing, 2011, 32(22): 7777-7804.

[73] Wu W, Yu Q, Peter V H, You L, Yang P, Tang H. How could agricultural land systems contribute to raise food production under global change? Journal of Integrative Agriculture, 2014, 13(7): 1432-1442.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Recent Progresses in Research of Crop Patterns Mapping by Using Remote Sensing

RichHTML

PDF

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	YANG GaiQing, WANG LinFeng, LI WenQing, ZHU HeShui, FU Tong, LIAN HongXia, ZHANG LiYang, TENG ZhanWei, ZHANG LiJie, REN Hong, XU XinYing, LIU XinHe, WEI YuXuan, GAO TengYun. Study on Milk Quality Based on Circadian Rhythm [J]. Scientia Agricultura Sinica, 2023, 56(2): 379-390.
[2]	FENG ZiHeng,SONG Li,ZHANG ShaoHua,JING YuHang,DUAN JianZhao,HE Li,YIN Fei,FENG Wei. Wheat Powdery Mildew Monitoring Based on Information Fusion of Multi-Spectral and Thermal Infrared Images Acquired with an Unmanned Aerial Vehicle [J]. Scientia Agricultura Sinica, 2022, 55(5): 890-906.
[3]	LU Xiang, GAO Yuan, WANG Kun, SUN SiMiao, LI LianWen, LI HaiFei, LI QingShan, FENG JianRong, WANG DaJiang. Analysis of Aroma Characteristics in Different Cultivated Apple Strains [J]. Scientia Agricultura Sinica, 2022, 55(3): 543-557.
[4]	HUANG Chong,HOU XiangJun. Crop Classification with Time Series Remote Sensing Based on Bi-LSTM Model [J]. Scientia Agricultura Sinica, 2022, 55(21): 4144-4157.
[5]	Yue GE,DeQuan ZHANG,ShaoBo LI,Li CHEN,XiaoChun ZHENG,Ce LIANG,TongJing YAN,JinHuo LI,ZhenYu WANG. Eating Quality Evaluation of Lamb in Different Postmortem Phases Based on Consumers’ Sensory Preferences [J]. Scientia Agricultura Sinica, 2022, 55(18): 3640-3651.
[6]	CHEN XueSen,WANG Nan,ZHANG ZongYing,MAO ZhiQuan,YIN ChengMiao. Understanding and Thinking About Some Problems of Fruit Tree Germplasm Resources and Genetic Breeding [J]. Scientia Agricultura Sinica, 2022, 55(17): 3395-3410.
[7]	GUO Can,YUE XiaoFeng,BAI YiZhen,ZHANG LiangXiao,ZHANG Qi,LI PeiWu. Research on the Application of a Balanced Sampling-Random Forest Early Warning Model for Aflatoxin Risk in Peanut [J]. Scientia Agricultura Sinica, 2022, 55(17): 3426-3436.
[8]	SHEN Zhe,ZHANG RenLian,LONG HuaiYu,XU AiGuo. Research on Spatial Distribution of Soil Texture in Southern Ningxia Based on Machine Learning [J]. Scientia Agricultura Sinica, 2022, 55(15): 2961-2972.
[9]	FENG JunJie,ZHAO WenDa,ZHANG XinQuan,LIU YingJie,YUAN Shuai,DONG ZhiXiao,XIONG Yi,XIONG YanLi,LING Yao,MA Xiao. DUS Traits Variation Analysis and Application of Standard Varieties of Lolium multiflorum Introduced from Japan [J]. Scientia Agricultura Sinica, 2022, 55(12): 2447-2460.
[10]	ZHAO Shan,ZHONG LingLi,QIN Lin,HUANG ShiQun,LI Xi,ZHENG XingGuo,LEI XinYu,LEI ShaoRong,GUO LingAn,FENG JunYan. Effects of Different Drying Methods on Functional Components and Antioxidant Activity in Sweet Potato Leaves [J]. Scientia Agricultura Sinica, 2021, 54(21): 4650-4663.
[11]	MI XiaoTian,SHI Lei,HE Gang,WANG ZhaoHui. Fertilizer Reduction Potential and Economic Benefits of Crop Production for Smallholder Farmers in Shaanxi Province [J]. Scientia Agricultura Sinica, 2021, 54(20): 4370-4384.
[12]	HUANG YuQing,SUN YanYan,LUO RongZheng,Awangcuomu ,LU SuWen,FAN XiuCai,WANG Chen,LIU ChongHuai,FANG JingGui. A New Classification Standard for Different Grape Cluster Shapes and Investigation on Cluster Shape Dynamic Development Process [J]. Scientia Agricultura Sinica, 2021, 54(11): 2389-2405.
[13]	LI MeiXuan,ZHU XiCun,BAI XueYuan,PENG YuFeng,TIAN ZhongYu,JIANG YuanMao. Remote Sensing Inversion of Nitrogen Content in Apple Canopy Based on Shadow Removal in UAV Multi-Spectral Remote Sensing Images [J]. Scientia Agricultura Sinica, 2021, 54(10): 2084-2094.
[14]	FengZhi SHI,RuiYan WANG,YuHuan LI,Hong YAN,XiaoXin ZHANG. LAI Estimation Based on Multi-Spectral Remote Sensing of UAV and Its Application in Saline Soil Improvement [J]. Scientia Agricultura Sinica, 2020, 53(9): 1795-1805.
[15]	ZHAO Jing,LI ZhiMing,LU LiQun,JIA Peng,YANG HuanBo,LAN YuBin. Weed Identification in Maize Field Based on Multi-Spectral Remote Sensing of Unmanned Aerial Vehicle [J]. Scientia Agricultura Sinica, 2020, 53(8): 1545-1555.