Research Progress and Challenges of Oil Crop Yield Monitoring by Remote Sensing

doi:10.12133/j.smartag.SA202303002

Abstract

Abstract:

[Significance] Oil crops play a significant role in the food supply, as well as the important source of edible vegetable oils and plant proteins. Real-time, dynamic and large-scale monitoring of oil crop growth is essential in guiding agricultural production, stabilizing markets, and maintaining health. Previous studies have made a considerable progress in the yield simulation of staple crops in regional scale based on remote sensing methods, but the yield simulation of oil crops in regional scale is still poor as its complexity of the plant traits and structural characteristics. Therefore, it is urgently needed to study regional oil crop yield estimation based on remote sensing technology. [Progress] This paper summarized the content of remote sensing technology in oil crop monitoring from three aspects: backgrounds, progressions, opportunities and challenges. Firstly, significances and advantages of using remote sensing technology to estimate the of oil crops have been expounded. It is pointed out that both parameter inversion and crop area monitoring were the vital components of yield estimation. Secondly, the current situation of oil crop monitoring was summarized based on remote sensing technology from three aspects of remote sensing parameter inversion, crop area monitoring and yield estimation. For parameter inversion, it is specified that optical remote sensors were used more than other sensors in oil crops inversion in previous studies. Then, advantages and disadvantages of the empirical model and physical model inversion methods were analyzed. In addition, advantages and disadvantages of optical and microwave data were further illustrated from the aspect of oil crops structure and traits characteristics. At last, optimal choice on the data and methods were given in oil crop parameter inversion. For crop area monitoring, this paper mainly elaborated from two parts of optical and microwave remote sensing data. Combined with the structure of oil crops and the characteristics of planting areas, the researches on area monitoring of oil crops based on different types of remote sensing data sources were reviewed, including the advantages and limitations of different data sources in area monitoring. Then, two yield estimation methods were introduced: remote sensing yield estimation and data assimilation yield estimation. The phenological period of oil crop yield estimation, remote sensing data source and modeling method were summarized. Next, data assimilation technology was introduced, and it was proposed that data assimilation technology has great potential in oil crop yield estimation, and the assimilation research of oil crops was expounded from the aspects of assimilation method and grid selection. All of them indicate that data assimilation technology could improve the accuracy of regional yield estimation of oil crops. Thirdly, this paper pointed out the opportunities of remote sensing technology in oil crop monitoring, put forward some problems and challenges in crop feature selection, spatial scale determination and remote sensing data source selection of oil crop yield, and forecasted the development trend of oil crop yield estimation research in the future. [Conclusions and Prospects] The paper puts forward the following suggestions for the three aspects: (1) Regarding crop feature selection, when estimating yields for oil crops such as rapeseed and soybeans, which have active photosynthesis in siliques or pods, relying solely on canopy leaf area index (LAI) as the assimilation state variable for crop yield estimation may result in significant underestimation of yields, thereby impacting the accuracy of regional crop yield simulation. Therefore, it is necessary to consider the crop plant characteristics and the agronomic mechanism of yield formation through siliques or pods when estimating yields for oil crops. (2) In determining the spatial scale, some oil crops are distributed in hilly and mountainous areas with mixed land cover. Using regularized yield simulation grids may result in the confusion of numerous background objects, introducing additional errors and affecting the assimilation accuracy of yield estimation. This poses a challenge to yield estimation research. Thus, it is necessary to choose appropriate methods to divide irregular unit grids and determine the optimal scale for yield estimation, thereby improving the accuracy of yield estimation. (3) In terms of remote sensing data selection, the monitoring of oil crops can be influenced by crop structure and meteorological conditions. Depending solely on spectral data monitoring may have a certain impact on yield estimation results. It is important to incorporate radar off-nadir remote sensing measurement techniques to perceive the response relationship between crop leaves and siliques or pods and remote sensing data parameters. This can bridge the gap between crop characteristics and remote sensing information for crop yield simulation. This paper can serve as a valuable reference and stimulus for further research on regional yield estimation and growth monitoring of oil crops. It supplements existing knowledge and provides insightful considerations for enhancing the accuracy and efficiency of oil crop production monitoring and management.

Key words: remote sensing, yield simulation, data assimilation, oil crops, yield monitoring, parameter inversion

MA Yujing, WU Shangrong, YANG Peng, CAO Hong, TAN Jieyang, ZHAO Rongkun. Research Progress and Challenges of Oil Crop Yield Monitoring by Remote Sensing[J]. Smart Agriculture, 2023, 5(3): 1-16.

Figures/Tables 2

References 111

1	TEH H F, NEOH B K, ITHNIN N, et al. Review: Omics and strategic yield improvement in oil crops[J]. Journal of the American oil chemists' society, 2017, 94(10): 1225-1244.
2	孙华, 余意雯, 黄萌, 等. 我国油料作物生产概况和空间集聚特征分析[J]. 江苏农业科学, 2022, 50(23): 67-74.
	SUN H, YU Y W, HUANG M, et al. Study on production situation and spatial agglomeration characteristics of oil crops in China[J]. Jiangsu agricultural sciences, 2022, 50(23): 67-74.
3	JIA Y Y, KUMAR D, WINKLER-MOSER J K, et al. Recoveries of oil and hydrolyzed sugars from corn germ meal by hydrothermal pretreatment: A model feedstock for lipid-producing energy crops[J]. Energies, 2020, 13(22): ID 6022.
4	DEMIREL C, KABUTEY A, HERÁK D, et al. Optimizing uniaxial oil extraction of bulk rapeseeds: Spectrophotometric and chemical analyses of the extracted oil under pretreatment temperatures and heating intervals[J]. Processes, 2021, 9(10): ID 1755.
5	王瑞元. 2021年我国粮油产销和进出口情况[J]. 中国油脂, 2022, 47(6): 1-7.
	WANG R Y. Introduction of grain and oil producfion, marketing, import and export in 2021 in China[J]. China oils and fats, 2022, 47(6): 1-7.
6	WANG J A, SI H P, GAO Z, et al. Winter wheat yield prediction using an LSTM model from MODIS LAI products[J]. Agriculture, 2022, 12(10): ID 1707.
7	ZHANG P P, ZHOU X X, WANG Z X, et al. Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat[J]. Scientific reports, 2020, 10(1): ID 5173.
8	JIN N, TAO B, REN W, et al. Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data[J]. Agricultural water management, 2022, 266: ID 107583.
9	BARBOUCHI M, LHISSOU R, ABDELFATTAH R, et al. The potential of using Radarsat-2 satellite image for modeling and mapping wheat yield in a semiarid environment[J]. Agriculture, 2022, 12(3): ID 315.
10	KRUPAVATHI K, RAGHUBABU M, MANI A, et al. Field-scale estimation and comparison of the sugarcane yield from remote sensing data: A machine learning approach[J]. Journal of the Indian society of remote sensing, 2022, 50(2): 299-312.
11	JOSHI V R, THORP K R, COULTER J A, et al. Improving site-specific maize yield estimation by integrating satellite multispectral data into a crop model[J]. Agronomy, 2019, 9(11): ID 719.
12	周青青, 胡永红, 段建南. 农作物遥感估产的方法综述[J]. 国土资源导刊, 2014, 11(5): 101-103.
	ZHOU Q Q, HU Y H, DUAN J N. Summary of methods of crop yield estimation by remote sensing[J]. Land & resources herald, 2014, 11(5): 101-103.
13	WEI C W, HUANG J F, MANSARAY L, et al. Estimation and mapping of winter oilseed rape LAI from high spatial resolution satellite data based on a hybrid method[J]. Remote sensing, 2017, 9(5): ID 488.
14	郭云开, 王杨. 经验模型与PROSPECT+4SAIL模型反演路域LAI比较研究[J]. 测绘与空间地理信息, 2013, 36(11): 1-5.
	GUO Y K, WANG Y. Comparative study on using the empirical model and PROSPECT + 4SAIL model for inversion LAI of road region[J]. Geomatics & spatial information technology, 2013, 36(11): 1-5.
15	ZHANG J A, WANG C F, YANG C H, et al. Assessing the effect of real spatial resolution of in situ UAV multispectral images on seedling rapeseed growth monitoring[J]. Remote sensing, 2020, 12(7): ID 1207.
16	SUN B, WANG C F, YANG C H, et al. Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter[J]. International journal of applied earth observation and geoinformation, 2021, 102: ID 102373.
17	QIU C R, LIAO G P, TANG H Y, et al. Derivative parameters of hyperspectral NDVI and its application in the inversion of rapeseed leaf area index[J]. Applied sciences, 2018, 8(8): ID 1300.
18	QI H X, ZHU B Y, KONG L X, et al. Hyperspectral inversion model of chlorophyll content in peanut leaves[J]. Applied sciences, 2020, 10(7): ID 2259.
19	ZHANG W F, CHEN E X, LI Z Y, et al. Rape (Brassica napus L.) growth monitoring and mapping based on radarsat-2 time-series data[J]. Remote sensing, 2018, 10(2): ID 206.
20	YUAN H H, YANG G J, LI C C, et al. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models[J]. Remote sensing, 2017, 9(4): ID 309.
21	QI H X, ZHU B Y, WU Z Y, et al. Estimation of peanut leaf area index from unmanned aerial vehicle multispectral images[J]. Sensors, 2020, 20(23): ID 6732.
22	MERCIER A, BETBEDER J, RAPINEL S, et al. Evaluation of Sentinel-1 and-2 time series for estimating LAI and biomass of wheat and rapeseed crop types[J]. Journal of applied remote sensing, 2020, 14(2): ID 024512.
23	GHOSH S S, DEY S, BHOGAPURAPU N, et al. Gaussian process regression model for crop biophysical parameter retrieval from multi-polarized C-band SAR data[J]. Remote sensing, 2022, 14(4): ID 934.
24	TOMÍČEK J, MIŠUREC J, LUKEŠ P. Prototyping a generic algorithm for crop parameter retrieval across the season using radiative transfer model inversion and Sentinel-2 satellite observations[J]. Remote sensing, 2021, 13(18): ID 3659.
25	LIU K, ZHOU Q B, WU W B, et al. Estimating the crop leaf area index using hyperspectral remote sensing[J]. Journal of integrative agriculture, 2016, 15(2): 475-491.
26	WANG S Q, GAO W H, MING J, et al. A TPE based inversion of PROSAIL for estimating canopy biophysical and biochemical variables of oilseed rape[J]. Computers and electronics in agriculture, 2018, 152: 350-362.
27	SUN Q, JIAO Q J, CHEN X D, et al. Machine learning algorithms for the retrieval of canopy chlorophyll content and leaf area index of crops using the PROSAIL-D model with the adjusted average leaf angle[J]. Remote sensing, 2023, 15(9): ID 2264.
28	NANDAN R, BANDARU V, HE J Y, et al. Evaluating optical remote sensing methods for estimating leaf area index for corn and soybean[J]. Remote sensing, 2022, 14(21): ID 5301.
29	LI W J, WEISS M, GARRIC B, et al. Mapping crop leaf area index and canopy chlorophyll content using UAV multispectral imagery: Impacts of illuminations and distribution of input variables[J]. Remote sensing, 2023, 15(6): ID 1539.
30	DUAN S B, LI Z L, WU H A, et al. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data[J]. International journal of applied earth observation and geoinformation, 2014, 26: 12-20.
31	王立辉, 杜军, 黄进良, 等. 基于GF-1号卫星WFV数据反演玉米叶面积指数[J]. 华中师范大学学报(自然科学版), 2016, 50(1): 120-127.
	WANG L H, DU J, HUANG J L, et al. Retrieving Leaf Area Index of maize based on GF-1 multispectral remote sensing data[J]. Journal of central China normal university (natural sciences), 2016, 50(1): 120-127.
32	李金帅. 遥感技术在农业中的应用[J]. 农业与技术, 2021, 41(11): 61-64.
	LI J S. Application of remote sensing technology in agriculture[J]. Agriculture and technology, 2021, 41(11): 61-64.
33	ALLIES A, ROUMIGUIÉ A, DEJOUX J F, et al. Evaluation of multiorbital SAR and multisensor optical data for empirical estimation of rapeseed biophysical parameters[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2021, 14: 7268-7283.
34	BAHRAMI H, HOMAYOUNI S, SAFARI A, et al. Deep learning-based estimation of crop biophysical parameters using multi-source and multi-temporal remote sensing observations[J]. Agronomy, 2021, 11(7): ID 1363.
35	王利民, 刘佳, 杨玲波, 等. 基于NDVI加权指数的冬小麦种植面积遥感监测[J]. 农业工程学报, 2016, 32(17): 127-135.
	WANG L M, LIU J, YANG L B, et al. Remote sensing monitoring winter wheat area based on weighted NDVI index[J]. Transactions of the Chinese society of agricultural engineering, 2016, 32(17): 127-135.
36	陈仲新, 任建强, 唐华俊, 等. 农业遥感研究应用进展与展望[J]. 遥感学报, 2016, 20(5): 748-767.
	CHEN Z X, REN J Q, TANG H J, et al. Progress and perspectives on agricultural remote sensing research and applications in China[J]. Journal of remote sensing, 2016, 20(5): 748-767.
37	SONG X P, POTAPOV P V, KRYLOV A, et al. National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey[J]. Remote sensing of environment, 2017, 190: 383-395.
38	SHANGGUAN Y L, LI X Y, LIN Y, et al. Mapping spatial-temporal nationwide soybean planting area in Argentina using Google Earth Engine[J]. International journal of remote sensing, 2022, 43(5): 1724-1748.
39	LI X Y, YU L, PENG D L, et al. A large-scale, long time-series (1984‒2020) of soybean mapping with phenological features: Heilongjiang province as a test case[J]. International journal of remote sensing, 2021, 42(19): 7332-7356.
40	YANG H, DENG F, FU H C, et al. Estimation of rape-cultivated area based on decision tree and mixed pixel decomposition[J]. Journal of the Indian society of remote sensing, 2021, 49(6): 1285-1292.
41	JIANG Y L, LU Z, LI S, et al. Large-scale and high-resolution crop mapping in China using Sentinel-2 satellite imagery[J]. Agriculture, 2020, 10(10): ID 433.
42	JIAO X F, KOVACS J M, SHANG J L, et al. Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data[J]. ISPRS journal of photogrammetry and remote sensing, 2014, 96: 38-46.
43	VALCARCE-DIÑEIRO R, ARIAS-PÉREZ B, LOPEZ-SANCHEZ J M, et al. Multi-temporal dual- and quad-polarimetric synthetic aperture radar data for crop-type mapping[J]. Remote sensing, 2019, 11(13): ID 1518.
44	ASAM S, GESSNER U, ALMENGOR GONZÁLEZ R, et al. Mapping crop types of Germany by combining temporal statistical metrics of Sentinel-1 and Sentinel-2 time series with LPIS data[J]. Remote sensing, 2022, 14(13): ID 2981.
45	REN T T, XU H T, CAI X M, et al. Smallholder crop type mapping and rotation monitoring in mountainous areas with Sentinel-1/2 imagery[J]. Remote sensing, 2022, 14(3): ID 566.
46	SORIA RUIZ J, FERNÁNDEZ ORDÓÑEZ Y, GRANADOS RAMÍREZ R. Methodology for prediction of corn yield using remote sensing satellite data in Central Mexico [J]. Investigaciones geográficas, 2012(55): ID 61.
47	RICHETTI J, JUDGE J, BOOTE K J, et al. Using phenology-based enhanced vegetation index and machine learning for soybean yield estimation in Paraná State, Brazil[J]. Journal of applied remote sensing, 2018, 12(2): ID 026029.
48	LI C C, MA C Y, CUI Y Q, et al. UAV hyperspectral remote sensing estimation of soybean yield based on physiological and ecological parameter and meteorological factor in China[J]. Journal of the Indian society of remote sensing, 2021, 49(4): 873-886.
49	FAN H Y, LIU S S, LI J, et al. Early prediction of the seed yield in winter oilseed rape based on the near-infrared reflectance of vegetation (NIRv)[J]. Computers and electronics in agriculture, 2021, 186: ID 106166.
50	龚龑, 肖洁, 候金雨, 等. 基于无人机遥感混合光谱分析的油菜估产模型[J]. 测绘地理信息, 2017, 42(6): 40-45.
	GONG Y, XIAO J, HOU J Y, et al. Rape yields estimation research based on spectral analysis for UAV image[J]. Journal of geomatics, 2017, 42(6): 40-45.
51	KPIENBAAREH D, MOHAMMED K, LUGINAAH I, et al. Estimating groundnut yield in smallholder agriculture systems using PlanetScope data[J]. Land, 2022, 11(10): ID 1752.
52	NARIN O G, ABDIKAN S. Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images[J]. Geocarto international, 2022, 37(5): 1378-1392.
53	ZHANG X Y, ZHAO J M, YANG G J, et al. Establishment of plot-yield prediction models in soybean breeding programs using UAV-based hyperspectral remote sensing[J]. Remote sensing, 2019, 11(23): ID 2752.
54	D'ANDRIMONT R, TAYMANS M, LEMOINE G, et al. Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series[J]. Remote sensing of environment, 2020, 239: ID 111660.
55	HAN J H, WEI C W, CHEN Y L, et al. Mapping above-ground biomass of winter oilseed rape using high spatial resolution satellite data at parcel scale under waterlogging conditions[J]. Remote sensing, 2017, 9(3): ID 238.
56	MA Y, FANG S H, PENG Y, et al. Remote estimation of biomass in winter oilseed rape (Brassica napus L.) using canopy hyperspectral data at different growth stages[J]. Applied sciences, 2019, 9(3): ID 545.
57	XIN Q C, GONG P, YU C Q, et al. A production efficiency model-based method for satellite estimates of corn and soybean yields in the Midwestern US[J]. Remote sensing, 2013, 5(11): 5926-5943.
58	ALGANCI U, OZDOGAN M, SERTEL E, et al. Estimating maize and cotton yield in southeastern Turkey with integrated use of satellite images, meteorological data and digital photographs[J]. Field crops research, 2014, 157: 8-19.
59	GASO D V, BERGER A G, CIGANDA V S. Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images[J]. Computers and electronics in agriculture, 2019, 159: 75-83.
60	SHARIFI A. Yield prediction with machine learning algorithms and satellite images[J]. Journal of the science of food and agriculture, 2021, 101(3): 891-896.
61	FERNANDEZ-BELTRAN R, BAIDAR T, KANG J A, et al. Rice-yield prediction with multi-temporal Sentinel-2 data and 3D CNN: A case study in Nepal[J]. Remote sensing, 2021, 13(7): ID 1391.
62	SIYAL AALI, DEMPEWOLF J, BECKER-RESHEF I. Rice yield estimation using Landsat ETM+ Data[J]. Journal of applied remote sensing, 2015, 9(1): ID 095986.
63	ZHUO W, HUANG J X, LI L, et al. Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation[J]. Remote sensing, 2019, 11(13): ID 1618.
64	ZHU B X, CHEN S B, CAO Y J, et al. A regional maize yield hierarchical linear model combining Landsat 8 vegetative indices and meteorological data: Case study in Jilin province[J]. Remote sensing, 2021, 13(3): ID 356.
65	贺振, 贺俊平. 基于NOAA-NDVI的河南省冬小麦遥感估产[J]. 干旱区资源与环境, 2013, 27(5): 46-52.
	HE Z, HE J P. Estimation of winter wheat yield based on the NOAA-NDVI data[J]. Journal of arid land resources and environment, 2013, 27(5): 46-52.
66	SONG X P, LI H J, POTAPOV P, et al. Annual 30 m soybean yield mapping in Brazil using long-term satellite observations, climate data and machine learning[J]. Agricultural and forest meteorology, 2022, 326: ID 109186.
67	ZAMANI-NOOR N, FEISTKORN D. Monitoring growth status of winter oilseed rape by NDVI and NDYI derived from UAV-based red-green-blue imagery[J]. Agronomy, 2022, 12(9): ID 2212.
68	HE H T, MA X D, GUAN H O, et al. Recognition of soybean pods and yield prediction based on improved deep learning model[J]. Frontiers in plant science, 2023, 13: ID 1096619.
69	PENG Y, ZHU T E, LI Y C, et al. Remote prediction of yield based on LAI estimation in oilseed rape under different planting methods and nitrogen fertilizer applications[J]. Agricultural and forest meteorology, 2019, 271: 116-125.
70	GONG Y, DUAN B, FANG S H, et al. Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis[J]. Plant methods, 2018, 14: ID 70.
71	LIU Y N, LIU S S, LI J, et al. Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images[J]. Computers and electronics in agriculture, 2019, 166: ID 105026.
72	BOGNÁR P, KERN A, PÁSZTOR S, et al. Testing the robust yield estimation method for winter wheat, corn, rapeseed, and sunflower with different vegetation indices and meteorological data[J]. Remote sensing, 2022, 14(12): ID 2860.
73	YOOSEFZADEH-NAJAFABADI M, TULPAN D, ESKANDARI M. Using hybrid artificial intelligence and evolutionary optimization algorithms for estimating soybean yield and fresh biomass using hyperspectral vegetation indices[J]. Remote sensing, 2021, 13(13): ID 2555.
74	SUN J, DI L P, SUN Z H, et al. County-level soybean yield prediction using deep CNN-LSTM model[J]. Sensors, 2019, 19(20): ID 4363.
75	YANG H, YANG G J, GAULTON R, et al. In-season biomass estimation of oilseed rape (Brassica napus L.) using fully polarimetric SAR imagery[J]. Precision agriculture, 2019, 20(3): 630-648.
76	NGUYEN L H, ROBINSON S, GALPERN P. Medium-resolution multispectral satellite imagery in precision agriculture: Mapping precision canola (Brassica napus L.) yield using Sentinel-2 time series[J]. Precision agriculture, 2022, 23(3): 1051-1071.
77	黎锐, 李存军, 徐新刚, 等. 基于支持向量回归(SVR)和多时相遥感数据的冬小麦估产[J]. 农业工程学报, 2009, 25(7): 114-117.
	LI R, LI C J, XU X G, et al. Winter wheat yield estimation based on support vector machine regression and multi-temporal remote sensing data[J]. Transactions of the Chinese society of agricultural engineering, 2009, 25(7): 114-117.
78	HERRERO-HUERTA M, RODRIGUEZ-GONZALVEZ P, RAINEY K M. Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean[J]. Plant methods, 2020, 16: ID 78.
79	MATEO-SANCHIS A, PILES M, MUÑOZ-MARÍ J, et al. Synergistic integration of optical and microwave satellite data for crop yield estimation[J]. Remote sensing of environment, 2019, 234: ID 111460.
80	PEJAK B, LUGONJA P, ANTIĆ A, et al. Soya yield prediction on a within-field scale using machine learning models trained on Sentinel-2 and soil data[J]. Remote sensing, 2022, 14(9): ID 2256.
81	SCHWALBERT R A, AMADO T, CORASSA G, et al. Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil[J]. Agricultural and forest meteorology, 2020, 284: ID 107886.
82	ABBASZADEH P, GAVAHI K, ALIPOUR A, et al. Bayesian multi-modeling of deep neural nets for probabilistic crop yield prediction [J]. Agriculture and forest meteorology, 2022, 314: ID 108773.
83	ZHOU J, ZHOU J, YE H, et al. Yield estimation of soybean breeding lines under drought stress using unmanned aerial vehicle-based imagery and convolutional neural network [J]. Biosystems engineering, 2021, 204: 90-103.
84	TEODORO P, TEODORO L P, BAIO F, et al. Predicting days to maturity, plant height, and grain yield in soybean: A machine and deep learning approach using multispectral data [J]. Remote sensing, 2021, 13(22): ID 4632.
85	REISI-GAHROUEI O, HOMAYOUNI S, MCNAIRN H, et al. Crop biomass estimation using multi regression analysis and neural networks from multitemporal L-band polarimetric synthetic aperture radar data[J]. International journal of remote sensing, 2019, 40(17): 6822-6840.
86	YU B, SHANG S H. Multi-year mapping of major crop yields in an irrigation district from high spatial and temporal resolution vegetation index[J]. Sensors, 2018, 18(11): ID 3787.
87	ZENG W Z, XU C, GANG Z, et al. Estimation of sunflower seed yield using partial least squares regression and artificial neural network models[J]. Pedosphere, 2018, 28(5): 764-774.
88	AMANKULOVA K, FARMONOV N, MUKHTOROV U, et al. Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology[J]. Geocarto international, 2023, 38(1): ID 2197509.
89	GOHAIN G B, SINGH K K, SINGH R S, et al. Application of CERES-sorghum crop simulation model DSSAT v4.7 for determining crop water stress in crop phenological stages[J]. Modeling earth systems and environment, 2022, 8(2): 1963-1975.
90	WANG Z Q, YE L, JIANG J Y, et al. Review of application of EPIC crop growth model[J]. Ecological modelling, 2022, 467: ID 109952.
91	DELIGIOS P A, FARCI R, SULAS L, et al. Predicting growth and yield of winter rapeseed in a Mediterranean environment: Model adaptation at a field scale[J]. Field crops research, 2013, 144: 100-112.
92	ALLIES A, ROUMIGUIÉ A, FIEUZAL R, et al. Assimilation of multisensor optical and multiorbital SAR satellite data in a simplified agrometeorological model for rapeseed crops monitoring[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2021, 15: 1123-1138.
93	LIAO C H, WANG J F, DONG T F, et al. Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean[J]. Science of the total environment, 2019, 650: 1707-1721.
94	TRÉPOS R, CHAMPOLIVIER L, DEJOUX J F, et al. Forecasting sunflower grain yield by assimilating leaf area index into a crop model[J]. Remote sensing, 2020, 12(22): ID 3816.
95	WU S R, REN J Q, CHEN Z X, et al. Evaluation of winter wheat yield simulation based on assimilating LAI retrieved from networked optical and SAR remotely sensed images into the WOFOST model[J]. IEEE transactions on geoscience and remote sensing, 2021, 59(11): 9071-9085.
96	GASO D V, DE WIT A, DE BRUIN S, et al. Efficiency of assimilating leaf area index into a soybean model to assess within-field yield variability[J]. European journal of agronomy, 2023, 143: ID 126718.
97	GASO D V, DE WIT A, BERGER A G, et al. Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model[J]. Agricultural and forest meteorology, 2021, 308/309: ID 108553.
98	TANG W C, TANG R X, GUO T, et al. Remote prediction of oilseed rape yield via Gaofen-1 images and a crop model[J]. Remote sensing, 2022, 14(9): ID 2041.
99	JIANG Z W, CHEN Z X, CHEN J, et al. Application of crop model data assimilation with a particle filter for estimating regional winter wheat yields[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2014, 7(11): 4422-4431.
100	杨金旻. 遥感技术在大豆种植情况监测中的应用[J]. 电脑知识与技术, 2020, 16(21): 221-223.
	YANG J M. Application of remote sensing technology in monitoring soybean planting situation[J]. Computer knowledge and technology, 2020, 16(21): 221-223.
101	BAI T C, WANG S G, MENG W B, et al. Assimilation of remotely-sensed LAI into WOFOST model with the SUBPLEX algorithm for improving the field-scale jujube yield forecasts[J]. Remote sensing, 2019, 11(16): ID 1945.
102	SILVESTRO P, PIGNATTI S, PASCUCCI S, et al. Estimating wheat yield in China at the field and district scale from the assimilation of satellite data into the aquacrop and simple algorithm for yield (SAFY) models[J]. Remote sensing, 2017, 9(5): ID 509.
103	DONG T F, LIU J G, QIAN B D, et al. Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data[J]. International journal of applied earth observation and geoinformation, 2016, 49: 63-74.
104	李岚涛, 任涛, 汪善勤, 等. 基于角果期高光谱的冬油菜产量预测模型研究[J]. 农业机械学报, 2017, 48(3): 221-229.
	LI L T, REN T, WANG S Q, et al. Prediction models of winter oilseed rape yield based on hyperspectral data at pod-filling stage[J]. Transactions of the Chinese society for agricultural machinery, 2017, 48(3): 221-229.
105	姚业浩, 李毅念, 陈玉仑, 等. 基于油菜角果长度图像识别的每角粒数测试方法[J]. 农业工程学报, 2021, 37(23): 153-160.
	YAO Y H, LI Y N, CHEN Y L, et al. Testing method for the seed number per silique of oilrape based on recognizing the silique length images[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37(23): 153-160.
106	李金霞, 章建新, 吕淑萍. 高产春大豆豆荚与叶片的光合性能研究[J]. 大豆科学, 2009, 28(6): 1026-1030.
	LI J X, ZHANG J X, LYU S P. Photosynthetic characteristics in pod and leaves of high-yield spring soybean[J]. Soybean science, 2009, 28(6): 1026-1030.
107	王春丽, 海江波, 田建华, 等. 油菜终花后角果和叶片光合对籽粒产量和品质的影响[J]. 西北植物学报, 2014, 34(8): 1620-1626.
	WANG C L, HAI J B, TIAN J H, et al. Influence of silique and leaf photosynthesis on yield and quality of seed of oilseed rape (Brassica na pus L.) after flowering[J]. Acta botanica boreali-Occidentalia sinica, 2014, 34(8): 1620-1626.
108	李静, 周杨果, 陆志峰, 等. 氮钾配施对冬油菜角果皮光合作用及光合器官氮分配的影响[J]. 植物营养与肥料学报, 2022, 28(5): 869-879.
	LI J, ZHOU Y G, LU Z F, et al. The effects of combined nitrogen and potassium application on photosynthesis and nitrogen allocation in photosynthetic organs of winter oilseed rape(Brassica napus L.) silique wall[J]. Journal of plant nutrition and fertilizers, 2022, 28(5): 869-879.
109	李俊, 袁金展, 官春云, 等. 油菜角果光合衰退的生理特征初步研究[J]. 中国油料作物学报, 2013, 35(6): 644-649.
	LI J, YUAN J Z, GUAN C Y, et al. Physiological characteristics of silique photosynthesis declining and its effect on rapeseed yield[J]. Chinese journal of oil crop sciences, 2013, 35(6): 644-649.
110	张耀文, 赵小光, 关周博, 等. 油菜角果光合特性研究现状及改良思路[J]. 中国油料作物学报, 2017, 39(5): 704-713.
	ZHANG Y W, ZHAO X G, GUAN Z B, et al. Review of silique photosynthetic characteristics and improvement in rapeseed[J]. Chinese journal of oil crop sciences, 2017, 39(5): 704-713.
111	税红霞, 汤天泽. 油菜器官与产量关系的研究进展[J]. 安徽农学通报, 2007, 13(16): 111-113.
	SHUI H X, TANG T Z. Progress of study on relationship between organs and yield in rape[J]. Anhui agricultural science bulletin, 2007, 13(16): 111-113.

作物	估产物候期
大豆	种子初期、豆荚期、灌浆期^{［47，48］}
油菜	开花期、角果期^{［49，50］}
花生	种子初生期^［51］
向日葵	开花期前的花序出现阶段^［52］

[1]	ZHANG Gan, YAN Haifeng, HU Gensheng, ZHANG Dongyan, CHENG Tao, PAN Zhenggao, XU Haifeng, SHEN Shuhao, ZHU Keyu. Identification Method of Wheat Field Lodging Area Based on Deep Learning Semantic Segmentation and Transfer Learning [J]. Smart Agriculture, 2023, 5(3): 75-85.
[2]	LIU Yixue, SONG Yuyang, CUI Ping, FANG Yulin, SU Baofeng. Diagnosis of Grapevine Leafroll Disease Severity Infection via UAV Remote Sensing and Deep Learning [J]. Smart Agriculture, 2023, 5(3): 49-61.
[3]	MAO Kebiao, ZHANG Chenyang, SHI Jiancheng, WANG Xuming, GUO Zhonghua, LI Chunshu, DONG Lixin, WU Menxin, SUN Ruijing, WU Shengli, JI Dabin, JIANG Lingmei, ZHAO Tianjie, QIU Yubao, DU Yongming, XU Tongren. The Paradigm Theory and Judgment Conditions of Geophysical Parameter Retrieval Based on Artificial Intelligence [J]. Smart Agriculture, 2023, 5(2): 161-171.
[4]	FAN Chengzhi, WANG Ziwen, YANG Xingchao, LUO Yongkai, XU Xuexin, GUO Bin, LI Zhenhai. Machine Learning Inversion Model of Soil Salinity in the Yellow River Delta Based on Field Hyperspectral and UAV Multispectral Data [J]. Smart Agriculture, 2022, 4(4): 61-73.
[5]	ZHUO Yue, DING Feng, YAN Haijun, XU Jing. Advances in Forage Crop Growth Monitoring by UAV Remote Sensing [J]. Smart Agriculture, 2022, 4(4): 35-48.
[6]	FU Hongyu, WANG Wei, LIAO Ao, YUE Yunkai, XU Mingzhi, WANG Ziwei, CHEN Jianfu, SHE Wei, CUI Guoxian. High Quality Ramie Resource Screening Based on UAV Remote Sensing Phenotype Monitoring [J]. Smart Agriculture, 2022, 4(4): 74-83.
[7]	ZHANG Zhibo, ZHAO Xining, GAO Xiaodong, ZHANG Li, YANG Menghao. Accurate Extraction of Apple Orchard on the Loess Plateau Based on Improved Linknet Network [J]. Smart Agriculture, 2022, 4(3): 95-107.
[8]	CHEN Ailian, ZHAO Sijian, ZHU Yuxia, SUN Wei, ZHANG jing, ZHANG Qiao. Application Scenarios and Research Progress of Remote Sensing Technology in Plant Income Insurance [J]. Smart Agriculture, 2022, 4(1): 57-70.
[9]	YANG Guofeng, HE Yong, FENG Xuping, LI Xiyao, ZHANG Jinnuo, YU Zeyu. Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle [J]. Smart Agriculture, 2022, 4(1): 1-16.
[10]	CHEN Meixiang, ZHANG Ruirui, CHEN Liping, TANG Qing, XIA Lang. Investigation on Advances of Unmanned Aerial Vehicle Application Research in Agriculture and Forestry [J]. Smart Agriculture, 2021, 3(3): 22-37.
[11]	DAI Shengpei, LUO Hongxia, ZHENG Qian, HU Yingying, LI Hailiang, LI Maofen, YU Xuan, CHEN Bangqian. Comparison of Remote Sensing Estimation Models for Leaf Area Index of Rubber Plantation in Hainan Island [J]. Smart Agriculture, 2021, 3(2): 45-54.
[12]	YANG Feifei, LIU Shengping, ZHU Yeping, LI Shijuan. Identification and Level Discrimination of Waterlogging Stress in Winter Wheat Using Hyperspectral Remote Sensing [J]. Smart Agriculture, 2021, 3(2): 35-44.
[13]	HAN Dong, WANG Pengxin, ZHANG Yue, TIAN Huiren, ZHOU Xijia. Progress of Agricultural Drought Monitoring and Forecasting Using Satellite Remote Sensing [J]. Smart Agriculture, 2021, 3(2): 1-14.
[14]	WANG Lin, LIANG Jian, MENG Fanyu, MENG Yang, ZHANG Yongtao, LI Zhenhai. Estimating Grain Protein Content of Winter Wheat in Producing Areas Based on Remote Sensing and Meteorological Data [J]. Smart Agriculture, 2021, 3(2): 15-22.
[15]	SHU Meiyan, CHEN Xiangyang, WANG Xiqing, MA Yuntao. Estimation of Maize Leaf Area Index and Aboveground Biomass Based on Hyperspectral Data [J]. Smart Agriculture, 2021, 3(1): 29-39.