Review of large scale crop remote sensing monitoring based on MODIS data

China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.

Satellite remote sensing of sea-ice and its operational monitoring method along the coast of China

Sea-ice is an important operational item for real timely monitoring and forecasting marine environment of China. This paper introduces an operational method of satellite remote sensing to monitor sea- ice using quantitative data of NOAA, and its contents include computer processing of AVHRR sounding data of NOAA and its program design, imagery processing of sea-ice imagery from satellite and their thematic analysis. The sea-ice satellite colour imageries processed via this software system are able to interpret sea-ice pattern, characterizing it by thickness, maximum position of ice boundary, floe concentration and dynamic process of ice changing. At the same time, analyses of the ice condition of the Bohai Sea for the two-year period (1986-1988) as monitored by satellite have been summarized.

Indication of the Expression of Transgene in Rice Plant Based on Hyperspectral Remote Sensing Technique Ⅱ——Growth Monitoring of Samples in the Contrast Experiment

Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works,the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively,fulfilling the growth monitoring of the transgenic samples.The spectral parameters(spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts,and thus could be applied as indicators of biophysical or biochemical processes changes of plant.By ASD portable field spectroradiometer with high-density probe,fine foliar spectra of 8 groups were obtained.By analyzing spectral angle and continuum removal,the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis.By investigating spectral indices of the samples,the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content.In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated.The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches.By this technique,quantitative and qualitative results of sample spectra could be provided as prior knowledge,as certain orientation,for laboratory professional advanced transgenic breeding study.

Irrigation Scheduling Using Remote Sensing Data Assimilation Approach

Remote sensing and crop growth models have enhanced our ability to understand soil water balance in irrigated agriculture. However, limited efforts have been made to adopt data assimilation methodologies in these linked models that use stochastic parameter estimation with genetic algorithm (GA) to improve irrigation scheduling. In this study, an innovative irrigation scheduling technique, based on soil moisture and crop water productivity, was evaluated with data from Sirsa Irrigation Circle of Haryana State, India. This was done by integrating SEBAL (Surface Energy Balance Algorithm for Land)-based evapotranspiration (ET) rates with the SWAP (Soil-Water-Atmosphere-Plant), a process-based crop growth model, using a GA. Remotely sensed ET and ground measurements from an experiment field were combined to estimate SWAP model parameters such as sowing and harvesting dates, irrigation scheduling, and groundwater levels to estimate soil moisture. Modeling results showed that estimated sowing, harvesting, and irrigation application dates were within ±10 days of observations and produced good estimates of ET and soil moisture fluxes. The SWAP-GA model driven by the remotely sensed ET moderately improved surface soil moisture estimates suggesting that it has the potential to serve as an operational tool for irrigation scheduling purposes.

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

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（P0.01, 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.

Establishment of Winter Wheat Regional Simulation Model Based on Remote Sensing Data and Its Application

Accurate crop growth monitoring and yield forecasting are significant to the food security and the sustainable development of agriculture. Crop yield estimation by remote sensing and crop growth simulation models have highly potential application in crop growth monitoring and yield forecasting. However, both of them have limitations in mechanism and regional application, respectively. Therefore, approach and methodology study on the combination of remote sensing data and crop growth simulation models are concerned by many researchers. In this paper, adjusted and regionalized WOFOST （World Food Study） in North China and Scattering by Arbitrarily Inclined Leaves-a model of leaf optical PROperties SPECTra （SAIL-PROSFPECT） were coupled through LAI to simulate Soil Adjusted Vegetation Index （SAVI） of crop canopy, by which crop model was re-initialized by minimizing differences between simulated and synthesized SAVI from remote sensing data using an optimization software （FSEOPT）. Thus, a regional remote-sensingcrop-simulation-framework-model （WSPFRS） was established under potential production level （optimal soil water condition）. The results were as follows： after re-initializing regional emergence date by using remote sensing data, anthesis, and maturity dates simulated by WSPFRS model were more close to measured values than simulated results of WOFOST; by re-initializing regional biomass weight at turn-green stage, the spatial distribution of simulated storage organ weight was more consistent with measured yields and the area with high values was nearly consistent with actual high yield area. This research is a basis for developing regional crop model in water stress production level based on remote sensing data.

Remote sensing-based global crop monitoring: experiences with China’s CropWatch system

Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices.China’s global crop-monitoring system(CropWatch)uses remote sensing data combined with selected field data to determine key crop production indicators:crop acreage,yield and production,crop condition,cropping intensity,crop-planting proportion,total food availability,and the status and severity of droughts.Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages.CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments.This paper presents a comprehensive overview of CropWatch as a remote sensingbased system,describing its structure,components,and monitoring approaches.The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach,as well as a comparison with other global crop-monitoring systems.

基于无人机遥感技术的大田作物精准施肥决策研究进展

近年来无人机遥感技术在作物施肥智慧决策与精准作业领域应用广泛,现将无人机遥感技术在监测作物长势并指导施肥、无人机精准作业等方面的研究现状作以综述。基于信息化方法的施肥决策可通过数据驱动和机理驱动2种方式制定,其中农情监测为施肥决策的制定提供了数据支撑,目前研究主要以机器学习、回归分析等数据驱动方法监测作物长势,存在农学机理与普适性不足等问题;部分研究通过耦合辐射传输模型、作物生长模型等机理模型提升了无人机遥感对作物大规模监测的物理意义与普适性。制定施肥决策方面重点探讨了通过无人机遥感数据建立回归模型等数据驱动方式制定作物精准施肥决策的研究现状,并对目前制定施肥决策缺乏机理性的问题进行了深入讨论与展望。无人机精准作业方面,综述无人机遥感、作业处方图生成和精准喷施技术等无人机执行精准施肥决策的关键作业环节,并对相关技术进行展望。深入分析基于无人机遥感方法开展大田作物精准施肥决策的研究进展及存在的问题,旨在为利用无人机实现作物精准施肥提供科学依据和参考。

基于数据同化系统的作物产量预测研究进展

数据同化系统融合了遥感数据和作物生长模型的优势,是实时监测农业生产状况的有力手段。本文在简要介绍作物产量遥感估测方法的基础上,重点对数据同化算法的发展情况、多源遥感数据在数据同化上的应用潜力、数据同化系统的不确定性以及数据同化系统的尺度效应4方面进行论述。并且针对农业应用现状,提出未来应充分挖掘多源遥感数据、多作物生长模型集合和数据算法的优势,最终实现以机理模型为纽带的作物估产模式,并为制定田间管理策略、规划粮食产业布局和制定进出口贸易政策提供有力的数据和技术支撑。

作物生长模型研究现状与展望

作物生长模型由最初的作物生长发育模型发展到农业决策支持模型,在科学研究、农业管理、政策制定等方面发挥着越来越重要的作用。本文首先回顾了作物生长模型的发展过程,并按照模型主要驱动因子,将作物生长模型分为土壤因子、光合作用因子和人为因子驱动3类并分别进行了归纳阐述;然后对典型的模型分别从模型模块、时空尺度、可模拟的作物类型等方面进行列表式对比;并对作物生长模型在气候变化评估、生产管理决策支持、资源管理优化等方面的应用,以及面临的极端条件、复杂农业景观和模型复杂度等挑战进行了总结,在此基础上认为遥感数据同化和孪生农场是其发展方向。

田块尺度水稻农情遥感监测平台设计与试验

为解决我国水稻种植过程中由于药肥施用不当和缺乏系统化管理所导致的单产低、农业面源污染严重等问题,并针对现有农情系统数据源单一的现状,以多源数据的协同监测为核心,基于WebGIS和Ant Design搭建前端框架,整合多源时空地理数据和分布式数据存储方法,采用Python、HTML、Javascript+CSS、ArcGIS Server、Mapbox Studio以及PostgreSQL等技术,构建一个前后端分离(F/B Separation)、云端实时更新的田块尺度水稻农情监测平台,以实现水稻生长参数反演、产量预估、田块参数查询、时空数据可视化与统计分析等功能。以江西兴桥镇与井冈山国家农业科技园为试验区,应用该系统的案例分析表明2022年兴桥镇水稻田块分布破碎,且镇内东北区域水稻产量高于西南,水稻田产量介于6750~8250 kg/hm^(2);同时发现试验区内田块水稻的长势与历史药肥施用量存在明显关联,药肥施用策略显著影响田块水稻长势。综上,本平台在多源数据协同作用下,能较好地满足大区域下田块水稻监测所要求的准确性、全面性,并在一定程度上实现水稻长势与产量的归因分析,可作为实现田块尺度水稻农情多源精细监测的有效示例。

基于无人机遥感的作物表型参数获取和应用研究进展

作物表型参数是由基因和环境因素决定或影响的作物生理、生化特征和性状。通过获取不同环境、不同生长时期的作物表型信息,可直观了解作物生长状况,以及时调整栽培管理措施,保障作物高效生产。无人机搭载RGB相机、光谱相机、激光雷达等传感器,可充分发挥灵活性好、获取数据效率高、成本相对较低等优势,实现作物表型参数信息的高效获取,同时,快速发展的图像处理和识别分类技术又为无人机遥感获取的作物表型参数信息提供了有效的处理和分析方法,从而使得作物监测更加便捷、高效。本文总结了无人机遥感获取作物表型参数信息的流程与方法,概括了基于无人机遥感开展作物株高、冠层覆盖度、叶面积指数、水分胁迫、生物量、产量等表型参数研究的现状,并对无人机遥感技术在作物表型参数信息解析方面的应用前景进行了展望,以期为充分发挥该技术在农业生产中的作用提供参考。

结合变异系数法和机器学习模型的棉花长势监测

为了更加准确地获取棉花关键物候期的长势信息,本文首先通过棉花制图指数提取棉花种植区域;然后利用变异系数法将反映棉花长势的株高、SPAD值、叶片湿重、叶片干重与叶面积5种指标构建为一个综合长势指标,即棉花长势指数(FBCGI);最后选取最优特征变量,结合随机森林模型构建棉花长势反演模型。结果表明:①棉花总体分类精度达到81.65%;②与5种单一长势指标相比,构建的FBCGI与植被指数的相关性更高;③基于最优特征变量和随机森林模型构建的棉花长势监测模型,在建模集和验证集中的R 2和RMSE分别为0.74、0.07和0.51、0.10。研究结果可为棉花长势监测提供重要参考。

理化复合参数和神经网络结合的冬小麦长势遥感监测

准确、及时地监测区域作物长势状况对农业规划和政策的制定与调整具有重要的意义。遥感技术作为一种收集大面积作物长势信息的有效手段,正日益受到关注。为提高冬小麦长势遥感监测的准确性和全面性,该研究基于田间实测的冬小麦拔节期地上鲜生物量(aboveground fresh biomass,AFB)、叶面积指数(leaf area index,LAI)、叶片叶绿素相对含量(soil and plant analyzer development,SPAD)和叶片氮含量(leaf nitrogen content,LNC)4种生长相关理化参数,利用熵值法获取各参数权重构建冬小麦理化复合参数(physico-chemical composite parameter,PCCP)。利用显著性检验和籽粒产量数据分析复合参数在量化冬小麦长势方面的性能。然后,以Sentinel-2A作为数据源,分析不同遥感指数与LAI、SPAD、AFB、LNC和PCCP的相关性。选取相关性较高的遥感指数作为反向传播(back propagation,BP)人工神经网络(artificial neural networks,ANN)的输入,建立冬小麦长势遥感监测模型,对PCCP进行估计。评价模型精度并用于监测研究区冬小麦长势分布特征。赋权结果表明,作物物理参数的权重大于生化参数,其中LAI的权重最大,为0.387,AFB和SPAD次之,LNC的权重最小,为0.105;PCCP性能评估结果表明,与单一理化参数相比,PCCP值能更好地揭示作物长势状况的差异,其与最终籽粒产量的相关性更好,决定系数提高0.035~0.468,均方根误差减少46.2~520.0 kg/hm^(2);在遥感监测过程中,PCCP比单一理化参数有更好的应用潜力,BP-ANN长势遥感监测模型模拟PCCP精度较高,在测试集中决定系数为0.830,均方根误差为0.080;研究区冬小麦总体长势稳定且分布集中,呈现"中部差,南北好"的空间分布特征。因此,构建作物理化复合参数用于量化作物长势是提高长势监测可靠性和准确性的一种有效方式,可为冬小麦田间管理提供科学依据,服务于发展智慧农业和建设农业强国的战略需求。

无人机高光谱遥感在作物表型监测中的应用进展

作物表型分析在精准农业中得到了广泛研究,主要用于监测作物生长情况,并分析基因型和环境对作物生长的影响。无人机搭载高光谱传感器能够快速、准确、无损地获取作物的详细光谱特征,可用于估测作物的叶绿素含量、叶面积指数、生物量、氮含量等关键生理生化指标。通过分析这些指标,可以对作物的健康状况、生长趋势和产量进行准确评估。本文分析了无人机平台和传感器的优劣,综述了高光谱信息的处理方法和应用于作物表型分析中的主要高光谱技术,及其在作物生长发育、病害监测和产量预测方面的研究进展。

基于高光谱遥感技术的棉花长势监测研究进展

高光谱遥感技术的快速发展使得作物长势监测方法更加简单、便捷、准确,同时还可获得大范围的作物信息,这为实现棉花生长状态的快速、连续监测提供了重要技术支撑。综述高光谱遥感技术在棉花长势监测方面的研究进展,并对高光谱遥感技术在棉花长势监测方面的未来发展进行展望。

基于Vision Transformer-LSTM(ViTL)的多时序遥感影像农作物分类方法

针对当前遥感农作物分类研究中深度学习模型对光谱时间和空间信息特征采样不足,农作物提取仍然存在边界模糊、漏提、误提的问题,提出了一种名为视觉Transformer-长短期记忆递归神经网络(Vision Transformer-long short term memory,ViTL)的深度学习模型,ViTL模型集成了双路Vision-Transformer特征提取、时空特征融合和长短期记忆递归神经网络(LSTM)时序分类等3个关键模块,双路Vision-Transformer特征提取模块用于捕获图像的时空特征相关性,一路提取空间分类特征,一路提取时间变化特征;时空特征融合模块用于将多时特征信息进行交叉融合;LSTM时序分类模块捕捉多时序的依赖关系并进行输出分类。综合利用基于多时序卫星影像的遥感技术理论和方法,对黑龙江省齐齐哈尔市讷河市作物信息进行提取,研究结果表明,ViTL模型表现出色,其总体准确率(Overall Accuracy,OA)、平均交并比(Mean Intersection over Union,MIoU)和F1分数分别达到0.8676、0.6987和0.8175,与其他广泛使用的深度学习方法相比,包括三维卷积神经网络(3-D CNN)、二维卷积神经网络(2-D CNN)和长短期记忆递归神经网络(LSTM),ViTL模型的F1分数提高了9%~12%,显示出显著的优越性。ViTL模型克服了面对多时序遥感影像的农作物分类任务中的时间和空间信息特征采样不足问题,为准确、高效地农作物分类提供了新思路。

高光谱遥感在农作物研究中的应用与发展趋势

高光谱遥感是目前农作物研究的重要技术手段。为促进农作物生长周期的健康发展和基础研究,提高遥感技术在农作物研究中的应用水平,笔者概述了高光谱遥感在农作物研究中的分析算法,重点讨论了高光谱遥感在农作物长势、农作物信息监测和农作物信息提取方面的研究进展。针对目前仍需深入开展的叶面积指数、生物量估算、氮素监测、叶绿素监测、病虫害监测、重金属监测、农作物碳氮比和水分含量等研究进行了系统综述。分析结果表明,在农作物研究中高光谱遥感技术与人工智能的结合已初见成效,但仍难以满足现代农业的智能化需求,若与新一代信息技术和大模型集成交叉将发挥巨大的研究潜力。

基于D-LinkNet与形态学计算的高分遥感图像车流量监测

针对城市交通路面车流量监测实时性和准确性高的需求,设计了一种利用高分遥感图像进行道路网自动提取和车辆自动监测的处理方法。综合利用D-LinkNet和形态学计算实现道路区域的二值化语义分割及连贯性、边缘扩展以及平滑性优化,同时将道路信息作为掩码并外溢后进行车辆目标检测,有效避免非道路区域车辆目标干扰。提出采用热力图的形式改进车流量监测方法,可以更直观显示道路拥挤程度。综合利用现有数据集对所提方法进行评价,车辆检测的平均精度达91.7%,道路提取平均交并比达85.3%,可以实现道路车流量的有效监测。

现代化技术在耕地种植作物监测中的运用

采用卫星遥感、AI识别解译、监管服务平台建设等现代化技术,改进传统的农业调查模式,提升耕地种植作物类型调查的工作效率和调查结果的准确性,及时制止耕地“非粮化”和“非农化”行为。为此,建立耕地种粮监测监管服务平台,对耕地种植作物的情况进行实时动态监测监管,为国家粮食生产安全提供有效支撑。