Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring

High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However,the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese "High Resolution Earth Observation Systems",China is able to receive superb high-resolution remotely sensed images(GF series) that equalizes or even surpasses foreign similar satellites in respect of spatial resolution,scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring,and identifies potential applications from regional to national scales. GF-1's high resolution(i.e.,2 m/8 m),high revisit cycle(i.e.,4 days),and its visible and near-infrared(VNIR) spectral bands enable a continuous,efficient and effective agricultural dynamics monitoring. Thus,it has gradually substituted the foreign data sources for mapping crop planting areas,monitoring crop growth,estimating crop yield,monitoring natural disasters,and supporting precision and facility agriculture in China agricultural remote sensing monitoring system(CHARMS). However,it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated,especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition,the thematic product series in terms of land cover,crop allocation,crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages,the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width,might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.     

Sensing technologies for precision specialty crop production

With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world. Accurate information concerning the spatial variability within fields is very important for precision farming of specialty crops. However, this variability is affected by a variety of factors, including crop yield, soil properties and nutrients, crop nutrients, crop canopy volume and biomass, water content, and pest conditions (disease, weeds, and insects). These factors can be measured using diverse types of sensors and instruments such as field-based electronic sensors, spectroradiometers, machine vision, airborne multispectral and hyperspectral remote sensing, satellite imagery, thermal imaging, RFID, and machine olfaction system, among others. Sensing techniques for crop biomass detection, weed detection, soil properties and nutrients are most advanced and can provide the data required for site specific management. On the other hand, sensing techniques for diseases detection and characterization, as well as crop water status, are based on more complex interaction between plant and sensor, making them more difficult to implement in the field scale and more complex to interpret. This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops. Some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed.     

Automatic delineation algorithm for site-specific management zones based on satellite remote sensing data

In light of the increasing demand for food production, climate change challenges for agriculture, and economic pressure, precision farming is an ever-growing market. The development and distribution of remote sensing applications is also growing. The availability of extensive spatial and temporal data—enhanced by satellite remote sensing and open-source policies—provides an attractive opportunity to collect, analyze and use agricultural data at the farm scale and beyond. The division of individual fields into zones of differing yield potential (management zones (MZ)) is the basis of most offline and map-overlay precision farming applications. In the process of delineation, manual labor is often required for the acquisition of suitable images and additional information on crop type. The authors therefore developed an automatic segmentation algorithm using multi-spectral satellite data, which is able to map stable crop growing patterns, reflecting areas of relative yield expectations within a field. The algorithm, using RapidEye data, is a quick and probably low-cost opportunity to divide agricultural fields into MZ, especially when yield data is insufficient or non-existent. With the increasing availability of satellite images, this method can address numerous users in agriculture and lower the threshold of implementing precision farming practices by providing a preliminary spatial field assessment.     

Zone mapping application for precision-farming: a decision support tool for variable rate application

A web-based decision support tool, zone mapping application for precision farming (ZoneMAP, ), has been developed to automatically determine the optimal number of management zones and delineate them using satellite imagery and field data provided by users. Application rates, such as of fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming equipment. ZoneMAP is linked to Digital Northern Great Plains, a web-based application which hosts an archive of satellite imagery, as well as high resolution imagery from airborne sensors. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well and were consistent with zone maps created by traditional means. The results demonstrated that ZoneMAP can serve as an effective and easy-to-use tool for those who practice precision agriculture.     

Comparison of QuickBird Satellite Imagery and Airborne Imagery for Mapping Grain Sorghum Yield Patterns

Timely and accurate information on crop conditions obtained during the growing season is of vital importance for crop management. High spatial resolution satellite imagery has the potential for mapping crop growth variability and identifying problem areas within fields. The objectives of this study were to use QuickBird satellite imagery for mapping plant growth and yield patterns within grain sorghum fields as compared with airborne multispectral image data. A QuickBird 2.8-m four-band image covering a cropping area in south Texas, USA was acquired in the 2003 growing season. Airborne three-band imagery with submeter resolution was also collected from two grain sorghum fields within the satellite scene. Yield monitor data collected from the two fields were resampled to match the resolutions of the airborne imagery and the satellite imagery. The airborne imagery was related to yield at original submeter, 2.8 and 8.4 m resolutions and the QuickBird imagery was related to yield at 2.8 and 8.4 m resolutions. The extracted QuickBird images for the two fields were then classified into multiple zones using unsupervised classification and mean yields among the zones were compared. Results showed that grain yield was significantly related to both types of image data and that the QuickBird imagery had similar correlations with grain yield as compared with the airborne imagery at the 2.8 and 8.4 m resolutions. Moreover, the unsupervised classification maps effectively differentiated grain production levels among the zones. These results indicate that high spatial resolution satellite imagery can be a useful data source for determining plant growth and yield patterns for within-field crop management.     

The application of small unmanned aerial systems for precision agriculture: a review

Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.     

Research advances of SAR remote sensing for agriculture applications: A review

Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.     

Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status

Remote sensing is a key technology for precision agriculture to assess actual crop conditions. Commercial, high-spatial-resolution imagery from aircraft and satellites are expensive so the costs may outweigh the benefits of the information. Hobbyists have been acquiring aerial photography from radio-controlled model aircraft; we evaluated these very-low-cost, very high-resolution digital photography for use in estimating nutrient status of corn and crop biomass of corn, alfalfa, and soybeans. Based on conclusions from previous work, we optimized an aerobatic model aircraft for acquiring pictures using a consumer-oriented digital camera. Colored tarpaulins were used to calibrate the images; there were large differences in digital number (DN) for the same reflectance because of differences in the exposure settings selected by the digital camera. To account for differences in exposure a Normalized Green–Red Difference Index [(NGRDI  = (Green DN  − Red DN)/(Green DN  + Red DN)] was used; this index was linearly related to the normalized difference of the green and red reflectances, respectively. For soybeans, alfalfa and corn, dry biomass from zero to 120 g m⁻² was linearly correlated to NGRDI, but for biomass greater than 150 g m⁻² in corn and soybean, NGRDI did not increase further. In a fertilization experiment with corn, NGRDI did not show differences in nitrogen status, even though areas of low nitrogen status were clearly visible on late-season digital photographs. Simulations from the SAIL (Scattering of Arbitrarily Inclined Leaves) canopy radiative transfer model verified that NGRDI would be sensitive to biomass before canopy closure and that variations in leaf chlorophyll concentration would not be detectable. There are many advantages of model aircraft platforms for precision agriculture; currently, the imagery is best visually interpreted. Automated analysis of within-field variability requires more work on sensors that can be used with model aircraft platforms.     

Adoption of precision agriculture technologies by German crop farmers

In recent years, precision farming has been receiving more attention from researchers. Precision farming, which provides a holistic system approach, helps farmers to manage the spatial and temporal crop and soil variability within a field in order to increase profitability, optimize yield and quality, and reduce costs. There has been considerable research in farmers’ adoption of precision agriculture technologies. However, most recent studies have considered only a few aspects, whereas in this study a wide range of farm characteristics and farmer demographics are tested to gain insight into the relevant aspects of adoption of precision farming in German crop farming. The results of a logistic regression analysis show that predictors with positive influence on the adoption of precision farming are agricultural contractor services such as an additional farming business, having under 5 years’ experience in crop farming, having between 16 and 20 years’ experience in crop farming, and having more than 500 ha of arable land. However, having a farm of less than 100 ha and producing barley are factors that exert a negative influence on the adoption of precision farming. The results of this study provide manifold starting points for the further proliferation of precision agriculture technologies and future research directions.     

农作物种植结构遥感提取研究进展

农作物种植结构信息对农业生产管理、农业可持续发展及国家粮食安全等具有重要意义。本文中概括了农作物种植结构遥感提取的理论基础,归类了近10年间不同农作物种植结构遥感提取技术方法,重点评述了不同技术方法的特点及应用情况,讨论和展望了未来农作物种植结构遥感提取研究的发展方向。当前,光谱特征、时相特征和空间特征是农作物种植结构遥感提取的三大理论基础。基于单一影像源的种植结构提取方法操作简单,但往往难以获取种植结构“最佳识别期”的遥感影像;基于多时序影像源的种植结构提取方法可以充分利用农作物季相节律特征,成为当前农作物种植结构遥感提取的主流方法。在基于多时序影像源的种植结构提取方法中,多特征参量法较单一特征参量法更适用于农作物种植结构复杂区域,基于多特征参量的统计模型法一定程度上解决了混合像元问题,但模型的鲁棒性有待提高。此外,遥感与统计数据融合的农作物种植结构提取法在国家及全球大尺度的农作物种植结构提取中具有优势,但较低的制图分辨率使得数据产品的区域适宜性较差。未来农作物种植结构遥感提取将以区域“作物一张图”为目标,充分发挥多源数据组合利用的优势,围绕多类型作物同步提取和大范围作物种植结构提取开展深入研究,重点加强遥感数据预处理、特征参量提取和分类器高效选择等关键技术研究,从而提升农作物种植结构遥感提取的时空尺度,满足多方位的农业应用需求。     

Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping

The joint use of satellite imagery and digital soil maps derived from soil sampling is investigated in the present paper with the goal of proposing site-specific management units (SSMU) within a commercial field plot. Very high resolution Quickbird imagery has been used to derive leaf area index (LAI) maps in maize canopies in two different years. Soil properties maps were obtained from the interpolation of ion concentrations (Na, Mg, Ca, K and P) and texture determined in soil samples and also from automatic readings of electromagnetic induction (EMI) readings taken with a mobile sensor.Links between the image-derived LAI and soil properties were established, making it possible to differentiate units within fields subject to abiotic stress associated with soil sodicity, a small water-holding capacity or flooding constraints. In accordance with the previous findings, the delineation of SSMUs is proposed, describing those field areas susceptible of variable-rate management for agricultural inputs such as water or fertilizing, or soil limitation correctors such as gypsum application in the case of sodicity problems. This demonstrates the suitability of spatial information technologies such as remote sensing and digital soil mapping in the context of precision agriculture.     

Mapping wheat nitrogen uptake from RapidEye vegetation indices

Mapping wheat nitrogen (N) uptake at 5 m spatial resolution could provide growers with new insights regarding nitrogen-use efficiency at the field scale. This study explored the use of spectral information from high resolution (5 × 5 m) RapidEye satellite data at peak leaf area index (LAI) to estimate end-of-season cumulative N uptake of wheat (Triticum spp.) in a heterogeneous, rainfed system. The primary objectives were to evaluate the usefulness of simple, widely used vegetation indices (VIs) from RapidEye as a tool to map crop N uptake over three growing seasons, farms and growing conditions, and to examine the usefulness of remotely sensed N uptake maps for precision agriculture applications. Data on harvested wheat N was collected at twelve plots over three seasons at four farms in the Palouse region of Northern Idaho and Eastern Washington. Seventeen commonly used spectral VIs were computed for images collected during ‘peak greenness’ (maximum LAI) to determine which VIs would be most appropriate for estimating wheat N uptake at harvest. The normalized difference red-edge index was the top performing VI, explaining 81 % of the variance in wheat N uptake with a regression slope of 1.06 and RMSE of 15.94 kg/ha. Model performance was strong across all farms over all three seasons regardless of crop variety, allowing the creation of high accuracy wheat N uptake maps. In conclusion, for this particular agro-ecosystem, mid-season VIs that incorporate the use of the NIR and red-edge bands are generally better predictors of end-of-season crop N uptake than VIs that do not include these bands, thereby further enabling their use in precision agriculture applications.     

Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat

This study investigates an imaging system based on a Rikola hyperspectral (HSI) and Nikon D800E (CIR) cameras installed on a manned ultralight aircraft Bekas Ch-32 for applications involving precision agriculture. The efficiency of this technical solution is compared with that of using Canon PowerShot SX260HS camera images acquired from helicopter-type unmanned aerial vehicle (UAV) to accomplish similar tasks. The criteria for comparison were the suitability of acquired images for modelling chlorophyll concentration in spring wheat and for estimating the normalized difference red edge (NDRE) index, which is conventionally obtained using OptRx proximal sensors. Hyperspectral image values used as explanatory variables in ordinary least squares regression explain 68 and 61% of the variance in chlorophyll concentration and NDRE, respectively and outperform other images. The advantage of hyperspectral imagery became negligible when applying geographically weighted regression to improve global regression models. The use of ultralight aircraft as a sensor platform for precision agriculture aimed aerial photography projects is suggested as currently the most cost-effective solution in Lithuania.     

Mapping crop ground cover using airborne multispectral digital imagery

Empirical relationships between remotely sensed vegetation indices and canopy density information, such as leaf area index or ground cover (GC), are commonly used to derive spatial information in many precision farming operations. In this study, we modified an existing methodology that does not depend on empirical relationships and extended it to derive crop GC from high resolution aerial imagery. Using this procedure, GC is calculated for every pixel in the aerial imagery by dividing the perpendicular vegetation index (PVI) of each pixel by the PVI of full canopy. The study was conducted during the summer growing seasons of 2007 and 2008, and involves airborne and ground truth data from 13 agricultural fields in the Southern High Plains of the USA. The results show that the method described in this study can be used to estimate crop GC from high-resolution aerial images with an overall accuracy within 3% of their true values.     

Agricultural remote sensing big data: Management and applications

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen-level (FLFL) satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level (FLTL) remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.     

农作物空间格局遥感监测研究进展

遥感技术因其高时效、宽范围和低成本等优点正被广泛应用于对地观测活动中,为大区域尺度掌握农作物空间格局提供了新的科学技术手段。本文系统总结了近10年来国内外农作物空间格局遥感监测在理论、方法、实践应用等方面取得的新进展,指出了亟待解决的问题,并对今后的发展方向进行了展望。研究认为,农作物种植面积遥感监测主要根据遥感传感器记录的不同农作物光谱特征的差异,进行不同农作物种植面积的识别,方法主要包括:基于光谱特征、基于作物物候特征和基于多源数据的农作物遥感识别方法。遥感技术应用于农作物复种模式监测主要根据时间序列植被指数描述的作物季节活动过程,利用不同的拟合方法得到作物生长曲线,实现作物复种模式有效监测。农作物种植方式遥感监测是更高层次的遥感应用,主要利用时间序列遥感数据,根据作物植被指数的变化规律区分不同作物生育周期,判断不同复种模式下作物的种植顺序和方式。在未来相当长的一段时间内,建立农作物空间格局遥感监测的理论和技术体系、发展和改进遥感影像分类方法、优化时间序列遥感数据平滑技术和提高信息提取的自动化与流程化将是农作物空间格局遥感监测需要重点解决的几个关键问题。     

遥感影像的空间分辨率对提取崩岗精度的影响

从0.6 m Quick Bird、2.5 m ALOS、10.0 m ALOS遥感影像中分别提取崩岗的特征数据,分析遥感影像的分辨率对崩岗数据精度的影响。结果表明:从0.6 m Quick Bird能容易地提取崩岗数据,且重现性较好,从2.5 m ALOS能提取崩岗数据,但重现性不强,从10.0 m ALOS无法提取崩岗。从0.6 m Quick Bird和2.5 m ALOS提取的崩岗,数量基本一致,但位置不一致、边界不重合、形状差别很大,从2.5m ALOS提取的崩岗周长平均减少20.83%,占58.33%的崩岗面积减少了,平均减少19.78%。因此,可断定0.6 m Quick Bird能反映崩岗的实际情况,2.5 m ALOS能较好地反映崩岗的实际情况,10.0 m ALOS完全不能提取崩岗数据。但高精度遥感影像的获取比较困难,且价格较贵。     

Ultra low-level airborne (ULLA) sensing of crop canopy reflectance: A case study using a CropCircle™ sensor

Operational airborne and satellite remote sensing in agriculture remains constrained by matching platform availability to suitable daytime weather and illumination conditions, crop development, and availability of ground staff. An ultra low-level aircraft carrying an active NIR/Red CropCircle™ sensor was successfully deployed to record and subsequently map crop vigour via the simple ratio (SR) index over a field of sorghum. Given the logging frequency of ≈20 Hz and the presence of alternate rows of bare soil, the Moiré effect reduced the contrast between crop and bare soil skip-rows. Such effects would not be expected to occur in non-skip-row crops. The ultra low-level airborne (ULLA)-SR map derived from the 20 m transect records compared favorably with the SR map derived from a meter-resolution airborne digital multispectral image that was re-sampled to a similar spatial resolution. This case study, involving a CropCircle™ sensor mounted in a low-level aircraft demonstrates another deployment option for users of this class of sensor. Moreover, an ULLA configuration offers the potential for greater flexibility in scheduling compared to airborne imaging, given it can be flown at any sun-angle, under cloud, at night, and may easily be incorporated into aircraft already conducting low-level operations, for example crop dusting and reconnaissance, over agricultural fields.     

Participative site-specific agriculture analysis for smallholders

Site-specific agriculture has been adopted in a high-tech context using, for instance, in situ sensors, satellite images for remote sensing analysis, and some other technological devices. However, farmers and smallholders without the economic resources and required knowledge to use and to access the latest technology seem to find an impediment to precision agricultural practices. This article discusses the possibility of adopting precision agriculture (PA) principles for site-specific management but in a low technology context for such farmers. The proposed methodology to support PA combines low technology dependency and a participatory approach by involving smallholders, farmers and experts. The case studies demonstrate how the interplay of low technology and a participative approach may be suitable for smallholders for site-specific agriculture analysis.     

Evaluating high resolution SPOT 5 satellite imagery for crop identification

High resolution satellite imagery offers new opportunities for crop monitoring and assessment. A SPOT 5 image acquired in May 2006 with four spectral bands (green, red, near-infrared, and short-wave infrared) and 10-m pixel size covering intensively cropped areas in south Texas was evaluated for crop identification. Two images with pixel sizes of 20 m and 30 m were also generated from the original image to simulate coarser resolution satellite imagery. Two subset images covering a variety of crops with different growth stages were extracted from the satellite image and five supervised classification techniques, including minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper (SAM), and support vector machine (SVM), were applied to the 10-m subset images and the two coarser resolution images to identify crop types. The effects of the short-wave infrared band and pixel size on classification results were also examined. Kappa analysis showed that maximum likelihood and SVM performed better than the other three classifiers, though there were no statistical differences between the two best classifiers. Accuracy assessment showed that the 10-m, four-band images based on maximum likelihood resulted in the best overall accuracy values of 91% and 87% for the two respective sites. The inclusion of the short-wave infrared band statistically significantly increased the overall accuracy from 82% to 91% for site 1 and from 75% to 87% for site 2. The increase in pixel size from 10 m to 20 m or 30 m did not significantly affect the classification accuracy for crop identification. These results indicate that SPOT 5 multispectral imagery in conjunction with maximum likelihood and SVM classification techniques can be used for identifying crop types and estimating crop areas.