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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Landsat-8 and Worldview-3 Data for Assessing Crop Residue Cover
摘要： Crop residues on the soil surface provide defense against erosive forces of water and wind. Quantifying crop residue cover is crucial for monitoring extent of conservation tillage practices. Current multispectral satellite sensors either lack appropriate spectral bands to reliably distinguish crop residue from soil or cannot provide global coverage. Our objective was to estimate crop residue cover in corn and soybean fields in central Iowa by combining data from two multispectral satellites - Landsat-8 and WorldView-3. Shortly after planting in 2016, we measured crop residue cover in >45 fields using the line-point transect method. Landsat Normalized Difference Tillage Index (NDTI) required local calibrations to account for variations in soils, crops, and moisture conditions. In contrast, WorldView-3 Shortwave Infrared Normalized Difference Residue Index (SINDRI) reliably estimated crop residue cover with minimal ground truth data. Although WorldView-3 images cannot provide global coverage, they can augment and extend ground truth observations for calibrating Landsat indices.
关键词： Soils,Crops,Conservation tillage,Non-photosynthetic vegetation,Agriculture
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 38th Central America and Panama Convention (CONCAPAN XXXVIII) - San Salvador, El Salvador (2018.11.7-2018.11.9)] 2018 IEEE 38th Central America and Panama Convention (CONCAPAN XXXVIII) - Sombreamiento de terrenos compartidos: plantas solares fotovoltaicas y cultivos Shared Fields Shading: Solar Power Plants and Crops
摘要： There is a global increasing trend to install fixed photovoltaic (PV) power plants on the ground. Its environmental impact compels us to think about other options on how to handle the soil use. This research examines a combination of a power plant and a secondary product, like a crop that actually requires shading to grow or improve its output. This kind of mix (PV&crops) is already under research, but no research initiative under the same focus was found in Latin America. This article reviews the traditional systems applied in agriculture to provide shading to crops (shade net and agroforestry), they are compared with a solar photovoltaic plant installed over the crop, and possible benefits are analyzed. Also, regional crops were studied where shading of a photovoltaic plant could provide benefits, a study of the final radiation distribution of a test plant was done. This research is part of an effort to establish value on new sustainable techniques in order to reduce the environmental impact of a photovoltaic power plant.
关键词： crops,shared use,soil use,photovoltaic energy,solar plant
更新于2025-09-23 15:23:52
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Assessment of red-edge vegetation indices for crop leaf area index estimation
摘要： This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.
关键词： Sensitivity analysis,Crops,RapidEye,Leaf area index,red-edge,Vegetation index
更新于2025-09-23 15:23:52
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Speed breeding short-day crops by LED-controlled light schemes
摘要： Key message A simple and rapid speed breeding system was developed for short-day crops that enables up to five generations per year using LED lighting systems that allow very specific adjustments regarding light intensity and quality. Abstract Plant breeding is a key element for future agricultural production that needs to cope with a growing human population and climate change. However, the process of developing suitable cultivars is time-consuming, not least because of the long generation times of crops. Recently, speed breeding has been introduced for long-day crops, but a similar protocol for short-day crops is lacking to date. In this study, we present a speed breeding protocol based on light-emitting diodes (LEDs) that allow to modify light quality, and exemplarily demonstrate its effectiveness for the short-day crops soybean (Glycine max), rice (Oryza sativa) and amaranth (Amaranthus spp.). Adjusting the photoperiod to 10 h and using a blue-light enriched, far-red-deprived light spectrum facilitated the growth of short and sturdy soybean plants that flowered ~ 23 days after sowing and matured within 77 days, thus allowing up to five generations per year. In rice and amaranth, flowering was achieved ~ 60 and ~ 35 days after sowing, respectively. Interestingly, the use of far-red light advanced flowering by 10 and 20 days in some amaranth and rice genotypes, respectively, but had no impact on flowering in soybeans, highlighting the importance of light quality for speed breeding protocols. Taken together, our short-day crops’ speed breeding protocol enables several generations per year using crop-specific LED-based lighting regimes, without the need of tissue culture tools such as embryo rescue. Moreover, this approach can be readily applied to a multi-storey 96-cell tray-based system to integrate speed breeding with genomics, toward a higher improvement rate in breeding.
关键词： light quality,LED lighting,photoperiod,short-day crops,speed breeding
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Yokohama, Japan (2019.7.28-2019.8.2)] IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium - Measuring Leaf Equivalent Water Thickness of Short-Rotation Coppice Willow Canopy Using Terrestrial Laser Scanning
摘要： Accurate measurements of leaf Equivalent Water Thickness (EWT) can help in early detection of vegetation stress. Terrestrial Laser Scanning (TLS) intensity data have the potential to provide 3D estimates of EWT, overcoming the limitations of the 2D estimates provided by remote sensing optical data. Such limitations include the sensors being solar illumination dependent and unable to provide information about the vertical variation in EWT. In this study, intensity data from the Leica P20 and P40 commercial TLS instruments were combined in a Normalized Difference Index (NDI). NDI was used to measure EWT in six short-rotation coppice willow (Salix spp.) plots from different varieties with an average error of 7.3% (R2 = 0.8, RMSE = 0.0011 g cm-2). The effects of wind and senescence of leaves on the accuracy of the EWT estimation were also investigated.
关键词： agricultural crops,ground LiDAR,biomass energy,water stress,Vegetation water content
更新于2025-09-19 17:13:59
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - A Method for Deriving Plant Temperature from UAV TIR Image
摘要： Crops are greatly affected by the temperature of farmland surface during their growing period. It is feasible to investigate the growth status of crops based on temperature information. For serving the research of crop growth status, the component temperature (e.g. temperature of vegetation and temperature of soil) are in need to be obtained. In this study, an unmanned aerial vehicle (UAV) temperature measurement system with a thermal infrared (TIR) imager and a charge-coupled device (CCD) camera is assembled and applied the brightness temperatures of farmland surface. The target areas were photographed by the UAV temperature measurement system according to a pre-set route, and obtain TIR and visible images. The component temperatures are obtained from the TIR image as following processes: (1) When shaded components are negligible at noon, two components, i.e. vegetation and soil, are divided by the OTSU algorithm; and (2) When shaded components cannot be ignored in the morning and afternoon, various components, i.e. vegetation, soil and concrete, the TIR image is divided into soil, vegetation and concrete by the corresponding classified visible images; Then, each of the components is divided into light and shaded components by the OTSU algorithm; thus, four components are obtained, including sunlit vegetation, shaded vegetation, sunlit soil, and shaded soil. The derived component temperatures can serve as inputs to agricultural and water resource models.
关键词： farmland,TIR,UAV,surface temperature of crops
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Crops Classification from Sentinel-2A Multi-spectral Remote Sensing Images Based on Convolutional Neural Networks
摘要： Deep learning technology such as convolutional neural networks (CNN) can extract the distinguishable and representative features of different land cover from remote sensing images in a hierarchical way to classify. However, in the field of agriculture, there are few application of crops classification from multi-spectral remote sensing images based on deep learning. In this context, we compared the classification methods of CNN and support vector machines (SVM) in extracting the spatial distribution of crops planting area from Sentineal-2A multi-spectral remote sensing images in Yuanyang county, China. For the region of study, both methods obtained reasonable spatial distribution of different crops, the verification results show that the overall accuracy of CNN is 95.6% which is superior to SVM.
关键词： multi-spectral,remote sensing,crops classification,Sentinel-2A,CNN
更新于2025-09-10 09:29:36
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Raspberry (Breeding, Challenges and Advances) || Use of Imaging Technologies for High Throughput Phenotyping
摘要： In this chapter we describe a high throughput phenotyping system that we have developed for raspberry and other soft fruit crops and its application to against individual (water stress regimes, vine weevil and Phytophthora root rot) and combined stresses. The term phenotype is used to describe the morphology, physiology, biochemistry and ontogeny of a plant, encompassing the diverse array of traits that contribute to the plant’s functional form. Plant phenotype is expressed as a consequence of the interaction between the plant genetic background (i.e. genotype) and the biotic and abiotic conditions experienced by the plant in its growing environment. A key focus of raspberry and other crop breeding is to understand the genetic control of desirable plant traits and the influence of environmental conditions on trait expression, which relies on the ability to collect quantitative information on target traits across genetically-characterised populations of plants. The process of characterising plant traits in detail, referred to as plant phenotyping, is a major challenge when relating plant genetic information to traits for plants in realistic growing environments.
关键词： high throughput phenotyping,raspberry,soft fruit crops,plant breeding,imaging technologies
更新于2025-09-10 09:29:36
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In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features
摘要： The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.
关键词： Red Green Ratio Index (RGRI),Normalized Difference Flood Index (NDFI),COSMO-SkyMed,Random Forest,Enhanced Vegetation Index (EVI),multi-temporal,summer crops,Landsat 8 OLI,rule-based classification,agriculture
更新于2025-09-10 09:29:36