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The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude–Pottier and Freeman–Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude–Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman–Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy. 相似文献
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Use of rainfall anomaly based Standardized Precipitation Index (SPI) and satellite-derived Vegetation Condition Index (VCI) are becoming common to assess the impacts of drought on crops. This study analysed spatio-temporal intra-seasonal and inter-seasonal relationships for 24 years between rainfall and NDVI and between SPI and VCI to understand crop response to water availability in the Rajasthan State, India. To separate the effect of weather and technology on crop growth over time, a modification in VCI was proposed and called “Trend Adjusted VCI” (VCITadj). The VCITadj was computed for early, mid, late and whole crop seasons by deriving pixel wise crop phenology metrics from NDVI profile. Significant linear relationships were found between NDVI and rainfall but phase of crop season affected the strength of this relationship. The SPI and VCITadj were linearly related in all the four seasons, the strength of relationship improved with the progress of crop season and these relationships were stronger than between rainfall and NDVI. These relationships broke down in irrigated croplands. As a result, the anomaly indices of SPI and VCITadj and their intra-seasonal relationships can be used to study the response of crops to water availability for early detection and better prognosis of agricultural drought. 相似文献
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该研究是加拿大Saskatchewan Scott农作物轮作系统(ACS)研究的一部分。研究始于1994年,历时18a,评价9个可耕种农作物产量系统的可靠性。由3种处理水平(organic,reduced,high)和3种作物多样性水平(low,diversified annual grains,diversified annual perennials)结合而产生的9个农作物产量系统,被用于监测和评价加拿大牧场不同处理和不同作物种植轮作下可耕种农作物的产量。在2003年生长季共收集了3次叶面积指数和光谱反射率的数据:生长季前期(6月)、生长季旺盛期(7月)、生长季后期(8月)。叶面积指数是由LAI-2000植物冠层分析仪监测的,光谱测量是由覆盖了350-2500 nm波长范围共2215个波段的ADS便携式高光谱仪完成的。结果显示,光学测量可以用于监测农作物生长状况的差异。从生长季的早期到中期,光谱和叶面积指数在不同处理下有显著差异。7月中期是用遥感资料监测农作物长势的最佳季节;红光波段与近红外波段反射率的比值和基于这两个波段构造的归一化植被指数,是检测农作物长势的最佳植被指数。 相似文献
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全球气候增暖对甘肃农作物生长影响的研究进展 总被引：16，自引：2，他引：14
系统总结了甘肃省科技攻关重大项目“甘肃干旱生态环境对全球气候变暖的响应研究”等3个课题的主要成果和研究进展。在揭示甘肃省现代气候变化基本特征是冬暖夏干的基础上,比较系统地综述了越冬作物（冬小麦、冬油菜）、喜凉作物（春小麦、马铃薯、胡麻）和喜温作物（玉米、棉花、酿酒葡萄）等8种主要农作物的生长发育、适生种植区高度和种植面积、产量和品质等对现代气候变化的响应特征。现代气候变化对甘肃农作物生长的影响非常突出,已对农作物安全生产与农业可持续发展构成了严重威胁。其研究结果为建立现代农业发展模式,旱作农业生产机制,农作物安全生产提供科学的指导意见。 相似文献
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