| 1 | 耕地复种指数遥感监测研究进展显示文摘复种指数是进行粮食估产、耕地集约利用评价、农业生态系统模拟等的关键参数,及时、准确地提取复种指数对于粮食安全、土地管理和生态环境安全具有重要意义。在传统的研究中,复种指数主要来源于地面统计数据。使用统计数据来计算复种指数虽然过程简单,但是计算结果存在信息滞后、无法体现统计单元内部的空间异质性、精度低等不足。遥感技术因具有大范围、高时效、低成本等优点而被用于耕地复种指数监测,已有学者对耕地复种指数的遥感监测开展了大量工作。本文以复种指数遥感提取的关键环节为主线,对1997—2020年国内外相关研究进行综述:首先,梳理了已有研究中的监测方法、高质量时间序列遥感数据获取方法及提取结果精度验证方法,并对不同方法的优缺点进行了总结;其次,对已有研究中存在的不足进行了探讨,并提出未来研究的侧重点:①开展已有监测方法的对比和分析;②加强地形复杂地区、小农尺度的监测力度;③提高遥感数据时空分辨率及处理效率;④对提取结果进行多尺度验证。 | 葛中曦 黄静 赖佩玉 郝斌飞 赵银军 马明国 | 2021 | 地球信息科学学报2021，23，7： | 5 |
| 2 | 时序滤波对农作物遥感识别的影响显示文摘获取长时序且高质量遥感观测数据是捕捉不同农作物关键物候节律信息,进而获取高精度农作物空间分布信息的关键。受云雨天气影响,卫星遥感易产生低质量观测,其往往不参与或采用时序滤波处理后再用于农作物遥感识别。然而,时序滤波对于农作物遥感识别的影响机制尚未摸清,为高效且高精度农作物遥感制图带来了较大挑战。该研究基于HLS(Harmonized Landsat Sentinel-2,30 m)和MODIS(Moderate-resolution Imaging Spectroradiometer,500 m)两种空间尺度的反射率产品分别构建时间序列数据集,以S-G(Savitzky–Golay)和HANTS(Harmonic Analysis of Time Series)滤波为例,采用随机森林分类器探究时序滤波分别对于30与500 m空间尺度农作物识别的影响。并通过植被指数时序曲线对比,深入分析时序滤波对于两种空间尺度农作物识别关键特征所带来的差异。结果表明,在该研究试验区域及观测时相内,针对中高分辨率HLS影像,相较于未经滤波处理数据,S-G和HANTS滤波农作物识别精度分别下降了1.73和5.17个百分点;而对于中低分辨率MODIS影像,未经滤波处理、S-G滤波和HANTS滤波后的农作物总体识别精度分别为84.73%、85.51%和83.05%,时序滤波前后农作物识别精度没有显著差异。此外,对比不同作物类型的植被指数时序曲线后发现,中高分辨率尺度下,对于大豆与玉米,其较小的类间差异会被时间滤波误认为噪声而进一步弱化,从而降低相似农作物的分类精度,而对于特征差异较明显的水稻与玉米、水稻与大豆,时序滤波则会减小其类内差异从而提高分类精度。对比而言,对于中低分辨率影像的农作物识别,受到混合像元等因素的干扰,同一作物类型中的光谱异质性是影响农作物识别精度的重要因素,因此时序滤波对其时序特征的影响较小。该研究通过深入分析时序滤波处理对不同空间尺度农作物识别的影响,为未来农作物空间分布的获取提供理论参考和技术支撑。 | 张馨予 蔡志文 杨靖雅 王聪 魏浩东 胡琼 徐保东 | 2022 | 农业工程学报2022，38，4： | 5 |
| 3 | Constructing a 30m African Cropland Layer for 2016 by Integrating Multiple Remote sensing,crowdsourced,and Auxiliary Datasets显示文摘Despite its essential importance to various spatial agriculture and environmental applications,the information on actual cropland area and its geographical distribution remain highly uncertain over Africa among remote-sensing products.Each of the African regions has its unique physical and environmental limiting factors to accurate cropland mapping,which leads to high spatial discre-pancies among remote sensing cropland products.Since no dataset could cope with all limitations,multiple datasets initially derived from various remote sensing sensors and classification techniques must be integrated into a more accurate cropland product than individual layers.Here,in the current study,four cropland products,produced initially from multiple sensors(e.g.Landsat-8 OLI,Sentinel-2 MSI,and PROBA-V)to cover the period(2015-2017),were integrated based on their cropland mapping accuracy to build a more accurate cropland layer.The four cropland layers’accuracy was assessed at Agro-ecological zones units via an inten-sive reference dataset(17,592 samples).The most accurate crop-land layer was then identified for each zone to construct the final cropland mask at 30 m resolution for the nominal year of 2016 over Africa.As a result,the new layer was produced in higher cropland mapping accuracy(overall accuracy=91.64%and cropland’s F-score=0.75).The layer mapped the African cropland area as 282 Mha(9.38%of the Continent area).Compared to earlier crop-land synergy layers,the constructed cropland mask showed a considerable improvement in its spatial resolution(30 m instead of 250 m),mapping quality,and closeness to official statistics(R^(2)=0.853 and RMSE=2.85 Mha).The final layer can be down-loaded as described under the“Data Availability Statement”section. | Mohsen Nabil Miao Zhang Bingfang Wu Jose Bofana Abdelrazek Elnashar | 2022 | Big Earth Data2022，6，1： | 1 |
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