Arid
DOI10.1080/15481603.2017.1414010
Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series
Loew, Fabian1,2; Biradar, Chandrashekhar1; Dubovyk, Olena3; Fliemann, Elisabeth1; Akramkhanov, Akmal1; Vallejo, Alejandra Narvaez1; Waldner, Francois2,4
通讯作者Loew, Fabian ; Biradar, Chandrashekhar
来源期刊GISCIENCE & REMOTE SENSING
ISSN1548-1603
EISSN1943-7226
出版年2018
卷号55期号:4页码:539-567
英文摘要

In the context of growing populations and limited resources, the sustainable intensification of agricultural production is of great importance to achieve food security. As the need to support management at a range of spatial scales grows, decision-support tools appear increasingly important to enable the timely and regular assessment of agricultural production over large areas and identify priorities for improving crop production in low-productivity regions. Understanding productivity patterns requires the timely provision of gapless, spatial information about agricultural productivity. In this study, dense 30-m time series covering the 2004-2014 period were generated from Landsat and MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images over the irrigated cropped area of the Fergana Valley, Central Asia. A light-use efficiency model was combined with machine learning classifiers to assess the crop yield at the field level. The classification accuracy of land cover maps reached 91% on average. Crop yield and acreage estimates were in good agreement (R-2=0.812 and 0.871, respectively) with reported yields and acreages at the district level. Several indicators of cropland intensity and productivity were derived on a per-field basis and used to highlight homogeneous regions in terms of productivity by means of clustering. Results underlined that regions with lower water-use efficiency were not only located further away from irrigation canals and intake points, but also had limited access to markets and roads. The results underline that yield could be increased by roughly 1.0 and 1.4t/ha for cotton and wheat, respectively, if the access to water would be optimized in some of the regions. The minimum calibration requirement of the method and the fusion of multi-sensor data are keys to cope with the constraints of operational crop monitoring and guarantee a sustained and timely delivery of the agricultural indicators to the user community. The results of this study can form the baseline to support regional land- and water-resource management.


英文关键词agricultural management cropland use intensity crop yield crop type classification water-use efficiency
类型Article
语种英语
国家Jordan ; Germany ; Belgium
收录类别SCI-E ; SSCI
WOS记录号WOS:000432160000004
WOS关键词DECISION-SUPPORT-SYSTEM ; DESERT LOCUST HABITAT ; CENTRAL-ASIA ; WATER PRODUCTIVITY ; FERGANA VALLEY ; VEGETATION INDEXES ; RANDOM FORESTS ; MODIS DATA ; IRRIGATED AGRICULTURE ; FURROW IRRIGATION
WOS类目Geography, Physical ; Remote Sensing
WOS研究方向Physical Geography ; Remote Sensing
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/209729
作者单位1.ICARDA, Geoinformat Unit, Amman, Jordan;
2.MapTailor Geospatial Consulting GbR, Bonn, Germany;
3.Ctr Remote Sensing Land Surfaces ZFL, Bonn, Germany;
4.Catholic Univ Louvain, Earth & Life Inst Environm, Louvain La Neuve, Belgium
推荐引用方式
GB/T 7714
Loew, Fabian,Biradar, Chandrashekhar,Dubovyk, Olena,et al. Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series[J],2018,55(4):539-567.
APA Loew, Fabian.,Biradar, Chandrashekhar.,Dubovyk, Olena.,Fliemann, Elisabeth.,Akramkhanov, Akmal.,...&Waldner, Francois.(2018).Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series.GISCIENCE & REMOTE SENSING,55(4),539-567.
MLA Loew, Fabian,et al."Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series".GISCIENCE & REMOTE SENSING 55.4(2018):539-567.
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