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Murali Krishna Gumma Prasad S. Thenkabail Pardharsadhi Teluguntla Mahesh N. Rao Irshad A. Mohammed Anthony M. Whitbread 《International Journal of Digital Earth》2016,9(10):981-1003
The goal of this study was to map rainfed and irrigated rice-fallow cropland areas across South Asia, using MODIS 250?m time-series data and identify where the farming system may be intensified by the inclusion of a short-season crop during the fallow period. Rice-fallow cropland areas are those areas where rice is grown during the kharif growing season (June–October), followed by a fallow during the rabi season (November–February). These cropland areas are not suitable for growing rabi-season rice due to their high water needs, but are suitable for a short -season (≤3 months), low water-consuming grain legumes such as chickpea (Cicer arietinum L.), black gram, green gram, and lentils. Intensification (double-cropping) in this manner can improve smallholder farmer’s incomes and soil health via rich nitrogen-fixation legume crops as well as address food security challenges of ballooning populations without having to expand croplands. Several grain legumes, primarily chickpea, are increasingly grown across Asia as a source of income for smallholder farmers and at the same time providing rich and cheap source of protein that can improve the nutritional quality of diets in the region. The suitability of rainfed and irrigated rice-fallow croplands for grain legume cultivation across South Asia were defined by these identifiers: (a) rice crop is grown during the primary (kharif) crop growing season or during the north-west monsoon season (June–October); (b) same croplands are left fallow during the second (rabi) season or during the south-east monsoon season (November–February); and (c) ability to support low water-consuming, short-growing season (≤3 months) grain legumes (chickpea, black gram, green gram, and lentils) during rabi season. Existing irrigated or rainfed crops such as rice or wheat that were grown during kharif were not considered suitable for growing during the rabi season, because the moisture/water demand of these crops is too high. The study established cropland classes based on the every 16-day 250?m normalized difference vegetation index (NDVI) time series for one year (June 2010–May 2011) of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using spectral matching techniques (SMTs), and extensive field knowledge. Map accuracy was evaluated based on independent ground survey data as well as compared with available sub-national level statistics. The producers’ and users’ accuracies of the cropland fallow classes were between 75% and 82%. The overall accuracy and the kappa coefficient estimated for rice classes were 82% and 0.79, respectively. The analysis estimated approximately 22.3?Mha of suitable rice-fallow areas in South Asia, with 88.3% in India, 0.5% in Pakistan, 1.1% in Sri Lanka, 8.7% in Bangladesh, 1.4% in Nepal, and 0.02% in Bhutan. Decision-makers can target these areas for sustainable intensification of short-duration grain legumes. 相似文献
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基于统计年鉴和MODIS的中国区域土地利用/覆盖变化特征研究 总被引：3，自引：0，他引：3
土地利用/覆盖变化(Land Use/Cover Change,LUCC)是影响区域气候变化不可忽略的因素,然而目前还没有十分可靠的土地利用/覆盖数据,因此其对气候变化影响的研究存在很大不确定性。基于此,利用中国统计年鉴(简称年鉴)和中分辨率成像光谱仪(MODerate-resolution Imaging Spectroradiometer,MODIS)遥感观测资料,以行政区为研究单元对3种典型土地利用/覆盖类型(森林、城市和农田)的变化进行了比较分析。结果表明:(1)2004~2011年,年鉴和MODIS森林覆盖率在中国各省(市、区)的分布和变化整体一致性较好,年鉴和MODIS数据都显示全国大部分省(市、区)份的森林覆盖率均有不同程度的增加,而仅MODIS数据中的北京、天津、吉林、黑龙江、上海、江苏森林覆盖率降低。MODIS数据能准确反映森林总体覆盖情况,由于类别精度不够,对单种森林类型及变化的描述存在较大误差。(2)年鉴表明中国东部城市建成区覆盖率及其增长远高于西部地区,黄淮海地区、东南部沿海地区城镇用地呈现加速扩张趋势,符合实际情况。但MODIS数据没有表征出中国区域近10年来的快速城市化进程,可能原因是城市面积较小,且地面地物分布复杂,受空间分辨率的限制,在离散的不集中的区域MODIS数据的土地覆盖类型分类精度较低,导致监测不到新城市的扩张。(3)MODIS农田面积和年鉴农作物播种面积、有效灌溉面积覆盖率的数值和空间分布均有较好的一致性,均是黄淮海地区覆盖率最大。但在2001~2011年,3种数据变化量的差异较大,尤其东部省(市、区)份,MODIS农田面积和年鉴有效灌溉面积增加,而年鉴农作物播种面积减少。(4)2004~2011年中国土地利用类型变化中年鉴城市建成区的变化速率最大(6.25%),其次为森林;耕地由于总量大,变化部分所占的比例较小,因而MOIDS农田及年鉴农作物播种面积和有效灌溉面积的变化速率均较小。 相似文献
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《International Journal of Digital Earth》2013,6(9):944-977
ABSTRACTMapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rule-based Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multi-temporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000–2015. A comparison between the ACCA-derived cropland products (ACPs) for the year 2014 (ACP2014) versus RCP2014 provided an overall agreement of 89.4% (kappa?=?0.814) with six classes: (a) producer’s accuracies varying between 72% and 90% and (b) user’s accuracies varying between 79% and 90%. ACPs for the individual years 2000–2013 and 2015 (ACP2000–ACP2013, ACP2015) showed very strong similarities with several other studies. The extent and vigor of the Australian croplands versus cropland fallows were accurately captured by the ACCA algorithm for the years 2000–2015, thus highlighting the value of the study in food security analysis. The ACCA algorithm and the cropland products are released through http://croplands.org/app/map and http://geography.wr.usgs.gov/science/croplands/algorithms/australia_250m.html 相似文献
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