Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultiv...Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.展开更多
针对黑河流域大尺度环境下水体提取难度大、演变规律尚不明晰等问题,基于谷歌地球引擎(Google Earth Engine,GEE)处理黑河流域1986—2024年Landsat影像,采集7.8×10^(4)个水体/非水体样本并构建逐年样本数据集,通过将多波段水体指数...针对黑河流域大尺度环境下水体提取难度大、演变规律尚不明晰等问题,基于谷歌地球引擎(Google Earth Engine,GEE)处理黑河流域1986—2024年Landsat影像,采集7.8×10^(4)个水体/非水体样本并构建逐年样本数据集,通过将多波段水体指数(Multi band water index,MBWI)、增强型水体指数(Enhanced water index,EWI)、改进归一化差异水体指数(Modified normalized difference water index,MNDWI)与光谱波段进行单独与统一组合,构建并筛选出最佳融合水体指数的随机森林(Random forest,RF)水体提取方法,提取了研究区39个时相的逐年地表水体影像,采用曼-肯德尔(Mann-Kendall,M-K)法揭示了黑河流域逐年地表水体面积变化特征,基于主成分与敏感性分析探究了影响地表水体演变的主要驱动因素。结果表明:融合3种水体指数(MBWI、EWI、MNDWI)的随机森林水体提取方法对黑河流域Landsat影像的水体提取效果最佳,平均总体精度(Overall accuracy,OA)为96.16%,平均Kappa系数为0.9128;经M-K法检验,黑河流域1986—2024年地表水体面积呈波动减少态势;年降水量、人口、年蒸散量为黑河流域地表水体演变的最主要驱动因素。研究结果可为全流域地表水体的快速准确提取提供理论支持。展开更多
This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration amo...This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration among the four most popular Landsat sensors. A total of 920 Landsat Collection 1 scenes were evaluated against the corresponding Pre-Collection images over a Pseudo-Invariant Site, Railroad Valley Playa Nevada, United States (RVPN). The radiometric performance of the six Landsat solar reflective bands, in terms of both Digital Numbers (DNs) and at-sensor Top of Atmosphere (TOA) reflectance, on the sensor cross-calibration was examined. Results show that absolute radiometric calibration at DNs level was applied to the Landsat-4 and -5 TM (L4 TM and L5 TM) by –1.119% to 0.126%. For L4 TM and L5 TM, the cross-calibration decreased the radiometric measurement level by rescaling at-sensor radiance to DN values. The radiometric changes, –0.77% for L4 TM, 0.95% for L5 TM, –0.26% for L7 ETM+, and –0.01% for L8 OLI, were detected during the cross-calibration stage of converting DNs into TOA reflectance. This study has also indicated that the long-term radiometric performance for the Landsat Collection 1 archive is promising. Supports of these conclusions were demonstrated through the time-series analysis based on the Landsat Collection 1 image stack. Nevertheless, the radiometric changes across the four Landsat sensors raised concerns of the previous Landsat Pre-Collection based results. We suggest that Landsat users should pay attention to differences in results from Pre-Collection and Collection 1 time-series data sets.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42101342,U2243205)the Third Comprehensive Scientific Expedition to Xinjiang(No.2021XJKK1403)。
文摘Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.
文摘针对黑河流域大尺度环境下水体提取难度大、演变规律尚不明晰等问题,基于谷歌地球引擎(Google Earth Engine,GEE)处理黑河流域1986—2024年Landsat影像,采集7.8×10^(4)个水体/非水体样本并构建逐年样本数据集,通过将多波段水体指数(Multi band water index,MBWI)、增强型水体指数(Enhanced water index,EWI)、改进归一化差异水体指数(Modified normalized difference water index,MNDWI)与光谱波段进行单独与统一组合,构建并筛选出最佳融合水体指数的随机森林(Random forest,RF)水体提取方法,提取了研究区39个时相的逐年地表水体影像,采用曼-肯德尔(Mann-Kendall,M-K)法揭示了黑河流域逐年地表水体面积变化特征,基于主成分与敏感性分析探究了影响地表水体演变的主要驱动因素。结果表明:融合3种水体指数(MBWI、EWI、MNDWI)的随机森林水体提取方法对黑河流域Landsat影像的水体提取效果最佳,平均总体精度(Overall accuracy,OA)为96.16%,平均Kappa系数为0.9128;经M-K法检验,黑河流域1986—2024年地表水体面积呈波动减少态势;年降水量、人口、年蒸散量为黑河流域地表水体演变的最主要驱动因素。研究结果可为全流域地表水体的快速准确提取提供理论支持。
文摘This study evaluates the long-term radiometric performance of the USGS new released Landsat Collection 1 archive, including the absolute calibration of each Landsat sensor as well as the relative cross-calibration among the four most popular Landsat sensors. A total of 920 Landsat Collection 1 scenes were evaluated against the corresponding Pre-Collection images over a Pseudo-Invariant Site, Railroad Valley Playa Nevada, United States (RVPN). The radiometric performance of the six Landsat solar reflective bands, in terms of both Digital Numbers (DNs) and at-sensor Top of Atmosphere (TOA) reflectance, on the sensor cross-calibration was examined. Results show that absolute radiometric calibration at DNs level was applied to the Landsat-4 and -5 TM (L4 TM and L5 TM) by –1.119% to 0.126%. For L4 TM and L5 TM, the cross-calibration decreased the radiometric measurement level by rescaling at-sensor radiance to DN values. The radiometric changes, –0.77% for L4 TM, 0.95% for L5 TM, –0.26% for L7 ETM+, and –0.01% for L8 OLI, were detected during the cross-calibration stage of converting DNs into TOA reflectance. This study has also indicated that the long-term radiometric performance for the Landsat Collection 1 archive is promising. Supports of these conclusions were demonstrated through the time-series analysis based on the Landsat Collection 1 image stack. Nevertheless, the radiometric changes across the four Landsat sensors raised concerns of the previous Landsat Pre-Collection based results. We suggest that Landsat users should pay attention to differences in results from Pre-Collection and Collection 1 time-series data sets.
