融合时间序列环境卫星数据与物候特征的水稻种植区提取

获取高精度的区域水稻种植面积对于农业规划、配置与决策具有重要意义。区域尺度的水稻面积获取依赖于高时空分辨率影像,但受卫星回访周期和气候影响,难以获取足够时间序列的高时空分辨率影像,从而影响水稻种植面积遥感提取的精度。为此,提出适应于中国南方多雨云天气地区,基于国产环境卫星(HJ-1A/1B)与MODIS融合数据的水稻种植面积提取的新方法。以洞庭湖区为实验区,利用STARFM模型融合环境卫星NDVI数据与MODIS13Q1数据,获取时间序列的环境卫星NDVI数据,利用水稻关键期的NDVI数据结合物候特征参数对水稻种植区域进行提取。结果表明,该方法能有效提取区域水稻种植的面积,水稻种植面积提取的总体精度与Kappa系数分别达到91.71%与0.9024,分类结果明显优于仅采用多光谱影像或NDVI数据。该研究为中国南方多雨云天气地区水稻种植面积提取提供了有效的方法。     

Analysis of differences in vegetation phenology cycle of abandoned farmland,using harmonic analysis of time-series vegetation indices data: the case of Gwangyang City,South Korea

ABSTRACT

Globally, countries have experienced substantial increases in farmland abandonment. Although vegetation phenology is a key factor for the classification of land use, understanding of the phenological change of abandoned farmland is lacking. Using harmonic analysis of NDVI and NDWI extracted from Landsat imagery, this study investigates the distinctive phenological characteristics of abandoned farmland, which contrasts with that of three other agricultural types (paddy, agricultural field, orchard) in the study site of Gwangyang City in Jeollanam Province, South Korea. The results suggest that abandoned farmland has higher overall greenness coverage and overall water content in vegetation than the other uses. In terms of both indices, abandoned farmlands changed with relatively less fluctuation than those of other uses, suggesting the existence of constant and unmanaged vegetation from ecological succession, which differs from crop fields that undergo cultivation procedures. The significant harmonic components differed among agricultural types and vegetation indices. In paddy, NDVI was explained with multiple, higher-order harmonic components, while in other types only first-order components met the 5% statistical significance level. With NDWI, land types were more clearly discernible, because of the different cultivation procedures involving water: wet-field method (paddy), dryland farming (orchard, agricultural field), and no cultivation (abandoned farmland). The analysis confirms that harmonic analysis could be useful in discerning abandoned farmland among areas of active agricultural use and shows that the statistical significance of harmonic terms can be employed as indicators of different agricultural types. The observed pattern of the geographic distribution of abandoned farmland has policy implications for the promotion of sustainable reuse of marginal farmland.     

Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes

Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user’s accuracies of sedge swamp and paddy respectively.     

Vegetation mapping with multitemporal NDVI in North Eastern China Transect (NECT)

Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI data in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. Research has also shown that NDVI provides potential to derive meaningful metrics that describe ecosystem functions. In this paper, we have applied both unsupervised “k-means” classification and supervised minimum distance classification as derived from temporal changes in NDVI measured in 1997 along the North Eastern China Transect (NECT), and we have also utilized the same two classification methods together with NDVI-derived metrics, namely maximum NDVI, mean NDVI, NDVI amplitude, NDVI threshold, total length of growing season, fraction of growing season during greenup, rate of greenup, rate of senescence, integrated NDVI during the growing season, and integrated NDVI during greenup/integrated NDVI during senescence to map vegetation. The main objectives of this study are: (1) to test the relative performance of NDVI temporal profile metrics and NDVI-derived metrics for vegetation cover discrimination in NECT; (2) to test the relative performance of unsupervised (k-means) and supervised (minimum distance) methods for vegetation mapping; (3) to test the accuracy of the IGBP-DIS released land cover map for NECT; (4) to provide an up-to-date vegetation map for NECT. The results suggest that the classifications based on NDVI temporal profile metrics have higher accuracies than those based on any other metrics, such as NDVI-derived metrics, or all (NDVI temporal profile metrics + NDVI-derived metrics), or 15 metrics (NDVI temporal profile + Rate of greenup, Rate of senescence, and Integrated NDVI in greenup/integrated NDVI in senescence) for both methods. And among them, unsupervised k-means classification had the highest overall accuracy of 52% and Kappa coefficient of 0.2057. Both unsupervised (k-means) and supervised (minimum distance) methods achieved similar accuracies for the same metrics. The accuracy of IGBP-DIS released land cover map had an overall accuracy of 37% and a Kappa coefficient is 0.1441, and can improve to 46% by decomposing the crop/natural vegetation mosaic to cropland and other natural vegetation types. The results support using unsupervised k-means classification based on NDVI temporal profile metrics to provide an up-to-date vegetation cover classification. However, new effort is necessary in the future in order to improve the overall performance on this issue.     

构建时空融合模型进行水稻遥感识别

传统变化检测手段进行水稻遥感识别受"云污染"和影像间配准误差导致的变化检测误差累积及"椒盐"现象的影响,水稻遥感识别精度低。本文提出时空融合模型(Temporal-Spatial-Fusion Model,TSFM)进行水稻遥感识别,旨在综合像元在时间、空间维度上的信息定义像元的水稻时空归属度,根据时空归属度划分阈值提取水稻。实验结果表明:在不同窗口尺度下,TSFM在整体和"云污染"区域对水稻提取均达到了较高精度。当窗口尺度为3×3时,水稻提取的用户精度、制图精度和总体精度分别达到93.4%、83.5%和87.9%。在不同窗口尺度下水稻提取的用户精度、制图精度、总体精度均高于分类后比较PCC(Post-Classification Comparison)和多数投票法(Majority Voting,MV);在"云污染"区域,水稻识别总体精度均在92.0%以上,水稻制图精度比PCC、MV分别至少提高了14.0%、7.6%。有效地解决了传统变化检测作物遥感识别存在的误差累积问题,在一定程度上避免了"云污染"和"椒盐"现象对识别结果的影响。另外,初步探讨了TSFM水稻提取精度与景观特征关系,发现在景观规整区域适宜采用较小的窗口,在破碎区域适宜采用较大的窗口。该方法的成功实施,为大范围开展秋粮作物遥感识别,消除"云"影响进行了前期实验探讨。     

Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images

Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between Rice_Landsat and Rice_NLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region.     

Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain

Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat–maize and winter wheat–cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement with in situ observations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region.     

基于多源国产高分卫星时空信息的米级分辨率耕地提取

及时准确地获取耕地空间分布数据对于农业生产管理、产量估算、种植结构调整等具有重要意义。目前的耕地提取多基于多时相中低分辨率影像或单时相高分辨率影像,难以满足耕地破碎,农作物种植模式复杂的区域精度需求。基于此,本研究通过协同国产高分一号（GF-1）、高分二号（GF-2）和高分六号（GF-6）卫星影像,探索米级分辨率尺度下的耕地高精度提取方法。该方法以深度神经网络UNet为基础,通过协同GF-1/6的多时相优势和GF-2影像的高空间分辨率构建了CEUNet（Cropland Extraction UNet）模型,以充分挖掘耕地的时相特征和空间几何特征。同时,将基于CEUNet模型提取的米级耕地结果分别与基于UNet和多源不同分辨率遥感影像的语义分割（UNet_m）、基于UNet和单时相高分辨率影像的语义分割（UNet_s）、基于对象的随机森林分类（OBIA）、基于像元的随机森林分类（RF）提取的耕地结果展开对比,分析所提出的方法在不同区域的适宜性。结果表明,基于CEUNet模型提取的米级耕地总体精度达到92.92%,且基于CEUNet提取的耕地的逐像元验证结果在平均F1-Score值上相较于基于对象和基于像元的随机森林分类分别提升了0.21和0.21,相较于UNet_m和UNet_s分别提升了0.04和0.11,其中针对地块破碎,景观异质性高等区域,CEUNet相较于UNet_m和UNet_s提升了0.09和0.26。本研究提出的CEUNet模型能够充分发挥多源国产高分卫星数据的空间和时间优势,两者结合能够快速、高效地提取不同农业景观及不同种植模式的耕地空间分布信息。     

Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China

ABSTRACT

Researchers, policy makers, and farmers currently rely on remote sensing technology to monitor crops. Although data processing methods can be different among different remote sensing methods, little work has been done on studying these differences. In order for potential users to have confidence in remote sensing products, an analysis of mapping accuracies and their associated uncertainties with different data processing methods is required. This study used the MOD09A1 and MYD09A1 products of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite, from which the Enhanced Vegetation Index (EVI) and the two-band EVI (EVI2) images were obtained. The objective of this study was to analyze the accuracy of different data processing combinations for multi-year rice area mapping. Sixteen combinations of EVI and EVI2 with two cloudy pixel removal methods (QA/BLUE) and four pixel replacement methods (MO/MY/MOY/MYO) were investigated over the Jiangsu Province of southeast China from 2006 to 2016. Different accuracy results were obtained with different data processing combinations for multi-year rice field mapping. Based on a comparison of the relative performance of different MODIS products and processing method combinations, EVI2_BLUE_MYO was proposed to be the optimal processing method, and was applied to forecasting the rice-planted area of 2017. Study results from 2006 to 2017 were validated against reference data and showed accuracies of rice area extraction of greater than 95%. The mean absolute error of transplanting, heading, and maturity dates were 11.55, 8.10, and 7.78 days, respectively. In 2017, two sample regions (A and B) were selected from places where rice fractional cover was greater than 75%. Rice area extraction accuracies of 85.0% (A) and 92.3% (B) were obtained. These results demonstrated the complementarity of MOD09A1 and MYD09A1 datasets in enhancing pixel spatial coverage and improving rice area mapping when atmospheric influences are significant. The optimal data processing combination indentified in this study is promising for accurate multi-year and large-area paddy rice information extraction and forecasting.     

Remote sensing classification approach to large-scale crop cultivation identification: A case study of the Aral Sea Basin

Since the collapse of the Soviet Union, the crop cultivation structure in the Aral Sea Basin has changed dramatically, and these changes are worth studying. However, historical crop remote sensing mapping at the watershed scale remains challenging, especially crop misclassification at the cropland edge due to mixed pixels. Therefore, we proposed a field segmentation approach to constrain field edges based on time-series Sentinel-2 remote sensing images and the Google Earth Engine platform and then employed the random forest algorithm to perform crop classification based on time series Landsat/Sentinel-2 images and crop phenology information to produce historical crop maps in the Aral Sea Basin from the 1990s onward. The results showed that the intersection over union between the extracted field edges and in situ-measured field size data was 0.65. The overall accuracy of crop mapping was 95.2% in 2019. Then, we extended our method to historical mapping over the 1991–2015 period with accuracies ranging from 82.8% to 91.3%. Moreover, our method applied to historical mapping works well in terms of accuracy and policy matching. These findings indicate that our method can accurately distinguish cropland edges to reduce classification errors due to mixed pixels. This method is promising for solving the cropland edge problem for historical crop mapping in the Aral Sea Basin and can potentially provide a reference for historical crop classification in other watersheds of the world.     

Global rain-fed,irrigated, and paddy croplands: A new high resolution map derived from remote sensing,crop inventories and climate data

Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.     

黄土地区高中低产农田成因及遥感分类方法研究——以山西省定襄县为例

利用空间遥感信息在地理信息系统支持下，以山西省定襄县为试验区，进行黄土地区高中低产农田监测时，选用与高中低产农田成因直接关系的盐碱程度、灌溉等级和地形坡度等专题信息与TM数据进行信息复合，采用先分枢分类最后合并的复合分层分类方法，改进了常规的遥感分类方法，大大提高了分类精度。     

Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain,Northeast China

Increasing interest in wetlands for environmental management requires an understanding of the location, spatial extent, and configuration of the resource. The National Wetlands Inventory is the most commonly used data source for this information. However, its accuracy is limited in some contexts, such as agricultural and forested wetlands. An large number of studies have mapped wetlands worldwide from the perspective of land use and land cover change. However, information on the actual wetland planting areas annually is limited, which greatly impacts ongoing research. In this case study of the West Songnen Plain, we developed a simple algorithm for the quick mapping of wetlands by utilizing their unique physical features, such as annual display of phenological land-cover change of exposed soils, shallow flooding water, and plants from multi-temporal Landsat images. Temporal variations of the Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) derived from Landsat images in 2010 for wetlands at different growth stages were analyzed. Results show that during the ante-tillering phase, the NDVI value (above zero) is lower than the LSWI value of paddies because of flooding of shallow water; during the reproductive and ripening phases, the NDVI value is higher than the LSWI value (above zero); and during the post-harvest wetland planting phase, the NDVI value is still higher than the LSWI value, but the LSWI value is negative. Wetland areas can be detected using one or two images in the optimum time window. The algorithm based on the difference of NDVI and LSWI values derived from Landsat images was used to extract the actual wetland planting area. Validated alongside statistical data, the algorithm showed high accuracy. Therefore, this algorithm highlights the unique features of wetlands and can help in mapping the actual wetland area annually on a regional scale. Results further indicate that the new method has a classification accuracy of 92 %. In comparison, two traditional methods based on Landsat-7/ETM registered accuracy rates of only 83 % and 87 % respectively.     

利用MODIS数据识别水稻关键生长发育期

利用遥感方法提取中国范围内的水稻关键生长发育期。首先, 对时间序列Terra MODIS-EVI(Enhanced Vegetation Index)进行傅里叶和小波低通滤波平滑处理, 然后, 根据水稻在移栽期、分蘖初期、抽穗期和成熟期的EVI变化特征, 实现对各个生长发育期的识别。通过将利用2005年MODIS数据识别的结果与当年气象台站的地面观测资料进行比较, 采用本研究中的识别方法得出的水稻各个生长发育期的绝对误差大部分小于16d, 经过F检验表明提取的结果与地面观测资料在0.05水平下具有显著一致性。研究中的信息提取方法可被用于其他年份的水稻生长发育期识别, 根据其他作物的生长发育特点, 也可能适合于提取其他作物的生长发育期。     

Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system

Cropland fallows are the next best-bet for intensification and extensification, leading to increased food production and adding to the nutritional basket. The agronomical suitability of these lands can decide the extent of usage of these lands. Myanmar’s agricultural land (over 13.8 Mha) has the potential to expand by another 50% into additional fallow areas. These areas may be used to grow short-duration pulses, which are economically important and nutritionally rich, and constitute the diets of millions of people as well as provide an important source of livestock feed throughout Asia. Intensifying rice fallows will not only improve the productivity of the land but also increase the income of the smallholder farmers. The enhanced cultivation of pulses will help improve nutritional security in Myanmar and also help conserve natural resources and reduce environmental degradation. The objectives of this study was to use remote sensing methods to identify croplands in Myanmar and cropland fallow areas in two important agro-ecological regions, delta and coastal region and the dry zone. The study used moderate-resolution imaging spectroradiometer (MODIS) 250-m, 16-day normalized difference vegetation index (NDVI) maximum value composite (MVC), and land surface water index (LSWI) for one 1 year (1 June 2012–31 May 2013) along with seasonal field-plot level information and spectral matching techniques to derive croplands versus cropland fallows for each of the three seasons: the monsoon period between June and October; winter period between November and February; and summer period between March and May. The study showed that Myanmar had total net cropland area (TNCA) of 13.8 Mha. Cropland fallows during the monsoon season account for a meagre 2.4% of TNCA. However, in the winter season, 56.5% of TNCA (or 7.8 Mha) were classified as cropland fallows and during the summer season, 82.7% of TNCA (11.4 Mha) were cropland fallows. The producer’s accuracy of the cropland fallow class varied between 92 and 98% (errors of omission of 2 to 8%) and user’s accuracy varied between 82 and 92% (errors of commission of 8 to 18%) for winter and summer, respectively. Overall, the study estimated 19.2 Mha cropland fallows from the two major seasons (winter and summer). Out of this, 10.08 Mha has sufficient moisture (either from rainfall or stored soil water content) to grow short-season pulse crops. This potential with an estimated income of US$ 300 per hectare, if exploited sustainably, is estimated to bring an additional net income of about US$ 1.5 billion to Myanmar per year if at least half (5.04 Mha) of the total cropland fallows (10.08 Mha) is covered with short season pulses.     

利用神经网络方法提取水稻种植面积—以湖北省双季早稻为例

在利用NOAA数据提取水稻种植面积的过程中，由于其地面分辨率较低，存在大量混合像元问题，使得提取精度不够理想，该文基于神经网络方法即可以提供多源数据的输入，又不受数据分布假设限制的特点，从NOAA图像演算最能反应朋稻分布信息的绿度指数（NDVI）和日夜温差值，将其重采样，然后加入对水稻生产区域有重要影响的土壤类型，土地利用类型及高程分布等信息，以TM图像作为准直值进行分类，获得较为理想的湖北省双季早稻种植面积。     

结合MNF变换与灰值形态学的三江平原多光谱、多时相MODIS遥感影像分类

通过对影像进行光谱特征分析,及对各种植被类型进行物候特征分析,选用以NDVI数据为主的多波段、多时相的MODIS影像数据进行最小噪声分离MNF变换,然后进行灰值形态学滤波,运用阈值分割法提取旱地,并运用自组织特征映射SOM神经网络聚类模型分离湿地和水田。实验结果与现有的研究成果相比,精度有较大提高。     

高分辨率遥感影像城中村提取的景观语义指数方法

城中村是中国一类特殊的非正式居民区。本文从城中村的物理特点出发,采用景观语义指数描述复杂的城中村场景,提出基于景观语义指数的高分辨率遥感影像城中村提取方法,并采用“分类置信度-反馈”机制进行实际可操作的大范围城中村制图。以广州市核心城区为例,城中村检测的总体精度达到了90%以上。试验结果表明相对于传统的光谱、纹理特征,景观语义指数能够更好地描述城中村的根本形态特点,更准确的城中村提取。此外,“分类置信度-反馈”机制能够充分参考机器学习的分类概率,以有限的人工干预生产更加准确的城中村制图产品。因此,本文方法能够有效应用于大范围的城中村提取与制图。     

Using NDVI Differencing and Temporal Logic to Enhance ISODATA Classification in Urban Environments

This paper presents a new approach to improving land use/cover mapping accuracy in an urban environment. Bi-temporal Landsat TM images (1987 and 1997) were initially classified using the ISODATA method. An NDVI difference image was derived and classified, with each class indicating certain land use/cover changes. Temporal logical reasoning was then performed on the classified NDVI difference map and the initial land use/cover maps. The procedure successfully resolved the confusion between forest clear-cuts/fallow cropland and urban, as well as between forest clear-cuts and cropland. The kappa analysis test led to a Z value of 1.837 with the p-value of 0.026 for the year 1987, and a Z value of 1.924 with the p-value of 0.014 for 1997, indicating significant enhancement at the 95% confidence level.     

多时相MODIS影像水田信息提取研究

水稻种植及其分布信息是土地覆被变化、作物估产、甲烷排放、粮食安全和水资源管理分析的重要数据源。基于遥感的水田利用监测中,通常采用时序NDVI植被指数法和影像分类法分别进行AVHRR和TM影像的水田信息获取。针对8天合成MODIS陆地表面反射比数据的特点和水稻生长特征,选取水稻种植前的休耕期、秧苗移植期、秧苗生长期和成熟期等多时相MODIS地表反射率影像数据,通过归一化植被指数、增强植被指数及利用对土壤湿度和植被水分含量较敏感的短波红外波段计算得到的陆表水指数进行水田信息获取。将提取结果与基于ETM＋影像的国土资源调查水田数据,通过网格化计算处理并进行对比分析,结果表明,利用MODIS影像的8天合成地表反射率数据,进行区域甚至全国的水田利用监测是可行的。