一种高时空分辨率NDVI数据集构建方法-STAVFM

ETM NDVI可以用来在30m的尺度上开展植被的监测,然而在Landsat卫星16天的重访周期和云污染等因素的影响下,常常会在相当长的一段时间内无法获取有效的ETM NDVI数据,给这一尺度下的植被动态监测带来了一定困难。相比之下,MODIS虽然在空间上只有250m分辨率的NDVI产品,却可以每天进行相同区域的监测。针对ETM空间分辨率高和MODIS时间分辨率高的特点,本研究选择实验区,基于对STARFM方法的改进,构建不同时空分辨率NDVI的时空融合模型-STAVFM,使用该模型对ETM NDVI与MODIS NDVI融合,构建了高时空分辨率NDVI数据集。研究结果表明,通过MODIS NDVI时间变化信息与ETM NDVI空间差异信息的有机结合,实现缺失高空间分辨率NDVI的有效预测（3景预测NDVI与实际NDVI的相关系数分别达到了0.82、0.90和0.91）,从而构建高时空分辨率NDVI数据集。所构建的高时空分辨率NDVI数据集在时间上保留了高时间分辨率数据的时间变化趋势,空间上又反映了高空间分辨率数据的空间细节差异。     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.     

融合高时空分辨率数据估算植被净初级生产力

植被净初级生产力NPP(Net Primary Production)遥感估算与分析,有赖于高时空分辨率的遥感数据,但目前中高分辨率的遥感数据受卫星回访周期及天气的影响,在中国南方地区难以获取连续时间序列的数据,从而影响了高精度的区域植被净初级生产力的遥感估算。为此,提出一种基于多源遥感数据时空融合技术与CASA模型估算高时空分辨率NPP的方法。首先,利用多源遥感数据,即Landsat8 OLI数据与MODIS13Q1数据,采用遥感数据时空融合方法,获得了时间序列的Landsat8 OLI融合数据;然后,基于Landsat8 OLI时空融合数据,并采用CASA模型,以长株潭城市群核心区为例,进行区域植被NPP的遥感估算。研究结果表明,基于时间序列Landsat融合数据估算的30m分辨率的NPP具有良好的空间细节信息,且估算值与实测值的相关系数达0.825,与实测NPP数据保持了较好的一致性。     

遥感影像时—空融合的“点”—“线”—“面”质量评价

遥感影像时—空融合可集成多源数据高空间分辨率和高时间分辨率互补优势,生成时间连续的高空间分辨率影像,在遥感影像的动态监测与时序分析等方面具有重要应用价值.然而,现有多数研究往往基于单一数据产品对时—空融合算法进行评价,而在实际生产应用中,需要验证算法在多种遥感产品数据的融合表现;此外,目前研究大多基于“单点时刻”进行评...     

Landsat8和MODIS融合构建高时空分辨率数据识别秋粮作物

本文利用Wu等人提出的遥感数据时空融合方法 STDFA(Spatial Temporal Data Fusion Approach)以Landsat 8和MODIS为数据源构建高时间、空间分辨率的遥感影像数据。以此为基础,构建15种30 m分辨率分类数据集,然后利用支持向量机SVM(Support Vector Machine)进行秋粮作物识别,验证不同维度分类数据集进行秋粮作物识别的适用性。实验结果显示,不同分类数据集的秋粮作物分类结果均达到了较高的识别精度。综合各项精度指标分析,Red+Phenology数据组合对秋粮识别效果最好,水稻识别的制图精度和用户精度分别达到91.76%和82.49%,玉米识别的制图精度和用户精度分别达到85.80%和74.97%,水稻和玉米识别的总体精度达到86.90%。     

Winter wheat biomass estimation using high temporal and spatial resolution satellite data combined with a light use efficiency model

Winter wheat biomass was estimated using HJ CCD and MODIS data, combined with a radiation use efficiency model. Results were validated with ground measurement data. Winter wheat biomass estimated with HJ CCD data correlated well with observed biomass in different experiments (coefficients of determination R² of 0.507, 0.556 and 0.499; n?=?48). In addition, R² values between MODIS estimated and observed biomass are 0.420, 0.502 and 0.633. Even if we downscaled biomass estimated using HJ CCD data to MODIS pixel size (9?×?9 HJ CCD pixels to approximate that MODIS pixel), R² values between estimated and observed biomass were still higher than those from MODIS. We conclude that estimation with remote sensing data, such as the HJ CCD data with high spatial resolution and shorter revisit cycle, can show more detail in spatial pattern and improve the application of remote sensing on a local scale. There is also potential for applying the approach to many other studies, including agricultural production estimation, crop growth monitoring and agricultural ecosystem carbon cycle studies.     

Comparison and assessment of NDVI time series for seasonal wetland classification

Satellite-based wetland mapping faces challenges due to the high spatial heterogeneity and dynamic characteristics of seasonal wetlands. Although normalized difference vegetation index (NDVI) time series (NTS) shows great potential in land cover mapping and crop classification, the effectiveness of various NTS with different spatial and temporal resolution has not been evaluated for seasonal wetland classification. To address this issue, we conducted comparisons of those NTS, including the moderate-resolution imaging spectroradiometer (MODIS) NTS with 500?m resolution, NTS fused with MODIS and Landsat data (MOD_LC8-NTS), and HJ-1 NDVI compositions (HJ-1-NTS) with finer resolution, for wetland classification of Poyang Lake. Results showed the following: (1) the NTS with finer resolution was more effective in the classification of seasonal wetlands than that of the MODIS-NTS with 500-m resolution and (2) generally, the HJ-1-NTS performed better than that of the fused NTS, with an overall accuracy of 88.12% for HJ-1-NTS and 83.09% for the MOD_LC8-NTS. Future work should focus on the construction of satellite image time series oriented to highly dynamic characteristics of seasonal wetlands. This study will provide useful guidance for seasonal wetland classification, and benefit the improvements of spatiotemporal fusion models.     

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r² = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions.     

基于超分辨率重建的多时相MODIS与Landsat反射率融合方法

提出一种基于超分辨率重建的MODIS与Landsat反射率图像融合方法,以STARFM算法与超分辨率重建为基础,使用观测的MODIS和Landsat地表反射率图像预测给定时刻的Landsat合成反射率图像。该方法利用基于稀疏表示的超分辨率重建方法对MODIS图像进行分辨率增强,实验结果表明这一操作能够增加原MODIS图像的空间细节,有助于提高STARFM算法的预测精度;另一方面,考虑输入两个基时刻图像相差较大时原STARFM算法预测的反射率会存在"时间平滑"的问题,限制每次只使用一个基时刻MODIS和Landsat图像对进行STARFM预测,使用逐图像块选择策略,从由两个基时刻图像分别进行预测得到的两组预测图像中选择最优的预测,同样得到了优于STARFM算法的预测结果。     

Extracting Regional Pattern of Wheat Sowing Dates Using Multispectral and High Temporal Observations from Indian Geostationary Satellite

Monitoring of Agricultural crops using remote sensing data is an emerging tool in recent years. Spatial determination of sowing date is an important input of any crop model. Geostationary satellite has the capability to provide data at high temporal interval to monitor vegetation throughout the entire growth period. A study was conducted to estimate the sowing date of wheat crop in major wheat growing states viz. Punjab, Haryana, Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan and Bihar. Data acquired by Charged Couple Detector (CCD) onboard Indian geostationary satellite INSAT 3A have continental (Asia) coverage at 1 km?×?1 km spatial resolution in optical spectral bands with high temporal frequency. Daily operational Normalized Difference Vegetation Index (NDVI) product from INSAT 3A CCD available through Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) was used to estimate sowing date of wheat crop in selected six states. Daily NDVI data acquired from September 1, 2010 to December 31, 2010 were used in this study. A composite of 7 days was prepared for further analysis of temporal profile of NDVI. Spatial wheat crop map derived from AWiFS (56 m) were re-sampled at INSAT 3A CCD parent resolution and applied over each 7 day composite. The characteristic temporal profiles of 7 day NDVI composite was used to determine sowing date. NDVI profile showed decreasing trend during maturity of kharif crop, minimum value after harvest and increasing trend after emergence of wheat crop. A mathematical model was made to capture the persistent positive slope of NDVI profile after an inflection point. The change in behavior of NDVI profile was detected on the basis of change in NDVI threshold of 0.3 and sowing date was estimated for wheat crop in six states. Seven days has been deducted after it reached to threshold value with persistent positive slope to get sowing date. The clear distinction between early sowing and late sowing regions was observed in study area. Variation of sowing date was observed ranging from November 1 to December 20. The estimated sowing date was validated with the reported sowing date for the known wheat crop regions. The RMSD of 3.2 (n?=?45) has been observed for wheat sowing date. This methodology can also be applied over different crops with the availability of crop maps.     

The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia

Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterised by large agricultural fields of around 75 ha on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross-checking the MODIS classification a Landsat ETM time series for four different dates in the season of 2002 was acquired and classified. We attempted to distinguish five different crop types and achieved satisfactory and good results for winter crops. Three hundred and sixty fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.     

基于HJ-1A/B卫星CCD数据的江汉平原农田物候探测

利用HJ-1A/B卫星CCD数据,提取2013—2015年三年江汉平原农田的归一化植被指数NDVI,构建时间序列曲线,利用小波变换对HJ-1A/B卫星所得的NDVI数据进行平滑降噪处理,结合地面调研资料,提取江汉平原农作物的物候信息。研究结果表明,HJ-1A/B卫星可用于农田物候监测,对于小区域的农田作物长势监测具有独特的优势。     

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas.     

Remote Sensing of Spatial and Temporal Vegetation Dynamics in Hispaniola: A Comparison of Haiti and the Dominican Republic

Abstract

Spatial and temporal vegetation contrasts between the nations of Haiti and the Dominican are analyzed using NDVI data derived from 30m resolution Landsat imagery and 8km resolution AVHRR imagery from the NOAA / NASA Pathfinder database. Analysis of vegetation dynamics in the Hispaniola border region indicates denser vegetation cover and a stronger correlation between elevation, slope, and NDVI on the Dominican side of the frontier. Temporal patterns of NDVI dynamics along the frontier suggest that changes in biomass are both more homogeneous and more extreme on the Haitian side. Analysis of 17 years of 8km resolution AVHRR imagery for the entire island of Hispaniola reveals consistently higher NDVI values for the Dominican Republic and a distinct intra‐annual pattern of mean monthly NDVI deviations that have important implications for future studies of vegetation dynamics in the region.     

鄱阳湖HJ-1A/B卫星CCD影像大气校正研究

以准同步的Terra/MODIS反演的气溶胶为辅助,采用FLAASH模型对2009-10-24鄱阳湖HJ-1A/B卫星CCD影像进行大气校正处理。结果表明,大气影响可以被有效去除,在水体遥感反射率较高的红、绿波段,大气校正精度较高,平均相对误差分别为13.4%和9.8%;而在水体遥感反射率较低的近红外、蓝波段,大气校正精度较低,这可能与波段不同的信噪比和陆地邻近像元效应有关。     

Water body mapping method with HJ-1A/B satellite imagery

This paper proposes an integrated water body mapping method with HJ-1A/B satellite imagery, the CCD (charge coupled device) data of the Chinese environmental satellites that were launched on September 6th, 2008. It combines the difference between NDVI and NDWI (NDVI–NDWI) with SLOPE and near-infrared (NIR) band. The NDVI–NDWI index is used to enhance the contrast between water bodies and the surrounding surface features; the topographic SLOPE is used to eliminate the mountain shadow; and the NIR band is used to reduce the effects of artificial construction land. The objectives are evaluating the potential of the HJ-1A/B imagery on water body monitoring, and proposing ideally mapping method. The test study results indicated that the NDVI–NDWI index is superior to the single index of NDVI and NDWI to enhance the contrast between water bodies and the rest of the features. On the basis of the accurately mapped water bodies in the HJ-1A/B CCD images of the study area, we conclude that the HJ-1A/B multi-spectral satellite images is an ideal data source for high spatial and temporal resolution water bodies monitoring. And the integrated water body mapping method is suitable for the applications of HJ-1A/B multi-spectral satellite images in this field.     

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。     

Quantifying winter wheat residue biomass with a spectral angle index derived from China Environmental Satellite data

Quantification of crop residue biomass on cultivated lands is essential for studies of carbon cycling of agroecosystems, soil-atmospheric carbon exchange and Earth systems modeling. Previous studies focus on estimating crop residue cover (CRC) while limited research exists on quantifying crop residue biomass. This study takes advantage of the high temporal resolution of the China Environmental Satellite (HJ-1) data and utilizes the band configuration features of HJ-1B data to establish spectral angle indices to estimate crop residue biomass. Angles formed at the NIR_IRS vertex by the three vertices at R, NIR_IRS, and SWIR (ANIR_IRS) of HJ-1B can effectively indicate winter wheat residue biomass. A coefficient of determination (R²) of 0.811 was obtained between measured winter wheat residue biomass and ANIR_IRS derived from simulated HJ-1B reflectance data. The ability of ANIR_IRS for quantifying winter wheat residue biomass using HJ-1B satellite data was also validated and evaluated. Results indicate that ANIR_IRS performed well in estimating winter wheat residue biomass with different residue treatments; the root mean square error (RMSE) between measured and estimated residue biomass was 0.038 kg/m². ANIR_IRS is a potential method for quantifying winter wheat residue biomass at a large scale due to wide swath width (350 km) and four-day revisit rate of the HJ-1 satellite. While ANIR_IRS can adequately estimate winter wheat residue biomass at different residue moisture conditions, the feasibility of ANIR_IRS for winter wheat residue biomass estimation at different fractional coverage of green vegetation and different environmental conditions (soil type, soil moisture content, and crop residue type) needs to be further explored.