Verification of vertically generalized production model and estimation of primary production in Sagami Bay,Japan

The Vertically Generalized Production Model (VGPM) was verified by the primary production data of the Sagami Bay, Japan. The VGPM with open ocean parameters including P ^B _opt , maximum primary production per unit of chlorophyll a in the water column, explained only 40% of the variability of integrated primary production. Formulations of the open ocean P ^B _opt showed no correlation with in situ P ^B _opt . Adjustment of the parameters of chlorophyll a and temperature dependent P ^B _opt improved the estimation of integrated primary production to 47% of the variation. Vertical integration parameters of VGPM also have to be adjusted to improve the estimation. Integrated primary production calculated with a stronger light dependency and with the adjusted P ^B _opt model can explain 74% of the variation. This model was used to estimate primary production of the Sagami Bay during 2003 with satellite data. In situ measurements on cloudy days indicate that the use of satellite data from sunny days only overestimates primary production.     

On the frequency of lake-effect snowfall in the Catskill Mountains

Meltwater from snow that falls in the Catskill/Delaware watershed in the Catskill Mountains in south-central New York contributes to reservoirs that supply drinking water to approximately nine million people in and near New York City (NYC). Using the Interactive Multisensor Snow and Ice Mapping System (IMS) 4-km snow maps from the National Oceanic and Atmospheric Administration’s National Ice Center, we identified and tracked 28 lake-effect (LE) storms that deposited snow in the Catskills (2004–2017). These storms, which generally originated from Lake Ontario, but sometimes from Lake Erie, represent an underestimate of the number of LE storms that contribute snowfall to the Catskills snowpack because snowstorms are not visible on IMS maps when they travel over already-snow-covered terrain. Using satellite, meteorological (including NEXRAD and National Weather Service Cooperative Observer Program), and reanalysis data, we identified conditions that contributed to the LE snowstorms and mapped snow-cover extent (SCE) following the storms. We show that LE snow makes an important contribution to the Catskills snowpack because of the frequency of events, even though the total amount of each event may, on average, be small. The IMS 4-km maps tend to overestimate SCE compared to MODerate-resolution Imaging Spectroradiometer and Landsat snow maps. Climate change could impact LE storms and the distribution of rain versus snow in the Catskills, which may affect future reservoir operations in the NYC Water Supply System and winter recreation in the Catskills.     

2000-2014年黄河源区ET时空特征及其与气候因子关系

黄河源区地处青藏高原东缘,具有独特的自然生境和丰富的自然资源,是中国西南区域经济发展的重要生态安全屏障。以MODIS ET产品作为研究黄河源区地表蒸散发（ET）的数据源,结合黄河源区内部及周边18个气象站数据、全国1∶100万植被类型图和黄河源区DEM数据,利用趋势分析、相对年际变化和相关分析法,研究2000-2014年黄河源区ET时空变化特征及不同土地利用类型下ET的变化规律,重点探讨了ET与气候因子的关系。结果表明：① 黄河源区多年ET区域分异规律明显,北部ET显著弱于中部和东南部,最强ET位于黄河源区的东南部,多年平均ET值为538.61 mm/a,距平相对变化显著,ET年际变化呈先减小后增加的趋势,平均趋势变化率为0.44 mm/a;② 年内ET呈周期性单峰变化趋势,7月达到峰值;2000-2014年黄河源区多年四季ET季节性差异明显,夏季ET最强达到188.14 mm/a,春秋季次之,冬季ET最弱仅97.15 mm/a;③ 研究时段内不同土地利用类型的ET大小表现为沼泽地>林地>草地>其他>裸地,整体上各土地利用类型的ET呈逐渐增加趋势;④ 相关分析结果表明,ET与同期气温、降水呈正相关关系,与相对湿度呈负相关关系,其中降水对ET的影响强于气温;驱动分区结果显示黄河源区ET受气候因子驱动的地区主要表现为降水驱动。     

GF-1卫星WFV影像间匀色方法

影像匀色方法是决定拼接后影像能否进一步用于遥感定量化应用的重要环节之一。本文针对我国高分辨率对地观测系统的首发星——GF-1卫星宽覆盖WFV传感器4相机拼接成像特点,提出了一种基于交叉辐射定标的影像间匀色方法。该方法首先利用传统的交叉定标方法获取某一台WFV相机在轨绝对辐射定标系数,然后采用统计分析和仿真模拟方法建立了相邻相机影像间DN值、表观辐亮度的相关模型,在完成相机间绝对辐射性能传递的同时,实现了影像间匀色处理。试验结果表明利用本文获取的交叉辐射定标系数能够有效地消除相邻相机影像重叠区域辐射不一致性的问题,实现WFV影像间匀色。该匀色方法可为其他类似卫星传感器影像间匀色提供参考依据。     

Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach

In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 10⁴ for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely.     

Empirical modeling and spatio-temporal patterns of urban evapotranspiration for the Phoenix metropolitan area,Arizona

In this study, an empirical model for predicting urban evapotranspiration (ET) is examined for the Phoenix metropolitan area that is in a subtropical desert climate using in situ ET measurements from a local flux tower and remotely sensed moderate-resolution imaging spectroradiometer land products. Annual ET maps of Phoenix are then created for the period from 2001 to 2015 using the empirical model developed. A time-series trend analysis is finally performed using predicted ET maps to discover the spatio-temporal patterns of ET changes during the study period. Results suggest that blue-sky albedo and land surface temperature are two statistically significant variables explanatory to model urban ET for Phoenix. Areas that have experienced significant increases of ET are highly spatially clustered, and are mainly found on the outskirts of the city, while areas of decreasing ET are generally associated with highly developed areas, such as downtown Phoenix.     

Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data

An improved methodology for the extraction and mapping of urban built-up areas at a global scale is presented in this study. The Moderate Resolution Imaging Spectroradiometer (MODIS)-based multispectral data were combined with the Visible Infrared Imager Radiometer Suite (VIIRS)-based nighttime light (NTL) data for robust extraction and mapping of urban built-up areas. The MODIS-based newly proposed Urban Built-up Index (UBI) was combined with NTL data, and the resulting Enhanced UBI (EUBI) was used as a single master image for global extraction of urban built-up areas. Due to higher variation of the EUBI with respect to geographical regions, a region-specific threshold approach was used to extract urban built-up areas. This research provided 500-m-resolution global urban built-up map of year 2014. The resulted map was compared with three existing moderate-resolution global maps and one high-resolution map in the United States. The comparative analysis demonstrated finer details of the urban built-up cover estimated by the resultant map.     

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.     

Testing estimation of water surface in Italian rice district from MODIS satellite data

Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R² = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.