Meta-analysis of influential factors on crop yield estimation by remote sensing

Crop yield estimation by remote sensing is an important task in food security. Despite numerous studies in this field, there is a lack of a systematic analysis on the past studies to enable researchers to link individual case studies together. In this article, meta-analysis is adopted to pool various studies around the world and reveal the influences of the factors (such as crop types, soil orders, and climate systems) on the correlations between crop yield and remote-sensing data. The meta-analysis synthesizes the data on the effectiveness of NDVI (normalized difference vegetation index) on crop-yield estimation and it has been found that the correlation coefficient (r) varies significantly. Different correlation values are found in different crop types, with the highest 0.88 in cotton and the lowest 0.79 in sugar cane. They are found to be related to the single-leaf blades of the crops except for sugar cane. In addition, the meta-analysis results show that the correlations are also affected by the soil orders and climates, as is evident from the positive correlation with fertility and the negative correlation with precipitation and temperature, respectively. The mean correlation in Mollisols is stronger than that in Oxisols, and the value in hot and humid climates (e.g. humid subtropical climate) is lower than that in dry and cold climates (e.g. temperate continental climate). The study provides useful information for future individual case studies and meta-analysis in this field.     

Mapping lake CDOM by satellite remote sensing

Given the importance of coloured dissolved organic matter (CDOM) for the structure and function of lake ecosystems, a method to estimate the amount of CDOM in lake waters over large geographic areas would be highly desirable. Advanced Land Imager (ALI) images were acquired in southern Finland (in 2002) and southern Sweden (in 2003) together with in situ measurements of bio-optical properties of 34 lakes (39 measuring stations). Based on this dataset, a band-ratio type algorithm was developed using ALI band 2 and band 3 for estimating CDOM content (absorption of filtrated water at 420 nm) in lakes. Correlation between in situ measured CDOM and the remote sensing estimate of CDOM was high, r²=0.73. The CDOM retrieval algorithm obtained on the basis of two images and in situ data was validated on a third ALI image (eastern Finland, 2002) that was available in the ALI image archive. In situ water-colour monitoring data from 22 lakes (27 measuring stations) in the third image were available in a database of the Finnish Environment Administration. The water-colour data were converted to CDOM absorption values, which were then compared to the results from a third ALI image. The correlation between remotely estimated and in situ CDOM values in the algorithm validation image was high, r²=0.83. These results support the conclusion that CDOM content in lakes over a wide range of concentrations (a_CDOM(420) between 0.68 and 11.13 m⁻¹) can be mapped using Advanced Land Imager data.     

Oil spill detection by satellite remote sensing

This paper presents the state of the art for oil spill detection in the world oceans. We discuss different satellite sensors and oil spill detectability under varying conditions. In particular, we concentrate on the use of manual and automatic approaches to discriminate between oil slicks and look-alikes based on pattern recognition. We conclude with a discussion of suggestions for further research with respect to oil spill detection systems.     

遥感卫星图像自动导航方法研究

遥感卫星图像自动导航方法用来自动地确定遥感卫星图像中每一个像素的地理经纬度,目前已经在气象、海洋、资源、环境、军事等领域得到了广泛应用,并且产生了巨大的社会和经济效益。介绍了一种新的基于最大相关系数的遥感卫星图像自动导航方法。首先给出了问题的描述;其次介绍了图像自动导航数据流程;然后设计并实现了一种新的基于最大相关系数的自动图像导航方法,它是整个遥感卫星自动导航的关键部分;最后给出了实例,验证了方法的可行性以及高精度。     

Locating tuna forage ground through satellite remote sensing

A technique has been developed to assess tuna habitat using satellite derived ocean colour, water transparency and sea surface temperature. The scientific approach is based on the food and feeding habit and the preferential temperature range of tuna. Time series chlorophyll-a images of Indian Remote Sensing Satellite P4-Ocean Colour Monitor (IRS P4-OCM) have been analysed to define the critical phytoplankton patch size, its persistence and the optimal water transparency depth. Sea surface temperature (SST) data derived from National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) have been analysed to study the optimum temperature range in the surrounding waters. Results based on hindcasting indicate that the tuna forage ground derived from satellite data yielded high catch of tuna (>2% hooking rate). Study reveals that a minimum time delay of 5–7 days is required for a phytoplankton patch to mature to the forage ground. In addition, water transparency for facilitating sight feeding should be about 25–30 m depth. The thermal limit and preferential temperature range observed is 26–30°C and 26–28°C, respectively. Preliminary results based on limited validation are highly promising. However, an experimental forecast is being planned to validate this approach.     

A method of modifying error for non-synchronicity of grass yield remote sensing estimation and measurement

A remote sensing yield estimation of aboveground phytomass in Hulunbeiermeng Leymus chinensis grassland, Inner Mongolia, China was made using Landsat Thematic Mapper data. Starting with the ecological principles of plant population, and based on observed data in fixed positions of the experimental region, a synchronous revision of the practical measurement of grass yield was made by using a logistic equation for the case that the measurement time and the satellite pass time are not synchronous. Statistical analysis results show that the interrelation between the revised grass yield and Ratio Vegetation Index (RVI) has been considerably enhanced. The correlation coefficient, r =0.856-0.944, shows a remarkable positive correlation, while the correlation coefficient of the unrevised grass yield is only 0.679-0.817, which does not pass the notability test of the f 0.01 level. The forecasting result of the grass yield of the Leymus chinensis grassland, in Ewenk Banner of Hulunbeiermeng, Inner Mongolia, China, also shows that the forecast value agrees well with the measured value.     

Use of remote sensing data for estimation of winter wheat yield in the United States

This paper shows the application of remote sensing data for estimating winter wheat yield in Kansas. An algorithm uses the Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)) computed for each week over a period of 23 years (1982–2004) from Advance Very High Resolution Radiometer (AVHRR) data. The weekly indices were correlated with the end of the season winter wheat (WW) yield. A strong correlation was found between winter wheat yield and VCI (characterizing moisture conditions) during the critical period of winter wheat development and productivity that occurs during April to May (weeks 16 to 23). Following the results of correlation analysis, the principal components regression (PCR) method was used to construct a model to predict yield as a function of the VCI computed for this period. The simulated results were compared with official agricultural statistics showing that the errors of the estimates of winter wheat yield are less than 8%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.     

Yield forecasting for wheat and corn in Hungary by satellite remote sensing

We have developed an advanced version of our yield estimation method [Ferencz et al., 2004 Cs, Ferencz, Bognár, P., Lichtenberger, J., Hamar, D., Gy, Tarcsai, Timár, G., Molnár, G., Sz, Pásztor, Steinbach, P., Székely, B., Ferencz, O.E. and Ferencz-Árkos, I. 2004. Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25: 4113–4149.  [Google Scholar], Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25, pp. 4113–4149], that is able to provide reliable forecasts for corn and wheat, several weeks before the harvest. The forecasting method is based on the data of the Advanced Very High Resolution Radiometer (AVHRR) instruments of the National Oceanic and Atmospheric Administration's (NOAA) Polar Orbiting Environmental Satellites (POES). The method was applied to Hungary between the years 1996 and 2000. The forecasted yield values are all within 5% reliability with respect to the actual yield data produced by classic (non-satellite based) methods and provided by the Hungarian Statistical Office, with the exception of 1997, where the absolute error is about 8%.     

Attitude control system specifications for a remote sensing satellite

Abstract

Though the literature is replete with papers on remote sensing via space satellites, the specifications in terms of jitter and drift rate that have to be met by the specific mission, are often either over-specified or under-specified. In this paper the authors endeavour to present, through heuristic arguments, the effect of jitter and drift on picture quality, and to arrive at the proper specifications on attitude and orbit control systems for remote sensing satellites.     

Prospects for satellite remote sensing of Adelie penguin rookeries

Future conservation efforts are expected to require population statistics for Southern Ocean species, including penguins. Preliminary laboratory spectral reflectance measurements are presented of penguin plumage plus some of the major components of the Antarctic environment. A sketch is presented of the prospects for employing remote sensing systems for monitoring penguin populations on a regional scale.     

Influence of atmospheric correction on the estimation of biophysical parameters of crop canopy using satellite remote sensing

A quantitative approach has been made for the estimation of biophysical parameters of a vegetation canopy by the inversion of a vegetation canopy reflectance model. Model inversion has been done using a non-linear optimization scheme against directional reflectance data over the canopy. A quasi-Newton algorithm has been employed that searches the minimum of a function iteratively using the functional values only. The technique provides a reasonably good estimation of the biophysical parameters. A study has been conducted to quantify the error related to the estimation of biophysical parameters of vegetation with simulated satellite data corrected with improper values of atmospheric aerosol and water vapour contents. In the visible, atmospheric correction of satellite data with improper values of atmospheric aerosol content results in a modification of the amplitude and angular pattern of the directional reflectance for both low-density and high-density vegetation canopies. However, in the near-infrared, the atmospheric correction of data with improper values of aerosol and water vapour contents changes the amplitude of directional reflectance, but, no significant changes in angular pattern are noticed. This study indicates that parameter estimation can be significantly influenced by using improper values of both aerosol and water vapour contents during data correction in the visible and near-infrared regions of the solar spectrum. The estimation accuracy is higher for a low-density canopy than for a dense vegetation canopy. Retrievals of all the surface parameters are not equally affected by such improper atmospheric correction of data. Particularly, estimations of soil reflectance and leaf area index are significantly influenced by such improper correction for a high-density vegetation canopy. However, the accuracy of the retrieved parameter values is higher in the near-infrared than in the visible for both high-density and low-density canopies.     

容差粗糙集卫星遥感数据分类

结合粗糙集理论和遥感数据中地物光谱特征空间分布信息,提出了一种基于光谱特征邻域的容差粗糙集分类方法,用来处理卫星遥感数据分类中的不确定性问题。利用北京地区Landsat-5 TM数据进行分类试验,对算法分类过程进行讨论及其分类结果进行验证分析;结果表明：文中方法在可理解性和稳定性上体现出比较好的性质,能够有效处理卫星遥感数据分类中存在的不确定性因素,在具有复杂光谱特征地物分类方面具有发展潜力。     

Detecting and measuring new snow accumulation on ice sheets by satellite remote sensing

A new technique is described that detects when and where new snow falls on ice sheets and then determines the thickness of new accumulation. Measurements of vertically polarized passive emission at 85 GHz are filtered with the Hilbert-Huang Transform to identify periods where the surface snow has changed significantly. These are shown to be commonly the result of new snow by comparison with both field observations and in situ instrumentation. Temperature, atmospheric emission and clouds all affect the passive microwave signal but each is examined and shown not to prevent the identification of new snow events. The magnitude of the brightness temperature change is not strongly correlated with snowfall amount. To quantify the amount of new snow, the spatial extent and timing of new snowfalls are examined with ICESat/GLAS laser altimetry data. Crossover differences between altimetric profiles taken before, during, and after the snowfall event provide a measure of the thickness of new snow. Specific cases are presented where 11 and 13 cm of new snow were detected over large regions.     

Barriers to adopting satellite remote sensing for water quality management

Sustainable practices require a long-term commitment to creating solutions to environmental, social, and economic issues. The most direct way to ensure that management practices achieve sustainability is to monitor the environment. Remote sensing technology has the potential to accelerate the engagement of communities and managers in the implementation and performance of best management practices. Over the last few decades, satellite technology has allowed measurements on a global scale over long time periods, and is now proving useful in coastal waters, estuaries, lakes, and reservoirs, which are relevant to water quality managers. Comprehensive water quality climate data records have the potential to provide rapid water quality assessments, thus providing new and enhanced decision analysis methodologies and improved temporal/spatial diagnostics. To best realize the full application potential of these emerging technologies an open and effective dialogue is needed between scientists, policy makers, environmental managers, and stakeholders at the federal, state, and local levels. Results from an internal US Environmental Protection Agency qualitative survey were used to determine perceptions regarding the use of satellite remote sensing for monitoring water quality. The goal of the survey was to begin understanding why management decisions do not typically rely on satellite-derived water quality products.     

Reconciling the global terrestrial water budget using satellite remote sensing

Recent retrievals of multiple satellite products for each component of the terrestrial water cycle provide an opportunity to estimate the water budget globally. In this study, we estimate the water budget from satellite remote sensing over ten global river basins for 2003-2006. We use several satellite and non-satellite precipitation (P) and evapo-transpiration (ET) products in this study. The satellite precipitation products are the GPCP, TRMM, CMORPH and PERSIANN. For ET, we use four products generated from three retrieval models (Penman-Monteith (PM), Priestley-Taylor (PT) and the Surface Energy Balance System (SEBS)) with data inputs from the Earth Observing System (EOS) or the International Satellite Cloud Climatology Project (ISCCP) products. GPCP precipitation and PM (ISCCP) ET have less bias and errors over most of the river basins. To estimate the total water budget from satellite data for each basin, we generate merged products for P and ET by combining the four P and four ET products using weighted values based on their errors with respect to non-satellite merged product. The water storage change component is taken from GRACE satellite data, which are used directly with a single pre-specified error value. In the absence of satellite retrievals of river discharge, we use in-situ gauge measurements. Closure of the water budget over the river basins from the combined satellite and in-situ discharge products is not achievable with errors of the order of 5-25% of mean annual precipitation. A constrained ensemble Kalman filter is used to close the water budget and provide a constrained best-estimate of the water budget. The non-closure error from each water budget component is estimated and it is found that the merged satellite precipitation product carries most of the non-closure error.     

Using satellite remote sensing to monitor rocket launch induced pollution

With the expected increase in the number of rocket launches, it is becoming necessary to monitor the pollution associated with their exhaust plumes. In this article, we attempt to use satellite remote-sensing data sets to detect rocket plumes. Specifically, we check to determine whether a plume that is seen in satellite true colour imagery is captured by the aerosol optical thickness spatial distribution. This leads to better assessment of rocket exhaust properties. Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data sets are used to observe the rocket plumes. We select case studies with various booster combinations (liquid propellant only or a combination of liquid and solid propellants). We also discuss the difficulties in identifying rocket plumes from remote-sensing data sets. However, our results from this work demonstrate a unique capability in monitoring human-made pollution and the extent the pollution can spread to surrounding areas.     

A satellite remote sensing technique for geological structure horizon mapping

A satellite remote sensing technique is demonstrated for generating near surface geological structure data. This technique enables the screening of large areas and targeting of seismic acquisition during hydrocarbon exploration. This is of particular advantage in terrains where surveying is logistically difficult. Landsat Thematic Mapper (TM) data and a high resolution digital elevation model (DEM), are used to identify the map outcropping horizons. These are used to reconstruct the near surface structure. The technique is applied in Central Yeman which is characterized by a 'layer-cake' stratigraphic section and low dipping terrain. The results are validated using two-dimensional seismic data. The near surface map images faults and structure not apparent in the raw data. Comparison with the structure map generated from two-dimensional seismic data indicates very good structural and fault correlation. The near surface map successfully highlights areas of potential closure at reservoir depths.     

Sea surface effects on the sea surface temperature estimation by remote sensing

By using a sea surface temperature profiler buoy, the behaviour of the vertical temperature profile near the sea surface was observed in Mutsu Bay. In the daytime under calm and strong sunshine condition, there occurred a large temperature difference between the uppermost sea surface and the 1 m depth. The difference disappeared when the wind began to blow with a speed greater than 4ms^?1. Besides the atmospheric effects the inhomogeneity of the vertical temperature distribution near the sea surface must be another major error factor in the sea surface temperature estimation by satellite remote sensing.     

Investigation into lossless hyperspectral image compression for satellite remote sensing

Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-infrared (IR), mid-IR and thermal IR portions of the spectrum, thus requiring large data storage on board the satellite and high bandwidth of the downlink transmission channel to ground stations. Image compression techniques are required to compensate for the limitations in terms of on-board storage and communication link bandwidth. In most remote-sensing applications, preservation of the original information is important and urges studies on lossless compression techniques for on-board implementation. This article first reviews hyperspectral spaceborne missions and compression techniques for hyperspectral images used on board satellites. The rest of the article investigates the suitability of the integer Karhunen–Loève transform (KLT) for lossless inter-band compression in spaceborne hyperspectral imaging payloads. Clustering and tiling strategies are employed to reduce the computational complexity of the algorithm. The integer KLT performance is evaluated through a comprehensive numerical experimentation using four airborne and four spaceborne hyperspectral datasets. In addition, an implementation of the integer KLT algorithm is ported to an embedded platform including a digital signal processor (DSP). The DSP performance results are reported and compared with the desktop implementation. The effects of clustering and tiling techniques on the compression ratio and latency are assessed for both desktop and the DSP implementation.     

高分辨率遥感卫星影像的河流提取方法研究

为了克服单纯采用光谱信息提取河流的缺陷,利用高分辨率遥感影像突出的高分辨率的特性提出一种综合影像中光谱、纹理、几何特性等多特征联合提取河流的方法。该方法分别对河流水体的光谱特征、纹理特征及河流几何形状进行描述,选取特征参数,构造综合特征矩阵,利用均值聚类分割最终得到河流目标。通过对真实高分辨率遥感影像Worldview1影像进行的实验验证了该方法的高精准性及快速性。