对建立遥感估产模式的几点初步认识

本文从分析遥感光谱参数的生物学意义着手，论证了正确建立遥感估产模型的可能途径。对几种有代表性的遥感估产模型作了分析，作者认为把可见光、近红外波段的遥感信息与热红外信息有机结合是解决遥感估产模型的最佳方案。对ＮＯＡＡ－ＡＶＨＲＲ的第１通道与第２通道光谱数值进行非朗伯体特性的纠正是必要的。遥感估产模型不仅可以使估产的空间尺度大大缩小而且参数数目亦可大大减小，更有利于实际运行。     

基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究

本文以LAI作为结合点，讨论了利用复合型混合演化（SCE—UA）算法实现CERES—Wheat模型与遥感数据同化的可行性。CERES—Wheat模型同化后主要生育期和产量的模拟值分别与真实条件下模型相应模拟值以及实测值进行比较。结果表明，同化后CERES—Wheat模型的模拟精度对LAI外部同化数据的误差并不十分敏感。并且在LAI同化数据较少时，也可获得较好的同化结果。这一特点体现了SCE—UA算法应用于同化过程的优越性，为同化策略在区域冬小麦长势监测及估产中的应用提供了基础。     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Estimation of Trophic State Index of Sukhna Lake Using Remote Sensing and GIS

The present study is focussing on that how Landsat 7 ETM⁺ can be effectively used for estimation of Trophic State Index for Sukhna Lake. Sukhna Lake in Chandigarh has undergone lot of changes in last few decades. The depth and area both reduced tremendously. The shrinkage of the lake is due to the siltation and inadequate water volumes flowing to it. The Trophic State Index has been estimated by using secchi disk transparency and Landsat 7 ETM⁺ data. The in situ observations for parameters like pH, DO were measured by using multiparameter water quality instrument TROLL 9500. The best tested interpolation technique has been used to generate in situ images. The results have shown that the lake is in Hypereutrophic condition since 2000.     

Crop Evapotranspiration Estimation for Wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) Using Remote Sensing Data in Semi-Arid Region of Maharashtra

Penman–Monteith method adapted to satellite data was used for the estimation of wheat crop evapotranspiration during the entire growth period using satellite data together with ground meteorological measurements. The IRS-1D/IRS-P6 LISS-III sensor data at 23.5 m spatial resolution for path 096 and row 059 covering the study area were used to derive, albedo, normalized difference vegetation index, leaf area index and crop height and then to estimate wheat crop evapotranspiration referred to as actual evapotranspiration (ETact). The ETact varied from 0.86 to 3.41 mm/day during the crop growth period. These values are on an average 16.40 % lower than wheat crop potential evapotranspiration (ETc) estimated as product of reference crop evapotranspiration estimated by Penman–Monteith method and lysimetric crop coefficient (Kc). The deviation of ETact from ETc is significant, when both the values were compared with t test for paired two sample means. Though the observations on ETact were taken from well maintained unstressed experimental plot of 120 × 120 m size, there was significant deviation. This deviation could be attributed to, the satellite images representing the actual crop evapotranspiration as function crop canopy biophysical parameters, condition of the crop stand, climatic and soil conditions and the microclimate variation over area of one hectare. However, Penman–Monteith method represents a flat rate of specific growth stage of the crop.     

Agricultural Area Diversification and Crop Water Demand Analysis: A Remote Sensing and GIS Approach

Large scale adoption of input intensive rice–wheat cropping system in the centrally located Jalandhar district of Indian Punjab has led to over-exploitation of ground water resources, intensive use of chemical fertilizers and deterioration of soil health. To overcome these shortfalls, in the present study, agricultural area diversification plan has been generated from agricultural area and crop rotation maps derived from remote sensing data (IRS P6-AWiFS and RADARSAT ScanSAR) along with few agro-physical parameters in GIS environment. Cropping system indices (area diversity, multiple cropping and cultivated land utilization) were also worked out from remote sensing data .Analysis of remote sensing data (2004–05) revealed that rice and wheat individually remained the dominant crops, occupy 57.8% and 64.9% of total agricultural area (TAA), respectively. Therefore, in the diversified plan, it is suggested that at least 39% of the current 40% TAA under rice–wheat rotation should be replaced by other low water requiring, high value and soil enriching crops, particularly in coarse textured alluvial plain having good quality ground water zones with low annual rainfall(<700 mm). This will reduce water requirement to the tune of 15,660 cm depth while stabilizing the production and profitability by crop area diversification without further degradation of natural resources.     

Improvement in Precision of Crop Acreage Estimation by Remote Sensing Using Frequency Distribution Based Stratification

A study was conducted to improve precision of crop acreage adopting stratified random sampling approach. Remotely sensed data was used to classify mustard crop for the states of Rajasthan, Madhya Pradesh, Uttar Pradesh, Gujarat and Haryana covering 81% of mustard area of India. A grid of size 5 × 5 km was super-imposed on classified image of study area and proportion of mustard crop within the grid was ascertained. Crop proportion was used to determine strata. Stratification was done based on equal interval of proportion, equal sample number and cumulative square root of frequency method. Cumulative square root of frequency method gave highest precision in all the cases.     

基于光能利用率模型的玉米遥感估产研究

农作物长势监测和产量预测对于国家制定相关粮食政策、农业发展等都具有重要的意义,如何获得高效、宏观、精确的估产方法一直是学者关注的重点问题。以吉林省德惠市的玉米作为研究对象,利用光能利用率模型对玉米进行产量估算的研究,并且使用空间数据插值方法中的反距离权重法获得了每月平均温度数据的格网数据。通过玉米的净初级生产力NPP的累计值以及玉米的收获指数来获得最终的玉米产量值,利用验证点实测产量值与估算值的相关性和相对误差进行精度验证,相关系数R^2为0.649 9,平均相对误差值为1.676%,证明基于光能利用率模型的玉米估产在研究区具有一定的可行性。     

Irrigation Water Requirements and Supply Analysis in Dehradun Region—An Integrated Remote Sensing and GIS Approach

The paper focuses on analysing the irrigation water supply and demand of different crops under three main canals for kharif and rabi seasons in Dehradun region of Uttaranchal state. Crop acreage maps of rabi and kharif seasons have been prepared using LANDSAT TM 5 digital data by applying different image processing and classification techniques. Crop water and irrigation water requirements of different crops have been computed using CROPWAT computer program. Canal discharges have been compared with the irrigation water planning and management and found to be more than the irrigation water requirements in many months, that shows the need of revising the irrigation water management.     

基于互补性参数的港湾初级生产力遥感及GIS探测模型

到目前为止,港湾初级生产力的遥感探测方法基本上集中在对叶绿素的光谱探测上,由于干扰因素的存在（海面波动,水中悬浮泥砂）,使遥感探测的精度受到影响。本文提出一种基于地理信息系统方法的具有互补性参数的港湾初级生产力遥感探测模型,由于互补性参数的存在,极大地提高了模型的抗干扰能力,使探测的精度得到明显改善。在广东大亚湾的初步试验表明：水交换状态参数的引用,模型的误差从６．５％降为０．２６％,精度大大提高,从而证明该方法的可行性和有效性。     

作物遥感分类研究进展

农作物精细分类是农业资源与环境监测的重要环节,提取不同作物种植信息能够为我国农业生产提供基础数据支撑.本文旨在梳理作物遥感分类关键技术的发展脉络,重点评述了分类特征、尺度问题以及分类方法3个方面的情况,最后讨论和展望了今后作物遥感分类研究的发展方向,希望提供现阶段作物遥感分类研究进展,为作物遥感研究方法的创新和改善提供理论支撑,为后续农业遥感应用提供参考.     

中国植被净第一性生产力遥感动态监测

植被净第一性生产力 (NPP)研究方法很多 ,运用NOAAAVHRR的可见光、近红外和热红外波段来提取和反演地面参数 ,进而准确估算陆地植被净第一性生产力 ,是一种全新的研究手段。利用遥感数据进行生物量和净第一性生产力的估算 ,主要是采用光能利用率模型 ,即通过NPP与植物吸收的光合有效辐射 (A PAR)和植被将所吸收的光合有效辐射转化为有机物的转化率 (ε)的关系来实现的。用数学公式可表达为 :NPP =(FPAR×PAR)× [ε ×σT×σE×σS× (1-Ym)× (1-Yg) ]在遥感和地理信息系统技术的支持下 ,以 1990年每旬的 8km分辨率的NOAAAVHRR 1— 5通道的影像为数据源 ,对中国每旬的陆地植被净第一性生产力进行估算 ,然后累加得出全年的NPP值。估算结果 :1990年我国陆地植被NPP总量为 6 13× 10 9t/a ,NPP最高值为 1812 9gC/m2 。就计算结果 ,对中国大陆植被NPP的分布规律进行了分析。遥感模型能够以面代点 ,比较真实地反映陆地植被NPP的时空分布状况 ,与我国植被分布的地理规律性相符 ,这是其它统计模型所无法比拟的     

Assessment of Groundwater Recharge Through Rainfall and Water Harvesting Structures in Jamka Microwatershed Using Remote Sensing and GIS

Water harvesting works had been conducted at Jamka micro-watershed of Saurashtra region of Gujarat in India for augmenting artificial groundwater recharge in hard rock aquifers of the semi arid region. In present study groundwater recharge of Jamka micro-watershed was estimated. The natural groundwater recharge through rainfall in the study area was estimated using empirical equations and the artificial groundwater recharge through water harvesting structures which was estimated using remote sensing and GIS. The area under submergence due to water harvesting structures is estimated using remote sensing images. The groundwater recharge in study area was also estimated using water table fluctuation method and compared with total recharge through rainfall and water harvesting structures. The natural groundwater recharge through rainfall in the study area was found varying from 11 to 16 per cent of annual rainfall. The total groundwater recharge in the study area was estimated 390.29?ha?m, in which the contribution of recharge through water harvesting structures was about 38.53%; this revealed that the water harvesting structures played an important role in increasing the groundwater recharge in the region.     

CO2倍增下遥感-光合作物产量响应模型的研究与应用

由于卫星散射计探视雷达回波的各向异性的双调和性质,同时由于散射物理模型函数的非线性及信号中存在噪声,使得常规点方式风场反演中风向有多至4 个解的多解存在。给出了一种改进的场方式反演方法,利用该方法从卫星散射计测量的后向散射强度的数据中唯一反演出大尺度海洋风场。通过数值模拟和实际算例计算表明反演结果在风向、风速上都与真解是吻合的。从结果可以看到,所采用的改进的场方式反演方法对模拟数据或真实散射计探测海面风场的反演是成功的。     

泰国水稻种植面积月变化的遥感监测

介绍了光学和微波遥感影像相结合进行泰国水稻种植面积监测方法。泰国雨季雨量充足 ,气温适合 ,同一时间耕地上水稻的物候多样 ,每月水稻种植面积都发生变化。利用旱季的TM影像 ,获得耕地信息。同时利用TM影像覆盖的雷达区域进行非耕地去除 ,进行非监督分类 ,提取反映水稻种植不同生长期的雷达影像后向反射系数特征 ,建立各种类型的分类模型 ,采用监督分类的方法对全景雷达数据进行水稻种植情况调查 ,并分别予以识别和统计 ,反映研究区水稻月种植情况。分类结果通过类别检验和面积量算检验进行精度评价和分析。     

Assessment and Management Flash Flood in Najran Wady Using GIS and Remote Sensing

Flash flood assessment and management are necessary for municipal, urban growth planning and emergency action plans. By using the Hydrologic Engineering Centers River Analysis System software, we can model flash flood events and calculate water surface profiles over the length of the modeled stream. After collecting elevation points by using GPS method, the digital elevation model can be calculated for the study area. Najran city has main flood stream passes beside King Abdullah Road based on facts and previous works. A small study area including the mainstream of Wady Najran and King Abdullah Road has chosen as test site. The used methodology has also been proved efficiently for identifying flood inundation maps. Water extent area overlapped by 52–86% for both used methods. At discharge Q = 15 m³/s, the road needs to be protected from the flood.     

利用双变量同化与PCA-Copula法的冬小麦单产估测

为了进一步提高冬小麦产量估测的精度,基于集合卡尔曼滤波算法和粒子滤波（particle filter, PF）算法,对CERES–Wheat模型模拟的冬小麦主要生育期条件植被温度指数（vegetation temperature condition index,VTCI）、叶面积指数（leaf area index, LAI）和中分辨率成像光谱仪（moderate-resolution imaging spectroradiometer, MODIS）数据反演的VTCI、LAI进行同化,利用主成分分析与Copula函数结合的方法构建单变量和双变量的综合长势监测指标,建立冬小麦单产估测模型,并通过对比分析选择最优模型,对2017—2020年关中平原的冬小麦单产进行估测。结果表明,单点尺度的同化VTCI、同化LAI均能综合反映MODIS观测值和模型模拟值的变化特征,且PF算法具有更好的同化效果;区域尺度下利用PF算法得到的同化VTCI和LAI所构建的双变量估产模型精度最高,与未同化VTCI和LAI构建的估产模型精度相比,研究区各县（区）的冬小麦估测单产与实际单产的均方根误差降低了56.25 kg/hm2,平均相对误差降低了1.51%,表明该模型能有效提高产量估测的精度,应用该模型进行大范围的冬小麦产量估测具有较好的适用性。     

森林地上生物量遥感估测研究进展

森林生物量是衡量生态系统生产力的重要指标,也是研究森林生态系统物质循环的重要基础,其估测方法可以分为传统地面实测法、遥感监测法和综合模型法.随着生物量估测从样地研究发展到区域应用,空间尺度的增大导致宏观资料和参数的获取存在很多困难.在深入分析目前应用遥感技术估算森林生物量的方法及原理基础上,系统评述了统计模型、物理模型...     

小麦生物量和真实叶面积指数的高光谱遥感估算模型

利用大田小麦的参数数据和冠层光谱数据,基于光谱一阶微分技术和光谱响应函数,构建等效MODIS植被指数,建立小麦生物量(本文指总干生物量,下同)和真实叶面积指数的高光谱遥感估算模型.结果表明:①小麦生物量与冠层光谱在552 nm,721 nm处呈现最显著相关关系,叶面积指数与冠层光谱的相关性在400～1100 nm范围内较显著;②红边位置与生物量的关系最为显著,相关系数R为0.818;③6种等效MODIS植被指数中,增强型植被指数对生物量最为敏感;④红边位置估算小麦总生物量的指数模型最优,决定系数R2为0.829;⑤增强型植被指数与小麦叶面积指数的指数模型拟合度最强,决定系数R2为0.94.利用实测光谱模拟MODIS等效反射率构建植被指数反演小麦参数的方法,可为利用卫星数据进行大面积、无破坏和及时获取地面植被信息研究提供重要手段.     

利用遥感和GIS的方法预测外来入侵物种的潜在分布

外来物种入侵已经成为人类历史上最重大的生态事件之一,它直接威胁到了包括中国在内的世界上很多国家的经济、公众健康、农业生产力和生态的完整性。该研究利用遥感和GIS的方法进行了外来入侵物种的相对适生区分析,预测了外来物种-豚草在中国的潜在分布,并在信息理论的框架下建立了一种改进的加权平均逻辑回归模型。利用logit阈值和频率统计的方法对入侵物种的生境进行相对适应性划分,减小了生物多样性数据的不对称带来的影响。