Remote sensing of biotic and abiotic stress for irrigation management of cotton

The applicability of commercially available remote sensing instrumentation was evaluated for site-specific management of abiotic and biotic stress on cotton (Gossypium hirsutum L.) grown under a center pivot low energy precision application (LEPA) irrigation system. This study was conducted in a field where three irrigation regimes (100%, 75%, and 50% ET_c) were imposed on areas of Phymatotrichum (root rot) with the specific objectives to (1) examine commercial remote sensing instrumentation for locating areas showing biotic and abiotic stress symptomology in a cotton field, (2) compare data obtained from commercial aerial infrared photography to that collected by infrared transducers (IRTs) mounted on a center pivot, (3) evaluate canopy temperature changes between irrigation regimes and their relationship to lint yield with IRTs and/or IR photography, and (4) explore the use of deficit irrigation and the use of crop coefficients for irrigation scheduling. Pivot-mounted IRTs and an IR camera were able to differentiate water stress among irrigation regimes. The IR camera distinguished between biotic (root rot) and abiotic (drought) stress with the assistance of groundtruthing. The 50% ET_c regime had significantly higher canopy temperatures than the other two regimes, which was reflected in significantly lower lint yields when compared to the 75% and 100% ET_c regimes. Deficit irrigation down to 75% ET_c had no impact on lint yield, indicating that water savings were possible without reducing yield.     

Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches

Different methods have been developed to estimate evapotranspiration from remote sensing data, from empirical approaches such as the simplified relationship to complex methods based on remote sensing data assimilation along with SVAT models. The simplified relationship has been applied from small spatial scale using airborne TIR images to continental scale with NOAA data. Assimilation procedures often require remote sensing data over different spectral domains to retrieve input parameters which characterize surface properties such as albedo, emissivity or Leaf Area Index. A brief review of these different approaches is presented, with a discussion about the main physical bases and assumptions of various models. The paper reports also some examples and results obtained over the experimental area of the Alpilles Reseda project, where various types of models have been applied to estimate surface fluxes from remote sensing data.     

Diagnosis of regional evaporation by remote sensing to support irrigation performance assessment

The success of water management in large irrigation schemes with composites of soil, crop, wetness and micro-meteorological conditions is difficult to quantify. Performance assessment indicators, being among others a function of evaporation, are useful tools to evaluate the actual functioning of an irrigation system. The inevitable spatial variability of evaporation in large irrigation schemes makes its determination with conventional point measurements almost impossible. A new remote sensing evaporation parameterization algorithm has been tested with high resolution Landsat Thematic Mapper data for the Eastern Nile Delta, Egypt. Although the implementation of such an algorithm requires assumptions to be made, the current case study shows that these assumptions do not hamper the estimation of actual and potential evaporation at regional scale. The actual evaporation has been used to express the uniformity of crop water use which is related to the equity of irrigation water distribution. The coefficient of variance in actual evaporation between 53 differenent irrigation districts is 10% on average. The relative evaporation was considered to determine whether the crop was adequately irrigated. The relative evaporation was more than 75% for 48 out of 53 irrigation districts. It is concluded, that improved information on actual crop growth conditions through remote sensing provides an essential insight into the planning of real-time and seasonal irrigation water deliveries.     

EMC单片机技术在粮仓库门控制系统中的应用

介绍了一种基于EMC单片机的粮仓库门自动控制系统.该系统分为红外遥控和接收两部分,其中红外遥控部分通过EM78P153控制红外无线遥控器发射指令信号,而红外接收部分通过接收模块接收到信号后由EM78P447来控制粮仓库门的开、关、停、限位和夜间自动点灯.系统结构简单,功能齐全,具有低成本、低功耗和可靠性高等特点.     

An entropy approach to spatial disaggregation of agricultural production

While agricultural production statistics are reported on a geopolitical – often national – basis we often need to know the status of production or productivity within specific sub-regions, watersheds, or agroecological zones. Such re-aggregations are typically made using expert judgments or simple area-weighting rules. We describe a new, entropy-based approach to making spatially disaggregated assessments of the distribution of crop production. Using this approach, tabular crop production statistics are blended judiciously with an array of other secondary data to assess the production of specific crops within individual ‘pixels’ – typically 25–100 square kilometers in size. The information utilized includes crop production statistics, farming system characteristics, satellite-derived land cover data, biophysical crop suitability assessments, and population density. An application is presented in which Brazilian state level production statistics are used to generate pixel level crop production data for eight crops. To validate the spatial allocation we aggregated the pixel estimates to obtain synthetic estimates of municipality level production in Brazil, and compared those estimates with actual municipality statistics. The approach produced extremely promising results. We then examined the robustness of these results compared to short-cut approaches to allocating crop production statistics and showed that, while computationally intensive, the cross-entropy method does provide more reliable estimates of crop production patterns.     

CO 2与土壤水分交互作用的番茄光合速率预测模型

为了实现不同土壤水分管理下的CO 2气肥精细控制,建立了番茄作物不同生长阶段的光合速率预测模型。实验设置了4个CO 2浓度与3个土壤水分条件的交互处理,利用无线传感器网络长期实时监测温室内环境信息,采用LI-6400XT型光合速率仪定时采集作物净光合速率信息;并用BP神经网络分别建立了番茄苗期、花期和果期的光合速率预测模型。预测模型的验证结果表明,对于苗期预测模型,预测值与实测值之间的决定系数 R 2为0.925;花期预测模型的决定系数 R 2为0.920,果期预测模型的决定系数 R 2为0.958;番茄各生长期的光合速率预测模型均具有较高的预测精度。在不同土壤水分条件下改变CO 2浓度,得到的CO 2浓度与光合速率预测曲线与实测值相近,可反映实际土壤水分管理下的CO 2浓度最优值,对指导不同土壤水分条件下CO 2气肥的精细调控具有重要意义。     

基于多源数据融合的盐分遥感反演与季节差异性研究

为提高多光谱盐分遥感反演的精度,利用实测高光谱与多光谱进行数据融合,并分析了不同季节盐分遥感的差异性。以河套灌区永济灌域为研究区域,以实测光谱仪测定的土壤高光谱数据和Landsat-8 OLI多光谱数据为基础,通过光谱变换和多元逐步回归方法筛选特征波段和特征光谱指数,构建了春、秋两季土壤盐分多光谱、高光谱反演模型,并利用特征光谱指数的线性回归构建了高-多光谱数据融合反演模型。结果表明:高光谱的反射率总体比多光谱高36. 83%,春季反射率比秋季平均高23. 78%。利用模型中最优变量-特征光谱指数对多光谱模型与高光谱模型进行融合,高-多光谱融合反演模型训练集和验证集R2平均值分别为0. 651和0. 635,RMSE平均值分别为2. 44 g/kg和2. 49 g/kg,精度明显高于对应的多光谱反演模型,其中训练集、验证集的R2平均值分别提高了36. 19%和35. 64%,RMSE平均值分别降低了34. 28%和41. 72%。春季多光谱、高光谱和融合反演模型的精度均高于秋季,其中训练集R2平均值比秋季模型分别提高了6. 03%、6. 05%和4. 40%,验证集R2平均值分别提高了19. 07%、12. 21%和1. 75%。构建的高-多光谱融合模型反演灌域春秋两季平均盐分含量分别为6. 05、5. 97 g/kg,平均相对误差分别为9. 65%和10. 68%,总体上该区域春季土壤主要为重盐化土,秋季土壤主要为中盐化土。     

喷头水量分布信息传感测试机理分

针对传统喷头水量分布特性测试手段落后的问题，研究了喷头水量分布特性自动测试的关键技术，并根据水量测试原理，建立了水量信息传感器的转换模型，实现了水量信息从模拟量到数字量的一步转换。采用基于分布式总线技术的水量信息采集方案，设计了喷头水量分布特性自动测试系统，使水量分布信息和其他测试数据快速直观地显示在计算机屏幕上，并可打印输出，提高了喷头性能测试的工作效率和水平，对新型喷头的试验研究提供了可靠数据和分析手段。     

电控发动机的氧传感器波形分析

通过汽车示波器对氧传感器的信号电压波形测试,介绍了氧传感器的标准信号电压波形、氧传感器波形分析法诊断发动机故障的一般流程及其具体应用。     

智能粮食水分测试仪的研制

给出以AT89C52单片机为核心的粮食水分测试仪的硬件结构组成,并详细介绍了信号处理和测量方法;通过对传感器的标定并进行相应的数据处理。通过实验发现,该仪器使用方便,测量精度高。