Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops

Irrigation performance and water productivity can be benchmarked if estimates of spatially distributed yield and crop water use are available. A commonly used method to estimate crop evapotranspiration in irrigated areas is to multiply reference evapotranspiration values by appropriate crop coefficients. This study evaluated convenient ways to derive such coefficients using multispectral vegetation indices obtained by remote sensing. Detailed ground radiometric measurements were taken in small plots perpendicular to the crop rows to obtain canopy reflectance values. Ancillary measurements of green ground cover, plant height, leaf area index and biomass were taken in the cropped strip covered by the radiometer field-of-view. The results were up-scaled using 10 Landsat-5 and 1 Landsat-7 images. Crop measurements and ground radiometry were made at the time of Landsat overpass on two commercial fields, one grown with sugarbeet and the other with cotton. Crop height and ground cover were determined weekly in these two fields, three additional sugarbeet fields and one additional cotton field. The ground and satellite observations of canopy reflectance yielded similar results. Two vegetation indices, the normalized difference vegetation index (NDVI) and the soil adjusted vegetation index (SAVI) were evaluated. Both indices described the crop growth well, but SAVI was used in further evaluations because it could be conveniently related to both ground cover and the basal crop coefficient using a simple model. Based on these findings, crop water use variability was analyzed in a large sample of sugarbeet and cotton fields, within a homogeneous irrigation scheme in Southern Spain. The yield versus evapotranspiration data points were highly scattered for both cotton and sugarbeet. The yield values obtained from the sugarbeet fields and cotton fields were substantially lower than values predicted by a linear yield function, and close to a curvilinear yield function, respectively. Evapotranspired water productivity varied in the cotton fields from 0.3 to 0.78 kg m⁻³, and in the sugarbeet fields from 7.15 to 14.8 kg m⁻³.     

Adapting to intersectoral transfers in the Zhanghe Irrigation System, China: Part I. In-system storage characteristics

The Zhanghe Irrigation System (ZIS), in Central China, has drawn attention internationally because it managed to sustain its rice production in the face of a dramatic reallocation of water to cities, industries and hydropower uses. Ponds, the small reservoirs ubiquitous in the area, are hypothesized to have been instrumental in this. Ponds are recharged by a combination of return flows from irrigation and runoff from catchment areas within the irrigated perimeter. They provide a flexible, local source of irrigation water to farmers. This paper assesses the storage capacity and some key hydrological properties of ponds in a major canal command within ZIS. Using remote sensing data (Landsat and IKONOS) and an area–volume relationship based on a field survey, we obtained an overall pond storage capacity of 96 mm (per unit irrigated area). A comparative analysis between 1978 and 2001 reveals that part of this capacity results from a very significant development of ponds (particularly in the smaller range of sizes) in the time interval, probably as a response to rapidly declining canal supplies. We developed a high-resolution digital elevation model from 1:10,000 topographic maps to support a GIS-based hydrological analysis. Pond catchments were delineated and found to extensively overlap, forming hydrological cascades of up to 15 units. In a 76-km² area within the irrigation system, we found an average of close to five ‘connected’ ponds downstream of each irrigated pixel. This high level of connectivity provides opportunities for multiple reuses of water as it flows along toposequences. A fundamental implication is that field ‘losses’ such as seepage and percolation do not necessarily represent losses at a larger scale. Such scale effects need to be adequately taken into account to avoid making wrong assumptions about water-saving interventions in irrigation.     

高光谱遥感森林信息提取研究进展

针对高光谱遥感技术森林信息提取,详细论述了国内外利用高光谱遥感数据进行森林类型识别、森林郁闭度估算和森林叶面积指数估测等森林物理参量,以及森林化学参量估计和森林健康状态评价等方面的应用研究状况,最后对高光谱遥感森林应用的发展趋势作了探讨.     

Characterizing rangeland vegetation using Landsat and 1-mm VLSA data in central Wyoming (USA)

As an alternative to ground-cover data collection by conventional and expensive sampling techniques, we compared measurements obtained from very large scale aerial (VLSA) imagery for calibrating moderate resolution Landsat data. Using a grid-based sampling scheme, 162 VLSA images were acquired at 100 m above ground level. The percent vegetation cover in each photo was derived using SamplePoint (a manual inventory method) and VegMeasure (a reflectance based, automated method). Approximately two-thirds of the VLSA images were used for calibrating Landsat data while the remainder was used for validation. Regression models with Landsat bands accounted for 55% of the VegMeasure-based measurements of vegetation, whereas models that included both Landsat bands and elevation data accounted for 67%. The relationship between the Landsat bands and the percent vegetation cover measured by SamplePoint was lower (R ² = 20%), highlighting the differences between the inventory and reflectance based protocols. Results from the model validation indicated that the model’s predictive power was lower when the vegetation cover was either <20% or >55%. Additional work is needed in these ecosystems to improve the calibration techniques for sites with low and high vegetation cover; however, these results demonstrate the VLSA imagery could be used for calibrating Landsat data and deriving rangeland vegetation cover. By adopting such methodologies the US Federal land management agencies can increase the efficiency of the monitoring programs in Wyoming and in other western states of the US. Mention of trade names is for information only and does not imply endorsement by USDA over comparable products or services.     

A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application

Nitrogen (N) fertilizer rates applied spatially according to crop requirements can improve the efficiency of N use. The study compares the performance of two commercial sensors, the Yara N-Sensor/FieldScan (Yara International ASA, Germany) and the GreenSeeker (NTech Industries Inc., Ukiah, California, USA), for assessing the status of N in spring wheat (Triticum aestivum L.) and corn (Zea mays L.). Four experiments were conducted at different locations in Quebec and Ontario, Canada. The normalized difference vegetation index (NDVI) was determined with the two sensors at specific growth stages. The NDVI values derived from Yara N-Sensor/FieldScan correlated with those from GreenSeeker, but only at the early growth stages, where the NDVI values varied from 0.2 to 0.6. Both sensors were capable of describing the N condition of the crop or variation in the stand, but each sensor had its own sensitivity characteristics. It follows that the algorithms developed with one sensor for variable-rate N application cannot be transferred directly to another sensor. The Yara N-Sensor/FieldScan views the crop at an oblique angle over the rows and detects more biomass per unit of soil surface compared to the GreenSeeker with its nadir (top-down) view of the crop. The Yara N-Sensor/FieldScan should be used before growth stage V5 for corn during the season if NDVI is used to derive crop N requirements. GreenSeeker performed well where NDVI values were >0.5. However, unlike GreenSeeker, the Yara N-Sensor/FieldScan can also record spectral information from wavebands other than red and near infrared, and more vegetation indices can be derived that might relate better to N status than NDVI.     

抚宁县景观空间格局变化研究

采用ERDAS IMAGINE、ArcGIS和景观结构分析软件FRAGSTATS,对抚宁县1986年和2003年的景观格局进行了对比分析。结果表明:在景观尺度上,研究区景观破碎度加大, 结构更加复杂,多样性下降,各组成部分大小差距加大;在景观要素类型尺度上,耕地和林地作为主要类型优势进一步扩大,林地更加规则连片,耕地却更趋破碎化;园地、水域和未利用土地减小和破碎;建设用地迅速增加,但趋于破碎。     

遥感监测土壤湿度的方法综述

通过对生态环境水热因子的遥感监测回顾,总结出定量地遥感监测土壤水分的方法主要有：微波遥感监测法、热惯量法、作物植被、亮温综合指数法和热红外法等,并对各方法的原理和具体的适用领域做了介绍和比较。     

高光谱遥感森林叶面积指数估测研究现状

文中简要概述了叶面积指数和高光谱遥感的概念, 分析比较了高光谱遥感与传统的宽波段遥感估测森林叶面积指数的优势, 以及利用高光谱遥感数据估测森林叶面积指数的常用方法, 并对目前高光谱遥感估测森林叶面积指数存在的问题行了讨论。     

小麦条锈病高光谱近地与高空遥感监测比较研究

 小麦条锈病是我国小麦生产中的重要病害,对该病防治的关键是做好监测工作。本研究利用ASD手持野外光谱仪[ASD FieldSpec HandHeld FR(325~1 075 nm)]和热气球在近地与高空研究了发病小麦冠层的高光谱遥感数据特征,获得了近地和对应高空2个不同平台光谱数据。经比较分析,发现高空数据与近地数据之间存在明显而有规律的变异关系,即高空获得的光谱反射率在可见光谱区域明显大于近地获得的光谱反射率。进一步对差异最显著的绿峰580 nm和黄边610 nm处数据进行回归分析,获得了高空光谱反射率值与近地光谱反射率值之间的回归模型,为进一步研究利用高平台遥感监测小麦条锈病奠定了一定的理论基础。     

3S技术在水土保持动态监测中的应用

近年来,随着社会和经济的发展,毁林开荒的现象比较普遍,林地面积锐减,植被大面积破坏,从而造成我国的水土流失现象日益严重,因此,对全国水土流失进行适时动态监测已势在必行。3S技术是集遥感、全球定位系统与地理信息系统于一体的高新技术手段,它可以快速、准确地获得地面各种信息。分别介绍了RS、GPS和GIS三种技术在水土保持动态监测中的应用,并对3S技术在水土保持动态监测方面的应用前景进行了展望。