文献 | 量子荧光|微型光纤光谱仪-ag贵宾会

土壤养分如总氮、有机质、速效钾和速效磷等是农作物生长的主要成分，这些土壤养分参数的检测一直沿用常规化学检测方法，对检测人员要求高，且需要昂贵的检测设备，存在检测成本高、效率低，不能大规模同时检测等问题。近红外光谱和高光谱成像分析技术具有成本低、快速和环保等优点，近年来在土壤养分测定方面提到了越来越广泛的应用。本文主要创新性成果有。

现代农业的发展，得益于化肥与农药的使用。施用化肥能够改善土壤肥力，提高农作物单位产量。然而，人们为了追求高产往往对作物大量盲目施肥。化肥施用量的增加和利用率的下降，不仅在经济上造成巨大损失，还会引起严重的环境污染，致使地表水富营养化，地下水和蔬菜中营养元素含量超标等问题。因此，大面积快速获取土壤养分含量信息，根据土壤养分的丰缺合理适量施肥，对于我国农业可持续发展具有重要意义。应用传统化学分析方法测量土壤养分，分析过程复杂、周期长、成本高、实时性差，很难大规模推广使用。农业生产上迫切需要一种快速，现场原位，连续且无污染的土壤养分检测方法。

green leaf area index (lai) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse applications. remotely sensed data provide considerable potential for estimating lai at local, regional, and global scales. the goal of this study was to retrieve green lai from modis 250-m vegetation index (vi) data for irrigated and rainfed maize and soybeans. the performance of both modis-derived ndvi and wide dynamic range vegetation index (wdrvi) were evaluated across three growing seasons (2002 through 2004) over a wide range of lai and also compared to the performance of ndvi and wdrvi derived from reflectance data collected at closerange across the same field locations. the ndvi vs. lai relationship showed asymptotic behavior with a sharp decrease in the sensitivity of the ndvi to lai exceeding 2 m2/m2 for both crops. wdrvi vs. lai relation was linear across the entire range of lai variation with determination coefficients above 0.93. importantly, the coefficients of the close-range wdrvi vs. lai equation and the modisretrieved wdrvi vs. lai equation were very close. the wdrvi was found to be capable of accurately estimating lai across a much greater lai range than the ndvi and can be used for assessing even slight variations in lai, which are indicative of the early stages of plant stress. these results demonstrate the new possibilities for analyzing the spatio-temporal variation of the lai of crops using multi-temporal modis 250-m imagery.

methods for chlorosis detection and physiological condition monitoring in vitis vinifera l, through accurate chlorophyll a and b content (cab) estimation at lead and canopy levels are presented in this manuscript. a total of 24 vineyards were identified for field and airborne data collection with the compact airborne spectrographic imager (casi), the reflective optics system imaging spectrometer (rosis) and the digital airborne imaging spectrometer (dais-7915) hyperspectral sensors in 2002 and 2003 in northem spain, comprising 103 study areas of 10 * 10 m in size, with a total of 1467 leaves collected for determination of pigment concentration. a subsample of 605 leaves was used for measuring the optical properties of reflectance and tranmittance with a li-cor 1800-12 integrating sphere coupled by a 200um diameter single mode fiber to an ocean optics model usb2000 spectrometer. several narrow-band vegetation indices were calculated from lead reflectance spectra, and the prospect leaf optical model was used for inversion using the extensive database od lead optical properties. results showed that the best indicators for chlorophyll content estimation in v. vinifera l. leaves were narrow-band hyperspectral indices calculated in the 700-750 nm spectral region (r2 ranging between 0.8 and 0.9), with poor performance of traditional indices such as the normalized difference vegetation index (ndvi). results for other biochemicals indicated that the structure insensitive pigment index (sipi) and the photochemical reflectance index (pri) were more sensitice to carotenoids c x c and chlorophyll-carotenoid ratios cab/cx c than to chlorophyll content cab. chlorophyll a and b estimation by inversion of the prospect leaf model on v. vinifera l. spectra was successful, yielding a determination coefficient of r2=0.95, with an rmse=5.3ug/cm2. the validity of leaf-level indices for chlorophyll content estimation at the canopy level in v. vinifera l. was studied using the scaling-up approach that links prospect and rowmcrm canpy reflectance simulation to account for the effects of vineyard structure, vine dimensions, row orientation and soil and shadow effects on the canopy reflectance. the index calculated as a combination of the transformed chlorophyll absorption in reflectance index (tcari), and the optimized soil-adjusted vegetation index (osavi) in the form rcari/osavi was the most consistent index for estimating cab on aggregated and pure vine pixels extracted from 1 m casi and rosis hyperspectral imagery. predictive relationships were developed with prospect-rowmcrm model between cab and tcari/osavi as function of lai, using field-measured vine dimensions and image-extracted siul background, row-orientation and viewing geometry values. prediction relationships for cab content with tcari/osavi were successfuly applied to the 103 study sites imaged on 24 fields by rosis and casi airborne sensors, yielding r2=0.67 and rmse-11.5ug/cm2.