Utility of Remote Sensing in Predicting Crop and Soil Characteristics

Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green–red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720nm). The most consistent correlation coefficients for soybean yield were obtained 89–93 DAP, corresponding to peak vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growth occurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields, R ² values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78, 55–130 DAP. The use of individual bands located in the green, red, and NIR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conducted to determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics.     

Crop water stress mapping for site-specific irrigation by thermal imagery and artificial reference surfaces

Variable-rate irrigation by machines or solid set systems has become technically feasible, however mapping crop water status is necessary to match irrigation quantities to site-specific crop water demands. Remote thermal sensing can provide such maps in sufficient detail and in a timely way. In a set of aerial and ground scans at the Hula Valley, Israel, digital crop water stress maps were generated using geo-referenced high-resolution thermal imagery and artificial reference surfaces. Canopy-related pixels were separated from those of the soil by upper and lower thresholds related to air temperature, and canopy temperatures were calculated from the coldest 33% of the pixel histogram. Artificial surfaces that had been wetted provided reference temperatures for the crop water stress index (CWSI) normalized to ambient conditions. Leaf water potentials of cotton were related linearly to CWSI values with R ² = 0.816. Maps of crop stress level generated from aerial scans of cotton, process tomatoes and peanut fields corresponded well with both ground-based observations by the farm operators and irrigation history. Numeric quantification of stress levels was provided to support decisions to divide fields into sections for spatially variable irrigation scheduling.     

Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield

Spectral unmixing techniques can be used to quantify crop canopy cover within each pixel of an image and have the potential for mapping the variation in crop yield. This study applied linear spectral unmixing to airborne hyperspectral imagery to estimate the variation in grain sorghum yield. Airborne hyperspectral imagery and yield monitor data recorded from two sorghum fields were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral imagery with sorghum plants and bare soil as two endmembers. A pair of plant and soil spectra derived from each image and another pair of ground-measured plant and soil spectra were used as endmember spectra to generate unconstrained and constrained soil and plant cover fractions. Yield was positively related to the plant fraction and negatively related to the soil fraction. The effects of variation in endmember spectra on estimates of cover fractions and their correlations with yield were also examined. The unconstrained plant fraction had essentially the same correlations (r) with yield among all pairs of endmember spectra examined, whereas the unconstrained soil fraction and constrained plant and soil fractions had r-values that were sensitive to the spectra used. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Results showed that the best plant and soil fractions provided better correlations than 96.3 and 99.9% of all the NDVIs for fields 1 and 2, respectively. Since the unconstrained plant fraction could represent yield variation better than most narrow-band NDVIs, it can be used as a relative yield map especially when yield data are not available. These results indicate that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping the variation in crop yield.     

基于星-机光谱融合的棉花叶片SPAD值反演

【目的】为提高棉花叶片叶绿素含量的反演精度,并掌握其在山东省夏津县的空间分布特征。【方法】本研究以山东省德州市夏津县为研究区,以夏津县大李庄棉田为试验区,通过SPAD（soil and plant analyzer development,SPAD）仪实地测定试验区棉花叶片叶绿素含量的相对值（SPAD值）,并获取同期试验区无人机（unmanned aerial vehicle,UAV）近地多光谱图像和研究区Sentinel-2A MSI（MSI）卫星影像;然后分别基于UAV和MSI的光谱反射率,构建并筛选最优光谱参量,采用多元线性回归（multiple linear regression,MLR）建立SPAD值定量反演模型;最后采用二次多项式拟合法融合UAV和Sentinel-2A MSI对应的最优光谱参量,对比分析融合前后模型效果,优选最佳反演模型,实现研究区SPAD值反演。【结果】研究表明,(REG-R)/(REG+R)、R/G、CL(red edge)、NDVI可作为SPAD值的最优光谱参量;基于UAV图像的定量反演模型精度优于基于MSI影像的模型;基于二次多项式拟合后建模R ²提高了0.015—0.057,RMSE降低了0.457—0.638,验证R ²提高了0.040—0.085,RMSE降低了0.387—0.397,RPD提高了0.020—0.139;将融合后的MSI光谱参量代入基于UAV图像的反演模型（Fused MSI-Mod_UAV）,也可获得较高的反演精度,建模R ²达0.672,RMSE为3.982,验证R ²达0.713,RMSE为3.859,RPD为1.685;基于上述模型进行研究区棉花叶片SPAD值反演分析,试验区整体呈南高北低的分布趋势,研究区呈中间低、四周高的分布趋势,均与实地情况一致,具有较好的预测效果。【结论】采用二次多项式拟合法融合无人机和卫星影像数据,可较好地实现区域高精度作物生长指标的定量反演,研究结果可丰富多源遥感融合理论与技术,为后续棉花长势监测与精准生产提供技术参考和数据支持。     

地膜厚度对作物产量与土壤环境的影响

农用地膜(以下简称地膜)已成为我国干旱、冷凉地区土壤增温、保墒和作物增产的重要措施。为摸清我国地膜厚度应用现状,研究地膜厚度对作物产量和土壤环境的影响,2011年,在全国范围内针对棉花、玉米、马铃薯、花生等主要覆膜作物分别布置172、99、30、58个调查点,采用问卷调查的方法对我国地膜厚度应用现状进行系统调查。根据调查结果,2011—2013年,在新疆、甘肃、内蒙古、山东4省分别针对以上4种作物,设置不同地膜厚度处理,系统分析了地膜厚度对土壤温度和含水量、作物产量、经济效益以及地膜残留强度等的影响。研究结果表明,我国主要覆膜作物现用地膜厚度较薄,96.7%的地膜厚度集中在0.004~0.008 mm之间。增加地膜厚度能够提高土壤温度和含水量,但对不同作物的产量影响不同;随着地膜厚度增加(0.004~0.012 mm),棉花和玉米产量不断增加,而马铃薯和花生产量先增加后减少;地膜厚度对作物经济效益有一定影响,但处理间无显著性差异(P0.05)。地膜厚度显著影响地膜残留强度(P0.05),除了马铃薯外,其余作物地膜残留强度和地膜厚度均呈显著负相关关系(P0.05)。综上所述,增加地膜厚度对于我国主要覆膜作物有一定的增产作用,但增产幅度有限,而在残膜回收的基础上增加地膜厚度能够显著降低地膜残留强度。为应对我国农田地膜残留问题,建议我国地膜厚度标准提高至0.010~0.012 mm较为适宜。     

Mapping wheat nitrogen uptake from RapidEye vegetation indices

Mapping wheat nitrogen (N) uptake at 5 m spatial resolution could provide growers with new insights regarding nitrogen-use efficiency at the field scale. This study explored the use of spectral information from high resolution (5 × 5 m) RapidEye satellite data at peak leaf area index (LAI) to estimate end-of-season cumulative N uptake of wheat (Triticum spp.) in a heterogeneous, rainfed system. The primary objectives were to evaluate the usefulness of simple, widely used vegetation indices (VIs) from RapidEye as a tool to map crop N uptake over three growing seasons, farms and growing conditions, and to examine the usefulness of remotely sensed N uptake maps for precision agriculture applications. Data on harvested wheat N was collected at twelve plots over three seasons at four farms in the Palouse region of Northern Idaho and Eastern Washington. Seventeen commonly used spectral VIs were computed for images collected during ‘peak greenness’ (maximum LAI) to determine which VIs would be most appropriate for estimating wheat N uptake at harvest. The normalized difference red-edge index was the top performing VI, explaining 81 % of the variance in wheat N uptake with a regression slope of 1.06 and RMSE of 15.94 kg/ha. Model performance was strong across all farms over all three seasons regardless of crop variety, allowing the creation of high accuracy wheat N uptake maps. In conclusion, for this particular agro-ecosystem, mid-season VIs that incorporate the use of the NIR and red-edge bands are generally better predictors of end-of-season crop N uptake than VIs that do not include these bands, thereby further enabling their use in precision agriculture applications.     

Using spectral distance, spectral angle and plant abundance derived from hyperspectral imagery to characterize crop yield variation

Vegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance (crop fractional cover estimated with spectral unmixing) derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band and three-band VIs based on selected wavelengths for quantifying crop yield variability. Airborne 102-band hyperspectral images acquired at the peak development stage and yield monitor data collected from two grain sorghum fields were used. A total of 64 VI images were generated based on the eight VIs and selected wavelengths for each field in this study. Two spectral distance images, two spectral angle images and two abundance images were also created based on a pair of pure plant and soil reference spectra for each field. Correlation analysis with yield showed that the eight VIs with the selected wavelengths had r values of 0.73–0.79 for field 1 and 0.82–0.86 for field 2. Although all VIs provided similar correlations with yield, the modified soil-adjusted vegetation index (MSAVI) produced more consistent r values (0.77–0.79 for field 1 and 0.85–0.86 for field 2) among the selected bands. Spectral distance, spectral angle and plant abundance produced similar r values (0.76–0.78 for field 1 and 0.83–0.85 for field 2) to the best VIs. The results from this study suggest that either a VI (MSAVI) image based on one near-infrared band (800 or 825 nm) and one visible band (550 or 670 nm) or a plant abundance image based on a pair of pure plant and soil spectra can be used to estimate relative yield variation from a hyperspectral image.     

沼肥对棉花生长发育及产量的影响

沼肥是一种优质的有机肥料,施用沼肥不仅能改良土壤,确保农作物生长所需的良好微生态环境,还有利于增强作物抗冻、抗旱能力,减少病虫害,提高作物产量.试验采用三因素三水平正交试验设计方法,对不同时间沼液浸种、不同浓度沼液叶面喷施、沼液和化肥追施等农艺措施组合进行田间试验研究.结果表明,沼液浸种和追施对棉花产量影响较大,其对产量的影响顺序依次是浸种＞追肥＞喷施.其中沼液浸棉种8 h、1 hm2追施150 kg尿素和15 t沼液对促进棉花产量效果最好.     

安徽省沿江棉区两种栽培方式下棉田杂草发生与危害研究

初步研究了安徽省沿江棉区直播棉田和移栽棉田杂草发生和危害。运用杂草群落的重要值(MDK)和相对多度(RA),结合K类中心聚类法分析两种模式棉田杂草发生和危害程度。在直播棉田和移栽棉田中,杂草发生种类和杂草演替规律差异不大。一般是以禾本科杂草牛筋草、马唐为重要杂草,由铁苋菜、狗尾草、马齿苋、通泉草、反枝苋和异型莎草等主要杂草组成的杂草群落;直播棉田杂草在棉花蕾期发生密度较大,对棉花的危害程度也较大。两种栽培方式下,不除草棉田的棉花前期株高、叶片数、果枝数和果节数显著低于常规除草棉田;后期的株高、单株铃数、单铃重显著低于常规除草棉田,皮棉产量减少极显著。     

地膜降解与土壤温度和含水量的关系及其对棉花产量的影响

为研究膜下滴灌棉田覆用降解地膜的保温保墒效果,通过田间试验,设DM1(普通聚乙烯地膜)、DM2(低降解率地膜)、DM3(中等程度降解率地膜)、DM4(高降解率地膜)4个覆膜处理,对各处理的地膜降解率、地温、土壤含水量、棉花产量进行调查测试,分析地膜不同降解率与土壤温度、含水量的相关关系。结果表明:棉花各生育期地膜降解率与产量呈负相关关系;地膜降解率与土壤温度呈线性负相关关系,其相关性在花期、花铃期、盛铃期达到极显著水平(P0.01),其相关系数在盛铃期达到0.924 9的最高值;地膜降解率与土壤含水量亦呈线性负相关关系,其相关性在各生育期均达显著水平(P0.05),其相关系数的大小顺序是:花铃期(0.980 2)盛铃期(0.978 9)花期(0.771 2)苗期(0.703 4)蕾期(0.695 6)。说明,地膜不同降解率对棉花产量影响显著,地膜降解率与棉田土壤温度、土壤含水量呈线性负相关关系,且地膜降解率高低对盛铃期地温影响最大,对花铃期土壤保墒作用调控最强。为实现环境和产量的双赢目标,在生产中选择适宜降解率的降解地膜十分必要。     

Predicting Cotton Lint Yield Maps from Aerial Photographs

It is generally accepted that aerial images of growing crops provide spatial and temporal information about crop growth conditions and may even be indicative of crop yield. The focus of this study was to develop a straightforward technique for creating predictive cotton yield maps from aerial images. A total of ten fields in southern Georgia, USA, were studied during three growing seasons. Conventional (true color) aerial photographs of the fields were acquired during the growing season in two to four week intervals. The aerial photos were then digitized and analyzed using an unsupervised classification function of image analysis software. During harvest, conventional yield maps were created for each of the fields using a cotton picker mounted yield monitor. Classified images and yield maps were compared quantitatively and qualitatively. A pixel by pixel comparison of the classified images and yield maps showed that spatial agreement between the two gradually increased in the weeks after planting, maintained spatial agreement of between 40% and 60% during weeks eight to fourteen, and then gradually declined again. The highest spatial agreement between a classified image and a yield map was 78%. The highest average agreement was 52% and occurred 9.9 weeks after planting. The visual similarity between the classified images and the yield maps were striking. In all cases, the dates with the best visual agreement occurred between eight and ten weeks after planting, and generally, during July for southern Georgia. This method offers great potential for offering cotton farmers early-season maps that predict the spatial distribution of yield. Although these maps can not provide magnitudes, they clearly show the resulting yield patterns. With inherent knowledge of past performance, farmers can use this information to allocate resources, address crop growth problems, and, perhaps, improve the profitability of their farm operation. These maps are well suited to be offered to farmers as a service by a crop consultant or a cooperative.     

基于PCA-WNN的玉米叶片叶绿素含量遥感反演建模方法

为寻找一种准确、非破坏性的叶绿素含量获取方法,实时掌握作物的生理状况,研究一种基于PCAWNN的玉米叶片叶绿素含量遥感反演模型。利用SVC HR-1024I光谱仪采集盆栽玉米叶片光谱,同时用SPAD-502便携式叶绿素计测定叶绿素含量。从包络线去除、微分处理后的光谱曲线中提取7个光谱特征参数(SCPs)并与修改型土壤调节植被指数(MSAVI)、归一化差值植被指数(NDVI)、修正植被指数(MVI)、比值植被指数(RVI)、差值植被指数(DVI)5种植被指数分别结合主成分分析(PCA),并提取前4个主分量作为小波神经网络(WNN)的输入因子,以Morlet母小波基函数作为激励函数,建立隐含层节点数为3的PCAWNN模型反演玉米叶片叶绿素含量。通过精度检验,表明7个SCPs与MSAVI组合的建模精度最高,验证小波神经网络反演玉米叶绿素含量的可行性以及其预测精度比BP神经网络更好。     

Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet

Diseases caused by nematodes and non-sporulating soil-borne fungi have low mobility and are likely to be suitable targets for precision agriculture applications. Sensors which assess the reflectance of plant leaves may be useful tools to detect soil-borne pathogens. The development of symptoms caused by the plant parasitic nematode Heterodera schachtii and the fungal pathogen Rhizoctonia solani anastomosis group 2-2IIIB alone or in combination was studied by leaf reflectance recorded with a hyperspectral imaging system (range 400–1000 nm) for 9 weeks twice per week. Three image processing methods were tested for their suitability to generate the most sensitive spectral information for disease detection. Nine spectral vegetation indices were calculated from spectra to correlate them to leaf symptom recordings. Supervised classification by spectral angle mapper was tested for the discrimination of leaf symptoms caused by the diseases. The symptoms of Rhizoctonia crown and root rot caused by R. solani and symptoms caused by H. schachtii induced modifications that could be detected by hyperspectral image analysis. Rhizoctonia crown and root rot symptom development in mixed inoculations was faster and more severe than in single inoculations, indicating complex interactions among fungus, nematode and plant. The results from this study under controlled conditions are currently used to transfer the sensor technology to the field.     

转双价基因棉SGK321不同秸秆还田量对土壤线虫群落的影响

为了更好地监测转基因作物秸秆还田对土壤生态系统的影响,通过盆栽试验研究了转双价(Bt+CpTI)基因棉秸秆不同还田量对土壤线虫密度、多样性以及群落结构的影响。以转双价(Bt+CpTI)基因棉SGK321和常规棉石远321为材料进行盆栽试验,设置5个还田量:不还田(对照)、半量还田1.0 g·kg~(-1)(2250 kg·hm~(-2))、全量还田2.0 g·kg~(-1)(4500 kg·hm~(-2))、1.5倍还田量3.0 g·kg~(-1)(6750 kg·hm~(-2))、2倍还田量4.0 g·kg~(-1)(9000 kg·hm~(-2)),取棉花蕾期和花铃期土壤进行线虫分离鉴定,并进行数据分析。结果表明:秸秆不还田时,两个棉花品种土壤线虫密度无显著差异;秸秆还田后,SGK321土壤线虫密度仅在蕾期3.0 g·kg~(-1)还田处理时高于石远321,存在显著差异,其他处理无显著差异;转双价(Bt+CpTI)基因棉SGK321土壤中鉴定出线虫19科34属,其中食细菌性线虫(Bacterivores)13属,食真菌性线虫(Fungivores)3属,植食性线虫(Plant-parasites)11属,杂食/捕食性线虫(Omnivores-predators)7属。常规棉石远321土壤中鉴定出线虫19科37属,其中食细菌性线虫13属,食真菌性线虫3属,植食性线虫12属,杂食/捕食性线虫9属。秸秆还田增加了食细菌性线虫的丰度,秸秆还田量超过3.0 g·kg~(-1)就会抑制植食性线虫的生长,使其丰度减小;从生态指标来看,两种棉花不同还田处理下的多样性指数(H′)、线虫通路指数(NCR)均无显著差异。研究表明,转双价(Bt+CpTI)基因棉SGK321的种植没有改变土壤线虫的营养类群,并且土壤线虫密度也无显著差异,两种棉花不同还田处理下的多样性指数(H′)、线虫通路指数(NCR)也均无显著差异。秸秆还田量在一定范围内会使土壤线虫增多,增加丰富度。多因素方差分析表明,相对于品种的影响,棉花生育期和秸秆还田量对线虫群落的影响更显著。     

Field methods for making productivity classes for site-specific management of wheat

Reducing the decision-making unit to classes within fields can improve yields, efficiency in the use of nutrients and profitability of crops. The objectives were to compare methods for class delimitation in wheat (Triticum aestivum L.) crops based on apparent productivity levels and establish similarities among them in terms of spatial overlapping, productive attributes and the use of nitrogen. In three wheat fields, high and low apparent productivity classes (APC) were defined based on eight methodologies: yield maps, soil maps, gramineae vegetation index, rotation crop index, interpretation of satellite images, management records, elevation and integrated soil and yield maps. In each APC, soil and crop yield components were determined under five nitrogen fertilization levels. Among delimitation methodologies, the degree of coincidence varied from 1.4 to 81.7%. The differences in soil properties, nitrogen use efficiency and grain yields were greater among fields than among APC within each field. In each field, the delimitation methodologies identified different single factors that discriminated among the potential management classes and were partially associated with the crop grain yields. The wheat crops at the low APC yielded 39% less and 12% less than at the high APC, respectively. The nitrogen fertilization, at the rate for maximum productivity for each ACP, reduced the yield differences between contrasting APC. Nitrogen fertilization also modified clustering of classes based on expected yields. Making management classes for wheat based on expected productivity is more accurate when based on previous crop production information under similar nitrogen fertilization conditions than the targeted crop.

     

Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data

In the context of a growing interest in remote sensing for precision agriculture applications, the utility of space-borne hyperspectral imaging for the development of a crop-specific spectral library and automatic identification and classification of three cultivars for each of rice (Oryza sativa L.), chilli (Capsicum annuum L.), sugarcane (Saccharum officinarum L.) and cotton (Gossipium hirsutum L.) crops have been investigated in this study. The classification of crops at cultivar level using two spectral libraries developed using hyperspectral reflectance data at canopy scale (in-situ hyperspectral measurements) and at pixel scale (Hyperion data) has shown promising results with 86.5 and 88.8% overall classification accuracy, respectively. This observation highlights the possible integration of in-situ hyperspectral measurements with space-borne hyperspectral remote sensing data for automatic identification and discrimination of various crop cultivars. However, considerable spectral similarity is observed between cultivars of rice and sugarcane crops which may pose problems in the accurate identification of various crop cultivars.     

Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability

Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton picker's cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensor's bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability.     

Spectral Detection of Emergence in Corn and Cotton

Multispectral reflectance of emerging cotton (Gossypium hirsutum) and corn (Zeamays) seedlings was measured during the 2000 and 2001 growing seasons. Reflectance in blue, green, red, and near infrared (NIR) wave lengths was used to detect seedling emergence, to monitor leaf area growth, and to measure the effect of bare soil reflectance on scene (bare soil and seedlings) reflectance. Cotton and corn seedlings were detected 1 day after initial emergence (1 DAE) in 2000 by the red band. The red band detected seedlings in 2001 at 9 and 8 DAE in early and late planted corn, respectively, and on 0 DAE for cotton. The normalized difference vegetative index (NDVI) detected seedlings at 1 DAE or 2 DAE in both years. Seedling ground cover in 2000 on the initial detection date in the target areas averaged 1.3% and 0.9%, respectively, for cotton and corn; comparable values in 2001 for cotton, early planted corn, and late planted corn, were 1.4%, 0.4%, and 0.8%, respectively. The red wave band was the most sensitive single band for detecting the presence of seedlings, but NDVI was the most sensitive spectral indicator, which was apparently due to the red band since the NIR band did not always detect seedlings. Seedling leaf area was linearly correlated with NDVI values beginning at 1 or 2 DAE. Bare soil was the major component of the scene during stand establishment and dominated single band reflectance and NDVI values. A dry soil surface that was smoothed and sealed by rain usually caused single band reflectance to increase. The high variability in spectral characteristics of bare soil restricted the interpretation of the spectral data to concluding whether or not seedlings were emerging, but without estimating numbers and seedling size.     

Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping

The joint use of satellite imagery and digital soil maps derived from soil sampling is investigated in the present paper with the goal of proposing site-specific management units (SSMU) within a commercial field plot. Very high resolution Quickbird imagery has been used to derive leaf area index (LAI) maps in maize canopies in two different years. Soil properties maps were obtained from the interpolation of ion concentrations (Na, Mg, Ca, K and P) and texture determined in soil samples and also from automatic readings of electromagnetic induction (EMI) readings taken with a mobile sensor.Links between the image-derived LAI and soil properties were established, making it possible to differentiate units within fields subject to abiotic stress associated with soil sodicity, a small water-holding capacity or flooding constraints. In accordance with the previous findings, the delineation of SSMUs is proposed, describing those field areas susceptible of variable-rate management for agricultural inputs such as water or fertilizing, or soil limitation correctors such as gypsum application in the case of sodicity problems. This demonstrates the suitability of spatial information technologies such as remote sensing and digital soil mapping in the context of precision agriculture.