氮肥不同施入量对水稻新品种富友33生育及产量的影响

采用小区对比试验方法,探讨了氮肥不同施入量对水稻新品种富友33生育及产量的影响。结果表明,增加氮肥施入量,可促进富友33单位面积收获穗数、齐穗期叶面积指数与剑叶净光合速率、成熟期干物质积累量的增加;不利于其成穗率、齐穗期高效叶面积率与有效叶面积率、齐穗后干物质积累量占籽粒产量百分比以及收获指数的提高。在本试验条件下,施氮量(N)240.0 kg/hm2的处理E3获得单产9.22 t/hm2,比E4、E2、E5、E1处理分别增产了0.2%、3.1%、3.4%和7.3%。     

Research advances of SAR remote sensing for agriculture applications: A review

Synthetic aperture radar (SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.     

Assessment of biomass and net primary productivity of a dry tropical forest using geospatial technology

This study quantifies biomass, aboveground and belowground net productivity, along with additional environmental factors over a 2-3 year period in Barnawapara Sanctuary of Chhattisgarh, India through satellite remotesensing and GIS techniques. Ten sampling quadrates20×20, 5×5 and 1×1 m were randomly laid for overstorey (OS), understorey (US) and ground vegetation(GS), respectively. Girth of trees was measured at breast height and collar diameters of shrubs and herbs at 0.1 m height. Biomass was estimated using allometric regression equations and herb biomass by harvesting. Net primary productivity (NPP) was determined by Ssumming biomass increment and litter crop values. Aspect and slope influenced the vegetation types, biomass and NPP in different forests. Standing biomass and NPP varied from 18.6 to101.5 Mg ha^-1 and 5.3 to 12.7 Mg ha^-1 a^-1, respectively,in different forest types. The highest biomass was found in dense mixed forest, while net production recoded in Teak forests. Both were lowest in degraded mixed forests of different forest types. OS, US and GS contributed 90.4, 8.7and 0.7%, respectively, for the total mean standing biomass in different forests. This study developed spectral models for the estimation of biomass and NPP using Normalized Difference Vegetation Index and other vegetation indices.The study demonstrated the potential of geospatial tools for estimation of biomass and net productivity of dry tropical forest ecosystem.     

SCE标定结合EnKF同化遥感和WOFOST模型模拟冬小麦时序LAI

WOFOST(world food studies)模型可用于模拟冬小麦全生育期内的时序叶面积指数(leaf area index, LAI),各器官生物量以及最终产量,对冬小麦的长势监测与产量预估有着重要意义。但将WOFOST模型用于中国具体区域的冬小麦生长模拟时,存在着参数定标困难、模拟结果不够准确等严重问题。目前对该模型的定标大多依靠研究者的经验进行,虽已总结出了一套从标定到模拟应用的研究方法,但在区域模拟时仍然存在很多问题。为此,该文以较易获取的LAI为参考指标,结合潜在生长水平模式下的WOFOST模型在衡水地区的应用,提出了一种"区域优化标定,像元同化修正"的研究方法:首先在区域尺度上对WOFOST模型进行优化标定,利用扩展傅里叶幅度灵敏度检验法(extend fourier amplitude sensitivity test, EFAST)分析模型各个参数的敏感性,在此基础上选择了可以迅速找到全局最优解的SCE(shuffled complex evolution)算法对总敏感度最高的5个参数进行优化,并将优化前后的时序LAI曲线进行对比;其次运用第一步确定的模型最优参数,在对区域内每个像元进行模拟时,结合Sentinel-2卫星数据反演所得的各个像元LAI,利用集合卡尔曼滤波(ensemble kalman filter, EnKF)在像元尺度上对LAI进行同化修正,并结合采样点的2次实测LAI数据对同化所得结果进行验证。试验发现,优化标定后的WOFOST模型模拟所得LAI曲线更接近所给的LAI真值,在此基础上结合数据同化模拟得出的衡水地区每个像元LAI的R2达到0.87,RMSE仅为0.62。因此,与原来只能通过经验进行定标的方法相比,该方法有效地解决了WOFOST模型在具体应用中亟待解决的复杂标定问题,并且结合同化修正有效地提高了模型在各个像元的模拟精度,R2由0.70～0.83提升至了0.87,RMSE由0.89～1.36降低至了0.62。同时该文也提供了从模型标定到具体模拟整个过程中各个环节的思路与方法,有利于促进WOFOST模型在区域尺度上的应用。     

基于Sentinel-2多光谱数据的棉花叶面积指数估算

棉花叶面积指数(leaf are index, LAI)的快速、准确获取对棉花长势监测、发育期诊断、面积提取以及产量估算等遥感监测具有重要意义。该研究利用2017年和2018年的Sentinel-2多光谱卫星数据及大面积田间试验观测获取的棉花不同发育期LAI实测数据,构建了基于单波段反射率及各类植被指数的棉花不同发育期及全发育期LAI估算模型,并采用留一验证(LOOCV, leave-one-out cross validation)和交叉验证对模型精度进行了检验。结果表明:1)对于单波段反射率,基于中心波长为842 nm波宽为145 nm的B8近红外波段对不同发育期LAI估算精度最优均方根误差(RMSE, root mean square error, RMSE=0.378);2)对于各类植被指数,花蕾期(20170616)和花铃期(20170802)时增强植被指数(EVI, enhanced vegetation index,)表现最佳(RMSE分别为0.352和0.367),开花期(20180623)时校正土壤调节植被指数(MSAVI2, modified soil adjusted vegetation index 2,)估算精度最高(RMSE=0.323);3)单波段反射率和各类植被指数对全发育期LAI的估算均要优于对单个发育期LAI的估算,其中基于IRECI指数的(invertedred-edge chlorophyllindex)全发育期LAI估算模型精度最佳,LOOCV检验RMSE=0.425,交叉检验RMSE=0.368;将基于IRECI的全发育期LAI估算模型应用到单个发育期LAI估算并与各单个发育期LAI估算模型精度对比,发现交叉验证RMSE平均值仅比LOOCV验证RMSE平均值高0.07,反映了全发育期LAI估算模型良好的普适性。该研究为农作物LAI估算提供了新的数据选择,完善了Sentinel-2卫星数据在LAI估算中的应用领域。     

Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation

Leaf chlorophyll content, a good indicator of photosynthesis activity, mutations, stress and nutritional state, is of special significance to precision agriculture. Recent studies have demonstrated the feasibility of retrieval of chlorophyll content from hyperspectral vegetation indices composed by the reflectance of specific bands. In this paper, a set of vegetation indices belonged to three classes (normalized difference vegetation index (NDVI), modified simple ratio (MSR) index and the modified chlorophyll absorption ratio index (MCARI, TCARI) and the integrated forms (MCARI/OSAVI and TCARI/OSAVI)) were tested using the PROSPECT and SAIL models to explore their potentials in chlorophyll content estimation. Different bands combinations were also used to derive the modified vegetation indices. In the sensitivity study, four new formed indices (MSR[705,750], MCARI[705,750], TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750]) were proved to have better linearity with chlorophyll content and resistant to leaf area index (LAI) variations by taking into account the effect of quick saturation at 670 nm with relatively low chlorophyll content. Validation study was also conducted at canopy scale using the ground truth data in the growth duration of winter wheat (chlorophyll content and reflectance data). The results showed that the integrated indices TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750] are most appropriate for chlorophyll estimation with high correlation coefficients R² of 0.8808 and 0.9406, respectively, because more disturbances such as shadow, soil reflectance and nonphotosynthetic materials are taken into account. The high correlation between the vegetation indices obtained in the developmental stages of wheat and Hyperion data (R² of 0.6798 and 0.7618 for TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750], respectively) indicated that these two integrated index can be used in practice to estimate the chlorophylls of different types of corns.     

Assessing the effects of the clumping phenomenon on BRDF of a maize crop based on 3D numerical scenes using DART model

Inverting radiative transfer (R-T) models against remote sensing observations to retrieve key biogeophysical parameters such as leaf area index (LAI) is a common approach. Even if new inversion techniques allow the use of three-dimensional (3D) models for that purpose, one-dimensional (1D) models are still widely used because of their ease of implementation and computational efficiency. Nevertheless, they assume a random distribution of foliage elements whereas most canopies show a clumped organization. Due to that crude simplification in the representation of the canopy structure, sizeable discrepancies can occur between 1D simulations and real canopy reflectance, which may further lead to false LAI values. The present investigation aims to appraise to which extent the incorporation of a clumping index (noted λ) into 1D R-T model could improve the simulations of Bidirectional Reflectance Distribution Function (BRDF). Canopy BRDF is simulated here for three growth stages of a maize crop with the Discrete Anisotropic Radiative Transfer (DART) model in the visible and near infrared spectral bands, for two contrasted soil types (dark and bright) and different levels of heterogeneity to represent the canopy structure. 3D numerical scenes are based on in-situ structural measurements and associated BRDF simulations are thus considered as references. 1D scenarios assume either that leaves are randomly distributed (λ = 1) or clumped (λ < 1). If BRDF simulations seem globally reliable under the assumption of a random distribution in near infrared, it can also lead to relative errors on the total BRDF up to 30% in the red spectral band. It comes out that the use of a clumping index in a 1D reflectance model generally improves BRDF simulations in the red considering a bright soil, which seems relatively independent of LAI. In the near infrared, best results are usually obtained with homogeneous canopies, except with the dark soil. Clearly, influent factors are mainly the LAI and the spectral contrast between soil and leaves.     

Slope correction for LAI estimation from gap fraction measurements

Digital hemispherical photography poses specific problems when deriving leaf area index (LAI) over sloping terrain. This study proposes a method to correct from the slope effect. It is based on simple geometrical considerations to account for the path length variation within the canopy for cameras pointing vertically. Simulations over sloping terrain show that gap fraction increases up-slope while decreasing down-slope. As a consequence of this balance between up- and down-slope effects, effective LAI estimates derived from inversion of the Poisson model are marginally affected for low to medium slopes (<25°) and LAI (LAI < 2). However, for larger slopes and LAI values, estimated LAI values may be strongly underestimated. The proposed correction was evaluated over four forested sites located over sloping terrain. Results indicate that in these conditions (LAI between 0.6 up to 3.0, clumped canopies with relatively erectophile leaf distribution), the effect of the slope (between 25° and 36°) was moderate as compared to other potential sources of problems when deriving LAI from gap fraction measurements, including clumping, leaf angle inclination and spatial sampling.