Common Spectral Bands and Optimum Vegetation Indices for Monitoring Leaf Nitrogen Accumulation in Rice and Wheat

Real-time monitoring of nitrogen status in rice and wheat plant is of significant importance for nitrogen diagnosis, fertilization recommendation, and productivity prediction. With 11 field experiments involving different cultivars, nitrogen rates, and water regimes, time-course measurements were taken of canopy hyperspeetral reflectance between 350-2 500 nm and leaf nitrogen accumulation （LNA） in rice and wheat. A new spectral analysis method through the consideration of characteristics of canopy components and plant growth status varied with phenological growth stages was designed to explore the common central bands in rice and wheat. Comprehensive analyses were made on the quantitative relationships of LNA to soil adjusted vegetation index （SAVI） and ratio vegetation index （RVI） composed of any two bands between 350-2 500 nm in rice and wheat. The results showed that the ranges of indicative spectral reflectance were largely located in 770-913 and 729-742 nm in both rice and wheat. The optimum spectral vegetation index for estimating LNA was SAVI （R822, R738） during the early-mid period （from jointing to booting）, and it was RVI （Rs22, R73s） during the mid-late period （from heading to filling） with the common central bands of 822 and 738 nm in rice and wheat. Comparison of the present spectral vegetation indices with previously reported vegetation indices gave a satisfactory performance in estimating LNA. It is concluded that the spectral bands of 822 and 738 nm can be used as common reflectance indicators for monitoring leaf nitrogen accumulation in rice and wheat.

Varietal difference in the correlation between leaf nitrogen content and photosynthesis in rice(Oryza sativa L.) plants is related to specific leaf weight

Increasing leaf photosynthesis per area（A） is of great importance to achieve yield further improvement. The aim of this study was to exploit varietal difference in A and its correlation with specific leaf weight（SLW）. Twelve rice cultivars, including 6 indica and 6 japonica varieties, were pot-grown under two N treatments, low N（LN） and sufficient N（SN）. Leaf photosynthesis and related parameters were measured at tillering stage. Compared with LN treatment, A, stomatal conductance（g_s）, mesophyll conductance（g_m）, leaf N content（N_（area））, and chlorophyll content were significantly improved under SN treatment, while SLW and photosynthetic N use efficiency（PNUE） were generally decreased. Varietal difference in A was positively related to both g_s and g_m, but not related to N_（area）. This resulted in a low PNUE in high N_（area） leaves. Varietal difference in PNUE was generally negatively related to SLW. Response of PNUE to N supply varied among different rice cultivars, and interestingly, the decrease in PNUE under SN was negatively related to the decrease in SLW. With a higher N_（area）, japonica rice cultivars did not show a higher A than indica rice cultivars because of possession of high-SLW leaves. Therefore, varietal difference in A was not related to N_（area）, and SLW can substantially interfere with the correlation between A and N_（area）. These findings may provide useful information for rice breeders to maximize A and PNUE, rather than over reliance on N_（area） as an indicator of photosynthetic performance.

Varietal Difference in Leaf Nitrogen Content and Leaf Area and Their Effects to Ripening Rate During Mature Period of japonica Rice

Employing the pot experiment of the complete random block design with 6 replications,four varieties of japonica rice (Fujisaka 5,Honenwase,Akitakomachi and Taichung 65) were used to study the varietal differences in leaf nitrogen content(LNC) and leaf area during mature period,their relation and effects to the ripening rate.The results showed that(1) thee were varietal differences in LNC at the heading stage and the LNC decrease rate during the matue period,the high LNC at the heading stage was related to the rapid LNC decrease.(2) There were two phases of the leaf area changing process during the mature period,first was the stable,and second was the decreased phase.There was varietal difference in the critical time of phase 1 and phase 2.The hign leaf area in the phase 1 was in relation to the rapid leaf area decrease in the phase 2.It was not found that there was relation between the leaf quality and quantity.(3)It wa unfavorable to the ripening rate for the high leaf area at the heading stage and the rapid decrease of the leaf area during the mature period.(4)It was put forward that the super high yield rice variety should possess the not very high leaf area and high LNC at the heading stage,slow senescence in the leaf area during the mature period.

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner.The nitrogen nutrition index(NNI)can quantitatively describe the nitrogen status of crops.Nevertheless,the NNI diagnosis for cotton with unmanned aerial vehicle(UAV)multispectral images has not been evaluated yet.This study aimed to evaluate the performance of three machine learning models,i.e.,support vector machine(SVM),back propagation neural network(BPNN),and extreme gradient boosting(XGB)for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images.The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI.The XGB model performed the best among the three models in predicting nitrogen weight.The prediction accuracy of nitrogen weight at the upper half-leaf level(R^(2)=0.89,RMSE=0.68 g m^(-2),RE=14.62%for calibration and R^(2)=0.83,RMSE=1.08 g m^(-2),RE=19.71%for validation)was much better than that at the all-leaf level(R^(2)=0.73,RMSE=2.20 g m^(-2),RE=26.70%for calibration and R^(2)=0.70,RMSE=2.48 g m^(-2),RE=31.49%for validation)and at the plant level(R^(2)=0.66,RMSE=4.46 g m^(-2),RE=30.96%for calibration and R^(2)=0.63,RMSE=3.69 g m^(-2),RE=24.81%for validation).Similarly,the XGB model(R^(2)=0.65,RMSE=0.09,RE=8.59%for calibration and R^(2)=0.63,RMSE=0.09,RE=8.87%for validation)also outperformed the SVM model(R^(2)=0.62,RMSE=0.10,RE=7.92%for calibration and R^(2)=0.60,RMSE=0.09,RE=8.03%for validation)and BPNN model(R^(2)=0.64,RMSE=0.09,RE=9.24%for calibration and R^(2)=0.62,RMSE=0.09,RE=8.38%for validation)in predicting NNI.The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields,which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.

Family-level leaf nitrogen and phosphorus stoichiometry of global terrestrial plants

Leaf nitrogen(N) and phosphorus(P) concentrations are critical for photosynthesis, growth, reproduction and other ecological processes of plants. Previous studies on large-scale biogeographic patterns of leaf N and P stoichiometric relationships were mostly conducted using data pooled across taxa, while family/genus-level analyses are rarely reported. Here, we examined global patterns of family-specific leaf N and P stoichiometry using a global data set of 12,716 paired leaf N and P records which includes 204 families, 1,305 genera, and 3,420 species. After determining the minimum size of samples(i.e., 35 records), we analyzed leaf N and P concentrations, N:P ratios and N^P scaling relationships of plants for 62 families with 11,440 records. The numeric values of leaf N and P stoichiometry varied significantly across families and showed diverse trends along gradients of mean annual temperature(MAT) and mean annual precipitation(MAP). The leaf N and P concentrations and N:P ratios of 62 families ranged from 6.11 to 30.30 mg g–1, 0.27 to 2.17 mg g–1, and 10.20 to 35.40, respectively. Approximately 1/3–1/2 of the families(22–35 of 62) showed a decrease in leaf N and P concentrations and N:P ratios with increasing MAT or MAP, while the remainder either did not show a significant trend or presented the opposite pattern. Family-specific leaf N^P scaling exponents did not converge to a certain empirical value, with a range of 0.307–0.991 for 54 out of 62 families which indicated a significant N^P scaling relationship. Our results for the first time revealed large variation in the family-level leaf N and P stoichiometry of global terrestrial plants and that the stoichiometric relationships for at least one-third of the families were not consistent with the global trends reported previously. The numeric values of the family-specific leaf N and P stoichiometry documented in the current study provide critical synthetic parameters for biogeographic modeling and for further studies on the physiological and ecological mechanisms underlying the nutrient use strategies of plants from different phylogenetic taxa.

Water,Nitrogen and Plant Density Affect the Response of Leaf Appearance of Direct Seeded Rice to Thermal Time

Field experiments were conducted in the Ebro Delta area （Spain）, from 2007 to 2009 with two rice varieties： Gleva and Tebre. The experimental treatments included a series of seed rates, two different water management systems and two different nitrogen fertilization times. The number of leaves on the main stems and their emergence time were periodically tagged. The results indicated that the final leaf number on the main stems in the two rice varieties was quite stable over a three-year period despite of the differences in their respective growth cycles. Interaction between nitrogen fertilization and water management influenced the final leaf number on the main stems. Plant density also had a significant influence on the rate of leaf appearance by extending the phyllochron and postponing the onset of intraspecific competition after the emergence of the 7th leaf on the main stems. Final leaf number on the main stems was negatively related to plant density. A relationship between leaf appearance and thermal time was established with a strong nonlinear function. In direct-seeded rice, the length of the phyllochron increases exponentially in line with the advance of plant development. A general model, derived from 2-year experimental data, was developed and satisfactorily validated; it had a root mean square error of 0.3 leaf. An exponential model can be used to predict leaf emergence in direct-seeded rice.

Distribution of Leaf Color and Nitrogen Nutrition Diagnosis in Rice Plant

Greenness and nitrogen content of each leaf on main stem of different japonica and indica rice varieties under different nitrogen levels were investigated. Results showed that the fourth leaf from the top exhibited active changes with the change of plant nitrogen status. When the plant nitrogen content was low, its color and nitrogen content were obviously lower than those of the three top leaves. With the increase of plant nitrogen content, the color and nitrogen content of the fourth leaf increased quickly, and the differences of color and nitrogen content between the fourth leaf and the three top leaves decreased. So, the fourth leaf was an ideal indication of plant nutrition status. In addition, color difference between the fourth and the third leaf from the top was highly related to the plant nitrogen content regardless of the variety and development stage. Therefore, color difference between the fourth and the third leaf could be widely used for diagnosis of plant nutrition. Results also indicated that the minimized color difference between the fourth and the third leaf at the critical effective tillering, the emergence of the second leaf from the top, and the heading was the symbol of high yield. Plant nitrogen content of 27 g kg-1 DW for japonica rice and 25 g kg-1 DW for indica were the critical nitrogen concentrations.

Determination of critical nitrogen dilution curve based on leaf area index for winter wheat in the Guanzhong Plain, Northwest China

Excessive use of nitrogen (N) fertilizers in agricultural systems increases the cost of production and risk of environmental pollution. Therefore, determination of optimum N requirements for plant growth is necessary. Previous studies mostly established critical N dilution curves based on aboveground dry matter (DM) or leaf dry matter (LDM) and stem dry matter (SDM), to diagnose the N nutrition status of the whole plant. As these methods are time consuming, we investigated the more rapidly determined leaf area index (LAI) method to establish the critical nitrogen (Nc) dilution curve, and the curve was used to diagnose plant N status for winter wheat in Guanzhong Plain in Northwest China. Field experiments were conducted using four N fertilization levels (0, 105, 210 and 315 kg ha?1) applied to six wheat cultivars in the 2013–2014 and 2014–2015 growing seasons. LAI, DM, plant N concentration (PNC) and grain yield were determined. Data points from four cultivars were used for establishing the Nc curve and data points from the remaining two cultivars were used for validating the curve. The Nc dilution curve was validated for N-limiting and non-N-limiting growth conditions and there was good agreement between estimated and observed values. The N nutrition index (NNI) ranged from 0.41 to 1.25 and the accumulated plant N deficit (Nand) ranged from 60.38 to –17.92 kg ha?1 during the growing season. The relative grain yield was significantly affected by NNI and was adequately described with a parabolic function. The Nc curve based on LAI can be adopted as an alternative and more rapid approach to diagnose plant N status to support N fertilization decisions during the vegetative growth of winter wheat in Guanzhong Plain in Northwest China.

Leaf area index based nitrogen diagnosis in irrigated lowland rice

Leaf area index （LAI） is used for crop growth monitoring in agronomic research, and is promising to diagnose the nitrogen （N） status of crops. This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice. Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014. Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice. LAI was determined by LI-3000, and estimated indirectly by LAI-2000 during vegetative growth period. Group and individual plant characters （e.g., tiller number （TN） and plant height （H）） were investigated simultaneously. Two N indicators of plant N accumulation （NA） and N nutrition index （NNI） were measured as well. A calibration equation （LAI=1.7787LAI2o00-0.8816, R2=0.870＂） was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI （R2=0.863^＊＊）. For the NNI, the relative LAI （R2=0.808-） was a relatively unbiased variable in the regression than the LAI （R^2=0.33^＊＊）. The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice （NA=29.778LAI-5.9397; NNI=0.7705RLAI＋0.2764）. Finally, a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H （LAI=-0.3375（THxHx0.01）2＋3.665（TH×H×0.01）-1.8249, R2=0.875＊＊）. With these models, the N status of rice can be diagnosed conveniently in the field.

Leaf Colour Chart vis-a-vis Nitrogen Management in Different Rice Genotypes

A field trial comprising 3 rice varieties (NDR-359, Sarju 52, HUBR 2-1) and 4 LCC scores (≤ 2, ≤ 3, ≤ 4, ≤ 5) along with the recommended dose of N was conducted in a split plot design to calibrate the LCC for nitrogen requirement of rice. Maximum grain yields of NDR-359, Sarju 52 at LCC ≤ 5 and HUBR 2-1 at LCC ≤ 4 were found to be 47.10, 40.66 and 36.04 q/ha respectively. The critical LCC score for real time nitrogen requirement for NDR 359 and Sarju 52 was found to be ≤ 5, while for HUBR 2-1 it was ≤ 4. Agronomic and recovery efficiency of nitrogen also followed the same trend. In the functional relationship between SPAD value and LCC score, while it was linear in NDR-359 and Sarju 52, for HUBR 2-1 it was quadratic. Further a positive correlation between SPAD values and LCC score was observed in all the 3 varieties.

Altered Expression of Transcription Factor Genes in Rice Flag Leaf under Low Nitrogen Stress

The response of transcription factor genes to low nitrogen stress was studied to provide molecular basis for improving the absorption and utilization efficiency of nitrogen fertilizer in rice. The agilent rice genome arrays were used to study the varied expression of transcription factor genes in two rice varieties （SN 196 and Toyonishhiki） with different chlorophyll contents under low nitrogen stress. The results showed that a total of 53 transcription factor genes （35 down-regulated and 18 up-regulated genes at the transcription level） in flag leaves of super-green rice SN196 and 27 transcription factor genes （21 down-regulated and 6 up-regulated genes at the transcription level） in flag leaves of Toyonishiki were affected by low nitrogen stress. Among those nitrogen-responsive genes, 48 transcription factor genes in SN196 and 22 in Toyonishiki were variety-specific. There were overlapped transcription factor genes responded to low nitrogen stress between SN196 and Toyonishiki, with 1 up-regulated and 4 down-regulated at the transcription level. Distributions of low nitrogen responsive genes on chromosomes were different in two rice varieties.

基于便携式作物生长监测诊断仪的红壤花生叶片氮积累量和叶面积指数监测

通过分析红壤花生不同生育期的生长指标动态变化特征及其与冠层光谱植被指数间的定量关系,以赣花5号和航花2号这2个花生品种为试验对象,设置4个施氮水平,在花生关键生育期(苗期、花针期、结荚期和饱果期)利用便携式作物生长监测诊断仪(CGMD-402)采集冠层光谱植被指数,并同步取样测定各处理的地上部生物量、叶片氮积累量(LNA)和叶面积指数(LAI),构建基于CGMD-402的红壤花生LNA和LAI监测模型。结果表明:施氮水平会对红壤花生植株的生长产生影响,地上部植株的生物量会随着施氮量的增加而增大;叶片氮积累量和叶面积指数均随生育进程的推进整体上表现为先升后降的动态变化特征;花针期与结荚期的花生冠层归一化植被指数(NDVI)与LAI和LNA均具有较好的相关性。因此,可利用便携式作物生长监测诊断仪CGMD-402监测红壤花生的LAI和LNA,为江西省红壤花生的精确施氮管理提供技术支撑。

不同氮源添加对椰子叶堆肥腐殖化效果的影响

为探究不同氮源对椰子叶堆肥腐殖化效果的影响,以鸡粪、猪粪、沼渣、尿素等不同氮源为控制变量,分别与椰子叶进行混合堆肥,对温度、发酵前后碳氮比的比值(T值)、腐殖质含量、腐殖化系数、腐殖化指数和腐殖化聚合度等指标进行分析。结果表明,各处理组的堆肥高温期(55.0℃以上)均超过15 d,达到无害化处理要求;T值均小于0.60,达到了腐熟要求;发酵后总有机碳含量下降,但各处理组的腐殖质碳含量上升,腐殖化程度增加。鸡粪处理组腐殖化效果最佳,其腐殖化系数增加19.28%、腐殖化指数增加65.80%,腐殖化聚合度达到2.38;猪粪处理组产品稳定性低于鸡粪处理,高于沼渣和尿素处理;沼渣处理组堆肥腐殖化系数最高,但腐殖化聚合度只有1.61,产品稳定性差;尿素处理组堆肥腐殖化指数和腐殖化聚合度降低,阻碍了其腐殖化进程。综上可知,鸡粪作为氮源添加对椰子叶堆肥的腐殖化效果最好,研究结果为海南地区椰子叶废弃物资源化利用提供科学依据。

亚热带树种在未成林造林地的凋落物量和周转与叶片性状的关系

【目的】测定亚热带树种的叶片功能性状、凋落叶质量、凋落物量和周转期,揭示叶片性状对凋落物量和周转的影响,为杉木采伐后如何选择造林树种以改善土壤肥力提供科学依据。【方法】选取在二代杉木林采伐迹地营造的17种亚热带树种,测定其在3年生未成林造林地的凋落物量和周转期,同时测定各树种的叶片功能性状(比叶面积、干物质含量、氮含量等)和凋落叶质量(碳氮比、单宁含量、可溶性糖含量等),建立叶片性状与凋落物量和周转期的回归关系。【结果】17种树种中,米老排凋落物量最高(6.67 t·hm^(-2)a^(-1)),杉木凋落物量最低(0 t·hm^(-2)a^(-1));江南桤木凋落叶周转期最短(0.09年);深山含笑凋落叶周转期最长(1.09年)。凋落物量随比叶面积增加而增加,随叶氮含量增加而降低;凋落叶周转期随凋落叶碳氮比和单宁含量增加而增加,随凋落叶最大持水率增加而降低。【结论】在亚热带未成林造林地中,凋落物量受比叶面积和叶氮含量的影响,凋落叶周转期受凋落叶碳氮比、单宁含量和最大持水率的影响;杉木在未成林造林地阶段的凋落物归还量极少。经营亚热带人工林时,要考虑种植比叶面积和凋落叶最大持水能力较高、凋落叶单宁含量和碳氮比较低的树种,以提高林地凋落物归还量和周转速率,改善退化人工林的土壤肥力。

大豆冠层叶片氮含量检测研究——基于无人机多光谱图像

为快速获取大豆冠层叶片氮素含量(Leaf Nitrogen Content,LNC)信息,采用无人机获取大豆冠层LNC多光谱影像光谱特征,通过分析光谱变量与LNC的相关性,选出对大豆冠层LNC敏感的光谱变量。利用逐步回归分析方法建立黑河43、龙垦310、龙垦3401在3个关键生育时期(R1、R3、R5)大豆LNC估测模型。研究结果表明:①在3个品种的3个生育期,除R5时期龙垦3401品种外,NDVI与LNC具有高度相关性,说明NDVI可以较好地进行大豆冠层LNC的反演。②在建模的过程中发现,在R1时期龙垦3401、黑河43、龙垦310所建模型的R2和RMSE依次为0.857、0.133,0.845、0.156,0.821、0.187;在R3时期龙垦3401、黑河43、龙垦310所建模型的R2和RMSE依次为0.835、0.204,0.881、0.113,0.849、0.162;在R5时期龙垦3401、黑河43、龙垦310所建模型的R2和RMSE依次为0.835、0.208,0.814、0.215,0.836、0.211。由此表明,利用无人机多光谱遥感图像数据可以很好地监测大豆LNC的空间分布情况。

机器学习结合高光谱植被指数与SPAD值估算冬小麦氮含量

冬小麦叶片氮含量与叶片光合作用和营养状况密切相关,直接影响植株生长发育,而茎秆中的氮含量与茎秆中纤维素、半纤维素和木质素的比例和含量密切相关,直接影响茎秆质量及植株的抗倒伏能力。然而,有关对冬小麦茎秆氮含量估算研究较为有限,限制了从氮含量角度判断茎秆质量及对倒伏的预测能力。为精准估算冬小麦不同器官(叶片、茎秆)氮含量,该研究通过2年田间试验,获取冬小麦4个关键生育期(拔节期、抽穗期、开花期、灌浆期)和3种施氮水平条件下(N1、N2和N3)的冠层光谱反射率、叶片、茎秆氮含量及叶片SPAD(soil and plant analyzer development,SPAD)值。分析了不同生育期和施氮水平条件下高光谱植被指数对叶片和茎秆氮含量的敏感性,并结合5种常用的机器学习算法:随机森林回归(random forest regression,RFR)、支持向量回归(support vector regression,SVR)、偏最小二乘回归(partial least squares regression,PLSR)、高斯过程回归(gaussian process regression,GPR)、深度神经网络回归(deep neural networks,DNN)构建冬小麦叶片和茎秆氮含量估算模型。结果表明:高光谱植被指数对叶片和茎秆氮含量的敏感性受到生育期和施氮水平的影响。在灌浆期,最佳植被指数双峰冠层植被指数DCNI(double-peak canopy nitrogen index)对叶片氮含量的敏感性最高,R^(2)为0.866。对茎秆氮含量,在抽穗期的敏感性最高,最佳植被指数归一化叶绿素比值指数NPQI(normalized phaeophytinization index)与氮含量决定系数R^(2)=0.677。施氮水平的提升增加了光谱植被指数对茎秆氮含量的敏感性。结合SPAD值的机器学习算法提升了氮含量的估算精度,对叶片氮含量,在不同生育期和施氮水平条件下估算精度提升了1%~7%,其中在全生育期的归一化均方根误差NRMSE从0.254降低到0.214,抽穗期的NRMSE提升最大,从0.201降低到0.128。对茎秆氮含量,全生育期的NRMSE从0.443降低到0.400,抽穗期的NRMSE变化最大,从0.323降低到0.268。在全生育期,结合SPAD值的DNN模型对叶片(R^(2)=0.782、NRMSE=0.214)和茎秆(R^(2)=0.802、NRMSE=0.400)氮含量的估算精度最佳。研究说明,SPAD值与光谱植被指数结合有利于提升冬小麦不同生育期和施氮水平条件下叶片和茎秆氮含量的估算精度。

基于植被指数融合的无人机冬小麦LNC反演

为了解无人机遥感平台用于快速、准确地监测冬小麦叶片氮含量(LNC)中的可行性,利用无人机遥感平台获取新疆喀什地区新疆农业科学院小麦育种基地冬小麦冠层光谱图像,分析和筛选可见光植被指数、多光谱植被指数与LNC的相关性,建立融合植被指数,比较多元线性回归(MLR)、逐步线性回归(SMLR)、随机森林回归(RF)在冬小麦各生育时期对叶片氮含量的适用性,筛选最优冬小麦叶片氮素含量估测模型。结果表明,小麦LNC与可见光植被指数(ExR、IKAW、VARI)、多光谱植被指数(RVI、RDVI、MSR、NDRE、RERDVI)、融合植被指数(ExR×RERDVI、IKAW×RERDVI和VARI×RERDVI)具有较高相关性,遥感监测效果在抽穗期最佳,灌浆期次之,成熟期最差。以融合植被指数作为自变量,采用随机森林回归模型构建的LNC估测模型在抽穗期的预测精度最佳,建模r^(2)、RMSE和nRMSE分别为0.866、0.95 g·kg^(-1)和6.23%,模型验证r^(2)、RMSE和nRMSE分别为0.71、1.61 g·kg^(-1)和10.83%。这说明基于无人机遥感平台利用融合植被指数能够实现对冬小麦LNC的快速、准确估测。

基于GF-6/WFV卫星遥感的大田冬小麦叶片氮素含量估测

为对大田冬小麦叶片氮素含量(LNC)进行快速、准确及无损监测,通过在江苏省泰州泰兴市、盐城大丰区和南通如皋市布设冬小麦遥感监测大田试验,在获取试验样点冬小麦冠层红光波段反射率(REDref)、近红外波段反射率(NIRref)和计算的十个光谱指数(RVI、NDVI、DVI、SAVI、OSAVI、MSR、RDVI、EVI2、NLI和SVI)基础上,将12个遥感光谱指标与冬小麦LNC进行相关分析,选出与LNC相关性较好的作为模型输入变量,构建基于BP神经网络的冬小麦LNC估测模型,并利用GF-6/WFV卫星遥感影像对县域冬小麦LNC的空间分布开展监测。结果表明,12个遥感光谱指标与冬小麦LNC之间存在不同程度的相关性,其中NDVI、RVI、MSR、OSAVI和NLI与冬小麦LNC的相关性较好(相关系数不低于0.65)。将优选的5个遥感光谱指标作为模型输入变量,构建基于BP神经网络的冬小麦LNC估测模型(LNC-BPEM),模型的估测精度r^(2)=0.866,RMSE=0.246%,ARE=12.9%。将冬小麦LNC-BPEM估测模型和GF-6/WFV影像结合对县域冬小麦LNC的空间信息监测,获得了如皋县域冬小麦LNC的空间分布特征,该区域冬小麦LNC范围在0.9%~2.0%(长势正常)的种植面积为29 693.3 hm^(2),占冬小麦总种植面积的74%。这说明利用GF-6/WFV卫星的多个遥感光谱指标与神经网络结合建模可有效估测县域大田冬小麦叶片氮素含量。

不同频率氮添加对内蒙古典型草原植物叶绿素的影响

大气氮沉降会影响植物功能性状的变异和进化,进而作用于植物个体和生态系统功能。研究草地生态系统植物功能性状在不同氮添加模式下的响应差异,对更准确地评估植物对环境变化的适应性至关重要。基于内蒙古草原野外长期氮沉降模拟实验平台,研究氮添加频率对优势物种羊草和冰草叶绿素含量的影响,结果表明每年一次氮添加使羊草叶绿素含量增加最多(15.21%),而每月一次氮添加对冰草叶绿素含量影响最大(增加了14.74%)。氮添加尤其是每年一次氮添加显著增加了土壤铵态氮、硝态氮和无机氮含量,并使土壤pH显著降低。这些结果表明:羊草叶绿素含量对低频率氮添加响应更明显,而高频率氮添加对冰草叶绿素含量的影响更显著,这两类物种间养分吸收策略存在明显差异。启示低频率氮添加可能高估了氮沉降对羊草叶绿素含量的影响,而低估了对冰草叶绿素含量的影响,这对准确预测植物叶片功能性状对大气氮沉降的变异具有重要意义,并将有助于应用到植物功能性状预测生态系统功能和过程响应未来全球变化的模型中。

基于无人机高光谱影像的冬小麦叶片氮浓度遥感估测

叶片氮浓度(LNC)是反应作物光合作用、营养状况和长势的重要指标,为精准高效地估测不同生育期冬小麦叶片氮浓度,以新冬22为研究对象,利用无人机搭载Pika L高光谱相机获取4个关键生育期冬小麦冠层反射率数据。基于波段优化算法和相关性分析筛选LNC敏感光谱指数,结合逐步回归、多元线性回归和偏最小二乘回归建立关键生育期冬小麦叶片氮浓度估测模型,并与单变量估测模型进行比较。结果表明:基于波段优化算法筛选的组合光谱指数与LNC的相关性优于传统植被指数,且达到极显著性相关;在单变量LNC估测模型中,组合光谱指数构建的模型精度优于传统植被指数,其中,扬花期差值光谱指数(DSI(R940、R968))建立的估测模型最好,R2为0.789;多变量估测模型精度均优于单变量估测模型,其中,基于偏最小二乘回归构建的LNC估算模型最好,孕穗期和扬花期拟合效果较优,模型决定系数均为0.923,均方根误差为0.082、0.084。本研究结果可以作为冬小麦LNC估测和长势监测的科学依据。