Nitrogen Reflectance Index Research Articles

Drought impacts African black beetle feeding on perennial ryegrass

interactions between plants, herbivorous insects and fungal Epichloë endophytes. However, water availability fluctuates, a phenomenon intensified by climate change, challenging the success of NewZealand’s pastoral industry. The impacts of climatic changes, such as intensifying drought, on pastoral insect pests have received little attention. In a no-choice bioassay, African black beetle (ABB) were fed semi-synthetic diets containing freeze-dried foliage material from drought-exposed and well-watered perennial ryegrass containing endophyte strains AR37 or NZCT, or with material free of endophyte (Nil). Diet consumption, beetle weight change, fungal alkaloid concentrations, phytohormones, and NRI (nitrogen reflectance index) concentrations were measured and compared. Except for AR37, ABB consumed more diets containing drought-exposed rather than well-watered material, with a 61% increase in Nil and a 50% increase in diets containing NZCT, though these effects were not accompanied by corresponding ABB weight changes. Here, we discuss the implications of these results on ABB damage in New Zealand pastures in the context of increasing drought and illustrate how spatial patterns of ABB damage could change over the next 75 years. Areas that do not currently have high populations of ABB such as Manawatū-Whanganui, Wairarapa and Marlborough will become increasingly vulnerable to damage.

Mapping Forage Biomass and Quality of the Inner Mongolia Grasslands by Combining Field Measurements and Sentinel-2 Observations

Grasslands provide essential forage sources for global livestock production. Remote sensing approaches have been widely used to estimate the biomass production of grasslands from regional to global scales, but simultaneously mapping the forage biomass and quality metrics (e.g., crude fiber and crude protein) is still relatively lacking despite an increasing need for better livestock management. We conducted novel gradient grass-cutting experiments and measured hyperspectral reflectance, forage biomass, crude fiber per area (CFarea), and crude protein per area (CParea) across 19 temperate grassland sites in the Xilingol region, Inner Mongolia, China. Based on these measurements, we identified sensitive spectral bands, calculated nine potential spectral indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, Red Edge Normalized Difference Vegetation Index, Red-Edge Inflection Point, Inverted Red-Edge Chlorophyll Index algorithm, Normalized Difference Red Edge Index, Nitrogen Reflectance Index, Normalized Greenness Index, Land Surface Water Index) and established Random Forest (RF) models that well predicted forage biomass (R2 = 0.67, NRMSE = 12%), CFarea (R2 = 0.59, NRMSE = 14%), and CParea (R2 = 0.77, NRMSE = 10%). Among these nine indices, Land Surface Water Index (LSWI, calculated by R785-900 and R2100-2280) was identified to be the most important predictor and was then used to establish empirical power law models, showing comparable prediction accuracies (forage biomass, R2 = 0.53; NRMSE = 14%; CFarea, R2 = 0.40, NRMSE = 17%; CParea, R2 = 0.72, NRMSE = 11%) in comparison to Random Forest models. Combining the empirical power law models with the LSWI calculated from Sentinel-2 observations, we further mapped the forage biomass and quality and estimated the livestock carrying capacity. The predicted forage biomass, CFarea, and CParea all showed a significant increase with higher mean annual precipitation, but showed no significant correlations with mean annual temperature. Compared with the estimates based on crude protein, the conventional approach solely based on forage biomass consistently overestimated livestock carrying capacity, especially in wetter areas. Our work provides an approach to simultaneously map the forage biomass and quality metrics and recommends a LSWI-based power law model for rapid and low-cost assessment of regional forage status to guide better livestock management.

Detection of Eucalyptus Leaf Disease with UAV Multispectral Imagery

Forest disease is one of the most important factors affecting tree growth and product quality, reducing economic values of forest ecosystem goods and services. In order to prevent and control forest diseases, accurate detection in a timely manner is essential. Unmanned aerial vehicles (UAVs) are becoming an important tool for acquiring multispectral imagery, but have not been extensively used for detection of forest diseases. This research project selected a eucalyptus forest as a case study to explore the performance of leaf disease detection using high spatial resolution multispectral imagery that had been acquired by UAVs. The key variables sensitive to eucalyptus leaf diseases, including spectral bands and vegetation indices, were identified by using a mutual information–based feature selection method, then distinguishing disease levels using random forest and spectral angle mapper approaches. The results show that green, red edge, and near-infrared wavelengths, nitrogen reflectance index, and greenness index are sensitive to forest diseases. The random forest classifier, based on a combination of sensitive spectral bands (green, red edge, and near-infrared wavelengths) and a nitrogen reflectance index, provided the best differentiation results for healthy and three disease severity levels (mild, moderate, and severe) with overall accuracy of 90.1% and kappa coefficient of 0.87. This research provides a new way to detect eucalyptus leaf diseases, and the proposed method may be suitable for other forest types.

Optimal models under multiple resource types for Brix content prediction in sugarcane fields using machine learning

In this paper, multispectral imageries were combined with estimated crop height, age of sugarcane, and weather data (rain event) to predict Brix content in sugarcane fields across multiple sugarcane fields grown with different crop management techniques and under tropical climate over four consecutive growing seasons (2017–2021). Four machine learning regressors were used to train Brix predictive models: (i) lasso, (ii) support vector machine (SVM), (iii) decision tree, and (iv) gradient boosting regression. We compared the model performance in terms of accuracy and practicability based on five different resource types, which used a combination of 10 vegetation indices: normalized difference vegetation index (NDVI), ratio vegetation index (RVI), chlorophyll index green (CIgreen), chlorophyll index Rededge (Cirededge), photosynthetic vigor ratio (PVR), plant pigment ratio (PPR), Red edge NDVI (NDRE), nitrogen reflectance index (NRI), green leaf index (GLI), and the simple inverted reflectance green; and combinations of 10 vegetation indices with estimated crop height, sugarcane age, and rain event information. Overall, gradient boosting regression gave the highest accurate models, followed by SVM, decision tree, and lasso regression. The highest accuracies obtained by models from each resource type are R2 0.54 (RMSEP 3.28 oBrix, RPD 1.5) for a resource that used only a combination of vegetation indices (CIrededge, GLI, PPR, and simple inverted reflectance green), R2 0.67 (RMSEP 2.87 oBrix, RPD 1.7) for a resource that used a combination of CIrededge, GLI, PPR, and simple inverted reflectance green with estimated sugarcane height data, R2 0.66 (RMSEP 2.97 oBrix, RPD 1.7) for a resource that used a combination of CIrededge, PPR, and GLI with the age of sugarcane data, R2 0.71 (RMSEP 2.81 oBrix, RPD 1.8) for a resource that used a combination of CIrededge, NRI, PPR, and simple inverted reflectance green with rain event data, and R2 0.75 (RMSEP 2.81 oBrix, RPD 1.8) for a resource that used a combination of CIrededge, PPR, GLI, and simple inverted reflectance green with estimated sugarcane height and rain event data. The model composed of vegetation indices, estimated sugarcane height, and rain event data is suitable for quantitative prediction, while the other four models are appropriate for screening prediction.

Predicting protein content of silage maize using remotely sensed multispectral imagery and proximal leaf sensing

Abstract Timely estimation of silage maize protein provides an effective decision to adapt optimized strategies for nitrogen fertilizer management and also harvesting time for farmers. So, this research aimed to investigate whether using vegetative indices (VIs) derived from UAV remotely sensed multispectral (with 520–900 nm wavelengths) imagery and also Soil Plant Analysis Development (SPAD) greenness index can be used to detect the leaf protein concentration (LPC) of silage maize, as a function of various nitrogen rates (0, 50, 100, and 150% of recommended dosage). Results of principal component analysis (PCA) suggested that LPC was highly correlated with leaf greenness index in all developmental stages. In addition, LPC was highly correlated with most of the VIs investigated. A PCA clustering showed the meaningful pattern of N rates. Higher LPC values, VIs, and greenness index were more concentrated in the higher nitrogen (N100% and N150%) sectors. Nitrogen Reflectance Index (NRI) was identified as the most important VIs to monitor and predict LPC in the silage maize field, showing a strong polynomial relationship with LPC in both eight-leaf collar (V8) (R2 = 0.81, p ≤ 0.01) and tasseling (VT) (R2 = 0.98, p ≤ 0.001) stages. In addition, among VIs, the Normalized Difference Vegetation Index (NDVI) demonstrated a significant linear regression relationship with LPC (R2 = 0.80, p ≤ 0.01) in the VT. Findings suggested the high potential of VIs extracted by UAV-taken multispectral imagery and also SPAD proximal sensing to help farmers rapidly diagnose LPC in silage maize, in line with the objectives of precision farming.

Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images.

Sheath blight (ShB), caused by Rhizoctonia solani AG1-I, is one of the most important diseases in rice worldwide. The symptoms of ShB primarily develop on leaf sheaths and leaf blades. Hyperspectral remote sensing technology has the potential of rapid, efficient and accurate detection and monitoring of the occurrence and development of rice ShB and other crop diseases. This study evaluated the spectral responses of leaf blade fractions with different development stages of ShB symptoms to construct the spectral feature library of rice ShB based on “three-edge” parameters and narrow-band vegetation indices to identify the disease on the leaves. The spectral curves of leaf blade lesions have significant changes in the blue edge, green peak, yellow edge, red valley, red edge and near-infrared regions. The variables of the normalized index between green peak amplitude and red valley amplitude (Rg − Ro)/(Rg + Ro), the normalized index between the yellow edge area and blue edge area (SDy − SDb)/(SDy + SDb), the ratio index of green peak amplitude and red valley amplitude (Rg/Ro) and the nitrogen reflectance index (NRI) had high relevance to the disease. At the leaf scale, the importance weights of all attributes decreased with the effect of non-infected areas in a leaf by the ReliefF algorithm, with Rg/Ro being the indicator having the highest importance weight. Estimation rate of 95.5% was achieved in the decision tree classifier with the parameter of Rg/Ro. In addition, it was found that the variety degree of absorptive valley, reflection peak and reflecting steep slope was different in the blue edge, green and red edge regions, although there were similar spectral curve shapes between leaf sheath lesions and leaf blade lesions. The significant difference characteristic was the ratio index of the red edge area and green peak area (SDr/SDg) between them. These results can provide the basis for the development of a specific sensor or sensors system for detecting the ShB disease in rice.

Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves

The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.

Identification of Wheat Yellow Rust Using Spectral and Texture Features of Hyperspectral Images

Wheat yellow rust is one of the most destructive diseases in wheat production and significantly affects wheat quality and yield. Accurate and non-destructive identification of yellow rust is critical to wheat production management. Hyperspectral imaging technology has proven to be effective in identifying plant diseases. We investigated the feasibility of identifying yellow rust on wheat leaves using spectral features and textural features (TFs) of hyperspectral images. First, the hyperspectral images were preprocessed, and healthy and yellow rust-infected samples were obtained by creating regions of interest. Second, the extraction of spectral reflectance characteristics and vegetation indices (VIs) were performed from the preprocessed hyperspectral images, and the TFs were extracted using the grey-level co-occurrence matrix from the images transformed by principal component analysis. Third, the successive projections algorithm was employed to choose the optimum wavebands (OWs), and correlation-based feature selection was employed to select the optimal VIs and TFs (those most sensitive to yellow rust and having minimal redundancy between features). Finally, identification models of wheat yellow rust were established using a support vector machine and different features. Six OWs (538, 598, 689, 702, 751, and 895 nm), four VIs (nitrogen reflectance index, photochemical reflectance index, greenness index, and anthocyanin reflectance index), and four TFs (correlation 1, correlation 2, entropy 2, and second moment 3) were selected. The identification models based on the OWs, VIs, and TFs provided overall accuracies of 83.3%, 89.5%, and 86.5%, respectively. The TF results were especially encouraging. The models with the combination of spectral features and TFs exhibited better performance than those using the spectral features or TFs alone. The accuracies of the models with the combined features (OWs and TFs, Vis, and TFs) were 90.6% and 95.8%, respectively. These values were 7.3% and 6.3% higher, respectively, than those of the models using only the OWs or VIs. The model with the combined feature (VIs and TFs) had the highest accuracy (95.8%) and was used to map the yellow rust lesions on wheat leaves with different damage levels. The results showed that the yellow rust lesions on the leaves could be identified accurately. Overall, the combination of spectral features and TFs of hyperspectral images significantly improved the identification accuracy of wheat yellow rust.

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.

Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model’s prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.

Disentangling the Relationship in Health of Zea Mays Crop using Photochemical Reflectance Index and Nitrogen Reflectance Index

Determining the spatial variation of different plant factors throughout growing season will help to resolve stress factors within a field in a timely basis. Whereas the spectral characterizes help to estimate the proper photosynthesis process. This research shows that the nitrogen reflectance index (NRI) help to predict the nitrogen level of healthy and diseased plants and photochemical reflectance index (PRI) affects the leaf spectral absorption. These indices are calibrated under the hyperspectral pushbroom camera Resonon PIKA-L (400-1000nm) which is non-destructive and less time consuming, it is available in RUSA lab in Dr. Babasaheb Ambedkar Marathawada University, Aurangabad, Maharashtra. The spectral bands considered for the calculation of NRI are 700nm, 670nm, 570nm and for PRI spectral bands considered were 531nm, 570nm. Statistical values for PRI were calculated like R-Square (0.727), RMSE (0.267), P-value (2.787), standard error (2.979) and the statistical values for NRI were R-Square (4.223), RMSE (0.512), P-value (0.968), standard error(2.648).Linear regression was calculated for finding the relation between the data.

A Remote Sensing Based Method to Detect Soil Erosion in Forests

Rainwater-induced soil erosion occurring in the forest is a special phenomenon of soil erosion in many red soil areas. Detection of such soil erosion is essential for developing land management to reduce soil loss in areas including southern China and other red soil regions of the world. Remotely sensed canopy cover is often used to determine the potential of soil erosion over a large spatial scale, which, however, becomes less useful in forest areas. This study proposes a new remote sensing method to detect soil erosion under forest canopy and presents a case study in a forest area in southern China. Five factors that are closely related to soil erosion in forest were used as discriminators to develop the model. These factors include fractional vegetation coverage, nitrogen reflectance index, yellow leaf index, bare soil index and slope. They quantitatively represent vegetation density, vegetation health status, soil exposure intensity and terrain steepness that are considered relevant to forest soil erosion. These five factors can all be derived from remote sensing imagery based on related thematic indices or algorithms. The five factors were integrated to create the soil erosion under forest model (SEUFM) through Principal Components Analysis (PCA) or a multiplication method. The case study in the forest area in Changting County of southern China with a Landsat 8 image shows that the first principal component-based SEUFM achieves an overall accuracy close to 90%, while the multiplication-based model reaches 81%. The detected locations of soil erosion in forest provide the target areas to be managed from further soil loss. The proposed method provides a tool to understand more about soil erosion in forested areas where soil erosion is usually not considered an issue. Therefore, the method is useful for soil conservation in forest.

Remotely assessing above-ground fresh biomass weight of wheat based on the combinations of pair vegetation indexes from HJ-CCD images

To improve the reliability of remote sensing assessment model of above-ground fresh biomass weight (AFBW) in wheat, we investigated the relationships of AFBW at different critical stages with their corresponding vegetation indexes obtained from HJ-CCD images, established and evaluated new AFBW models based on paired vegetation indexes. The results showed that combination of normalized difference vegetation index (NDVI) and structure intensive pigment index (SIPI), namely N(NDVI, SIPI), could be used to accurately assess AFBW at jointing stage with R2 and RMSE of 0.84 and 379.14 kg ha−1, respectively, and more feasible than the single vegetation index model with accuracy increased by 10.95%. Moreover, the ratio combination of NDVI and green normalized difference vegetation index (GNDVI), namely R(NDVI, GNDVI), could be used to accurately assess AFBW at booting stage with R2 of 0.87 and RMSE of 987.64 kg ha−1 and accuracy increased by 12.56%. The difference combination of NDVI and nitrogen reflectance index (NRI), namely D(NDVI, NRI), could be used to accurately assess AFBW at anthesis with R2 of 0.86 and RMSE of 1786.37 kg ha−1 and accuracy increased by 13.37%. In totally, N(NDVI, SIPI), R(NDVI, GNDVI) and D(NDVI, NRI) are potential indicators of AFBW at different stages and can be applied as a new method for more accurate assessment of wheat growth.

Remote sensing detection of vegetation health status after ecological restoration in soil and water loss region

This paper proposed a vegetation health index (VHI) to rapidly monitor and assess vegetation health status in soil and water loss region based on remote sensing techniques and WorldView-2 imagery. VHI was constructed by three factors, i.e., the normalized mountain vegetation index, the nitrogen reflectance index and the reflectance of the yellow band, through the principal component transformation, in order to avoid the deviation induced by subjective method of weighted summation. The Hetian Basin of Changting County in Fujian Province, China, was taken as a test area to assess the vegetation health status in soil and water loss region using VHI. The results showed that the VHI could detect vegetation health status with a total accuracy of 91%. The vegetation of Hetian Basin in good, moderate and poor health status accounted for 10.1%, 49.2% and 40.7%, respectively. The vegetation of the study area was still under an unhealthy status because the soil was poor and the growth of newly planted vegetation was not good in the soil and water loss region.

Estimation of nitrogen vertical distribution by bi-directional canopy reflectance in winter wheat.

Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively.

Nitrogen vertical distribution by canopy reflectance spectrum in winter wheat

Nitrogen is a key factor for plant photosynthesis, ecosystem productivity and leaf respiration. Under the condition of nitrogen deficiency, the crop shows the nitrogen deficiency symptoms in the bottom leaves, while excessive nitrogen will affect the upper layer leaves first. Thus, timely measurement of vertical distribution of foliage nitrogen content is critical for growth diagnosis, crop management and reducing environmental impact. This study presents a method using bi-directional reflectance difference function (BRDF) data to invert foliage nitrogen vertical distribution. We developed upper-layer nitrogen inversion index (ULNI), middle-layer nitrogen inversion index (MLNI) and bottom-layer nitrogen inversion index (BLNI) to reflect foliage nitrogen inversion at upper layer, middle layer and bottom layer, respectively. Both ULNI and MLNI were made by the value of the ratio of Modified Chlorophyll Absorption Ration Index to the second Modified Triangular Vegetation Index (MCARI/MTVI2) referred to as canopy nitrogen inversion index (CNII) in this study at ±40° and ±50°, and at ±30° and ±40° view angles, respectively. The BLNI was composed by the value of nitrogen reflectance index (NRI) at ±20° and ±30° view angles. These results suggest that it is feasible to measure foliage nitrogen vertical-layer distribution in a large scale by remote sensing.

Estimation of winter wheat grain crude protein content from in situ reflectance and advanced spaceborne thermal emission and reflection radiometer image

The advanced technology in site-specific and spaceborne determination of grain crude protein content (CP) by remote sensing can help optimize the strategies for buyers in aiding purchasing decisions, and help farmers to maximize the grain output by adjusting field nitrogen (N) fertilizer inputs. We performed field experiments to study the relationship between grain quality indicators and foliar nitrogen concentration (FNC). FNC at anthesis stage was significantly correlated with CP, while spectral vegetation index was significantly correlated to FNC. Based on the relationships among nitrogen reflectance index (NRI), FNC and CP, a model for CP prediction was developed. NRI was able to evaluate FNC with a higher coefficient of determination of R2= 0.7302 in Experiment A. The relationship between laboratory measured and remotely sensed FNC had a coefficient of determination of R2=0.7279 in Experiment B. The method developed in this study could contribute towards developing optimal procedures for evaluating wheat grain quality by in situ canopy-reflected spectrum and ASTER image at anthesis stage. CP derived from both in situ spectrum and the ASTER image exhibited high accuracy and the precision in Experiment C. The RMSE were 0.893 % for in situ spectrum model and 1.654 % for ASTER image model, and the R2 were 0.7661 and 0.7194 for both, respectively. It is thus feasible to forecast grain quality by NRI derived from in situ canopy-reflected spectrum and ASTER image. Our results indicated that the inversion of FNC and the evaluation of CP by NRI were surprisingly good.

Radiometric Field Measurements of Maize for estimating Soil and Plant Nitrogen

Conventional recommendations of nitrogen (N) fertiliser are based on composite soil samples. This may result in either underfertilisation or overfertilisation due to the neglected spatial variability. This paper shows a procedure to estimate in season plant and soil nitrogen by using remote sensing. The study was conducted on two experimental sites. The first experiment consisted of six non-replicated fertiliser plots. These data were used to develop the relationships between reflectance data and the plant and soil N. The second experiment had four plots with various N and water treatments on which the developed relationships were verified. Plant N, soil N and reflectance data from both nadir and 75° view angles were collected almost weekly. The nitrogen reflectance index was employed to estimate plant N. Regression analysis was used to develop relationships. Spatial variability was mapped in ArcView geographical information system. Regression analysis between reflectance data and plant N, and soil N and plant N showed good relationships at various stages. The nadir view tended to underestimate the plant and soil parameters, whereas the 75° view slightly overestimated. Geographic information system mapping of measured and estimated soil N showed an agreement except in locations where high N spots were encountered.

Potential Use of Nitrogen Reflectance Index to estimate Plant Parameters and Yield of Maize

Estimating the spatial variability of various plant parameters during the growing season can assist in timely correction of stress conditions within a field. This research illustrates that the nitrogen reflectance index (NRI) developed to estimate plant nitrogen status can be used to estimate plant parameters and yield potential. The study was conducted on two experimental maize sites. Selected maize hybrids were ‘Pioneer 3790’, which was a planophile canopy architecture and ‘NC+ 1598’ with an erectophile canopy architecture. The first site consisted of six non-replicated fertiliser plots. Data from these plots were used to develop the relationships between reflectance data and the plant parameters. The second site contained four plots with various nitrogen (N) and water treatments on which the developed relationships were verified. Leaf area, biomass, and plant reflectance data were collected almost weekly from both sites during the 1996 growing season. Measured and estimated yield, leaf area index (LAI) and dry matter were mapped in ArcVIEW geographical information system. Results showed that the NRI was a comparable estimator of potential yield to the normalised difference vegetation index or to the modified soil adjusted vegetation index. For the LAI and biomass, all vegetation indices produced similar coefficients of determination. Results showed that the NRI could be used to estimate the within-field variation of yield potential and plant parameters.