Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions

Early prediction of crop yield can be an important tool for identifying promising genotypes in breeding programmes. To assess whether measurements of canopy reflectance at given stages of development could be used for yield forecasting and to identify the most appropriate indices, locations and growth stages for durum wheat yield assessment, nine field experiments, each including 20 or 25 durum wheat (Triticum turgidum L. var durum) genotypes, were carried out under a wide range of Mediterranean conditions. Canopy reflectance was recorded with a portable field spectroradiometer at several times from booting to physiological maturity, and nine indices were further derived. Grain yield was measured at harvesting. The results indicated that milk-grain stage was the most appropriate developmental stage for yield assessment. However, some indices were also sensitive to yield variations when determined at anthesis or even heading or booting. The capacity of spectral reflectance indices to forecast grain yield increased on locations that allowed genotypes to express their yield potentiality. Reflectance at 550?nm (R550), water index (WI), photochemical reflectance index (PRI), structural independent pigment index (SIPI), normalized difference vegetation index (NDVI) and simple ratio (SR) explained jointly a 95.7% of yield variability when all the experiments were analysed together, 92% being explained by R550. When regression analyses were carried out separately for each experiment, spectral reflectance indices explained from 17.3% to 65.2% of total variation in yield, and the indices that best explained differences in yield were experiment-dependent. Our data suggest that reflectance at 680?nm (R680), WI and SR may be suitable estimators of durum wheat grain yield under Mediterranean conditions, when determined at milk-grain stage.     

Spectral estimation of absorbed photosynthetically active radiation in corn canopies

Most models of crop growth and yield require an estimate of canopy leaf area index or absorption of radiation; however, direct measurement of LAI or light absorption can be tedious and time-consuming. The object of this study was to develop relationships between photosynthetically active radiation (PAR) absorbed by corn (Zea mays L.) canopies and the spectral reflectance of the canopies. Absorption of PAR was measured near solar noon in corn canopies planted in north-south rows at densities of 50,000 and 100,000 plants ha.^?1 Reflectance factor data were acquired with a radiometer with spectral bands similar to the Landsat MSS. Three spectral vegetation indices (ratio of near infrared to red reflectance, normalized difference, and greenness) were associated with more than 95% of the variability in absorbed PAR from planting to silking. The relationships developed between absorbed PAR and the three indices were tested with reflectance factor data acquired from corn canopies planted in 1979–1982 that excluded those canopies from which the equations were developed. Treatments included in these data were two hybrids, four planting densities (25, 50, 75, and 100 thousand plantsha^?1), three soil types (Typic Argiaquoll, Udollic Ochraqualf, and Aeric Ochraqualf), and several planting dates. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR, estimated indirectly from the multispectral reflectance of the canopies, were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index.     

Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions

The objective of this work is to study the effect of changing the sensor view angle on spectral reflectance indices and their relationships with yield and other agronomic traits. Canopy reflectance spectra of 25 durum wheat genotypes were measured with a field spectroradiometer at two view angles, nadir and 30°, from anthesis to maturity in two years and two water regimes. Nine spectral reflectance indices were calculated from reflectance measurements for correlation with yield and several agronomic traits. At off-nadir position more reflected radiation was collected, associated with the reflective characteristics of stems. The performance of the indices predicting the yield and the agronomic traits varied as a function of sensor view angle, and were moreover affected by leaf area index (LAI) value. At high LAI, simple ratio (SR) and normalized difference vegetation index (NDVI), calculated at off-nadir position, were better predictors of traits related to the density of stems and poorer predictors of traits related to green area. On the other hand, at low LAI the indices normalized pigment chlorophyll index (NPCI) and water index (WI) were better predictors of yield and all the other traits when the sensor view angle was at nadir, whereas no differences due to sensor angle were accounted for the other three indices. The different performance of indices at low and high LAI is discussed.     

Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices

Abstract

Prediction models were developed for wheat to assess crop growth in terms of leaf area index, dry matter production and grain yield from remotely-sensed temperature and spectral indices. The cumulative stress degree days (SDD) for the period of flowering to grain formation stage showed significantly higher correlation with dry matter (r= — 0940) and grain yield (r= —0-939) whereas that, for the period grain formation to harvest stage, showed significantly higher correlation lpar;r= —0-967) for crop water use. Significant and positive correlations between dry matter, leaf area and grain yield with infrared/red, normalised difference (ND), transformed vegetation index and greenness index were attained with the latter providing the highest degree of predictability. Spectral indices measured between flowering to milking stages gave the best prediction indicating the suitability of this period for crop growth assessment by this technique. Inter-stage sensitivity analysis by using multiple regression approach also revealed that greenness and transformed vegetation indices could provide better prediction of dry matter and grain yield. From the values of regression coefficients the jointing to beginning of milk formation period of the crop was found to be the most sensitive stage influencing the yield of crop.     

Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra

Remote sensing is a promising tool that provides quantitative and timely information for crop stress detection over large areas. Nitrogen (N) is one of the important nutrient elements influencing grain yield and quality of winter wheat (Triticum aestivum L.). In this study, canopy spectral parameters were evaluated for N status assessment in winter wheat. A winter wheat field experiment with 25 different cultivars was conducted at the China National Experimental Station for Precision Agriculture, Beijing, China. Wheat canopy spectral reflectance over 350–2500 nm at different stages was measured with an ASD FieldSpec Pro 2500 spectrometer (Analytical Spectral Devices, Boulder, CO, USA) fitted with a 25° field of view (FOV) fibre optic adaptor. Thirteen narrow-band spectral indices, three spectral features parameters associated with the absorption bands centred at 670 and 980 nm and another three related to reflectance maximum values located at 560, 920, 1690 and 2230 nm were calculated and correlated with leaf N concentration (LNC) and canopy N density (CND). The results showed that CND was a more sensitive parameter than LNC in response to the variation of canopy-level spectral parameters. The correlation coefficient values between LNC and CND, on the one hand, and narrow-band spectral indices and spectral features parameters, on the other hand, varied with the growth stages of winter wheat, with no predominance of a single spectral parameter as the best variable. The differences in correlation results for the relationships of CND and LNC with narrow-band spectral indices and spectral features parameters decreased with wheat plant developing from Feekes 4.0 to Feekes 11.1. The red edge position (REP) was demonstrated to be a good indicator for winter wheat LNC estimation. The absorption band depth (ABD) normalized to the area of absorption feature (NBD) at 670 nm (NBD670) was the most reliable indicator for winter wheat canopy N status assessment.     

A non-linear regression form for vegetation index-crop yield relation incorporating acquisition date normalization

A non-linear form relating vegetation indices (VI) to crop grain yields which normalizes for differences in acquisition date is suggested. It is based on the assumption that deviations in VI near the peak VI follow a quadratic behaviour. This form gave a higher R2 value than a simple VI-yield linear model on a multi-year, multi-location data set of IRS (Indian Remote Sensing Satellite-1A) LISS-I(Linear Imaging Self Scanner-I) derived near-infrared (NIR)/red radiance ratios and wheat grain yields in a study site in Madhya Pradesh (India). As the suggested model includes time of peak as a variable, it allows integration of results from other sources, such as, weather-based crop phenology model or high repetivity spectral data into the VI-yield relation.     

Spectral response of rice crop and its relation to yield and yield attributes

Ground-based radiometric measurements were conducted on six varieties of rice crop during an entire growth cycle using a hand-held seven-band radiometer. Concomitant measurements of some of the yield attributes were also made. Spectral data were also collected on a single variety grown under 12 different fertilizer treatments. Spectral data have been correlated with leaf area index, total wet biomass, total dry biomass, plant water content and final grain, straw and total yield. The results show similar temporal spectral responses of all six varieties and a strong correlation of agronomic parameters with spectral parameters derived from the near-infrared and red radiances. Red and near-infrared radiance ratio and normalized differences were found sensitive to the N fertilizer application but not to the P and K fertilizers. Linear correlations were observed between spectral parameters and final grain, straw and total yield     

利用光谱指数进行冬小麦变量施肥的可行性及其效益评价

变量施肥技术作为精准农业一个新的发展方向，如果能以适时获得的高光谱数据代替传统繁琐的实验室土壤养分测定数据来指导变量施肥实践，那将对我国精准农业的发展具有重要的实践意义。研究根据冬小麦起身拔节期冠层光谱数据，选用反映冬小麦长势信息的优化土壤调节植被指数（OSAVI）进行变量施肥，对光谱指数（OSAVI）指导变量施肥实践的可行性进行了探讨，结果表明起身拔节期的冠层光谱特征值与产量之间表现出很好的线性相关关系，可以根据起身期的冠层光谱特征值预测当李作物目标产量。对变量施肥效益也做了研究，结果显示通过变量施肥能够改善冬小麦的长势差异状况，显著提高了冬小麦籽粒产量，降低各处理产量之间的变异，但各处理籽粒品质之间的差异却略有增加。     

Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat

While certain spectral reflectance indices have been shown to be sensitive to the expression of a range of performance-related traits in crops, knowledge of the potentially confounding effects associated with plant anatomy could help improve their application in phenotyping. Morphological traits (leaf and spike wax content, leaf and spike orientation, and awns on spikes) were studied in 20 contrasting advanced wheat lines to determine their influence on spectral indices and in their association with grain yield under well-irrigated conditions. Canopy reflectance (400–1100 nm) was determined at heading and grain filling during two growing seasons and three vegetation indices (VIs; red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), and simple ratio (SR)), and five water indices (WIs; one simple WI and four normalized WIs (NWI-1, NWI-2, NWI-3, and NWI-4)) were calculated. The major reflectance fluctuations caused by the differences in leaf and spike morphology mainly occurred in the infrared region (700–1100 nm) and little variation in the visible region (400–700 nm). The NWI-3 ((R₉₇₀ – R₈₈₀)/(R₉₇₀ + R₈₈₀)) consistently showed a stronger association with yield than the RNDVI by using uncorrected canopy reflectance (original raw data) and data adjusted by scattering and smoothing. When canopy reflectance was corrected by a scattering method, the NWI-3 and a modified RNDVI with 958 nm showed the strongest correlations with grain yield by grouping lines for waxy leaves and spikes, curved leaves, and erect and awnless spikes. The results showed that the relationship between the spectral indices and grain yield can be improved (higher correlations) by correcting canopy reflectance for confounding effects associated with differences in leaf and spike morphology.     

Spectral components analysis Rationale,and results for three crops

Abstract

The spectral components analysis identities, LAI/VI × APAR/LAI = APAR/VI and LAI/VI × YIELD/LAI = YIELD/VI, where VI denotes any one of several spectral vegetation indices available, LAI is the leaf area index, APAR is the absorbed photosynthetically active radiation and YIELD is the saleable plant part (grain, fibre or root), express the information conveyed by canopies about their development, response to stresses and yield capability. The rationale includes the concepts that vegetation indices adequately measure the amount of photosynthetic-ally active tissue in plant canopies and that high yields cannot be achieved unless growing conditions permit canopies to develop that effectively intercept the available light during reproduction. For wheat, cotton and maize the coefficients of determination (r ²) usually exceeded 0.90 for exponential, power or linear expressions relating the numerator (dependent variable, y) to the denominator (x) variable of each term in the first equation. Results show that APAR can be reliably estimated from VI, and that the relation is nearly linear. The equations help to quantify remote assessments of crop productivity, to unify field-observed interrelations among LAI, APAR and YIELD and to validate remotely observable LAI and APAR inputs for plant process crop growth and yield models, or for growth analysis.     

Polarization of light reflected from grain crops during the heading growth stage

The linear polarization of light reflected from different cereal crops was studied to determine its potential to remotely detect differences in crop morphology. Photographs taken at intervals of 1–4 days, over a period of 11 weeks, were used to obtain polarization imagery of four cereal grain crops. The mean value of the linear polarization of light reflected from common wheat, barley, and durum wheat canopies decreased over a 15-day period during which the crops reached 50% heading. Over the succeeding 16-day period, the mean value of polarization showed a further decrease for the barley, but an increase for both the wheats due to lodging. These results indicate that linear polarization is a useful indicator of crop differences and crop maturity.     

Influence of wind on crop canopy reflectance measurements

Remote sensing techniques of measuring red and far-red crop canopy reflectance are frequently used to estimate crop canopy characteristics. The variability introduced in reflectance data from nonvegetative factors such as wind decreases the usefulness of the techniques. The objective of this study was to quantify and minimize the variability from wind on spectral reflectances. Red and far-red reflectances were acquired above wheat, barley, and alfalfa canopies throughout days of changing wind conditions. Periods of 312 s with little changes in irradiance values were used for the analysis. Wind had negligible effect on reflectances of a short canopy such as cut alfalfa, while it had a significant effect on reflectances from canopies with a higher vertical structure, particularly during gusty conditions. Within the windy and calm periods, extreme values of spectral reflectance differed by 60% and 12%, respectively, in the red, and by 40% and 8% in the far-red for the barley canopy. For the compact and dense canopy structure of alfalfa, these differences reached a maximum of 10% under windy conditions in both spectral regions. The plant canopy architecture, the wind conditions, and the spectral regions all affected the magnitude of the influence of wind on crop canopy spectral reflectances. The mean reflectance of a canopy overestimated the true reflectance by 2–4% while the use of the median reduced this overestimation. Sampling requirements for this sensor are evaluated, and the possibility of decreasing either the sampling rate or the sampling period is discussed.     

Use of Landsat TM and EOS MODIS imaging technologies for estimation of winter wheat yield in the North China Plain

This study focuses on the methodologies of winter wheat yield prediction based on Land Satellite Thematic Map (TM) and Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS) imaging technologies in the North China Plain. Routine field measurements were initiated during the periods when the Landsat satellite passed over the study region. Five Landsat TM images were acquired. Wheat yields of the experimental sites were recorded after harvest. Spectral vegetation indices were calculated from TM and MODIS images. The correlation analysis among wheat yield and spectral parameters revealed that TM renormalized difference water index (RDWI) and MODIS near-infrared reflectance had the highest correlation with yield at grain-filling stages. The models from the best-fitting method were used to estimate wheat yield based on TM and MODIS data. The average relative error of the root mean square error (RMSE) of the predicted yield was smaller from TM than from MODIS.     

Spectral response of architecturally different wheat canopies

The spectral response of two architecturally different spring wheat canopies having similar single leaf reflectance, green leaf area index (GLAI), and total dry phytomass, was measured throughout a growing season. Experimental results and supporting model calculations showed that the more planophile canopy had a higher spectral reflectance (measured at nadir) than the erectophile canopy. During the period of peak GLAI, the ratio of near-infrared to red reflectances (IR/red) for the erectophile canopy was about 30% higher than for the planophile canopy. The perpendicular vegetation index (PVI), however, was about 30% higher for the planophile canopy than for the erectophile canopy. When ground measured reflectances were transformed to radiances exiting the top of either a clear or a turbid atmosphere, the differences between the erectophile and the planophile canopies remained for the PVI but were obscured for the IR/red ratio. The results demonstrate the importance of architectural effects on the spectral response of canopies, and the interpretation of that response for estimating GLAI and dry phytomass by use of vegetation indices.     

Improving leaf area index retrieval using spectral characteristic parameters and data splitting

ABSTRACT

Spectral variables such as spectral characteristic parameters (SCPs) commonly change with intraday phenology. Empirical retrieval methods, which are generally used in leaf area index (LAI) retrieval due to their simplicity and computational efficiency, typically relate the biophysical parameter of interest to the spectral variable during the whole observation period. Whilst information regarding diurnal changes in spectral variables is necessary and useful in applied contexts. We analysed the diurnal change characteristics of canopy spectral reflectance and SCPs of winter wheat in the jointing stage based on field data collected at fixed sampling points with different vegetation canopies, and validated the effectiveness of data splitting strategy with field data collected in random sample pattern. The key results are as follows: (i) Canopy spectral reflectance of winter wheat in the jointing stage exhibited clear intraday variability, typically presenting a double-peak characteristic occurring from 11:35 to 12:34, where the reflectance changed substantively during this period. (ii) The SCPs of winter wheat in the jointing stage exhibited different diurnal patterns. Specifically, the blue edge position presented ‘blue shifts’, the yellow edge position generally exhibited steady fluctuations, and the red edge position followed divergent trends between the two sampling points due to differences in the vegetation canopy. Amplitude and area parameters exhibited a double-peak characteristic but there were slight differences between them. (iii) By dividing the whole observation period into sub-periods, the coefficient of variation (CV) of each spectral characteristic parameter can be greatly reduced, whilst the coefficient of determination (R²) of LAI retrieval can be greatly increased. Optimal spectral parameters and sub-periods for LAI retrieval were confirmed based on the diurnal variation of SCPs. To optimize LAI retrieval the suggested spectral parameters are blue edge amplitude, red edge amplitude, and red edge area, and the sub-periods are 09:50–11:35, 11:35–12:34, 12:34–13:50, and 13:50–15:00, respectively. The 11:35–12:34 sub-period should be carefully considered due to possible midday depression of photosynthesis.     

Assessment of water-stress effects on crops

The present paper describes the results of a ground experiment conducted on major crops of India during December 1980 to April 1981 and October 1981 to April 1982. The crops selected were wheat, chickpea and mustard. The experiment was jointly conducted between the Indian Space Research Organization (ISRO) and the Indian Agricultural Research Institute. Canopy spectral reflectance and physiological variables were measured over the complete crop cycle. The results of the 2 year experiment led to the following conclusions: (i) spectral parameters can be used for detecting water and nutrient stress in crops, (ii) spectral parameters are highly correlated to physiological variables and final production, (iii) correlation is further improved by including the effect of weather parameters, (iv) stress degree day is highly correlated to grain yield and (v) a yield model based on photosynthesis calculation worked well for the wheat experiment.     

Soil water and plant canopy effects on remotely measured surface temperatures

Abstract.

Thermal infrared remote sensing of diurnal crop canopy temperature variations represents a possible method for determining the availability of soil water to plants. This study was performed to assess the effects of soil water and crop canopy on apparent temperatures observed by means of remote sensors, and to determine the impact of these effects on remote soil water monitoring. Airborne thermal scanner and apparent reflectance data (one date) and ground PRT-5 data (three dates) were collected primarily over barley and other small grain canopies. Plant heights, cover, and available soil water for four layers in the top 20 cm were determined. Analysis of the data showed a close inverse linear relationship between the available water and the day minus night temperature difference δT, for thick barley canopies (plant cover above 90 per cent) only. The use of apparent reflectance values in the visible region did not improve available soil water regression equations substantially. These results suggest that the available water or plant stress could only be accurately determined for thick canopies, and that the reflectance data could probably be used to identify such canopies but would not improve regression estimates of soil water from remote sensing data.     

Prospects for quantifying structure,floristic composition and species richness of tropical forests

Airborne spectral and light detection and ranging (lidar) sensors have been used to quantify biophysical characteristics of tropical forests. Lidar sensors have provided high-resolution data on forest height, canopy topography, volume, and gap size; and provided estimates on number of strata in a forest, successional status of forests, and above-ground biomass. Spectral sensors have provided data on vegetation types, foliar biochemistry content of forest canopies, tree and canopy phenology, and spectral signatures for selected tree species. A number of advances are theoretically possible with individual and combined spectral and lidar sensors for the study of forest structure, floristic composition and species richness. Delineating individual canopies of over-storey trees with small footprint lidar and discrimination of tree architectural types with waveform distributions is possible and would provide scientists with a new method to study tropical forest structure. Combined spectral and lidar data can be used to identify selected tree species and identify the successional status of tropical forest fragments in order to rank forest patches by levels of species richness. It should be possible in the near future to quantify selected patterns of tropical forests at a higher resolution than can currently be undertaken in the field or from space.     

A spectral directional reflectance model of row crops

A computationally efficient reflectance model for row planted canopies is developed in this paper through separating the contributions of incident direct and diffuse radiation scattered by row canopies. The row model allows calculating the reflectance spectrum in any given direction for the optical spectral region. The performance of the model is evaluated through comparisons with field measurements of winter wheat as well as with an established 3D computer simulation model. Especially the systematic comparisons with the computer simulation model demonstrate that the model can adequately simulate the characteristic distribution of directional reflectance factors of row canopies, which is shown in the polar map of reflectance as a high or low value stripe approximately parallel to the row orientation, besides the hotspot effect. Physical mechanisms causing the dynamics were proposed and supported by comparison studies. The features of reflectance distributions of row canopies, which are distinctively different from those of homogeneous canopy, imply that it is problematic to use one-dimensional radiation transfer model to interpret radiation data and estimate the structural or spectral parameters of row canopies from reflectance measurements. Finally, further improvements needed for the current model are briefly discussed.     

Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R² = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.