R. N. Sahoo

Assessment of plant nitrogen stress in wheat Triticum aestivum L. through hyperspectral indices

A field experiment with wheat was conducted with four different nitrogen and four different water stress levels, and hyperspectral reflectances in the 350–2500 nm range were recorded at six crop phenostages for two years (2009–2010 and 2010–2011). Thirty-two hyperspectral indices were determined using the first-year reflectance data. Plant nitrogen (N) status, characterized by leaf nitrogen content (LNC) and plant nitrogen accumulation (PNA), showed the highest R 2 with the spectral indices at the booting stage. The best five predictive equations for LNC were based on the green normalized difference vegetation index (GNDVI), normalized difference chlorophyll index (NDCI), normalized difference705 (ND705) index, ratio index-1dB (RI-1dB) and Vogelman index a (VOGa). Their validation using the second-year data showed high R 2 (>0.80) and ratio of performance to deviation (RPD; >2.25) and low root mean square error (RMSE; <0.24) and relative error (<10%). For PNA, five predictive equations with simple ratio pigment index (SRPI), photochemical reflectance index (PRI), modified simple ratio705 (mSR705), modified normalized difference705 (mND705) and normalized pigment chlorophyll index (NPCI) as predicting indices yielded the best relations with high R 2 > 0.80. The corresponding RMSE and RE of these ranged from 1.39 to 1.13 and from 24.5% to 33.3%, respectively. Although the predicted values show good agreement with the observed values, the prediction of LNC is more accurate than PNA, as indicated by higher RMSE and very high RE for the latter. Hence, the plant nitrogen stress of wheat can be accurately assessed through the prediction of LNC based on the five identified reflectance indices at the booting stage.

Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

Abstract The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for parameters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

Development of Critical Values for the Leaf Color Chart, SPAD and Fieldscout CM 1000 for Fixed Time Adjustable Nitrogen Management in Aromatic Hybrid Rice (Oryza sativa L.)

The nitrogen (N) requirement of hybrid rice is generally greater than in conventional rice varieties. Recommendations for N monitoring at regular intervals of 7–10 days through leaf greenness are available, but farmers are accustomed to apply fertilizer N at selected growth stages only. An inexpensive leaf color chart (LCC) and nondestructive chlorophyll meters were evaluated for site-specific N management strategy in world’s first aromatic rice hybrid PRH-10 at the Indian Agricultural Research Institute, New Delhi. Two field experiments were conducted on PRH-10 with four levels of N (0, 70, 140, and 210 kg ha−1) during June–October of 2010 and 2011 to determine the LCC, soil–plant analysis development (SPAD), and Fieldscout CM 1000 (CM 1000) values for achieving economic optimum grain yield at three critical growth stages (tillering, panicle initiation, and flowering). Quadratic regression between N levels and grain yield were used to determine economic optimum grain yield (6427 kg ha−1 in 2010 and 6399 kg ha−1 in 2011) corresponding to optimum economical dose of 151 kg N ha−1 (2010) and 144 kg N ha−1 (2011). Nitrogen concentration in fully expanded youngest leaf correlated significantly (P < 0.01) and positively with LCC score, SPAD value, CM 1000 value, and total chlorophyll concentration at tillering, panicle initiation, and flowering for both years. The critical LCC score, SPAD, CM 1000 values, chlorophyll concentration, and leaf N concentration obtained were at tillering 4.4, 42.3, 285, and 2.16 mg g−1 fresh weight and 3.29%; at panicle initiation 4.4, 43.0, 276, and 2.16 mg g−1 fresh weight and 3.02%; and at flowering 4.5, 41.7, 270, and 2.05 mg g−1 fresh weight and 2.83%, respectively. Corrective N application should be done when observed leaf N indicator values at a particular growth stage reach or go below the critical values.

For assessing yields under extreme climatic events using crop simulation models: aerosol layer effects on growth and yield of wheat, rice, and sugarcane

Aerosol presence reduces sunshine hours and the amount of radiation received. The extent of reduction in radiation during this extreme event (January-March 1999) was relatively lower, as the extent of the diffused radiation increases. During this time, the reduction ranged from 5-12%. The differential response of the crops (wheat, rice and sugarcane) under changed proportion of direct and diffused radiation due to haze was seen through using crop simulation models (WTGROWS for wheat, DSSAT for rice and sugarcane). The growing conditions were optimal. Regions chosen for simulation were north-west India for wheat, coastal and southern regions for rice and north-eastern, western and southern regions for sugarcane. Simulation results were obtained in terms of phenology, biomass and economic yield at harvest. There was slight reduction in the yield of these three crops due to reduction in the radiation, but coupled weather changes (lowering of temperature, etc.) due to cloudy condition could benefit the crops through phenology modifications and other crop process activities, which can some times give higher yields of crops under the aerosol layer when compared to no haze layer situation. Diffused radiation is more photo-synthetically active, and this feature has still to be included in most of the existing crop growth models, as the existing crop models do not differentiate between direct and diffused radiation. The scope of using remote sensing for assessing the haze layer (spatial and temporal extent) could be employed in the crop simulation models for regional impact analysis.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Study of the Anisotropic Reflectance Behaviour of Wheat Canopy to Evaluate the Performance of Radiative Transfer Model PROSAIL5B

A field experiment was conducted on wheat to analyze its bi-directional reflectance in relation to sun-target-sensor geometry. To achieve a large variation in crop parameters, two extreme nitrogen treatments were applied. The study reconfirms the strong and consistent anisotropic patterns of wheat bi-directional reflectance in visible (VIS) and near infra-red (NIR) and its magnitude was highest in the principal plane. This anisotropic pattern extended equally in shortwave infra-red (SWIR). The hotspot broadened with crop growth due to increase in leaf area index (LAI), leaf size and planophilic orientation. The shape and magnitude of PROSAIL5B simulated spectra was in close agreement with the observed spectra in the optical region for most of the view zenith and azimuth angle combinations. In the NIR and SWIR, the magnitude of the model simulations showed good match in the principal plane, whereas underestimation was found in the backward scattering direction at higher view zenith angles in the VIS. The typical bowl shape of observed reflectance in principal plane was very well simulated in NIR by the model but failed in other wavebands. The model performed best in the NIR region followed by SWIR and maximum relative error was in VIS. Over the whole optical region and view zenith angles, the model simulations showed an average error of 26%. The model simulations were poor at low LAI indicating the need to improve soil reflectance algorithm in the model. Results of the study have implications for understanding the strengths/shortcomings in the model and its inversion to derive crop biophysical parameters from multispectral sensors.

Total information content as an index of soil moisture

Accurate estimation of soil moisture through remote sensing technique has been a challenge till date. In optical and thermal remote sensing, there is no index developed to detect the changes in soil moisture levels. In microwave region, soil roughness and other target parameters equally affect the technique for soil moisture estimation. Therefore, a computational technique in C language based on Shannon’s Information Theory (Shannon, 1948) has been developed to calculate total information content from multispectral radiometer data. The total information content is a compressed single value, which quantifies the information content of soil spectral reflectance in the electromagnetic spectrum range (400–1100 nm) under study. This technique was tested over a wide range of soil moisture levels. The study revealed that as compared to other techniques total information content index is very sensitive to change in moisture content of soil. This technique could not only quantify the soil moisture content in optical and near infra red region, but also led to a simplified one dimensional separability and clustering analysis.

Trends and Change-Point in Satellite Derived Phenology Parameters in Major Wheat Growing Regions of North India During the Last Three Decades

The virtual certainty of the anticipated climate change will continue to raise many questions about its aggregated impact of environmental changes on our regional food security in imminent future. Crop responses to these changes are certain, but its exact characteristics are hardly understood at regional scale due to complex overlapping effects of climate change and anthropogenic manipulation of agro-ecosystem. This study derived phenology of wheat in north India from satellite data and analyzed trends of phenology parameters over last three decades. The most striking change-point period in phenology trends were also derived. The phenology was derived from two sources: (1) STAR-Global vegetation Health Products-NDVI, and (2) GIMMS-NDVI. The results revealed significant earliness in start of growing season (SOS) in Punjab and Haryana while delay was found in Uttar Pradesh (UP). End of the wheat season almost always occurred early, to even those place where SOS was delayed. Length of growing season increased in most of Punjab and northern Haryana whereas its decrease dominated in UP. The early sowing practice of the farmers of the Punjab and Haryana may be one of the adaptation strategies to manage the terminal heat stress in reproductive stage of the crop in the region. The change-point occurred in late 1990s (1998–2000) in Punjab and Haryana, while in eastern UP it was in early 1990s (1990–1995). Despite the difference in temporal aggregation and spatial resolution, both the datasets yielded similar trends, confirming both the robustness of the results and applicability of the datasets over the region. The results demands further research for proper attribution of the effects into its causes and may help devising crop adaption practices to climatic stresses.

Acreage estimation of mango orchards using hyperspectral satellite data

Horticultural crop plays a unique role in Indias economy, therefore reliable and timely estimates of area under horticulture crops are of vital importance. Present methods of crop acreage estimation rely heavily on sample survey approach which is time consuming for a diversified and large country like India. Modern space technology with advance tools of Remote Sensing, GIS and GPS may be an alternative option for estimating areaunder horticultural crops. The advantage of using satellite data is that it provides both synoptic view and the economies of scale, since data over large areas could be gathered quickly from such platforms. This study has been undertaken to estimate the acreage under mango and to map existing orchards of Mango using hyperspectralsatellite data. The study was conducted for Meerut district of Uttar Pradesh. The hyperion hyperspectral satellitedata was evaluated to estimate the area under all mango orchards. These estimates were compared with actual area under mango orchards measured using Global Positioning System (GPS) and the total area under mangowas predicted as 961.88 ha which was 92% close to ground data 889.65 ha. The results indicated the scope of hyperspectral remote sensing in acreage estimation of fruit crops.

Discrimination of rice genotypes using field spectroradiometry

Abstract Spectral discrimination of rice genotypes was investigated using canopy reflectance in the range of 350 to 2500 nm. The pre-processed reflectance spectra were statistically analysed using one-way analysis of variance (ANOVA) followed by classification and regression tree (CART) technique to find significantly sensitive wavelengths for discrimination. The CART was able to select seventeen wavelengths (4 in visible, 5 in near-infra-red and 8 in shortwave infra-red region) well distributed over the entire spectrum. The spectral separability between each pair of rice genotypes at the selected wavebands was quantified using Jeffries–Matusita (J–M) distance analysis. The J–M distance analysis taking 91 pairs of genotypes showed that all the pairs were separable. This result was further validated by quadratic discriminant analysis (QDA) with an overall accuracy of 98%. The variation in biophysical and biochemical attributes of genotypes has been captured through differential spectral reflectance at selected wavebands which could make the discrimination possible.