Sanatan Pradhan

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.

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.

Impact of Tillage and Residue Management on Water and Thermal Regimes of a Sandy Loam Soil under Pigeonpea-Wheat Cropping System

Tillage and crop residue management are the most critical components of the cultivation, which significantly affect the soil-plant-water relations. Conservation tillage (CT) aims to improve soil condition and conserve soil water, although limited information is available on seasonal variation of soil water and temperature under cropping systems. A medium duration (6 years) experiment with different tillage and residue management option on a sandy loam soil in pigeonpea-wheat cropping system was selected to monitor the changes in soil structure through adoption of conservation agriculture (CA), and the subsequent impact on soil water and thermal regimes over the growing period of wheat crop. Treatments were: conventional tillage with incorporation of previous crop residue (CT+R); conventional tillage with residue removal (CT-R); no-tillage with residue retained over surface as mulch (NT+R); and no-tillage with residue removed (NT-R). The impact was quantified in terms of change in basic soil physical parameters viz., bulk density (BD), penetration resistance (as Cone Index, CI) and porosity, and their effect on soil water dynamics, and seasonal and diurnal soil temperature. At the initial growth stages (9 to 51 DAS), NT+R and NT-R recorded 5–10 per cent higher BD at 0–10 cm layer, but was comparable with CT+R and CT-R at the later stages. Soil pore ( 30 μm) and meso-(5–30 μm) pore volumes had marginal differences. A sub-surface (10–20 cm) hard layer (CI 1.7–1.9 MPa) was omnipresent, although omission of tillage resulted in marginal reduction in CI at this layer. Marginally higher macro-pores in NT systems caused higher initial rate of infiltration and the cumulative infiltration. Throughout the growing season of wheat, NT+R retained higher soil water than other tillage-residue combinations [17% (0 to 48), 11% (−5 to 32), and 14% (−2 to 36), higher compared to NT-R, CT+R and CT-R]. Soil water content in NT+R was also higher by 20% (−5 to 41) before irrigation cycles. Similarly, soil temperature was the most regular in NT+R, even at 3 cm depth due to the presence of surface residue and higher amount of soil water. The difference between temperatures at 10 am and 3: 30 pm was 2.9 (0.2–6.3)°C in NT+R, compared to 3.8 (0.2–7)°C in NT-R, 4 (0.8–8)°C in CT+R, and 3.1 (0.7–5.5)°C in CT-R. Therefore, role of tillage and crop residues in modifying soil physical environment and maintaining better soil water and thermal conditions have been clearly documented.

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (

Assessment and mapping of spatial variation of soil physical health in a farm

\sigma ^{\mathrm{o}}_{\mathrm{RH}}

Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices

σRHo), differences of circular vertical and horizontal