Net surface radiation defines the availability of radiation energy on and near the surface to drive many physical and physiological processes such as latent heat, sensible heat fluxes, and evapotranspiration. One of the prime challenges of modeling radiation budget is estimation of net longwave radiation. Incoming or downwelling longwave radiation (LWin) flux is one of the two key components of net longwave radiation. Its estimation in cloudy conditions has always been a challenge due to lack of instrumentation and regular measurements at different spatial scales. In this study, two artificial neural network (ANN) multi-layer perceptron (MLP) models were developed for LWin flux estimation under cloudy-sky during daytime and nighttime using half-hourly flux measurements over different agro-climatic settings and several atmospheric parameters from measurements, satellite-based observations, and model outputs. A comparative evaluation was made between existing or newly developed multivariate linear regression (MVR) models and ANN-based models. The latter set of models were found to be superior to the best MVR model during both daytime and nighttime. The ANN models were found to have consistent performance across different sites and cloud types except less accuracy in sub-humid or humid climate and in deep convection cloud. The ANN models showed overall accuracies of 2.7% and 3.3% of measured mean and R2 of 0.86 and 0.85 for daytime and nighttime, respectively, when compared with independent data of in-situ measurements.
The main objective of current study is to investigate the potential of decomposition methods for monitoring crops and discrimination of other land cover targets using c-band hybrid polarimetric Risat-1 SAR images. There are two study areas namely Burdwan and Bharatpur in India chosen for analysing various existing decomposition methods in this paper. The Risat-1 hybrid polarimetric SAR Single look complex (SLC) data by ascending observation mode were utilized in our experiment and acquired in the month of December 22nd, 2014 and August 3rd, 2016 from parts of Burdwan and Bharatpur area respectively. The Stokes classical parameters G0, G1, G2 and G3 are derived from hybrid SAR images for further analysis. From these Stokes parameters, the relative phase, degree of polarization, orientation, ellipticity and polarization angle are calculated. Furthermore, four decomposition techniques namely m-δ, m-α, m-χ and modified m-χ are performed and expressed in the form of odd bounce, even bounce and volume components for monitoring crops and surrounding land cover targets in our study areas. The preliminary results have been observed from supervised classification on the basis of decomposition methods for identification of various crops, barren land, urban areas and waterbodies in the study sites. It has been shown that volume component among all decompositions is over estimated in comparison to odd and even bounce components. Risat-1 hybrid SAR data is found to be more suitable, convenient and cost-effective for discrimination of various land cover targets whereas cloud free optical data is a prime hindrance to the crop inventory.
Microwave remote sensing is one of the most promising tools for soil moisture estimation owing to its high sensitivity to dielectric properties of the target. Many ground-based scatterometer experiments were carried out for exploring this potential. After the launch of ERS-1, expectation was generated to operationally retrieve large area soil moisture information. However, along with its strong sensitivity to soil moisture, SAR is also sensitive to other parameters like surface roughness, crop cover and soil texture. Single channel SAR was found to be inadequate to resolve the effects of these parameters. Low and high incidence angle RADARSAT-1 SAR was exploited for resolving these effects and incorporating the effects of surface roughness and crop cover in the soil moisture retrieval models. Since the moisture and roughness should remain unchanged between low and high angle SAR acquisition, the gap period between the two acquisitions should be minimum. However, for RADARSAT-1 the gap is typically of the order of 3 days. To overcome this difficulty, simultaneously acquired ENVISAT-1 ASAR HH/VV and VV/VH data was studied for operational soil moisture estimation. Cross-polarised SAR data has been exploited for its sensitivity to vegetation for crop-covered fields where as co-pol ratio has been used to incorporate surface roughness for the case of bare soil. Although there has not been any multi-frequency SAR system onboard a satellite platform, efforts have also been made to understand soil moisture sensitivity and penetration capability at different frequencies using SIR-C/X-SAR and multi-parametric Airborne SAR data. This paper describes multi-incidence angle, multi-polarised and multi-frequency SAR approaches for soil moisture retrieval over large agricultural area.
In this paper an attempt to model wheat yield is made by exploiting characteristic interaction of cross-polarised SAR with wheat crop. SAR backscatter from a crop field is affected by the density, structure, volume and the moisture content of various components of plant (viz. head, stem, leaf) alongwith soil moisture. Hence, to effectively handle the influence of each of these components of the plant on SAR backscatter, a plant parameter, termed as Interaction Factor (IF) is conceptualised by combining volume, moisture, height for each of the component and density of plant. For this purpose, detailed experiment over farmers' fields was carried out in synchrony with SAR acquisition involving in-depth measurements on volume, moisture content and height of various components of wheat plant, number of grains, plant density and soil moisture. Stepwise regression analysis revealed that IFHead significantly affects the shallow incidence angle, cross-polarised C-band SAR backscatter. IFHead is also highly correlated to the number of grains. This is attributed to the fact that parameters of the wheat head from which IFHead is calculated, namely moisture, volume and height, determine eventual number of grains. The study offers an approach for estimating wheat yield by retrieving number of grains from shallow incidence angle cross-polarised SAR data.
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