R. R. Navalgund

Large-Area Soil Moisture Estimation Using Multi-Incidence-Angle RADARSAT-1 SAR Data

The sensitivity of synthetic aperture radar (SAR) backscatter to soil moisture has been adequately established. However, monitoring of soil moisture over large agricultural areas is still difficult because SAR backscatter is also sensitive to other target properties like surface roughness, crop cover, and soil texture (soil type), along with its strong sensitivity to soil moisture. Hence, to develop a methodology for large-area soil moisture estimation using SAR, it is necessary to incorporate the effects of surface roughness, crop cover, and soil texture in the soil moisture retrieval model. In this paper, a methodology for soil moisture estimation over a large area is developed using a pair of low- and high-incidence-angle RADARSAT-1 SAR data over parts of Agra, Mathura, and Bharatpur districts, India, during March 1999. The methodology requires acquisition of synthetic aperture radar data at low and high incidence angles, such that the soil moisture changes are negligible between the two acquisitions. In order to demonstrate the applicability of the developed methodology, the same was validated over a different area (parts of Saharanpur and Haridwar districts, India) during March 2005. Both test sites provided the variety of agricultural heterogeneity required for development and validation of the methodology for large-area soil moisture estimation. The proposed methodology offers an approach to incorporate the effects of surface roughness, crop cover, and soil texture in the soil moisture retrieval model from the space platform, without making any assumptions on the distributions of these parameters or without knowing the actual values of these parameters on ground.

Estimation of rice yield using IRS-1A digital data in coastal tract of Orissa

An attempt to derive a relation between spectral data of rice canopies and their grain yield has been made in the Cuttack and Puri districts of Orissa for Kharif rice using Landsat MSS digital data...

Incorporating soil texture in soil moisture estimation from extended low‐1 beam mode RADARSAT‐1 SAR data

Sensitivity of microwaves towards soil moisture is well understood; still, development of a practical algorithm for soil moisture estimation using microwaves is difficult. This is due to the fact that along with their strong sensitivity to soil moisture, microwave signals are also sensitive to other target properties such as soil texture, surface roughness, and crop cover. In this paper, an attempt has been made to incorporate the effect of soil texture in large area soil moisture mapping using extended low‐1 beam mode RADARSAT‐1 SAR data in such a way that knowledge of soil texture is not a prerequisite.

How far sar has fulfilled its expectation for soil moisture retrieval

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.

Retrieval of surface roughness using multi-polarized Envisat-1 ASAR data

Spatial distribution of surface roughness is very critical information for many application areas. Surface roughness is often characterized using statistical distribution. However, due to the huge complexity associated with spatial soil surfaces it is difficult to accurately characterize surface roughness over large areas using statistical distribution. Surface roughness influences SAR backscatter significantly and therefore for bare soil surfaces, surface roughness plays a critical role in determining the degree of depolarization of the SAR signal. In this paper surface roughness is retrieved using multi-polarized Envisat-1 ASAR data. The depolarization ratio [σ°VH − σ°VV] has been found to be very sensitive to surface roughness. This study demonstrates an approach that can be used to retrieve quantitative surface roughness values from a space platform without making any assumptions regarding distribution of surface roughness on the ground.

Relation of wheat yield with parameters derived from a spectral growth profile

An attempt has been made to generate crop growth profiles using multi-date NOAA AVHRR data of wheat-growing season of 1987–88 for the districts of Punjab and Haryana states of India. A profile model proposed by Badhwar was fitted to the multi-date Normalised Difference Vegetation Index (NDVI) values obtained from geographically referenced samples in each district. A novel approach of deriving a set of physiologically meaningful profile parameters has been outlined and the relation of these parameters with district wheat yields has been studied in order to examine the potential of growth profiles for crop-yield modelling. The parameter ‘area under the profile’ is found to be the best estimator of yield. However, with such a parameter time available for prediction gets reduced. Combination of different profile parameters shows improvement in correlation but lacks the consistency for individual state data.

A New Technique for Temperature and Humidity Profile Retrieval From Infrared-Sounder Observations Using the Adaptive Neuro-Fuzzy Inference System

The accuracy of the atmospheric profiles of temperature and humidity, retrieved from infrared-sounder observations using physical retrieval algorithms, depends directly on the quality of the first-guess profiles. In the past, forecasts from the numerical-weather-prediction models were extensively used as the first guess. During the past few years, the first guess for physical retrieval is being estimated using regression techniques from sounder observations. In this study, a new nonlinear technique has been described to improve the first guess using simulated infrared brightness temperatures for GOES-12 Sounder channels. The present technique uses fuzzy logic and data clustering to establish a relationship between simulated sounder observations and atmospheric profiles. This relationship is further strengthened using the Adaptive Neuro-Fuzzy Inference System (ANFIS) by fine-tuning the existing fuzzy-rule base. The results of ANFIS retrieval have been compared with those from the nonlinear (polynomial) regression retrieval. It has been found that ANFIS is more robust and reduces root mean squared error by 20% in humidity profile retrieval compared with the nonlinear regression technique. In addition, it has been shown that the ANFIS technique has an added advantage of its global application without any need for classification of the training data that is required in the regression techniques.

Estimation of wheat leaf area index from IRS LISS-III data using Price model

A satellite sensor image based model suggested by Price was investigated for the estimation of Leaf Area Index (LAI) using data acquired by Linear Imaging Self Scanner-III (LISS-III) onboard Indian Remote Sensing Satellite-1C (IRS-1C) over two wheat growing sites in India (Karnal and Delhi) for crop seasons 1996-97 and 1997-98, respectively. Besides red and near-infrared (NIR) measurements over vegetation canopy, the model only requires a priori crop specific attentuation constants. These constants were computed for wheat using published and field ground reflectance measurements. Application of the model over 36 fields on which ground estimates of LAI were available, indicated a RMSE of 1.28 and 1.07 for the Karnal and Delhi sites, respectively.

Pre-harvest wheat yield and production estimation for the Punjab, India

Abstract Two different approaches to relate wheat yield with spectral indices derived from remotely-sensed data have been explored for the state of Punjab, India. In the study based on site-level approach yield obtained from crop-cutting sites was found to be linearly related to NIR/Red ratio derived from Landsat MSS data of corresponding sites in Ludhiana and Patiala districts of Punjab. Incorporation of agrometeorological data was also tried. Certain inherent limitations of the site-level approach led to the district-level studies which focused on the relation of district yields with corresponding average spectral indices derived from satellite sensors like Landsat MSS and lRS-LISS-i. Significant correlations were observed in all cases and the relation based on Landsat MSS/IRS LISS-I data was used for trial forecast of wheat yields for 1989–90 season. A comparison of remote-sensing based production forecast showed good agreement with the conventional estimate of Bureau of Economics and Statistics at sta...

Comparison of ocean color chlorophyll algorithms for IRS-P4 OCM sensor usingin-situ data

In-situ chlorophyll concentration data and remote sensing reflectance (Rrs) measurements collected in six different ship campaigns in the Arabian Sea were used to evaluate the accuracy, precision, and suitability of different ocean color chlorophyll algorithms for the Arabian Sea. The bio-optical data sets represent the typical range of biooptical conditions expected in this region and are composed of 47 stations encompassing chlorophyll concentration, between 0.072 and 5.90 mg m-3, with 43 observations in case I water and 4 observations in case II water. Six empirical chlorophyll algorithms [i.e. Aiken-C, POLDER-C, OCTS-C, Morel-3, Ocean Chlorophyll-2 (OC2) and Ocean Chlorophyll-4 (OC4)] were selected for analysis on the Arabian Sea data set. Numerous statistical and graphical criterions were used to evaluate the performance of these algorithms. Among these six chlorophyll algorithms two chlorophyll algorithms (i.e. OC2 and OC4) performed well in the case I waters of the Arabian Sea. The OC2 algorithm, a modified cubic polynomial function which uses ratio of Rrs490 nm and Rrs555 nm (where, Rrs is remote sensing reflectance), performed well with r2=0.85; rms =0.15. The OC4 algorithm, a four-band (443, 490, 510, 555 nm), maximum band ratio formulation was found best on the basis of statistical analysis results with r2=0.85 and rms=0.14. Both OC2 and OC4 algorithms failed to estimate chlorophyll inTrichodesmium dominated waters. The OC2 algorithm was preferred over OC4 algorithm for routine processing of the OCM data to generate chlorophyll-a images, as it uses a band ratio of 490/555 nm and atmospheric correction is more accurate in 490 nm compared to 443 nm band, which is used by OC4 algorithm.