Normalized Difference Vegetation Index (NDVI) values extracted from remotely sensed optical imagery are used ubiquitously to monitor crop condition. However, challenges in the operational use of optical imagery are well documented making it difficult to capture measures of crop condition during critical phenology stages when clouds obscure. This study investigates the integration of Synthetic Aperture Radar (SAR) and optical imagery to characterize the condition of crop canopies in order to deliver daily measures of NDVI during the entire growing season. Multitemporal C-band polarimetric RADARSAT-2 SAR data and RapidEye images were acquired in 2012 for a study site in western Canada. SAR polarimetric parameters and NDVI were extracted. The temporal variations in SAR polarimetric parameters and NDVI were interpreted with respect to the development of the canola canopy. Optical NDVI was statistically related with SAR polarimetric parameters over test canola fields. Significant correlations were documented between a number of SAR polarimetric parameters and optical NDVI, in particular with respect to HV backscatter, span, volume scattering of the Freeman Durden decomposition and the radar vegetation index, with R-values of 0.83, 0.72, 0.81 and 0.71 respectively. Based on the statistical analysis, SAR polarimetric parameters were calibrated to optical NDVI, creating a SAR-calibrated NDVI (SARc-NDVI)). A canopy structure dynamics model (CSDM) was fitted to the SARc-NDVI, providing a seasonal temporal vegetation index curve. The coupling of NDVI from optical and SAR imagery with a CSDM demonstrates the potential to derive daily measures of crop condition over the entire growing season.
Accurate crop growth stage estimation is important in precision agriculture as it facilitates improved crop management, pest and disease mitigation and resource planning. Earth observation imagery, specifically Synthetic Aperture Radar (SAR) data, can provide field level growth estimates while covering regional scales. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization SAR data and ground truth growth stage data are used to model the influence of canola growth stages on SAR imagery extracted parameters. The details of the growth stage modeling work are provided, including a) the development of a new crop growth stage indicator that is continuous and suitable as the state variable in the dynamic estimation procedure; b) a selection procedure for SAR polarimetric parameters that is sensitive to both linear and nonlinear dependency between variables; and c) procedures for compensation of SAR polarimetric parameters for different beam modes. The data was collected over three crop growth seasons in Manitoba, Canada, and the growth model provides the foundation of a novel dynamic filtering framework for real-time estimation of canola growth stages using the multi-sensor and multi-mode SAR data. A description of the dynamic filtering framework that uses particle filter as the estimator is also provided in this paper.
Accurate and frequent monitoring of land surface changes arising from oil and gas exploration and extraction is a key requirement for the responsible and sustainable development of these resources. Petroleum deposits typically extend over large geographic regions but much of the infrastructure required for oil and gas recovery takes the form of numerous small-scale features (e.g., well sites, access roads, etc.) scattered over the landscape. Increasing exploitation of oil and gas deposits will increase the presence of these disturbances in heavily populated regions. An object-based approach is proposed to utilize RapidEye satellite imagery to delineate well sites and related access roads in diverse complex landscapes, where land surface changes also arise from other human activities, such as forest logging and agriculture. A simplified object-based change vector approach, adaptable to operational use, is introduced to identify the disturbances on land based on red–green spectral response and spatial attributes of candidate object size and proximity to roads. Testing of the techniques has been undertaken with RapidEye multitemporal imagery in two test sites located at Alberta, Canada: one was a predominant natural forest landscape and the other landscape dominated by intensive agricultural activities. Accuracies of 84% and 73%, respectively, have been achieved for the identification of well site and access road infrastructure of the two sites based on fully automated processing. Limited manual relabeling of selected image segments can improve these accuracies to 95%.
In response to the increasing demand on in-season crop inventory, this study presents results of early season crop
identification and acreage estimates based on a random forest classifier using RADARSAT-2 fine quad (FQ) SAR
data. Thirty RADARSAT-2 FQ SAR scenes acquired over Indian Head, Canada, during the 2009 AgriSAR
campaign led by the European Space Agency (ESA) were analyzed. Consistent with results from other researches,
this study revealed that the highest classification accuracies are achieved in mid to late season (early July to mid
August) when most of the crops experiencing vegetative growth and early reproduction. In addition by incorporating
multi-beam images, an increase in classification accuracy of 2% to 12% can be achieved. For images acquired close
in time, shallower incidence angles usually give better classification accuracy compared with steeper incidence
angles. In order to achieve optimal classification performance, both multi-temporal and multi-beam acquisitions
should be combined. For major crops such as canola, spring wheat, lentil, and field peas, over 85% accuracies can
be reached early in the growing season (early July) when multi-temporal multi-beam RADARSAT-2 FQ data are
used.
The object of this paper is to investigate the relationship between polarimetric SAR information and LAI. RADARSAT-
2 Fine Quad-pol SLC data with shallower and steeper incidence angles were programmed throughout the 2008 growing
season. Optical data were acquired using a hyperspectral CASI airborne sensor as well as the SPOT-4 multi-spectral
satellite. The optical data were used to generate LAI map for the entire study site. Backscatter coefficients, ratios of
backscatter intensity, three polarimeric variables and three Cloude-pottier Decomposition parameters were extracted
from the polarimetric data set. Temporal variations of the backscatter coefficient were analyzed. The results show an
increase in backscatter with corn and soybean growth. The statistical analysis quantified the relationship between the
radar parameters and LAI revealing a strong sensitivity for some radar configurations. For both corn and soybean,
RADARSAT-2 cross-polarization (HV) backscatter at either shallow or steep incidence angles was well correlated with
LAI. To avoid sensitivity to sensor calibration and changing target moisture conditions, ratios of backscatter intensity,
polarimetric variables and Cloude-pottier Decomposition parameters were investigated. For corn, the ratio of HV/HH
and HV/VV as well as pedestal height, total power, correlation coefficient, Entropy and alpha angle were highly
correlation with LAI at steeper incidence angle. For soybean, the higher correlations were found with the ratio of HV/HH
as well as pedestal height, total power, Entropy and alpha angle at shallow incidence angle. In general, the best results
were observed for corn using the FQ6 acquisition. For soybean, the FQ20 data provided the most promising results.
Remote Sensing technology has been used in agricultural statistics since early 1970s in developed countries and since late 1970s in China. It has greatly improved the efficiency with its accurate, timingly and credible information. But agricultural monitoring using remote sensing has not yet been assessed with credible data in China and its accuracy seems not consistent and reliable to many users. The paper reviews different methods and the corresponding assessments of agricultural monitoring using remote sensing in developed countries and China, then assesses the crop area estimating method using Landsat TM remotely sensed data as sampling area in Northeast China. The ground truth is ga-thered with global positioning system and 40 sampling areas are used to assess the classification accu-racy. The error matrix is constructed from which the accuracy is calculated. The producer accuracy, the user accuracy and total accuracy are 89.53%, 95.37% and 87.02% respectively and the correlation coefficient between the ground truth and classification results is 0.96. A new error index δ is introduced and the average δ of rice area estimation to the truth data is 0.084. δ measures how much the RS classification result is positive or negative apart from the truth data.
China is one of the main soybean production countries in the world and soybean is of great importance in agricultural
industry, domestic consumption and international trade. In recent years, however, China has become the largest
soybean importer in the world. Therefore timely credible information about soybean planting area and production is
essential for government decision making and agricultural management on domestic consumption and international
trade. Moreover, information on soybean planting and continuous planting location is critical for distributing farmer
subsidies and production management. In this paper, an operational system based on multi-resolution remotely sensed
data was developed for the soybean area inventory and continuous cropping area monitoring. A stratified sampling
method is employed to extract and locate major soybean-planting regions, which are later surveyed using remote
sensing data. At the same time, sub regions are constructed based on cropping systems in which remotely sensed data of
different resolutions are applied for the soybean area estimation and replanting area location assessment.
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