Terrain Corrected Research Articles

Improving Landsat Multispectral Scanner (MSS) geolocation by least-squares-adjustment based time-series co-registration

The Landsat Multispectral Scanner (MSS) data sensed by the Landsat 1–5 satellites make up a significant portion of the early Landsat data record. However, accurate MSS image geolocation has been difficult to achieve systematically due to a number of factors associated primarily with the older sensor and satellite technology. As of August 2019, only 49% of the Landsat MSS archive could be processed at the highest-level L1TP (precision and terrain corrected) level, and the remainder were processed as L1GS (systematically corrected) with inaccurate geolocation and no terrain correction. This paper presents a methodology to improve the geolocation of MSS time series. The methodology uses an area- and feature-based least-squares matching scale-space algorithm, with a time series registration implementation, that we developed previously using Landsat-8 and Sentinel-2 imagery. The methodology requires that at least three L1TP images in the time series acquired over a given path/row are available. A linear combination of a polynomial transformation and multiple radially symmetric radial-basis-functions (RBFs) to model local uncorrected terrain relief effects present in the L1GS images are used. The processing is automated and applied in two passes. The first pass screens L1TP images to select the well-aligned ones that are used as references. The second pass registers the target images, including the L1GS images and any misaligned L1TP images, to all the reference L1TP images. The transformation coefficients for each registered target image are derived by least-squares adjustment using densely-matched tie-points between the target and the reference images. The methodology is demonstrated using 12 months of Landsat-4 MSS images at four Landsat path/row locations that contain agricultural, mountainous, and coastal regions, including a total of 43 L1TP and 31 L1GS images. There were sufficient tie-points to characterize the degree of misregistration of 14 L1GS images that had significant mean misregistration shifts ranging from 7.33 to 17.42 60 m pixels. In addition, at one site, two L1TP images were found to be misaligned and have mean misregistration shifts of 1.27 and 2.20 60 m pixels. The methodology provides sub-pixel registration accuracy - after registration, the mean misregistration shifts for the 14 L1GS and two misaligned L1TP images varied from only 0.10 to 0.41 60 m pixels. The methodology does not use a digital elevation model, and examples illustrate that although the RBF transformations can compensate terrain relief distortion effects, larger (~0.5 to 1.0 pixel) misregistration errors can remain in areas with highly variable terrain relief. Results are also provided for Landsat-1 MSS imagery to demonstrate the applicability of the methodology to even the earliest part of the Landsat record. Detailed qualitative and quantitative results are presented and indicate the potential of the methodology to improve the geolocation of the Landsat MSS data record that is discussed with recommendations for future research.

Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine

Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications.

Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits

Optical satellite products are available at different processing levels. Of these products, terrain corrected (i.e., orthorectified) products are the ones mostly used for glacier displacement estimation. For terrain correction, a digital elevation model (DEM) is used that typically stems from various data sources with variable qualities, from dispersed time instances, or with different spatial resolutions. Consequently, terrain representation used for orthorectifying satellite images is often in disagreement with reality at image acquisition. Normally, the lateral orthoprojection offsets resulting from vertical DEM errors are taken into account in the geolocation error budget of the corrected images, or may even be neglected. The largest offsets of this type are often found over glaciers, as these may show strong elevation changes over time and thus large elevation errors in the reference DEM with respect to image acquisition. The detection and correction of such orthorectification offsets is further complicated by ice flow which adds a second offset component to the displacement vectors between orthorectified data. Vice versa, measurement of glacier flow is complicated by the inherent superposition of ice movement vectors and orthorectification offset vectors. In this study, we try to estimate these orthorectification offsets in the presence of terrain movement and translate them to elevation biases in the reference surface. We demonstrate our method using three different sites which include very dynamic glaciers. For the Oriental Glacier, an outlet of the Southern Patagonian icefield, Landsat 7 and 8 data from different orbits enabled the identification of trends related to elevation change. For the Aletsch Glacier, Swiss Alps, we assess the terrain offsets of both Landsat 8 and Sentinel-2A: a superior DEM appears to be used for Landsat in comparison to Sentinel-2, however a systematic bias is observed in the snow covered areas. Lastly, we demonstrate our methodology in a pipeline structure; displacement estimates for the Helheim-glacier, in Greenland, are mapped and corrected for orthorectification offsets between data from different orbits, which enables a twice as dense a temporal resolution of velocity data, as compared to the standard method of measuring velocities from repeat-orbit data only. In addition, we introduce and implement a novel matching method which uses image triplets. By formulating the three image displacements as a convolution, a geometric constraint can be exploited. Such a constraint enhances the reliability of the displacement estimations. Furthermore the implementation is simple and computationally swift.

LAND COVER MAPPING USING SENTINEL-1 SAR DATA

In this paper, the potential of using free-of-charge Sentinel-1 Synthetic Aperture Radar (SAR) imagery for land cover mapping in urban areas is investigated. To this aim, we use dual-pol (VV+VH) Interferometric Wide swath mode (IW) data collected on September 16th 2015 along descending orbit over Istanbul megacity, Turkey. Data have been calibrated, terrain corrected, and filtered by a 5x5 kernel using gamma map approach. During terrain correction by using a 25m resolution SRTM DEM, SAR data has been resampled resulting into a pixel spacing of 20m. Support Vector Machines (SVM) method has been implemented as a supervised pixel based image classification to classify the dataset. During the classification, different scenarios have been applied to find out the performance of Sentinel-1 data. The training and test data have been collected from high resolution image of Google Earth. Different combinations of VV and VH polarizations have been analysed and the resulting classified images have been assessed using overall classification accuracy and Kappa coefficient. Results demonstrate that, combining opportunely dual polarization data, the overall accuracy increases up to 93.28% against 73.85% and 70.74% of using individual polarization VV and VH, respectively. Our preliminary analysis points out that dual polarimetric Sentinel-1SAR data can be effectively exploited for producing accurate land cover maps, with relevant advantages for urban planning and management of large cities.

LAND COVER MAPPING USING SENTINEL-1 SAR DATA

Abstract. In this paper, the potential of using free-of-charge Sentinel-1 Synthetic Aperture Radar (SAR) imagery for land cover mapping in urban areas is investigated. To this aim, we use dual-pol (VV+VH) Interferometric Wide swath mode (IW) data collected on September 16th 2015 along descending orbit over Istanbul megacity, Turkey. Data have been calibrated, terrain corrected, and filtered by a 5x5 kernel using gamma map approach. During terrain correction by using a 25m resolution SRTM DEM, SAR data has been resampled resulting into a pixel spacing of 20m. Support Vector Machines (SVM) method has been implemented as a supervised pixel based image classification to classify the dataset. During the classification, different scenarios have been applied to find out the performance of Sentinel-1 data. The training and test data have been collected from high resolution image of Google Earth. Different combinations of VV and VH polarizations have been analysed and the resulting classified images have been assessed using overall classification accuracy and Kappa coefficient. Results demonstrate that, combining opportunely dual polarization data, the overall accuracy increases up to 93.28% against 73.85% and 70.74% of using individual polarization VV and VH, respectively. Our preliminary analysis points out that dual polarimetric Sentinel-1SAR data can be effectively exploited for producing accurate land cover maps, with relevant advantages for urban planning and management of large cities.

TVDI模型的农业旱情时空变化遥感应用研究

温度植被干旱指数（TVDI）是一种通过反演土壤湿度来反映农业干旱状况的重要方法，其中能量平衡和植被指数的变化是影响TVDI模型精度的主要因子。在研究比值植被指数（RVI）、归一化植被指数（NDVI）、增强型植被指数（EVI）和修正土壤调节植被指数（MSAVI）和下垫面温度（<em>T</em>_s）的基础上，引入DEM数据并对<em>T</em>_s做地形校正，减少了地形起伏对能量平衡的影响，建立不同植被指数的<em>T</em>_s-Ⅵ特征空间，选择与土壤湿度相关性最好的温度植被干旱指数（TVDI），获取研究区2005年作物生长季（5-9月）的干旱状况，并用同步的气象信息对干旱状况进行验证。结果表明：根据不同时期采用不同植被指数的TVDI模型，经过地形校正后能够更好地反映研究区的农业干旱状况。;Temperature Vegetation Dryness Index (TVDI) is an important tool that reflects agriculture dry situation by inverting soil moisture. The changes of energy balance and vegetation index are two main factors to influence the precision of the TVDI. The MODIS (Moderate….) data products, as RVI(Ratio Vegetation Index), NDVI(Normalized Difference Vegetation Index), EVI(Enhanced Vegetation Index), MSAVI(Modified Soil Adjusted Vegetation Index), and <em>T</em>_s (Land Surface Temperatures), are applied and the DEM (ASTER-GDEM) data are used to correct the <em>T</em>_s data for the reduction of the topographic influences by topographic relief. The TVDI is then employed by comparison of different vegetation index, where the TVDI is more sensitive to soil moisture. Thus the dry situation in the study area is analyzed during the plant growth time and compared by the synchronous meteorology data. The results indicate that: (1) terrain correction can effectively prevent the decrease of TVDI value from a lower surface temperature for a higher pixel. The correlation between <em>T</em>_s-NDVI index and measured values on May is compared, <em>R</em>< sup>2</sup> will increase from 0.4634 to 0.5859 by terrain correction. It shows that the terrain corrected TVDI can improve effectively the estimation of soil moisture. (2) By comparing the correlation between <em>T</em>_s-NDVI, <em>T</em>_s-EVI, <em>T</em>_s-RVI, <em>T</em>_s-MSAVI and soil moisture,all the TVDIs present the negative correlations with soil moisture. The best correlations between the soil moisture and TVDIs can be always found, such as <em>T</em>_s-MSAVI in June, July and September 2005, <em>T</em>_s-EVI in May, and <em>T</em>_s-NDVI in August. Thus a TVDI feature space for different periods by these vegetation indexes are built for inversion of drought conditions. By comparison with agricultural meteorology, the results are acceptable. (3) Large area of the study area was humid from May to September 2005, drought occurred in the West on August, and humid was located in East on June. Therefore, compared with the measured data, the terrain corrected TVDI model is robust to eliminate the terrain and land cover influences to land surface temperature for inversion of soil moisture in the study area. And it is faithful to predict the agricultural drought condition in the study area during 2005 crop growth season.

AN IMPROVED SAR RADIOMETRIC TERRAIN CORRECTION METHOD AND ITS APPLICATION IN POLARIMETRIC SAR TERRAIN EFFECT REDUCTION

A new SAR radiometric terrain correction method was proposed to reduce the terrain efiects in sloped regions. Based on this method, a procedure for polarimetric SAR terrain efiect reduction was proposed, including geometric correction, shadow detection, radiometric terrain correction, and polarization orientation angle shift compensation. Experiments using RADARSAT-2 polarimetric SAR data of the Three Gorges Area, China demonstrated the efiectiveness of the proposed radiometric terrain correction method. Both visual and quantitative analyses showed that after the proposed radiometric terrain correction method was applied, the contrast between difierent slopes that caused by local incidence angle difierences, foreshortening, and layover was signiflcantly reduced. The difierence of backscattering intensity on slopes facing the radar sensor and facing away from the sensor was reduced from 12.5dB before radiometric correction to 1.3dB. The overall accuracy of land use/land cover classiflcation was improved by 11.2 percent using the terrain corrected polarimetric SAR data.

Regional Gravity Terrain Corrections: examples from South Australia

Traditional Bouguer corrections assume that the Earth behaves as a flat slab (Bouguer reduction), an assumption that is incorrect. A more accurate method of removing the effects of an Earth model is to take into account the terrain information, and assume a curved Earth. Terrain corrections are becoming more commonplace in individual surveys as computer power increases. We have produced a regional (state-wide) scale terrain corrected gravity grid, using a 1-second DEM to calculate the correction. The correction adds a subtle change to the state wide gravity, removing some regional lows. It can be used as a simple visual tool to determine where terrain corrections are more appropriately undertaken.

Generation of geometrically and radiometrically terrain corrected SAR image products

Terrain undulations affect the geometric and radiometric quality of synthetic aperture radar images. The correction of these effects becomes indispensable when quantitative image analysis is performed with respect to the derivation of geo- and biophysical parameters. The paper presents a rigorous approach for geometric and radiometric correction of SAR images. Using a digital elevation model, the imaging geometry is reconstructed and is used to perform geometric and radiometric correction of terrain induced distortions. The importance of a stringent radiometric correction based on the integration of the image brightness is emphasized. The approach guarantees that the energy contained in the image data is preserved throughout the geocoding process. The resulting backscattering images are fully terrain corrected and can be used for further quantitative investigations and may also improve qualitative studies as e.g. land cover classifications. The technique is applicable for different sensor types and image products, including already geocoded SAR images. The effect of different resolutions of digital elevation models used for the correction of the backscattering coefficient is investigated.