Satellite Pour l'Observation De La Terre Research Articles

Assessment of the Ecological Condition of Informal Settlements Using the Settlement Surface Ecological Index

To manage urban ecological ecosystems adequately, understanding the urban areas’ biophysical characteristics is required. This study developed a settlement surface ecological index (SSEI) using tree, soil, impervious surface and grass covers, land surface temperature (LST), and soil moisture derived from Satellite Pour L’Observation de la Terre (SPOT) 7 and Landsat 8 satellite images. The assessment of the SSEI was conducted over twelve sites of 300 m by 300 m. The selected sites contained formal and informal settlements of varying building densities. The SSEI values ranged from −0.3 to 0.54. Seven assessed areas are in the worst ecological condition with an SSEI below zero. Only three settlement types had an SSEI index value of 0.2 and above, and two of these areas were informal settlements. The formal low-density settlement with higher tree coverage displayed the highest index value of 0.54, slightly higher than the medium-density informal settlement. Overall, there is no significant difference in the SSEI values between the surface ecological condition of formal and informal settlements. The results achieved in this study can be used to understand urban ecology better and develop urban greening strategies at a city or settlement level.

Automated Identification of Thermokarst Lakes Using Machine Learning in the Ice-Rich Permafrost Landscape of Central Yakutia (Eastern Siberia)

The current rate and magnitude of temperature rise in the Arctic are disproportionately high compared to global averages. Along with other natural and anthropogenic disturbances, this warming has caused widespread permafrost degradation and soil subsidence, resulting in the formation of thermokarst (thaw) lakes in areas of ice-rich permafrost. These lakes are hotspots of greenhouse gas emissions (CO2 and CH4), but with substantial spatial and temporal heterogeneity across Arctic and sub-Arctic regions. In Central Yakutia (Eastern Siberia, Russia), nearly half of the landscape has been affected by thermokarst processes since the early Holocene, resulting in the formation of more than 10,000 partly drained lake depressions (alas lakes). It is not yet clear how recent changes in temperature and precipitation will affect existing lakes and the formation of new thermokarst lakes. A multi-decadal remote sensing analysis of lake formation and development was conducted for two large study areas (~1200 km2 each) in Central Yakutia. Mask Region-Based Convolutional Neural Networks (R-CNN) instance segmentation was used to semi-automate lake detection in Satellite pour l’Observation de la Terre (SPOT) and declassified US military (CORONA) images (1967–2019). Using these techniques, we quantified changes in lake surface area for three different lake types (unconnected alas lake, connected alas lake, and recent thermokarst lake) since the 1960s. Our results indicate that unconnected alas lakes are the dominant lake type, both in the number of lakes and total surface area coverage. Unconnected alas lakes appear to be more susceptible to changes in precipitation compared to the other two lake types. The majority of recent thermokarst lakes form within 1 km of observable human disturbance and their surface area is directly related to air temperature increases. These results suggest that climate change and human disturbances are having a strong impact on the landscape and hydrology of Central Yakutia. This will likely affect regional and global carbon cycles, with implications for positive feedback scenarios in a continued climate warming situation.

A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l’Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data.

Fine-scale multi-temporal and spatial analysis of agricultural drought in agro-ecological regions of Zimbabwe

This study utilised multi-temporal satellite data to characterize droughts across the five Agro-ecological zones (AEZ) of Zimbabwe at monthly, seasonal, sub-seasonal and annual scales (2000 to 2018) using a combination of Satellite Pour l'Observation de la Terre (SPOT) Vegetation (VGT) and Project for On-Board Autonomy-Vegetation (PROBA-V) derived Vegetation Condition Index (VCI). The characterized droughts were validated, using maize yield and meteorological station derived Standardised Precipitation Index (SPI). The study showed differential spatial and temporal expression of drought in Zimbabwe, over the 19-year study period. November had the highest frequency of drought while the first quarter of the season experienced more droughts than the second quarter. Spatially, virtually all AEZ in Zimbabwe are prone to droughts and the frequency is increasingly, with the Agro-ecological region five (southern Zimbabwe) being the worst affected. Further, the study found a strong positive relationship between the VCI and maize yield (r = 0.701, P = 0.000), demonstrating the utility of the index in drought monitoring at national level. Overall, findings from this work highlight the significance of using multi-date and national-scale analysis of agricultural drought occurrence and trends. Moreover, this work provides critical baseline information for developing drought resilience and mitigation strategies in drought-prone areas.

Application of Satellite Data on Water Pollution: An Intensive Review

Evaluation of water pollution is a priority work nowadays. The signature of the waterbody reveals its excellence or mediocrity and reflectance that can measure by a sensor used to analyse the health status of the waterbody. The remote sensing analysis has become the latest state of art technologies for monitoring large-scale waterbodies. High-resolution satellite data are now available to estimate water pollution through various water quality parameters like clarity, chlorophyll, suspended solids, turbidity, temperature, salinity, organic matter, etc. In this review study, a special emphasise has been given to the various satellites like Landsat, sentinel, satellite pourl’Observation de la terre (SPOT), moderate resolution imaging spectroradiometer (MODIS), medium resolution imaging spectrometer (MERIS), Indian remote sensing satellites (IRS) and its application on water pollution. Availability of satellite data, algorithms, and models to assess water quality has also been reviewed in detailed. The review suggests development and innovation in satellites, sensors and techniques to assess the non-optically active constituents of water quality for better understanding and management of water pollution.

Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.

Panchromatic and multispectral image fusion using the spatial frequency and the à trous wavelet transform

We propose a panchromatic (Pan) and multispectral (MS) image fusion method in order to synthesize an MS image with enhanced spatial resolution and preserved spectral information of the original MS image. The proposed method is based on the spatial frequency and the à trous wavelet transform (SFATWT). The à trous wavelet transform (ATWT) is used to decompose Pan and MS images into low and high frequencies. The spatial frequency is exploited in the image fusion rules to improve the details or the high-frequency information which will be injected into the MS image. The ATWT reconstruction makes it possible to produce MS image with high spatial resolution. To validate the proposed method, two image sets are used. The first one is acquired by the SPOT (Satellite Pour l’Observation de la Terre) satellite with a resolution ratio of 2. The second one is the Algerian satellite AlSat 2A images with a resolution ratio of 4. The fused results are evaluated by means of qualitative and quantitative comparisons. The assessment of fused products has shown that the proposed method is promising.

The Use of SPOT 6 and RapidEye Imageries for Mangrove Mapping in the Kembung River, Bengkalis Island, Indonesia

High-resolution satellite imageries believed to map the mangrove area well. Two satellite imageries with almost similar spatial resolution were expected to achieve this objective, including SPOT (Satellite Pour l’Observation de la Terre) 6 and a RapidEye. Two images covered the same area and acquired under the equivalent season at Kembung River, Bengkalis Island, Riau Province. A comparison was tested on several objectives. First, a comparison was conducted band by band through examining their statistical spectral analysis. RapidEye had a higher variance in all visible bands but low at NIR. Second, spectral separability using Jeffrey-Matusita distance was executed with three-band composites (SPOT 6/VNIR; RAPIDEYE/VNIR and RAPIDEYE/VNIR+RedEdge). SPOT 6 had better separability than RapidEye imageries compositions (VNIR+RedEdge and VNIR), with an overall accuracy of 79.9%, 75.3%, and 67.3%, respectively. Third, mangrove land cover classification. Three pixel-based classification rules were used, including a general classifier, maximum likelihood (ML), and two advanced classifiers, i.e., neural network (NN) and support vector machine (SVM). Results indicated that the use of SPOT 6 produced better overall accuracy than the other two image composition for each classifier. SVM was promising in the mangrove mapping algorithm compare to NN and ML.

Agricultural Field Detection from Satellite Imagery Using the Combined Otsu’s Thresholding Algorithm and Marker-Controlled Watershed-Based Transform

An accurate detection of agricultural fields is often needed for agricultural-related applications, such as subsidies monitoring, field-based crop yield estimation and agricultural statistics extraction. High-resolution space images have become the fundamental source to extract agricultural field boundaries. Manual boundary delineation is not practical. In this study, we present an approach to detect agricultural fields from satellite images on the basis of agricultural field blocks. An agricultural field block consists of one or more fields that are owned by the farmers. The approach combines the Otsu’s thresholding algorithm and marker-controlled watershed (MCW)-based segmentation. First, the well-separated field segments within a field block being considered are detected through recursive processing of the Otsu’s thresholding algorithm. Then, these distinct field segments are used to generate a marker image, and further extraction of individual fields is carried out through a marker-controlled watershed (MCW)-based segmentation. The approach was tested using 10-m resolution Satellite Pour l’Observation de la Terre (SPOT)-5 multi-spectral (XS) image, 4-m resolution IKONOS XS image, 2.40-m resolution QuickBird XS image, and 0.60-m resolution QuickBird pan-sharpened (PS) image. The results were evaluated using the reference field boundary dataset. The achieved overall accuracies were 89.7, 83.2, 81.0, and 77.4% for the IKONOS XS, QuickBird XS, SPOT-5 XS, and QuickBird PS images, respectively. The results are promising and indicate that the approach can be used for the extraction of agricultural fields from space imagery.

Analyzing Potential Tree-Planting Sites and Tree Coverage in Mexico City Using Satellite Imagery

Locating potential tree-planting sites and analyzing tree canopy cover is important in the planning and management of urban forests. This paper reports the quantification of potential planting sites as well as tree canopy cover in the urban area of Mexico City, estimated by means of SPOT (Satellite Pour l’Observation de la Terre) 6 satellite images and a supervised pixel-based classification approach. Results showed an estimated area of 3100.7 ha of potentially useful sites, including places with bare soil and grass-covered areas such as median strips, roundabouts and parks. An average tree canopy cover of 10.6% and an average impervious surface of 79.2% for the 15 boroughs were also analyzed. The area of potential planting sites would represent a 5% gain for the current tree canopy cover if it were to be planted. With an overall accuracy of 92.4%, the use of both images from the SPOT 6 sensor and the classification approach proved to be appropriate for obtaining thematic covers in the urban environment of Mexico City.

A Relief Dependent Evaluation of Digital Elevation Models on Different Scales for Northern Chile

Many geoscientific computations are directly influenced by the resolution and accuracy of digital elevation models (DEMs). Therefore, knowledge about the accuracy of DEMs is essential to avoid misleading results. In this study, a comprehensive evaluation of the vertical accuracy of globally available DEMs from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topography Mission (SRTM), Advanced Land Observing Satellite (ALOS) World 3D and TanDEM-X WorldDEM™ was conducted for a large region in Northern Chile. Additionally, several very high-resolution DEM datasets were derived from Satellite Pour l’Observation de la Terre (SPOT) 6/7 and Pléiades stereo satellite imagery for smaller areas. All datasets were evaluated with three reference datasets, namely elevation points from both Ice, Cloud, and land Elevation (ICESat) satellites, as well as very accurate high-resolution elevation data derived by unmanned aerial vehicle (UAV)-based photogrammetry and terrestrial laser scanning (TLS). The accuracy was also evaluated with regard to the existing relief by relating the accuracy results to slope, terrain ruggedness index (TRI) and topographic position index (TPI). For all datasets with global availability, the highest overall accuracies are reached by TanDEM-X WorldDEM™ and the lowest by ASTER Global DEM (GDEM). On the local scale, Pléiades DEMs showed a slightly higher accuracy as SPOT imagery. Generally, accuracy highly depends on topography and the error is rising up to four times for high resolution DEMs and up to eight times for low-resolution DEMs in steeply sloped terrain compared to flat landscapes.

Urban Expansion and the Loss of Prairie and Agricultural Lands: A Satellite Remote-Sensing-Based Analysis at a Sub-Watershed Scale

Prairies or grasslands together with areas designated as agricultural lands are one of the largest types of land cover and land use that exist today. While prairies provide habitat to a wide variety of animals and organisms, and agricultural lands support human populations, these lands, especially those in the immediate vicinities of large urban areas, are giving way to urbanization at alarming rates. In particular, prairies are often viewed as wastelands because their benefit to the effective functioning of the urban ecosystem is often not fully understood. On the other hand, many agricultural lands are being converted for several urban uses because of the high economic returns from their sale. In this study, we classified SPOT (Satellite Pour l’Observation de la Terre) satellite data of the study area using the supervised maximum likelihood classification approach in order to investigate the loss of prairies and agricultural lands due to urban expansion in six sub-watersheds in the Kansas City metropolitan area of the States of Kansas and Missouri in the U.S. Based on the classified maps, we computed the magnitude and rate of urban expansion, and the proportion of loss in prairies and agricultural lands that was a result of urban expansion. Results from the 22-year study revealed that in all six sub-watersheds, agricultural lands and grassland were depleted at alarming rates with no sustainable effort to conserve them. These results provide baseline information that can support a data-driven and sustainable path for urban expansion in the examined sub-watersheds.

Can We Use Satellite-Based FAPAR to Detect Drought?

Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l’Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.

Mangrove Vegetation Health Assessment Based on Remote Sensing Indices for Tanjung Piai, Malay Peninsular

Abstract Mangroves critically require conservation activity due to human encroachment and environmental unsustainability. The forests must be conserving through monitoring activities with an application of remote sensing satellites. Recent high-resolution multispectral satellite was used to produce Normalized Difference Vegetation Index (NDVI) and Tasselled Cap transformation (TC) indices mapping for the area. Satellite Pour l’Observation de la Terre (SPOT) SPOT-6 was employed for ground truthing. The area was only a part of mangrove forest area of Tanjung Piai which estimated about 106 ha. Although, the relationship between the spectral indices and dendrometry parameters was weak, we found a very significant between NDVI (mean) and stem density (y=10.529x + 12.773) with R2=0.1579. The sites with NDVI calculated varied from 0.10 to 0.26 (P1 and P2), under the environmental stress due to sand deposition found was regard as unhealthy vegetation areas. Whereas, site P5 with NDVI (mean) 0.67 is due to far distance from risk wave’s zone, therefore having young/growing trees with large lush green cover was regard as healthy vegetation area. High greenness indicated in TC means, the bands respond to a combination of high absorption of chlorophyll in the visible bands and the high reflectance of leaf structures in the near-infrared band, which is characteristic of healthy green vegetation. Overall, our study showed our tested WV-2 image combined with ground data provided valuable information of mangrove health assessment for Tanjung Piai, Johor, Malay Peninsula.

Chronology of the 2014–2016 Eruptive Phase of Volcán de Colima and Volume Estimation of Associated Lava Flows and Pyroclastic Flows Based on Optical Multi-Sensors

The eruption at Volcán de Colima (México) on 10–11 July 2015 represents the most violent eruption that has occurred at this volcano since the 1913 Plinian eruption. The extraordinary runout of the associated pyroclastic flows was never observed during the past dome collapse events in 1991 or 2004–2005. Based on Satellite Pour l’Observation de la Terre (SPOT) and Earth Observing-1 (EO-1) ALI (Advanced Land Imager), the chronology of the different eruptive phases from September 2014 to September 2016 is reconstructed here. A digital image segmentation procedure allowed for the mapping of the trajectory of the lava flows emplaced on the main cone as well as the pyroclastic flow deposits that inundated the Montegrande ravine on the southern flank of the volcano. Digital surface models (DSMs) obtained from SPOT/6 dual-stereoscopic and tri-stereopair images were used to estimate the volumes of some lava flows and the main pyroclastic flow deposits. We estimated that the total volume of the magma that erupted during the 2014–2016 event was approximately 40 × 107 m3, which is one order of magnitude lower than that of the 1913 Plinian eruption. These data are fundamental for improving hazard assessment because the July 2015 eruption represents a unique scenario that has never before been observed at Volcán de Colima. Volume estimation provides complementary data to better understand eruptive processes, and detailed maps of the distributions of lava flows and pyroclastic flows represent fundamental tools for calibrating numerical modeling for hazard assessment. The stereo capabilities of the SPOT6/7 satellites for the detection of topographic changes and the and the availability of EO-1 ALI imagery are useful tools for reconstructing multitemporal eruptive events, even in areas that are not accessible due to ongoing eruptive activity.

Seasonal forecasts for the Limpopo Province in estimating deviations from grazing capacity

Application of seasonal forecasts in agriculture has significant potential and realized utility. Other sectors that may also benefit from using seasonal forecasts include (but are not limited to) health, hydrology, water, and energy. This paper shows that seasonal forecast model data, satellite Pour l’Observation de la Terre (SPOT), dry matter productivity (DMP) data (proxy of grass biomass) along with other sets of data are effectively used to estimate grazing capacity (GC) over a 12-year test period (1998/1999–2009/2010) in Limpopo Province. GC comprises a vital consideration in agricultural activities, particularly for a province in South Africa like Limpopo, due to its varying climate. The Limpopo Province capitalizes on subsistence farming, including livestock and crop production. Grazing should thus be regulated in order to conserve grass, shrubs, and trees, thereby ensuring sustainability of rangelands. In a statistical downscaling model, the predictor is the 850 geopotential height fields of a coupled ocean–atmosphere general circulation (CGCM) over Southern Africa to predict seasonal DMP values. This model shows that the mid-summer rainfall totals are important predictors for the November through April (NDJFMA) DMP (as well as grazing capacity) growing season. Forecast verification is conducted using the relative operating characteristics (ROC) and reliability diagrams. The CGCM model shows skill in discriminating high and low DMP (GC) seasons in the Limpopo Province, as well as reliability in the probabilistic forecasts. This paper demonstrates the development of a tailored forecast, an avenue that should be explored in enhancing relevance of forecasts in agricultural production.

Textural measures for estimating oil palm age

ABSTRACTIn oil palm management, age is one of the yield determinant factors. The conventional field investigations are often exhaustive and costly methods when implemented on a large scale. Despite much attention to classify individual oil palm ages by using various remote sensing images, none of the studies depicted satisfying overall accuracies. The overall aim of this study was to optimize window size and number of texture measurements for oil palm ages classification. The study was conducted in a commercial oil palm plantation comprised of palms of multiple ages planted from 1991 to 2008. Three Satellite Pour l’Observation de la Terre (SPOT)-5 multispectral images, acquired on 12 April 2012, 4 April 2013, and 14 April 2014, were evaluated. The individual ages were successfully classified with accuracy ranging from 59% to 97%, with an average overall accuracy of 84%. The results illustrated that the largest window size related to the smallest oil palm planting block in the study area, 390 m × 390 m on-the-ground window size, and seven combination of texture measurements, resulted in the highest classification overall accuracy. The utilization of texture measurements produced synergistic effects able to discriminate the oil palm age, with mean, entropy, homogeneity, and angular second moment as among the significant textures.

Radiometric homogenisation of aerial images by calibrating with satellite data

ABSTRACTThe use of very high resolution (VHR) aerial imagery for quantitative remote sensing has been limited by unwanted radiometric variation over temporal and spatial extents. In this paper we propose a simple yet effective technique for the radiometric homogenisation of the digital numbers of aerial images. The technique requires a collocated and concurrent, well-calibrated satellite image as surface reflectance reference to which the aerial images are calibrated. The bands of the reference satellite sensor should be spectrally similar to those of the aerial sensor. Using radiative transfer theory, we show that a spatially varying local linear model can be used to approximate the relationship between the surface reflectance of the reference image and the digital numbers of the aerial images. The model parameters for each satellite pixel location are estimated using least squares regression inside a small sliding window. The technique was applied to a set of aerial images captured over multiple days with an Intergraph Digital Mapping Camera (DMC) system. A near-concurrent Moderate Resolution Imaging Spectroradiometer (MODIS) nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance image was used as the reflectance reference dataset. The resulting DMC mosaic was compared to a near-concurrent Satellite Pour l’Observation de la Terre (SPOT) 5 reflectance image of a portion of the same area, omitting the blue channel from the DMC mosaic due to its absence in the SPOT 5 data. The mean absolute reflectance difference was found to be 3.43% and the mean coefficient of determination (R2) over the bands was 0.84. The technique allows the production of seamless mosaics corrected for coarse scale atmospheric and BRDF effects and does not require the manual acquisition (or provision) of ground reflectance references. The accuracy of corrections is limited by the resolution of the reference image, which is generally significantly coarser than VHR imagery. The method cannot correct for small scale BRDF or other variations not captured at the reference resolution. Nevertheless, results show a significant improvement in homogeneity and correlation with SPOT 5 reflectance.

Look-up-table approach for leaf area index retrieval from remotely sensed data based on scale information

Leaf area index (LAI) is a key structural characteristic of vegetation and plays a significant role in global change research. Several methods and remotely sensed data have been evaluated for LAI estimation. This study aimed to evaluate the suitability of the look-up-table (LUT) approach for crop LAI retrieval from Satellite Pour l’Observation de la Terre (SPOT)-5 data and establish an LUT approach for LAI inversion based on scale information. The LAI inversion result was validated by in situ LAI measurements, indicating that the LUT generated based on the PROSAIL (PROSPECT+SAIL: properties spectra + scattering by arbitrarily inclined leaves) model was suitable for crop LAI estimation, with a root mean square error (RMSE) of ∼0.31m2 / m2 and determination coefficient (R2) of 0.65. The scale effect of crop LAI was analyzed based on Taylor expansion theory, indicating that when the SPOT data aggregated by 200 × 200 pixel, the relative error is significant with 13.7%. Finally, an LUT method integrated with scale information was proposed in this article, improving the inversion accuracy with RMSE of 0.20 m2 / m2 and R2 of 0.83.

Geodetic Mass Balance of the Northern Patagonian Icefield from 2000 to 2012 Using Two Independent Methods

We compare two independent estimates of the rate of elevation change and geodetic mass balance of the Northern Patagonian Icefield (NPI) between 2000 (3856 km²) and 2012 (3740 km²) from space-borne data. The first is obtained by differencing the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) from February 2000 and a Satellite pour l’Observation de la Terre 5 (SPOT5) DEM from March 2012. The second is deduced by fitting pixel-based linear elevation trends over 118 DEMs calculated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) stereo images acquired between 2000 and 2012. Both methods lead to similar and strongly negative icefield-wide mass balances of -1.02±0.21 and -1.06±0.14 m w.e. yr-1 respectively, which is in agreement with earlier studies. Contrasting glacier responses are observed, with individual glacier mass balances ranging from -0.15 to -2.30 m w.e. yr-1 (standard deviation = 0.49 m w.e. yr-1; N = 38). For individual glaciers, the two methods agree within error bars, except for small glaciers poorly sampled in the SPOT5 DEM due to clouds. Importantly, our study confirms the lack of penetration of the C-band SRTM radar signal into the NPI snow and firn except for a region above 2900 m a.s.l. covering less than 1% of the total area. Ignoring penetration would bias the mass balance by only 0.005 m w.e. yr-1. A strong advantage of the ASTER method is that it relies only on freely available data and can thus be extended to other glacierized areas.