Land surface temperature and evapotranspiration estimation in the Amazon evergreen forests using remote sensing data

Amazonian tropical forests play a significant role in global water, carbon and energy cycles. Considering the relevance of this biome and the climate change projections which predict a hotter and drier climate for the region, the monitoring of the vegetation status of these forests becomes of significant importance. In this context, vegetation temperature and evapotranspiration (ET) can be considered as key variables. Vegetation temperature is directly linked with plant physiology. In addition, some studies have shown the existing relationship between this variable and the CO2 absorption capacity and biomass loss of these forests. Evapotranspiration resulting from the combined processes of transpiration and evaporation links the terrestrial water, carbon and surface energy exchanges of these forests. How this variable will response to the changing climate is critical to understand the stability of these forests. Satellite remote sensing is presented as a feasible means in order to provide accurate spatially-distributed estimates of these variables. Nevertheless, the use of satellite passive imagery for analysing this region still has some limitations being of special importance the proper cloud masking of the satellite data which becomes a difficult task due to the continuous cloud cover of the region. Under the light of the aforementioned issues, the present doctoral thesis is aimed at estimating the land surface temperature and evapotranspiration of the Amazonian tropical forests using remote sensing data. In addition, as cloud screening of satellite imagery is a critical step in the processing chain of the previous magnitudes and becomes of special importance for the study region this topic has also been included in this thesis. We have mainly focused on the use of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) which is amongst major tools for studying this region. Regarding the cloud detection topic, the potential of supervised learning algorithms for cloud masking was studied in order to overcome the cloud contamination issue of the current satellite products. Models considered were: Gaussian Naïve Bayes (GNB), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forests (RF), Support Vector Machine (SVM) and Multilayer Perceptron (MLP). These algorithms are able to provide a continuous measure of cloud masking uncertainty (i.e. a probability estimate of each pixel belonging to clear and cloudy class) and therefore can be used under the light of a probabilistic approach. Reference dataset (a priori knowledge) requirement was satisfied by considering the collocation of Cloud Profiling Radar (CPR) and Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) observations with MODIS sensor. Model performance was tested using three independent datasets: 1) collocated CPR/CALIOP and MODIS data, 2) MODIS manually classified images and 3) in-situ ground data. For the case of satellite image and in-situ testing, results were additionally compared to current operative MYD35 (version 6.1) and Multi-Angle Implementation of the Atmospheric Correction (MAIAC) cloud masking algorithms. These results showed that machine learning algorithms were able to improve MODIS operative cloud masking performance over the region. MYD35 and MAIAC tended to underestimate and overestimate the cloud cover, respectively. Amongst the models considered, LDA stood out as the best candidate because of its maximum accuracy (difference in Kappa coefficient of 0.293/0.155 (MYD35 /MAIAC respectively)) and minimum computational associated. Regarding the estimation of land surface temperature (LST), the aim of this study was to generate specific LST products for the Amazonian tropical forests. This goal was accomplished by using a tuned split-window (SW) equation. Validation of the LST products was obtained by direct comparison between LST estimates as derived from the algorithms and two types of different LST observations: in-situ LST (T-based validation) and LST derived from the R-based method. In addition, LST algorithms were validated using independent simulated data. In-situ LST was retrieved from two infrared radiometers (SI-100 and IR-120) and a CNR4 net radiometer, situated at Tambopata test site (12.832 S, 62.282 W) in the Peruvian Amazon. Apart from this, current satellite LST products were also validated and compared to the tuned split-window. Although we have mainly focus on MODIS LST products which derive from three different LST algorithms: split-window, day and night (DN) and Temperature Emissivity Separation (TES), we have also considered the inclusion of the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. In addition, a first assessment of the Sea and Land Surface Temperature Radiometer (SLSTR) is presented. Validation was performed separately for daytime and nighttime conditions. For MODIS sensor, current LST products showed Root Mean Square Errors (RMSE) in LST estimations between 2 K and 3K for daytime and 1 K and 2 K for nighttime. In the best case (with a restrictive cloud screening) RMSE errors decrease to values below 2K and around 1 K, respectively. The proposed LST showed RMSE values of 1K to approximately 2 K and 0.7-1.5 K (below 1.5 K and below 1 K in the best case) for daytime and nighttime conditions, thus improving current LST MODIS products. This is also in agreement with the R-based validation results, which show a RMSE reduction of 0.7 K to 1.7 K in comparison to MODIS LST products. For the case of VIIRS sensor daytime conditions, VIIRS-TES algorithm provides the best performance with a difference of 0.2 K to around 0.3 K in RMSE regarding the split window algorithm (in the best case it reduces to 0.2 K). All VIIRS LST products considered have RMSE values between 2 K and 3K. At nighttime, however VIIRS-TES is not able to outperform the SW algorithm. A difference of 0.7 K to 0.8 K in RMSE is obtained. Contrary to MODIS and the SW LST products, VIIRS-TES tends to overestimate in-situ LST values. Regarding SLSTR sensor, the L2 product provides a better agreement with in-situ observations than the proposed algorithm (daytime difference in RMSE around 0.6 K and up 0.07 K at nighttime). In the estimation of the ET, we focused on the evaluation of four commonly used remote-sensing based ET models. These were: i) Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), ii) Penman-Monteith MODIS operative parametrization (PM-Mu), iii) Surface Energy Balance System (SEBS), and iv) Satellite Application Facility on Land Surface Analysis (LSASAF). These models were forced using remote-sensing data from MODIS and two ancillary meteorological data sources: i) in-situ data extracted from Large-Scale Biosphere-Atmosphere Experiment (LBA) stations (scenario I), and ii) three reanalysis datasets (scenario II), including Modern-Era Retrospective analysis for Research and Application (MERRA-2), European Centre for Medium-range Weather Forecasts (ECMWF) Re-Analysis-Interim (ERA-Interim), and Global Land Assimilation System (GLDAS-2.1). Performance of algorithms under the two scenarios was validated using in-situ eddy-covariance measurements. For scenario I, PT-JPL provided the best agreement with in-situ ET observations (RMSE = 0.55 mm/day, R = 0.88). Neglecting water canopy evaporation resulted in an underestimation of ET measurements for LSASAF. SEBS performance was similar to that of PT-JPL, nevertheless SEBS estimates were limited by the continuous cloud cover of the region. A physically-based ET gap-filling method was used in order to alleviate this issue. PM-Mu also with a similar performance to PT-JPL tended to overestimate in-situ ET observations. For scenario II, quality assessment of reanalysis input data demonstrated that MERRA-2, ERA-Interim and GLDAS-2.1 contain biases that impact model performance. In particular, biases in radiation inputs were found the main responsible of the observed biases in ET estimates. For the region, MERRA-2 tends to overestimate daily net radiation and incoming solar radiation. ERA-Interim tends to underestimate both variables, and GLDAS-2.1 tends to overestimate daily radiation while underestimating incoming solar radiation. Discrepancies amongst these inputs resulted in large absolute deviations in spatial patterns (deviations greater than 500 mm/year) and temporal patterns

CIRA annual report FY 2016/2017

Reporting period April 1, 2016-March 31, 2017

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Estimation of Surface Moisture Content and Evapotranspiration Using Weightage Approach.

Soil moisture (MC) and evapotranspiration (ET) are considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes. However, monitoring them continuously over large areas using the high temporal-resolution optical satellites is very demanding. Satellites such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), have a coarse spatial resolution in their images. Thus it not only impedes the acquisition of an accurate MC and ET but also represents multispectral reflections from the holistic surface features. This beside their dependence on vegetation and ground coefficient when assessing MC and ET. The study aims to enhance the spatial accuracy by weighting the MC produced from different surface cover classes within the pixel. MC for each pixel is segmented into three (3) different classes namely urban, vegetation and multi surface cover according to their respective MC weightage. Secondly, to generate an improved actual ETa map by overlaying the segmented MC with a rectified ETo. Images from AVHRR and MODIS satellites were selected in order to generate MC and ET maps. Two powerful MC algorithms were used based on land Surface Temperature (Ts), vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths to examine the depth influence on MC and Ts magnitudes

CIRA annual report FY 2015/2016

Reporting period April 1, 2015-March 31, 2016

Advances in Remote Sensing-based Disaster Monitoring and Assessment

Remote sensing data and techniques have been widely used for disaster monitoring and assessment. In particular, recent advances in sensor technologies and artificial intelligence-based modeling are very promising for disaster monitoring and readying responses aimed at reducing the damage caused by disasters. This book contains eleven scientific papers that have studied novel approaches applied to a range of natural disasters such as forest fire, urban land subsidence, flood, and tropical cyclones

Hydrology

In this book, an attempt is made to highlight the recent advances in Hydrology. The several topics examined in this book form the underpinnings of larger-scale considerations, including but not limited to topics such as large-scale hydrologic processes and the evolving field of Critical Zone Hydrology. Computational modeling, data collection, and visualization are additional subjects, among others, examined in the set of topics presented

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

CIRA annual report FY 2014/2015

Reporting period July 1, 2014-March 31, 2015