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

Evaluation of Sentinel-1, SMAP and SMOS surface soil moisture products for distributed eco-hydrological modelling in Mediterranean forest basins

[EN] Reliable distributed hydrological modeling, especially in semi-arid areas, must consider the inclusion of surface soil moisture (SSM) spatial information during the calibration process. This variable plays a key role in the evapotranspiration processes that determine the hydrological cycle. The coarse resolution of the SSM estimates by satellite remote sensing has restricted the application of this approach to only large basins, focusing most of the studies in the consideration of simply the temporal dynamics of this variable. The growing efforts in providing higher spatial resolution through disaggregating methodologies or new sensor estimates facilitates the application of this spatial approach to small basins. This paper explores the applicability of the currently available satellite surface soil moisture estimates for distributed eco-hydrological modelling in Mediterranean forest basins. On one hand, this study contributes to fill the existing research gap on the use of remote sensing SSM spatial patterns within the distributed hydrological modelling framework in small basins. On the other hand, it serves as an indirect validation method for the spatial performance of satellite SSM products. To achieve this goal, we implemented the eco-hydrological model TETIS in three case studies named: Hozgarganta (southern Spain), Ceira (western Portugal) and Carraixet (eastern Spain). The SSM estimates selected for comparison were Sentinel-1 SSM provided by the Copernicus Global Land Services (CGLS), SMAP SSM disaggregated using Sentinel-1 (SPL2SMAP_S) provided by the National Aeronautics and Space Administration (NASA), SMOS SSM provided by the Barcelona Expert Center (BEC), and SMOS and SMAP SSM disaggregated using the DISPATCH algorithm provided by Lobelia Earth. The methodology employed involved a multi-objective and multi-variable calibration in terms of remote sensing SSM spatial patterns and in-situ streamflow, using the Spatial Efficiency Metric (SPAEF) and the Nash-Sutcliffe efficiency index (NSE) respectively. Before model calibration a sensitivity analysis of the most influent variables was performed. The temporal and spatial comparison of the reference SSM products revealed inconsistencies amongst products. The disaggregating methodology determined the spatial agreement to a greater degree than the sensor itself (i.e. SMAP, SMOS). In spite of the differences amongst products, the multi-objective calibration approach proposed increased the robustness of the hydrological modelling.This study was founded by the Spanish AEI within the program WaterJPI through the project iAqueduct (PCI2019-103729) , by the EC Life project ResilientForests (LIFE17 CCA/ES/000063) , and by the project Water4Cast funded by Generalitat Valenciana (PROMETEO/2021/074) . We also acknowledge the following hydrometeorological data providers institutions: SiAR, SAIH-HIDROSUR, SAIH Jucar and SNIRH.Gomis-Cebolla, J.; Garcia-Arias, A.; Perpinyà-Vallès, M.; Francés, F. (2022). Evaluation of Sentinel-1, SMAP and SMOS surface soil moisture products for distributed eco-hydrological modelling in Mediterranean forest basins. Journal of Hydrology. 608:1-19. https://doi.org/10.1016/j.jhydrol.2022.12756911960

MODIS-Based Monthly LST Products over Amazonia under Different Cloud Mask Schemes

One of the major problems in the monitoring of tropical rainforests using satellite imagery is their persistent cloud coverage. The use of daily observations derived from high temporal resolution sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), could potentially help to mitigate this issue, increasing the number of clear-sky observations. However, the cloud contamination effect should be removed from these results in order to provide a reliable description of these forests. In this study the available MODIS Land Surface Temperature (LST) products have been reprocessed over the Amazon Basin (10 N–20 S, 80 W–45 W) by introducing different cloud masking schemes. The monthly LST datasets can be used for the monitoring of thermal anomalies over the Amazon forests and the analysis of spatial patterns of warming events at higher spatial resolutions than other climatic datasets

Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests

Evapotranspiration (ET) is considered a key variable in the understanding of the Amazonian tropical forests and their response to climate change. Remote-Sensing (RS) based evapotranspiration models are presented as a feasible means in order to provide accurate spatially-distributed ET estimates over this region. In this work, the performance of four commonly used ET RS models was evaluated over Amazonia using Moderate Resolution Imaging Spectroradiometer (MODIS) data. RS models included 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 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). 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 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 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 tends to overestimate daily radiation while underestimating incoming solar radiation. Discrepancies amongst these reanalysis inputs generally explain the observed discrepancies in model spatial and temporal patterns

The impact of a multi-criteria calibration on the performances of the DREAM model

Water resources observation and modelling are essential to better understand hydrological processes and improve water resource management. However, the reliability of hydrological simulation is strongly controlled by the quality and type of field observations used for the calibration and validation processes. Therefore, it is critical to develop proper strategies for model calibration and validation in order to reduce prediction uncertainties. Standard hydrological calibration relies mainly on the time series of total streamflow at the catchment outlet; nevertheless, this leads to a limited insight into the spatial behaviour of a river basin. In this work, we use simulations from the physically-based distributed DREAM model to discuss the importance of multi-criteria calibration to obtain consistent parameter sets. The calibration methodology exploits a physical based filter to decompose the streamflow times series in two time series referring to the surface component and the baseflow. Therefore, we adopted a multi-criteria calibration procedures which optimizes: (a) the total streamflow measured at the basin outlet (used as a reference study case); b) both the surface runoff and baseflow measured at the basin outlet; and (c) the combination the time series of the two components along with the annual water balance components. In addition, we also explored the use of a lumped parametrization against a spatial parametrization derived from the soil type characteristics of the river basin. In all cases, parameter optimization was carried out using an automatic calibration performed by a genetic algorithm (GA) tool. The study was carried out for two experimental catchments located in Basilicata and Campania regions (Southern Italy). The performed experiments showed that the inclusion of physical information during the calibration process results in a general improvement of model reliability. This research is a part of iAqueduct project funded under the Water JPI 2018 Joint Call, Closing the Water Cycle Gap – on Sustainable Management of Water Resources - Water Works 2017