CRS-ISLA Past Research

CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data

Abstract: The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during January–March 2011 at the research site of the National Weather Service office, Caribou, ME, USA. In this experiment dual-polarized microwave (37 and 89 GHz) observations were accompanied by detailed synchronous observations of meteorology and snowpack physical properties. The objective of this long-term field experiment was to improve understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations. In this paper we present an overview of the field experiment and comparative preliminary analysis of the continuous microwave and snowpack observations and simulations. The observations revealed a large difference between the brightness temperature of fresh and aged snowpack even when the snow depth was the same. This is indicative of a substantial impact of evolution of snowpack properties such as snow grain size, density and wetness on microwave observations. In the early spring we frequently observed a large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization corresponding to daytime snowmelt and nighttime refreeze events. SNTHERM (SNow THERmal Model) and the HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures, respectively. Simulated snow depth and snowpack temperature using SNTHERM were compared to in situ observations. Similarly, simulated microwave brightness temperatures using the HUT model were compared with the observed brightness temperatures under different snow conditions to identify different states of the snowpack that developed during the winter season.

Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation

Abstract: An increasing number of satellite sensors operating in the optical and microwave spectral bands represent an opportunity for utilizing multi-sensor fusion and data assimilation techniques for improving the estimation of snowpack properties using remote sensing. In this paper, the strength of a synergistic approach of leveraging optical, active and passive microwave remote sensing measurements to estimate snowpack characteristics is discussed and examples from recent work are given. Observations with each type of sensor have specific technical constraints and limitations. Optical sensor data has high spatial resolution but is limited to cloud free days, whereas passive microwave sensors have coarse spatial resolution and are sensitive to multiple snowpack properties. Multi-source and multi-temporal remote sensing data therefore hold great promise for moving the monitoring and analysis of snow toward estimates of a suite of snow properties at high spatial and temporal resolution.

Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

Abstract: Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth’s surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth’s hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature.

Evaluation of the Snow Thermal Model (SNTHERM) through Continuous in situ Observations of Snow’s Physical Properties at the CREST-SAFE Field Experiment

Abstract: Snowpack properties like temperature or density are the result of a complex energy and mass balance process in the snowpack that varies temporally and spatially. The Snow Thermal Model (SNTHERM) is a 1-dimensional model, energy and mass balance-driven, that simulates these properties. This article analyzes the simulated snowpack properties using SNTHERM forced with two datasets, namely measured meteorological data at the Cooperative Remote Sensing Science and Technology-Snow Analysis and Field Experiment (CREST-SAFE) site and the National Land Data Assimilation System (NLDAS). The study area is located on the premises of Caribou Municipal Airport at Caribou (ME, USA). The model evaluation is based on properties such as snow depth, snow water equivalent, and snow density, in addition to a layer-by-layer comparison of snowpack properties. The simulations were assessed with precise in situ observations collected at the CREST-SAFE site. The outputs of the SNTHERM model showed very good agreement with observed data in properties like snow depth, snow water equivalent, and average temperature. Conversely, the model was not very efficient when simulating properties like temperature and grain size in different layers of the snowpack.

Proof of Concept: Development of Snow Liquid Water Content Profiler Using CS650 Reflectometers at Caribou, ME, USA

Abstract: The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness’s plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task and a demanding issue to measure the snowpack’s liquid water content (LWC) and its temporal evolution with conventional in situ techniques. We propose an approach based on the use of time-domain reflectometry (TDR) and CS650 soil water content reflectometers to measure the snowpack’s LWC and temperature profiles. For this purpose, we created an easily-applicable, low-cost, automated, and continuous LWC profiling instrument using reflectometers at the Cooperative Remote Sensing Science and Technology Center-Snow Analysis and Field Experiment (CREST-SAFE) in Caribou, ME, USA, and tested it during the snow melt period (February–April) immediately after installation in 2014. Snow Thermal Model (SNTHERM) LWC simulations forced with CREST-SAFE meteorological data were used to evaluate the accuracy of the instrument. Results showed overall good agreement, but clearly indicated inaccuracy under wet snow conditions. For this reason, we present two (for dry and wet snow) statistical relationships between snow LWC and dielectric permittivity similar to Topp’s equation for the LWC of mineral soils. These equations were validated using CREST-SAFE in situ data from winter 2015. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Additionally, the equations seemed to be able to capture the snowpack state (i.e., onset of melt, medium, and maximum saturation). Lastly, field test results show advantages, such as: automated, continuous measurements, the temperature profiling of the snowpack, and the possible categorization of its state. However, future work should focus on improving the instrument’s capability to measure the snowpack’s LWC profile by properly calibrating it with in situ LWC measurements. Acceptable validation agreement indicates that the developed snow LWC, temperature, and wetness profiler offers a promising new tool for snow hydrology research.

Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE

Abstract:

This article presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration Terra and Aqua Earth Observing System satellites using in-situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center – Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January–April) and 2014 (February–April). A total of 314 day-and-night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. CREST-SAFE is a synoptic ground station, located in the cold county of Caribou in Maine, USA, with a distinct advantage over most meteorological stations because it provides automated and continuous LST observations via an Apogee Model SI-111 Infrared Radiometer. This article also attempts to answer the question of whether a single pixel (1 km²) or several spatially averaged pixels should be used for satellite LST validation by increasing the MODIS window size to 5 × 5, 9 × 9, and 25 × 25 windows.

Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy (about 1°C), both suggesting that MODIS LST retrievals are reliable for similar land-cover classes and atmospheric conditions. Increasing the MODIS window size showed an overestimation of in-situ LST and some improvement in the daytime Terra and night-time Aqua biases, with the highest accuracy achieved with the 5 × 5 window. A comparison between MODIS emissivity from bands 31, 32, and in-situ emissivity showed that emissivity errors (relative error = −0.30%) were insignificant.

Comparison and Downscale of AMSR2 Soil Moisture Products with In-Situ Measurements from the SCAN-NRCS Network over Puerto Rico

Abstract:

A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing with ground-based measurements and perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used and their performances were studied. The results revealed that AMSR2 products tend to underestimate. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Abstract:

Soil moisture is placed at the interface between land and atmosphere which influences water and energy flux. However, soil moisture information has a significant importance in hydrological modelling and environmental processes. Recent advances in acquiring soil moisture from the satellite and its effective utilization provide an alternative to the conventional soil moisture methods. In this study, an attempt is made to apply physically based, distributed-parameter, Soil and Water Assessment Tool (SWAT) to validate Advanced Microwave Scanning Radiometer (AMSR2) soil moisture in parts of Puerto Rico. For this, calibration is performed for the years 2010 to 2012 with known observed discharge sites, Rio Guanajibo and Rio Grande de Añasco in Puerto Rico and validation, with the observed stream flow for the year 2013 using the AMSR2 soil moisture. Moreover, the SWAT and AMSR2 soil moisture outcome are compared on a monthly basis. The model capability and performance in simulating the stream flow are evaluated utilizing the statistical method. The results indicated a negligible difference in SWAT soil moisture and AMSR2 soil moisture for stream flow estimation. Finally, the model retrievals show a satisfactory agreement between observed and simulated streamflow.

Comparison and Downscale of AMSR2 Soil Moisture Products with In Situ Measurements from the SCAN–NRCS Network over Puerto Rico

Abstract:

A continuous spatio-temporal database of accurate soil moisture (SM) measurements is an important asset for agricultural activities, hydrologic studies, and environmental monitoring. The Advanced Microwave Scanning Radiometer 2 (AMSR2), which was launched in May 2012, has been providing SM data globally with a revisit period of two days. It is imperative to assess the quality of this data before performing any application. Since resources of accurate SM measurements are very limited in Puerto Rico, this research will assess the quality of the AMSR2 data by comparing it with ground-based measurements, as well as perform a downscaling technique to provide a better description of how the sensor perceives the surface soil moisture as it passes over the island. The comparison consisted of the evaluation of the mean error, root mean squared error, and the correlation coefficient. Two downscaling techniques were used, and their performances were studied. The results revealed that AMSR2 products tend to underestimate soil moisture. This is due to the extreme heterogeneous distributions of elevations, vegetation densities, soil types, and weather events on the island. This research provides a comprehensive study on the accuracy and potential of the AMSR2 products over Puerto Rico. Further studies are recommended to improve the AMSR2 products.

Impacts of Hurricane Maria on Land and Convection Modification Over Puerto Rico

Abstract:

Hurricane Maria drastically altered the landscape across the island of Puerto Rico. This article investigates modifications to surface-atmospheric interactions due to Hurricane Maria induced land damage and the associated impacts on local convective dynamics. Herein, we employed LANDSAT-8 image mosaics to quantify the hurricane induced land modification. Results of the analysis indicate that the island suffered significant forest damage—much of which registered as a 28.35% increase in barren land and a 10.85% increase in pasture. Smaller changes included a decrease in cultivated agricultural land cover by 0.76%, along with wetland and water increases of 0.62% and 0.25%, respectively. Pre and postMaria land classifications were then assimilated into the Regional Atmospheric Modeling System cloud resolving model for the simulation of the June 23 to July 2, 2018 period under two land conditions. Results of the numerical experiments indicate that surface to atmosphere interactions were significantly modified when the land cover was altered, and that the highest deviations between pre and postMaria convection occurred over elevated areas with extreme hurricane induced land changes, such as the Cordillera Central mountain range and the El Yunque rainforest.

Fertilization and precise irrigation scheduling for mature avocado

Abstract:

Irrigation scheduling (IS) and fertilization are among the most important practices in the production of horticultural crops because they affect fruit quality and quantity directly. Thus, a 15-year-old avocado orchard (cv. ‘Simmonds’) was used to determine precise IS, based on monitoring soil moisture content (SMC), remote sensing technologies [Unmanned Aerial Vehicle (UAV)] under two fertilization levels using granular formulation 15-3-19. In October 2015, all trees were pruned (topped and hedged) to 3.05 m height and 2.44 m diameter. In December 2015, soil moisture (SM) sensors were installed at five (10, 30, 50, 70 and 90 cm) soil depths in six locations. Trees received two fertilizer treatments: F1-9.06 kg and F2-12.07 kg of 15-3-19/tree/year every three months. Precipitation and SM data were recorded daily for 21 months; SM data was corrected with a quadratic equation (y = -4.1881x² + 3.6886x – 0.3083) generated specifically for the Coto soil series (Typic Hapludox). The SM values recorded were always greater than 41%, indicating that the avocado orchard was growing under water saturation conditions; thus, micro-irrigation was not needed. The UAV data at 5, 13 and 20 months after pruning (MAP) showed quick closure of the avocado canopy; acquiring a denser and more cylindrical shape (from 17.6 ± 2.65 m² to 52.7 ± 6.10 m²), regardless of fertilizer level. Based on correlation of UAV and manual results, F2-treated trees indicated stronger correlation at 13 and 20 MAP (R2 >0.75) than F1-trees. Yield production (110 avocados per tree = 13,200 per hectare) and leaf nutrient content did not differ significantly with fertilizer level. For commercial avocado farmers the use of SMC sensors and UAV technology could be an advantage, albeit an expensive one. Soil moisture content sensors have been shown to be very effective in irrigation water conservation. In terms of fertilization, the results suggest not using more than 9.06 kg of 15-3-19/tree/year as this amount seems enough to satisfy avocado requirements, under the experiment’s conditions. Future evaluations will determine if it is possible to use less fertilizer and still maintain an optimal avocado production.

Assessing streamflow forecast accuracy for flash flood events in Puerto Rico