Tarendra Lakhankar

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that the Tropical Rainfall Measuring Mission (TRMM) 3B-43 precipitation product exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Compared to station observations, the TRMM precipitation product showed an overall Nash-Sutcliffe efficiency of 0.49, which is similar to the skill of the gridded station-based product Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The other satellite precipitation products considered (Global Satellite Mapping of Precipitation (GSMaP), the Climate Prediction Center Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)) were less skillful, as judged by Nash-Sutcliffe efficiency, and, on average, substantially underestimated precipitation compared to station observations, despite their, in some cases, higher nominal spatial resolution compared to TRMM. None of the products fully captured the dependence of mean precipitation on elevation seen in the station observations. Overall, the TRMM product is promising for use in water resources applications.

Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations.

Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States

In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006 to 2010. IMS provides daily maps of snow and sea ice extent within the Northern Hemisphere using data from combination of geostationary and polar orbiting satellites in visible, infrared and microwave spectrums. Statistical correspondence between the IMS and in situ point measurements has been evaluated assuming that ground measurements are discrete and continuously distributed over a 4 km IMS snow cover maps. Advanced Very High Resolution Radiometer (AVHRR) land and snow classification data are supplemental datasets used in the further analysis of correspondence between the IMS product and in situ measurements. The comparison of IMS maps with in situ snow observations conducted over a period of four years has demonstrated a good correspondence of the data sets. The daily rate of agreement between the products mostly ranges between 80% and 90% during the Northern Hemisphere through the winter seasons when about a quarter to one third of the territory of continental US is covered with snow. Further, better agreement was observed for stations recording higher snow depth. The uncertainties in validation of IMS snow product with stationed NCDC data were discussed.

Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data

This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger footprint are critical in remote sensing based soil moisture retrieval. This study analyzes the sub-pixel variability (standard deviation of sub- grid pixels) of Normalized Difference Vegetation Index and SAR backscatter. Back- propagation neural network was used for soil moisture retrieval from active microwave remote sensing data from Southern Great Plains of Oklahoma. The effect of land cover heterogeneity (number of different vegetation species within pixels) on soil moisture retrieval using active microwave remote sensing data was investigated. The presence of heterogeneous vegetation cover reduced the accuracy of the derived soil moisture using microwave remote sensing data. The results from this study can be used to characterize the uncertainty in soil moisture retrieval in the context of Soil Moisture Active and Passive (SMAP) mission which will have larger footprint.

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.

Sensitivity analysis of b-factor in microwave emission model for soil moisture retrieval: a case study for SMAP mission.

Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates the sensitivity of the b-factor, which is function of vegetation type. The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS) and measured field soil moisture from Southern Great Plains experiment (SGP99). The results show that the relative sensitivity of the b-factor is 86% in wet soil condition and 88% in high vegetated condition compared to the sensitivity of the soil moisture. Apparently, the b-factor is found to be more sensitive than the vegetation water content, surface roughness and surface temperature; therefore, the effect of the b-factor is fairly large to the microwave emission in certain conditions. Understanding the dependence of the b-factor on the soil and vegetation is important in studying the soil moisture retrieval algorithm, which can lead to potential improvements in model development for the Soil Moisture Active-Passive (SMAP) mission.

Soil Moisture Retrieval from Radarsat Data: A Neuro-Fuzzy Approach

The spatial dynamic of soil moisture is generally affected by the variation in soil surface characteristics such as: land cover, vegetation density, soil texture, and soil material. The mapping of soil moisture by remote sensing tools has several advantages over the conventional field measurement techniques especially in the case of heterogeneous landscapes. The use of microwave remote sensing offers fast and reliable ways in mapping the spatial distribution of soil moisture. Microwave remote sensing systems is used to measure soil moisture on the basis of a large contrast that exists between the dielectric constant values for dry and wet soils. This study describes the use of non-parametric classifiers for the soil moisture retrieval from active microwave remote sensing data. The difficulty facing the soil moisture retrieval process is due, in large part, to the lack of a precise mathematical description of the observed land cover parameters and to the extent of their variability. Two non-parametric techniques have been used: Neural Networks and Fuzzy Logic. Different configurations of these two classifiers have been tested and compared by assessing the accuracy of soil moisture.

Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal

Global change affects vegetation cover and processes through multiple pathways. Long time series of surface land surface properties derived from satellite remote sensing give unique abilities to observe these changes, particularly in areas with complex topography and limited research infrastructure. Here, we focus on Nepal, a biodiversity hotspot where vegetation productivity is limited by moisture availability (dominated by a summer monsoon) at lower elevations and by temperature at high elevations. We analyze the normalized difference vegetation index (NDVI) from 1981 to 2015 semimonthly, at an 8 km spatial resolution. We use a random forest (RF) of regression trees to generate a statistical model of the NDVI as a function of elevation, land use, CO 2 level, temperature, and precipitation. We find that the NDVI increased over the studied period, particularly at low and middle elevations and during the fall (post-monsoon). We infer from the fitted RF model that the NDVI linear trend is primarily due to CO 2 level (or another environmental parameter that is changing quasi-linearly), and not primarily due to temperature or precipitation trends. On the other hand, interannual fluctuation in the NDVI is more correlated with temperature and precipitation. The RF accurately fits the available data and shows promise for estimating trends and testing hypotheses about their causes.

The Effect of Vegetation Cover on Snow Cover Mapping from Passive Microwave Data

Snow-cover parameters are being increasingly used as inputs to hydrological models. Having an accurate estimation of the snow cover characteristics during the snowmelt season is indispensable for an efficient hydrological modeling and for an improved snowmelt runoff forecasts. In this paper, we used an adaptive neural network system to generate the spatial distribution of snow accumulation from multi-channel SSM/I data in the Northern Midwest of the United States. Five SSM/I channels were used in this experiment (19H, 19V, 22V, 37V, and 85V). Three snow days with high snow accumulation and no precipitation have been selected during the 2001/2002 winter season to train and test the neural network system. Snow depth measurements have been collected from the National Climatic Data Center (NCDC) through the Cooperative Observer Network for the U.S. snow Monitoring. The snow depths have been compiled and gridded into 25 km times 25 km grid to match the final SSM/I resolution. Different vegetation-related parameters (NDVI, optical depth, homogeneity) have been collected and gridded over the study area. The current results have shown a significant effect of vegetation cover properties on the mapping accuracy. Furthermore, the addition of vegetation related information to the mapping process has shown to have a positive impact on mapping performance, especially for areas with shallow snow cover (less than 5 cm)

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

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.