SMAP Mission Status and Plan

The prime mission phase of National Aeronautics Space Administration's (NASA's) Soil Moisture Active Passive (SMAP) project was successfully completed in June 2018. The extended phase has been approved by NASA for operation through 2021 (with option to 2023). SMAP data have been well calibrated and have enabled diverse scientific investigations in water, energy and carbon cycle research, terrestrial ecology and ocean science. This paper will provide the highlights of algorithm updates to radiometric calibration and soil moisture retrieval algorithms. A summary of extended phase activities, in particular the SMAPVEX19 campaign, for product enhancements will be provided

Synergistic integration of optical and microwave satellite data for crop yield estimation

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regressionand its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R 2 ≥ 0.76; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation

The Evaluation of the Additive Information Contained in New Ecohydrological Measurements

The development of ecohydrological frameworks and theories under the ongoing global climate crisis depends on the development of new and advanced ecohydrological measurements. Currently, numerous of datasets have been collected at plot and ecosystem levels to understand the complex interactions of along the soil, plant, and atmosphere continuum. The development of new measurements costs considerable effort, time, and funding. Therefore, it is important to quantify if new measurements contain useful information for ecohydrological studies, and how the information encoded in the new measurements are transferred to understand and predict key environmental variables. In this dissertation, we show that predictions in vegetation dynamics can be improved by adding observations from advanced satellite and in situ sensor systems. First, we evaluated new satellite soil moisture and vegetation optical depth measurements by Soil Moisture Passive Active (SMAP). We find that there is more opportunity for SMAP soil moisture and vegetation observations to be useful in locations where the daily vegetation climatology cannot adequately reflect observed vegetation dynamics. We also find that the uncertainties SMAP dual channel algorithm (DCA) are largely contributed by uncertainty of the algorithm’s inputs (horizontal and vertical polarized brightness temperature), while the informational uncertainty of the SMAP DCA model itself is more related to the retrieval quality of soil moisture. The informational redundancy and synergy of the two brightness temperature measurements from SMAP are tightly related to the SMAP soil moisture retrieval quality. Finally, we apply a similar informational analysis to new isotopic measurements at the National Ecological Observation Network (NEON). We find that majority of the information from the isotope measurements collected by NEON is unique, which cannot be obtained by other meteorological variables. Carbon isotope (δ13C) provides more additional information about LH in arid locations, while the water isotope (δ2H) provides more additional information about LH at locations with higher aridity, lower mean annual temperature, and lower mean site elevation. These studies show that informational analysis is useful to evaluated how additional information is encoded in new ecohydrological measurements

A Compact, High Resolution Hyperspectral Imager for Remote Sensing of Soil Moisture

Measurement of soil moisture content is a key challenge across a variety of fields, ranging from civil engineering through to defence and agriculture. While dedicated satellite platforms like SMAP and SMOS provide high spatial coverage, their low spatial resolution limits their application to larger regional studies. The advent of compact, high lift capacity UAVs has enabled small scale surveys of specific farmland cites. This thesis presents work on the development of a compact, high spatial and spectral resolution hyperspectral imager, designed for remote measurement of soil moisture content. The optical design of the system incorporates a bespoke freeform blazed diffraction grating, providing higher optical performance at a similar aperture to conventional Offner-Chrisp designs. The key challenges of UAV-borne hyperspectral imaging relate to using only solar illumination, with both intermittent cloud cover and atmospheric water absorption creating challenges in obtaining accurate reflectance measurements. A hardware based calibration channel for mitigating cloud cover effects is introduced, along with a comparison of methods for recovering soil moisture content from reflectance data under varying illumination conditions. The data processing pipeline required to process the raw pushbroom data into georectified images is also discussed. Finally, preliminary work on applying soil moisture techniques to leaf imaging are presented

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

MULTI-SENSOR ASSIMILATION OF AMSR-E SPECTRAL DIFFERENCES AND GRACE TERRESTRIAL WATER STORAGE RETRIEVALS TO IMPROVE MODELED SNOW ESTIMATES

Snow, a key component of terrestrial water storage (TWS) in many watersheds across the globe, is a significant contributor to the Earth’s hydrologic cycle, energy cycle, and climate system. This study explores multi-sensor, multi-variate data assimilation (DA) using synthetic Advanced Microwave Scanning Radiometer for EOS (AMSR-E) passive microwave (PMW) brightness temperature spectral differences ($\Delta$$T_b$) and synthetic Gravity Recovery and Climate Experiment (GRACE) TWS retrievals in order to improve estimates of snow water equivalent (SWE), subsurface water storage, and TWS over snow-covered terrain. A series of synthetic twin experiments are conducted using NASA Catchment land surface model as the prognostic model. AMSR-E $\Delta$$T_b$ DA using a support vector machine as the observation operator improves SWE estimates, but adds little value to subsurface storage estimates. A physically-informed GRACE TWS DA approach significantly enhances the TWS vertical resolution via discretization into SWE and subsurface components more accurately. When AMSR-E $\Delta$$T_b$ and GRACE TWS are assimilated simultaneously, dual assimilation significantly improves the SWE estimates with a 14.1\% reduction of RMSE (relative to the Open Loop without assimilation) and leads to the largest improvement in TWS estimates (RMSE = 66.4 mm) and most reliable subsurface water storage ensemble spread (spread-error ratio = 1.08) as compared to the single-sensor DA scenarios. However, dual DA does not always yield complementary updates, and can at times, lead to conflictory changes to SWE. That is, the assimilation of $\Delta$$T_b$ often generates positive SWE increments whereas assimilation of TWS often removes SWE in the dual DA system, which can ultimately degrade the posterior SWE estimates. This synthetic experiment provides valuable insight for future DA experiments merging real-world AMRS-E/AMSR-2 $\Delta$$T_b$ and GRACE/GRACE-FO TWS retrievals in order to better characterize terrestrial freshwater storage across regional and continental scales

A Deep Learning Framework in Selected Remote Sensing Applications

The main research topic is designing and implementing a deep learning framework applied to remote sensing. Remote sensing techniques and applications play a crucial role in observing the Earth evolution, especially nowadays, where the effects of climate change on our life is more and more evident. A considerable amount of data are daily acquired all over the Earth. Effective exploitation of this information requires the robustness, velocity and accuracy of deep learning. This emerging need inspired the choice of this topic. The conducted studies mainly focus on two European Space Agency (ESA) missions: Sentinel 1 and Sentinel 2. Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their open access policy. The increasing interest gained by these satellites in the research laboratory and applicative scenarios pushed us to utilize them in the considered framework. The combined use of Sentinel 1 and Sentinel 2 is crucial and very prominent in different contexts and different kinds of monitoring when the growing (or changing) dynamics are very rapid. Starting from this general framework, two specific research activities were identified and investigated, leading to the results presented in this dissertation. Both these studies can be placed in the context of data fusion. The first activity deals with a super-resolution framework to improve Sentinel 2 bands supplied at 20 meters up to 10 meters. Increasing the spatial resolution of these bands is of great interest in many remote sensing applications, particularly in monitoring vegetation, rivers, forests, and so on. The second topic of the deep learning framework has been applied to the multispectral Normalized Difference Vegetation Index (NDVI) extraction, and the semantic segmentation obtained fusing Sentinel 1 and S2 data. The S1 SAR data is of great importance for the quantity of information extracted in the context of monitoring wetlands, rivers and forests, and many other contexts. In both cases, the problem was addressed with deep learning techniques, and in both cases, very lean architectures were used, demonstrating that even without the availability of computing power, it is possible to obtain high-level results. The core of this framework is a Convolutional Neural Network (CNN). {CNNs have been successfully applied to many image processing problems, like super-resolution, pansharpening, classification, and others, because of several advantages such as (i) the capability to approximate complex non-linear functions, (ii) the ease of training that allows to avoid time-consuming handcraft filter design, (iii) the parallel computational architecture. Even if a large amount of "labelled" data is required for training, the CNN performances pushed me to this architectural choice.} In our S1 and S2 integration task, we have faced and overcome the problem of manually labelled data with an approach based on integrating these two different sensors. Therefore, apart from the investigation in Sentinel-1 and Sentinel-2 integration, the main contribution in both cases of these works is, in particular, the possibility of designing a CNN-based solution that can be distinguished by its lightness from a computational point of view and consequent substantial saving of time compared to more complex deep learning state-of-the-art solutions

Deep Learning based data-fusion methods for remote sensing applications

In the last years, an increasing number of remote sensing sensors have been launched to orbit around the Earth, with a continuously growing production of massive data, that are useful for a large number of monitoring applications, especially for the monitoring task. Despite modern optical sensors provide rich spectral information about Earth's surface, at very high resolution, they are weather-sensitive. On the other hand, SAR images are always available also in presence of clouds and are almost weather-insensitive, as well as daynight available, but they do not provide a rich spectral information and are severely affected by speckle "noise" that make difficult the information extraction. For the above reasons it is worth and challenging to fuse data provided by different sources and/or acquired at different times, in order to leverage on their diversity and complementarity to retrieve the target information. Motivated by the success of the employment of Deep Learning methods in many image processing tasks, in this thesis it has been faced different typical remote sensing data-fusion problems by means of suitably designed Convolutional Neural Networks