Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression with a Pre-Classification Approach

Global Navigation Satellite System-Reflectometry (GNSS-R) can retrieve Earth's surface parameters, such as soil moisture (SM) using the reflected signals from GNSS constellations with advantages of non-contact, all-weather, real-time, and continuity, particularly the space-borne Cyclone GNSS (CYGNSS) mission. However, the accuracy and efficiency of SM estimation from CYGNSS still need to improve. In this paper, the global SM is estimated using Machine Learning (ML) regression aided by a pre-classification strategy. The total observations are classified by land types and corresponding subsets are built for constructing ML regression submodels. Ten-fold cross-validation technique is adopted. The overall performance of SM estimation with/without pre-classification are compared, and the results show that the SM estimations using different ML algorithms all have substantial improvement with the pre-classification strategy. Then the optimal XGBoost predicted model with root mean square error (RMSE) of 0.052 cm3/cm3 is adopted. In addition, the satisfactory daily and seasonal SM prediction outcomes with an overall correlation coefficient value of 0.86, and an RMSE value of 0.056 cm3/cm3 are achieved at a global scale, respectively. Furthermore, the extensive temporal and spatial variations of CYGNSS SM predictions are evaluated. It shows that the reflectivity plays a main role among the predictors in SM estimation, and the next is vegetation. In some extremely dry places, the roughness may become more important. The value of SM is positively correlated with RMSE and also another limit condition that will constrain the variation of predictors, thus affecting correlation coefficient R and RMSE. Also, we compare both SMAP and CYGNSS SM predictions against in situ SM measurements from 301 stations. Similar low median unbiased RMSEs are obtained, and the daily averaged CYGNSS-based SM against the in situ networks is 0.049 cm3/cm3. The presented approach succeeds in providing SM estimation at a global scale with employing the least ancillary data with superior results and this paper reveals the spatio-temporal heterogeneity for SM estimation using CYGNSS data

Parameter considerations for the retrieval of surface soil moisture from spaceborne GNSS-R

The Microwave Interferometric Reflectometer (MIR) is an airborne GNSS-R instrument developed by Universitat Politècnica de Catalunya. In 2018, it was flown twice over the agricultural Yanco area, New South Wales, Australia, once after a very dry period, and a further time the day after a strong rain event. This rain event resulted in many crop fields being entirely flooded, producing a saturation in the GNSS-R reflectivity value. In this work, the received data set is processed to identify the optimum integration time with the goal to minimize pixel blurring. This issue is assessed for airborne conditions, and then extra-polated to the spaceborne case. The presented results show that the blurring of the GNSS waveform is produced even from an airborne sensor with short integration times. Following the determination of an optimal integration time for the platform in use, the surface roughness term in the reflectivity equation can be isolated due to the signal saturation during very wet surface conditions. The final results from the two channels (L1 C/A and L5) are subsequently presented. In this case, it is shown that most reflectivity variations in GNSS-R measurements are linked to surface roughness and Speckle noise fluctuations rather than soil moisture changes.Postprint (updated version

Soil moisture estimation synergy using GNSS-R and L-Band microwave radiometry data from FSSCat/FMPL-2

The Federated Satellite System mission (FSSCat) was the winner of the 2017 Copernicus Masters Competition and the first Copernicus third-party mission based on CubeSats. One of FSSCat’s objectives is to provide coarse Soil Moisture (SM) estimations by means of passive microwave measurements collected by Flexible Microwave Payload-2 (FMPL-2). This payload is a novel CubeSat based instrument combining an L1/E1 Global Navigation Satellite Systems-Reflectometer (GNSS-R) and an L-band Microwave Radiometer (MWR) using software-defined radio. This work presents the first results over land of the first two months of operations after the commissioning phase, from 1 October to 4 December 2020. Four neural network algorithms are implemented and analyzed in terms of different sets of input features to yield maps of SM content over the Northern Hemisphere (latitudes above 45° N). The first algorithm uses the surface skin temperature from the European Centre of Medium-Range Weather Forecast (ECMWF) in conjunction with the 16 day averaged Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate SM and to use it as a comparison dataset for evaluating the additional models. A second approach is implemented to retrieve SM, which complements the first model using FMPL-2 L-band MWR antenna temperature measurements, showing a better performance than in the first case. The error standard deviation of this model referred to the Soil Moisture and Ocean Salinity (SMOS) SM product gridded at 36 km is 0.074 m3/m3. The third algorithm proposes a new approach to retrieve SM using FMPL-2 GNSS-R data. The mean and standard deviation of the GNSS-R reflectivity are obtained by averaging consecutive observations based on a sliding window and are further included as additional input features to the network. The model output shows an accurate SM estimation compared to a 9 km SMOS SM product, with an error of 0.087 m3/m3. Finally, a fourth model combines MWR and GNSS-R data and outperforms the previous approaches, with an error of just 0.063 m3/m3. These results demonstrate the capabilities of FMPL-2 to provide SM estimates over land with a good agreement with respect to SMOS SM.This work was supported by the 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). This work was (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21 / AEI / 10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work was also (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). Joan Francesc Munoz-Martin received support from the grant for the recruitment of early-stage research staff FI-DGR 2018 of the AGAUR - Generalitat de Catalunya (FEDER), Spain; Christoph Herbert received the support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050 and funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 713673; David Llavería received support from an FPU fellowship from the Spanish Ministry of Education FPU18/06107.Peer ReviewedPostprint (published version

Information retrieval from spaceborne GNSS Reflectometry observations using physics- and learning-based techniques

This dissertation proposes a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The proposed methodology has been built upon the literature review, analyses, and findings from a number of published studies throughout the dissertation research. Namely, a Sig- nals of Opportunity Coherent Bistatic scattering model (SCoBi) has been first developed at MSU and then its simulator has been open-sourced. Simulated GNSS-Reflectometry (GNSS-R) analyses have been conducted by using SCoBi. Significant findings have been noted such that (1) Although the dominance of either the coherent reflections or incoher- ent scattering over land is a debate, we demonstrated that coherent reflections are stronger for flat and smooth surfaces covered by low-to-moderate vegetation canopy; (2) The influ- ence of several land geophysical parameters such as SM, vegetation water content (VWC), and surface roughness on the bistatic reflectivity was quantified, the dynamic ranges of reflectivity changes due to SM and VWC are much higher than the changes due to the surface roughness. Such findings of these analyses, combined with a comprehensive lit- erature survey, have led to the present inversion algorithm: Physics- and learning-based retrieval of soil moisture information from space-borne GNSS-R measurements that are taken by NASA’s CYGNSS mission. The study is the first work that proposes a machine learning-based, non-parametric, and non-linear regression algorithm for CYGNSS-based soil moisture estimation. The results over point-scale soil moisture observations demon- strate promising performance for applicability to large scales. Potential future work will be extension of the methodology to global scales by training the model with larger and diverse data sets

An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data

Recently, soil moisture (SM) has been estimated using Cyclone Global Navigation Satellite System (CYGNSS) data. Machine learning (ML) algorithms for CYGNSS SM estimation can minimize unpredictable influences and help improve the accuracy of SM retrieval. However, ML-based CYGNSS SM estimation requires ancillary data from other sources, and thus, the uncertainty, internal errors, and even dependence on external parameters of this process may complicate and limit SM estimation. In this article, a simple land type (LT) digitization strategy that incorporates the idea of classification is proposed with feature optimization to achieve an effective and independent SM retrieval without any other auxiliary data. The input features are chosen from the CYGNSS data themselves, and the corresponding labels (digitized stable LTs) are used in the training stage of the SM estimation model. During the fine-tuning stage, several input features (such as the dielectric constant and incident angle) are compared and selected after optimization to achieve better results. Moreover, the CYGNSS data are gridded at 9 × 9 km to validate the enhanced soil moisture active passive mission SM products at a resolution of 9 km. Only three input variables are adopted for the SM learning model, which are directly derived from the CYGNSS data for independently estimating SM at a high spatial resolution. Powerful performance is achieved by extreme gradient boosting based on a LT digitalization strategy, with root-mean-square error (RMSE) and unbiased RMSE (ubRMSE) values of 0.063 cm3/cm3 and a correlation coefficient (R) of 0.71 for the entire dataset. The performances of different ML learning models for various LTs are presented. The mean ubRMSE and RMSE are 0.041 cm3/cm3 and 0.057 cm3/cm3, respectively. The results demonstrate the effectiveness of the proposed LT digitization strategy for retrieving SM from CYGNSS data with various ML methods and the capability of SM estimation using the CYGNSS product as a new independent source

An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission

HydroGNSS (Hydrology using Global Navigation Satellite System reflections) has been selected as the second European Space Agency (ESA) Scout earth observation mission to demonstrate the capability of small satellites to deliver science. This article summarizes the case for HydroGNSS as developed during its system consolidation study. HydroGNSS is a high-value dual small satellite mission, which will prove new concepts and offer timely climate observations that supplement and complement the existing observations and are high in ESAs earth observation scientific priorities. The mission delivers the observations of four hydrological essential climate variables as defined by the global climate observing system using the new technique of GNSS reflectometry. These will cover the world's land mass to 25 km resolution, with a 15-day revisit. The variables are soil moisture, inundation or wetlands, freeze/thaw state, and above-ground biomass

A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture

Agricultural research is essential for increasing food production to meet the requirements of an increasing population in the coming decades. Recently, satellite technology has been improving rapidly and deep learning has seen much success in generic computer vision tasks and many application areas which presents an important opportunity to improve analysis of agricultural land. Here we present a systematic review of 150 studies to find the current uses of deep learning on satellite imagery for agricultural research. Although we identify 5 categories of agricultural monitoring tasks, the majority of the research interest is in crop segmentation and yield prediction. We found that, when used, modern deep learning methods consistently outperformed traditional machine learning across most tasks; the only exception was that Long Short-Term Memory (LSTM) Recurrent Neural Networks did not consistently outperform Random Forests (RF) for yield prediction. The reviewed studies have largely adopted methodologies from generic computer vision, except for one major omission: benchmark datasets are not utilised to evaluate models across studies, making it difficult to compare results. Additionally, some studies have specifically utilised the extra spectral resolution available in satellite imagery, but other divergent properties of satellite images - such as the hugely different scales of spatial patterns - are not being taken advantage of in the reviewed studies.Comment: 25 pages, 2 figures and lots of large tables. Supplementary materials section included here in main pd

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

Exploring Neural Networks For Predicting Sentinel-C Backscatter Between Image Acquisitions

Measuring moisture dynamics in soil and overlying vegetation is key to understanding ecosystem and agricultural dynamics in many contexts. For many applications, moisture information is demanded at high temporal frequency over large areas. Sentinel-1 C-band radar backscatter satellite images provide a repeating sequence of fine-resolution (10-m) observations that can be used to infer soil and vegetation moisture, but the 12-day interval between satellite observations is infrequent relative to the sensed moisture dynamics. Machine learning approaches have been used to predict soil moisture at higher spatial resolutions than the original satellite images, but little effort has been made to increase the temporal resolution of the images. This study extends machine learning approaches to infer fine-resolution backscatter between observations relying on auxiliary data observations, including elevation and daily gridded weather. Several variations of Multi-modal Fully Convolutional Neural Network architectures, problem setup, and training methods are explored for a predominantly rural area in southwest Oklahoma near the transition between humid subtropical and semiarid climates. The training area lies in the overlap zone for adjacent Sentinel-1 satellite tracks, allowing for training with several different temporal offsets. We find that the UNET architecture produced the most accurate and robust estimated backscatter patterns, with superior prediction compared to a prior observation baseline in nearly all cases investigated when geography was included in the training data. This superior performance also generalized to nearby areas when training data for a given geography was not available, where 86% of predictions performed superior compared to a prior observation baseline

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security