The Water Cloud Model (WCM) is a semi-empirical model widely used to simulate microwave backscattering from vegetated soil surfaces. Combined with a soil scattering model, the WCM is also employed to retrieve soil moisture content. However, the relatively simple conceptual structure of the WCM with a few model parameters may result in ill-posed calibration and poorly transferable soil moisture retrievals. In order to investigate the optimal parameter space and calibration uncertainty, we calibrated the WCM combined with a linear soil scattering model using a Markov Chain Monte Carlo (MCMC) approach and data from the SMAPVEX12 field campaign. With the validation dataset, we then retrieved the soil moisture using the optimal parameter values for VV, HH and VH polarisations. The results showed superior accuracy of VH- and HH-pol soil moisture retrievals over VV-pol retrievals in terms of the root mean squared error (RMSE) and Pearson’s correlation coefficient (R).

Agricultural field boundary information is an essential input for precision agriculture. This paper proposes a Multi-scale Multi-task Boundary Detection Deep Learning (DL) Network (MMBDNet) based on spatial attention mechanisms to delineate agricultural fields using high-resolution optical satellite imagery. The designed DL architecture simultaneously learns three tasks - a major task for field prediction and two auxiliary tasks for boundary prediction and distance estimation. We experimented with the agricultural landscape of Île-de-France, France, using the cloud-free time-series images from PlanetScope satellite that capture key phenological stages of crops. The segmentation results from different months are combined and post-processed using hierarchical watershed segmentation to extract field instances. We compared the MMBDNet with the baseline single-task U-Net and multitask BsiNet models at pixel- and object-level. Our results show that the MMBDNet has the highest pixel-level (above 85%) and object-level (above 70%) accuracy compared to U-Net and BsiNet.

Monitoring vegetation water content (VWC) over large spatial extents has become an important part of healthy ecosystem management, drought & wild fire risk assessment and precision agriculture. In this work we use two currently operational satellite synthetical aperture radar (SAR) systems, X-band KOMPSAT 5 and C-band Sentinel 1, to retrieve radar vegetation index (RVI) values over a wheat cropping field located in the state of Victoria, Australia. One quad-polarized RVI (KOMPSAT 5) and two types of dual-polarized RVIs (Sentinel 1 and KOMPSAT 5) were compared with the Normalized Difference Water Index (NDWI) derived Sentinel 2 as an indicator of VWC. The efficacy of RVI values in estimating VWC over wheat cropping field was examined at four different growth stages in May (sowing), June (Tillering), July (Stem Extension), and September (Heading) in 2019. Our results show that, for both Sentinel-1 and KOMPSAT-5, RVIs tested have a moderate skill to predict VWC when the comparison and evaluation were performed over multiple crop growth stages, but they present poor skill to predict VWC for a single snapshot of NDWI maps. This is in part due to the small range of VWC variation within the experimental field for each snapshot map. Quad-pol RVI outperformed dual-pol RVIs in predicting NDWI in multi-temporal application. However, all examined RVIs exhibited saturation for high NDWI conditions (NDWI > 0.5). RVI values also presented widely scattered variation for all examined growth stages. Sub-footprint-scale heterogeneity of the surface roughness and biomass, along with inherent noise of SAR, may contribute to high scatter of RVIs at individual snapshot level mapping.

In-season crop type mapping can assist in early yield estimation, however, such data are not widely available. Currently available crop type maps mostly rely on either optical imagery or synthetic aperture radar (SAR), but there is a growing number of research that demonstrates the potential of synergistic optical and SAR data fusion. This research investigates the performance of machine learning approaches that account for both optical and SAR features to generate in-season crop type maps. Classification performance of three supervised machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were tested. Experimental results demonstrate that the best performance for the in-season classification of corn and soybeans was obtained four months after the sowing (April – July) from the fusion of optical (Sentinel-2) and SAR (Sentinel-1) images. The in-season classification from SVM and RF demonstrated 81.2 % (overall accuracy) agreement with ground truth.

In this paper, we present a methodology for retrieving soil moisture using radar-derived vegetation parameters from multi-polarization SAR data. The semi-empirical Water Cloud Model (WCM) is used to retrieve soil moisture in wheat cropping fields. The ground and airborne data collected during the Soil Moisture Active Passive Validation EXperiment 2012 (SMAPVEX12) are used for this study. In a comparison of the two vegetation descriptors used in the WCM, the Radar Vegetation Index (RVI) and Dual-pol Radar Vegetation Index (DpRVI), the DpRVI in VH polarisation provides greater retrieval accuracy with an RMSE of 0.047 m3m−3and Pearson’s correlation coefficient (R) of 0.83. Our results also indicate that HH polarization outperforms the VV polarization for the wheat crop.

Among all essential nutrients, nitrogen (N) is required by plants in large quantities throughout the entire developmental process. This is due to its importance for plant growth and development and as a primary source of energy for photosynthesis. Previous research has demonstrated that solar-induced chlorophyll fluorescence (SIF) coupled with chlorophyll a+b content (Cab) improved the estimation of leaf N, outperforming standard vegetation indices. The present study investigates the contribution of leaf Cx, a measure of the de-epoxidation state of the xanthophyll cycle, for explaining leaf N variability, concluding that it ranks third after Caband SIF consistently over two growing seasons. Among the rest of the biochemical constituents estimated by model inversion, Cxcontributed more than anthocyanins (Anth), the total carotenoid content (Ccar), and crown-level structural traits.

This study evaluate the performance of a Wheat Canopy Scattering Model (WCSM) at L-band, which was initially developed to simulate the backscatter of C-band Synthetic Aperture Radar (SAR), using the L-band UAVSAR data and ground-based measurements of soil moisture, soil surface roughness and crop parameters collected from wheat fields during the SMAPVEX12 field campaign. Results show that WCSM is capable of estimating HH-pol backscatter with an error less than 2.48 dB. On the other hand, relatively large RMSE of 4.38 dB and 4.34 dB were observed for VV and VH backscatter coefficients, respectively. Furthermore, it was observed that model tends to overestimate VV backscatter. It is also observed that co-pol total backscatter from a wheat canopy is sensitive to incidence angle followed by root mean square (RMS) height and soil moisture while cross-pol backscatter showing high sensitivity to wheat crop biophysical parameters.

Traditional methods to estimate leaf nitrogen (N) from satellite imagery rely on structural and chlorophyll$a+b\,(\mathrm{C}_{\text{ab}})$vegetation indices. Recent progress with airborne hyperspectral imagery identified Cab and SIF as critical indicators for evaluating leaf N variability, yielding superior performance than standard vegetation indices. In tree orchards, accurate physiological assessments require high-spatial-resolution hyperspectral imagery to minimize canopy architecture and soil background effects. Understanding the potential of coarse-spatial-resolution spaceborne hyperspectral imagery for leaf N estimation is critical. In this study, DESIS hyperspectral imagery collected on board the International Space Station was used to assess the quantification of leaf N, evaluating the relative contributions of physiological plant traits and SIF. High-resolution airborne hyperspectral imagery and ground N data were used for validation. Results show that Cab and SIF were the most critical parameters explaining leaf N both from DESIS and from airborne hyperspectral imagery, yielding strong correlations against ground truth N data ($r^{2}=0.90, p < 0.0001$) and with airborne-predicted$\mathrm{N}\,(r^{2}=0.75, p < 0.0001)$.

Standard remote sensing methods for nitrogen (N) assessment in precision agriculture rely on empirical relationships built with chlorophyll a+b (Cab) sensitive vegetation indices. Nevertheless, methods of N estimation based on the Cab vs. N relationships are strongly affected by the saturation of these indices at high N levels, and by canopy structure, shadows and soil background variability. These effects are even more pronounced in heterogeneous orchards where the tree crown structural variability is a major factor that limits the transferability of the algorithms within- and across-tree crop species. Solar-induced fluorescence (SIF) has been proposed in precision agriculture as a plant functional trait related to N due to its link with photosynthesis. However, retrieving SIF from orchards is challenging due to the mixture of sunlit and shaded crown components. The present study explored the retrieval of airborne SIF in almond orchards from hyperspectral imagery, assessing its contribution to the estimation of N. Results show that the assessment of N improved when SIF was coupled to the model estimated Cab (e.g., Cab+SIF; r2=0.95) as compared with using Cab alone (r2=0.87).

The Fifth KOrea Multi-Purpose SATellite (KOMPSAT-5) is the first X-band (9.66 GHz) Synthetic Aperture Radar (SAR) mission of Korea that has been operational since its launch on August 22, 2013. It has been administered and managed by the Korea Aerospace Research Institute (KARI) from the initial design, development and building to calibration/validation and the subsequent normal operation. Primary aims of the KOMPSAT-5 mission are to extend KARI's existing capability of Earth observation via optical satellites to the all-day and all-weather conditions, and to meet a range of advanced remote sensing needs in general Geographical Information System (GIS) survey and monitoring the ocean, land, ice/glacier, disaster and environment. Besides the primary aims, the KOMPSAT-5 images have been distributed to international organizations such as the International Charter since 2011 and AOGEO (Asia-Oceania Group on Earth Observation) since 2019. The KOMPSAT-5, is still operating normally beyond the original design mission operation. Imaging modes had been enhanced and added to existing modes. The system is also operating orbit maintenance for the InSAR application and the output sigma naught ( σ0) have been used for multitemporal SAR images analysis. This paper introduces overall operation, acquisition, utilization and application related to the status of the KOMPSAT-5 SAR mission. We also discuss the ways to facilitate a wider and active adoption of the KOMPSAT-5 and introduce the future continuation mission, KOMPSAT-6, that KARI is developing as her second SAR mission.

X-band microwave data from a Synthetic Aperture Radar (SAR) satellite, KOMPSAT-5, was used to produce high-resolution (3 m) surface soil moisture maps over agricultural fields in Victoria, Australia during the winter cropping season of 2019. Primary aim of the experiment was to evaluate the X-band SAR soil moisture for agricultural fields at different crop growth stages. Three paddocks of approximately 100 ha planted with dryland wheat were chosen as experimental fields where ground samples for calibration and validation were collected over five field campaigns scheduled to capture the major growth stages of wheat. The Water-Cloud Model (WCM) was used to delineate the at-surface scattering coefficient into vegetation and soil surface components, then a linear model associating the surface soil moisture directly to the soil scattering was combined to retrieve soil moisture from the surface scattering coefficient. The retrieved X-band soil moisture content in comparison with the ground-based soil moisture measurement exhibited accuracy in the range of 0.025-0.058 m3m-3for the Root Mean Squared Error (RMSE), and 0.22-0.52 for the coefficient of determination (R2). The overall retrievals presented the consistent accuracy for both the pre-sowing bare soil and vegetated conditions, however, the poor performance in July (Stem Extension stage) was attributed to the rainfall event occurred during the ground sampling.

KOMPSAT-5 is an X-band satellite that has a great potential for high-resolution soil moisture retrieval over agricultural regions. In the present paper, comparison between the soil moisture estimations from the widely used Sentinel-1 (C-band) and KOMPSAT-5 (X-band) is conducted for a dryland wheat field site located in Victoria State of Australia. This study uses a semi-empirical vegetation scattering model to invert radar signal and retrieve soil moisture based on KOMPSAT-5 and Sentinel-1 images and the retrieval model is calibrated by field measurements. In this model, normalized difference water index (NDWI) is applied for the parameterization of vegetation water content and the estimation of volume scattering from vegetation canopy. The results demonstrated that the accuracy of soil moisture retrieved from both satellite instruments was consistent with the field measurements, with the root mean square error (RMSE) reached 0.016 cm3/cm3at X-band and 0.015 cm3/cm3at C-band, and the correlation coefficients (R) were 0.726 and 0.517, respectively. In addition, the validating results showed that the X-band provided slightly consistent accurate soil moisture retrieval in comparison with the C-band, with the RMSE of 0.017 cm3/cm3. Consequently, this study indicated the prospect and applicability of estimating soil moisture retrieved from both KOMPSAT-5 and Sentinel-1 remote sensing data and could also provide support and reference for other crop yield estimation effectively.

This paper investigated the sensitivity of passive microwave L-band soil moisture (SM) retrieval from multiangle airborne brightness temperature data obtained under morning and afternoon conditions from the National Airborne Field Experiment conducted in southeast Australia in 2006. Ground measurements at a dryland focus farm including soil texture, soil temperature, and vegetation water content were used as ancillary data to drive the retrieval model. The derived SM was then in turn evaluated with the ground-measured near-surface SM patterns. The results of this paper show that the Soil Moisture and Ocean Salinity target accuracy of 0.04 m3·m-3for single-SM retrievals is achievable irrespective of the 6 A.M. and 6 P.M. overpass acquisition times for moisture conditions ≤0.15 m3·m-3. Additional tests on the use of the air temperature as proxy for the vegetation temperature also showed no preference for the acquisition time. The performance of multiparameter retrievals of SM and an additional parameter proved to be satisfactory for SM modeling-independent of the acquisition time-with root-mean-square errors less than 0.06 m3·m-3for the focus farm.

For Earth observation data to be useful for a wide range of land surface applications, a kilometer or finer resolution is required. Unfortunately, passive microwave observations at low microwave frequencies (1-10 GHz), already providing important information on soil moisture and vegetation dynamics, are generally only available at a resolution of tens of kilometers. This letter presents a new downscaling method relating L-band radiometer and C-band radar observations for downscaling purposes. The data were obtained from two extensive airborne field experiments across a 80 000-km2catchment in south-eastern Australia and coinciding Envisat Advanced Synthetic Aperture Radar acquisitions, performed during the Austral summer and spring of 2010. The novel approach of this study is in the downscaling of coarse-scale emissivities as observed by the radiometer with a new interpretation of the change detection methodology for the radar signal to relate the spatiotemporal changes of those two types of observations at 1 km. It is shown that, for most land surface conditions, a good spatial representation at high resolution is achieved, without considering land surface specific parameterizations, which is promising for using very high resolution radar data from the Sentinel-1 platform for downscaling of passive microwave data from current missions, such as National Aeronautics and Space Administration's Soil Moisture Active Passive and European Space Agency's SMOS.

NASA's Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil moisture products: i) high-resolution from radar (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP mission. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5-10, 2010), SMAPEx-2 (December 4-8, 2010) and SMAPEx-3 (September 5-23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature (~1 km) and radar backscatter (~10 m) observations were collected over an area the size of a single SMAP footprint (38 km × 36 km at 35° latitude) with a 2-3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiometer-radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0-5 cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.

Once launched in late 2014, NASA's Soil Moisture Active Passive (SMAP) mission will use a combination of a four-channel L-band radiometer and a three-channel L-band radar to provide high resolution global mapping of soil moisture and landscape freeze/thaw state every 2-3 days. These measurements are valuable to improved understanding of the Earth's water, energy, and carbon cycles, and to many applications of societal benefit. In order for soil moisture and freeze/thaw to be retrieved accurately from SMAP microwave data, prelaunch activities are concentrating on developing improved geophysical retrieval algorithms for each of the SMAP baseline products using data from simulations, from existing satellite missions such as SMOS, and from field campaign data, such as the SMAPEx airborne study in Australia discussed in this paper.

The recently launched Soil Moisture and Ocean Salinity (SMOS) satellite is providing soil moisture observations at continental scales by measuring L-band microwave radiation emitted from the land surface. While its retrieval algorithms will correct for factors such as vegetation and surface roughness, it will not correct for soil salinity. This letter tests the assumption that soil salinity will have a negligible impact on L-band brightness temperature (Tb) at SMOS scales using field data; airborneTbobservations were collected in a saline groundwater discharge area near Nilpinna Station, South Australia. At the 500-m scale, the airborne observations ofTbcould not be reproduced using the baseline algorithm of the SMOS Level 2 retrieval scheme, without accounting for soil salinity in the model. The analysis in this letter shows that soil moisture retrieval errors of at least 0.04 m3m-3(i.e., the entire SMOS error budget) will occur due to salinity alone in SMOS footprints with saline coverage as low as 25% (possibly even much less). Consequently, fractional salinity coverage cannot be considered a negligible factor by microwave soil moisture satellite missions.

The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m3/m3. Further analysis found the following: 1) The roughness valueHRwas too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameterstthandttvrequired optimization, yielding new values oftth= 0.2 andttv= 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.

Surface soil moisture was retrieved from the L-band radiometer data collected in semiarid regions during the Soil Moisture Experiment in 2004. The 2-D synthetic aperture radiometer (2D-STAR) was flown over regional-scale study sites located in AZ, USA, and Sonora, Mexico (SO). The study sites are characterized by a range of topographic relief with a land cover that varies from bare soil to grass and scrubland and includes areas with high rock fraction near the soil surface. The 2D-STAR retrieval of soil moisture was in good agreement with the ground-based estimates of surface soil moisture in both AZ (raise = 0.012 m3m-3) and SO (rmse = 0.011 m3m-3). The 2D-STAR also showed a good performance in the Walnut Gulch Experimental Watershed (rmse = 0.014 m3m-3) where the surface soil featured high rock fraction was as high as 60%. Comparison of the results with the Polarimetric Scanning Radiometer at the Cand X-band data indicates the superior soil moisture retrieval performance of the L-band data over the regions with high rock fraction and moderate vegetation density.

Soil moisture retrievals made using data from the airborne L-band microwave radiometer, 2D-STAR, over a wide range of land cover types are presented. The 2D-STAR was flown over six regional-scale sites during Soil Moisture Experiments in 2003 and 2004. Four sites located in Alabama, Georgia, Arizona, and Sonora were selected for this work. Land cover types included bare soil, bare soil with gravelly surface, shrub, crop field, and forest. Topographic conditions varied from flat or gently rolling plains to high-relief hilly or mountainous area. Results indicate fairly good soil moisture retrieval performance of the 2D-STAR over the various land cover types and moisture conditions (overall RSME=0.22 m3/m3). The 2D-STAR also showed improved soil moisture retrieval over a C- and X-band microwave instrument (PSR-C/X) for densely vegetated areas and gravelly soil surfaces.

Microwave radiometry at low frequencies (L-band, ~ 1.4 GHz) has been known as an optimal solution for remote- sensing of soil moisture. However, the antenna size required to achieve an appropriate resolution from space has limited the development of spaceborne L-band radiometers. This problem can be addressed by interferometric technology called aperture synthesis. The Soil Moisture and Ocean Salinity (SMOS) mission will apply this technique to monitor global-scale surface parameters in the near future. The first airborne experiment using an aircraft prototype of this approach, the Two-Dimensional Synthetic Aperture Radiometer (2D-STAR), was performed in the Soil Moisture Experiment in 2003 (SMEX03). The L-band brightness temperature data acquired in Alabama by the ID- STAR was compared with ground-based measurements of soil moisture and with C-band data collected by the Polarimetric Scanning Radiometer (PSR). Our results demonstrate a good response of the 2D-STAR brightness temperature to changes in surface wetness, both in agricultural and forest lands. The behavior of the horizontally polarized brightness temperature data with increasing view-angle over the forest area was noticeably different than over bare soil. The results from the comparison of 2D-STAR and PSR indicate a better response of the 2D-STAR to the surface wetness under both wet and dry conditions. Our results have important implications for the performance of the future SMOS mission.