Active Microwave Data Research Articles

Characterization of the Moon’s South polar craters using optical, microwave and thermal remote sensing

The peculiar illumination and insolation conditions at the poles of the Moon lead to the formation of Permanently Shadowed Regions (PSR) that act as cold traps for volatiles and are important for In Situ Resource Utilization (ISRU). However, since these regions lie in shadows of surrounding topographic highs, the ability of optical remote sensing in imaging these regions for photo reconnaissance is limited and using other modes of remote sensing becomes imperative. In this work, reflectance from optical data, backscatter from active microwave data and temperature from thermal data from the surfaces of three craters – de Gerlache, Haworth and Shoemaker – on the South Pole of the Moon, in close proximity to the Artemis III candidate landing sites, has been studied. Images from Clementine UVVIS, LRO WAC and LRO NAC have been compared with fully polarimetric data from the L-bands sensor of Chandrayaan-2 DFSAR. Further, the temperatures at the surface of the craters have been studied using data from LRO DIVINER. Since large parts of the craters under study lie in permanent shadows, Clementine UVVIS and LRO WAC could image only some parts of the rims. LRO NAC, with its longer exposure times, could image some parts within the crater however the walls of the crater and the floor near the walls remained in shadow. DFSAR was found to be useful in viewing the permanently shadowed regions of the craters. Overall, the backscatter in HH and VV (co-pol) channels was higher than the backscatter in HV and VH (cross-pol) channels. The elevated rims of the craters were clearly visible in co-pol channels and the rough texture of the ejecta of the craters had a distinct high backscatter in cross-pol channels. This fully polarimetric data can be used to map the interiors of the craters where optical data has low albedo. Analysis of the thermal data revealed that within the craters, in the regions that were in perpetual shadows, the maximum summer temperatures were less than 150 K making these regions cold traps for volatiles and possible locations of surface water–ice. The parts of craters lying in the shadows of surrounding higher features had lower maximum temperatures in summer and the parts facing them had higher maximum temperatures due to reflected insolation. Seleno Referenced Image (SRI) product from Chandrayaan-2 DFSAR was found to be a viable alternative to optical data for viewing the PSRs and for their geologic mapping.

A 41-year (1979–2019) passive-microwave-derived lake ice phenology data record of the Northern Hemisphere

Abstract. Seasonal ice cover is one of the important attributes of lakes in middle- and high-latitude regions. The annual freeze-up and breakup dates as well as the duration of ice cover (i.e., lake ice phenology) are sensitive to the weather and climate; hence, they can be used as an indicator of climate variability and change. In addition to optical, active microwave, and raw passive microwave data that can provide daily observations, the Calibrated Enhanced-Resolution Brightness Temperature (CETB) dataset available from the National Snow and Ice Data Center (NSIDC) provides an alternate source of passive microwave brightness temperature (TB) measurements for the determination of lake ice phenology on a 3.125 km grid. This study used Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) data from the CETB dataset to extract the ice phenology for 56 lakes across the Northern Hemisphere from 1979 to 2019. According to the differences in TB between lake ice and open water, a threshold algorithm based on the moving t test method was applied to determine the lake ice status for grids located at least 6.25 km away from the lake shore, and the ice phenology dates for each lake were then extracted. When ice phenology could be extracted from more than one satellite over overlapping periods, results from the satellite offering the largest number of observations were prioritized. The lake ice phenology results showed strong agreement with an existing product derived from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) data (2002 to 2015), with mean absolute errors of ice dates ranging from 2 to 4 d. Compared with near-shore in situ observations, the lake ice results, while different in terms of spatial coverage, still showed overall consistency. The produced lake ice record also displayed significant consistency when compared to a historical record of annual maximum ice cover of the Laurentian Great Lakes of North America. From 1979 to 2019, the average complete freezing duration and ice cover duration for lakes forming a complete ice cover on an annual basis were 153 and 161 d, respectively. The lake ice phenology dataset – a new climate data record (CDR) – will provide valuable information to the user community about the changing ice cover of lakes over the last 4 decades. The dataset is available at https://doi.org/10.1594/PANGAEA.937904 (Cai et al., 2021).

Arctic Sea Ice Type Classification by Combining CFOSCAT and AMSR‐2 Data

AbstractFirst‐year ice (FYI) and multi‐year ice (MYI) are the two most common ice types in the Arctic. In this article, the classification of FYI and MYI over the Arctic region in the winter of 2019/2020 and 2020/2021 is investigated by combining the data of the scatterometer on Chinese‐French Oceanography Satellite (CFOSCAT) and the Advanced Microwave Scanning Radiometer‐2 (AMSR‐2) based on the Tree Augmented Naive Bayes (TAN) classifier. The CFOSCAT/AMSR ice type classification results are validated by using Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, Canadian Ice Service ice charts, and synthetic‐aperture radar data. The results showed that the overall MYI extent change trend retrieved from CFOSCAT/AMSR was consistent with the OSI SAF product with a correlation coefficient of 0.89 for winter of 2019/2020 and 0.88 for 2020/2021. In addition, CFOSCAT/AMSR identified a slightly larger MYI extent than OSI SAF and the average deviation between them is 10.1% for 2019/2020 while 8.3% for 2020/2021. Besides, CFOSCAT/AMSR can identify more MYI pixels when the MYI concentration is relatively low in the Western Arctic region. We also used CFOSCAT data only to retrieve ice type and found that the active and passive microwave data fusion could capture more MYI pixels located near the boundary of MYI and FYI main body, and the introduction of AMSR‐2 data in ice type classification could reduce the error caused by the abnormal values of CFOSCAT parameters.

Freeze/thaw onset detection combining SMAP and ASCAT data over Alaska: A machine learning approach

In the context of global warming, permafrost degrades gradually. To cope with the instability of the cryosphere, it is very important to strengthen the monitoring of the seasonal freeze–thaw cycle. At present, active and passive microwave remote sensing data are widely used in freeze/thaw (F/T) onset detection. There is some potential to improve accuracy through the combination of active and passive microwave data. Compared with the traditional method for combination, the machine learning algorithm has a stronger nonlinear expression ability. Therefore, it is advisable to use machine learning to combine multi-source data for freeze/thaw onset detection. In this study, the temporal change detection method is applied to SMAP data and ASCAT data respectively for preliminary detection. Then the Random Forest algorithm (RF) is used to combine the preliminary results of active and passive microwave data with site observation to estimate the freeze/thaw onsets more accurately. The method is validated with data obtained in Alaska from 2015 to 2019. The accuracy evaluation shows that the proposed method can effectively improve the accuracy of freeze/thaw onset detection. The predicted distribution of the freeze/thaw cycle indicates that the variation of the freeze–thaw cycle is closely related to latitude. In general, the proposed method based on machine learning is promising in the research of freeze–thaw state monitoring.

A Calibration/Disaggregation Coupling Scheme for Retrieving Soil Moisture at High Spatio-Temporal Resolution: Synergy between SMAP Passive Microwave, MODIS/Landsat Optical/Thermal and Sentinel-1 Radar Data.

Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite

Vegetation optical depth (VOD), as a microwave-based vegetation index for vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. Currently, VOD is mainly retrieved from passive microwave data and few studies focused on VOD retrievals from active microwave data. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at Vertical-Vertical (VV) polarization. In this study, a new ASCAT INRAE Bordeaux (IB) VOD (hereafter, IB VOD), was developed based on the Water Cloud Model (WCM) coupled with the Ulaby linear model for soil backscattering. The main features of IB VOD are that (i) the ERA5-Land soil moisture (SM) dataset was used as an auxiliary SM dataset in the retrievals, (ii) pixel-based soil model parameters were mapped using Random Forest (RF), and (iii) the vegetation model parameter was calibrated for each day. The IB VOD product was retrieved over Africa during 2015–2019, and its performances were evaluated in space and time by comparing with aboveground biomass (AGB), lidar tree height (TH), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index (LAI). Results were inter-compared with three other VOD products at the same frequency. In terms of spatial correlation with AGB (R = 0.92) and TH (R = 0.89), IB VOD outperforms the other VOD products, suggesting IB VOD has a strong ability to capture spatial patterns of AGB and TH. By comparing all VOD products against NDVI, EVI and LAI, we found that the highest temporal correlation with NDVI (EVI, LAI) was obtained with IB VOD over 29.94% (36.65%, 30.19%) of the study region. Considering all three vegetations indices, highest temporal correlation values with IB VOD could be particularly noted for deciduous broadleaf forests, woody savannas and savannas.

A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province

Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatial-resolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data.

Urban Classification Using Multi-temporal Sentinel-1 Data Based On Coherence Characteristics

Classification urban features plays an important part in monitoring and development planning of the area. Optical remote sensing data is currently used in study land use/land cover. However, optical remote sensing data are affected by clouds and weather. Hence, it is difficult to update information. Sentinel-1 is the satellite mission which conducted by the European Space Agency (ESA). Sentinel-1 is composed of two satellites, Sentinel-1A and Sentinel-1B which carried C-band Synthetic Aperture Radar (SAR) instrument, 10m spatial resolution and provided free of charge. SAR images, which is an active microwave data, is not affected by weather, day and night. In this article, the authors present the experimental results of using coherence technique of two SAR images acquised at different times to classify urban features. The classification accuracy by using VV and VH polarization images were respectively 89% and 93%. VH polarization image data used in classification urban feature is better than VV polarization image.

Paddy area estimation in Nagapattinam district using sentinel-1A SAR data

A Research study was conducted to estimate the paddy area in the Nagapattinam district of Tamil Nadu using Sentinel 1A SAR data through multi-temporal feature extraction. Multi-temporal Sentinel 1A data of GRD data at VH polarizations were obtained for the study area at 12 days intervals. Collected data were processed using MAP scape-RICE software. Sentinel 1A is an active SAR microwave data, which captures the crop characteristics irrespective of the weather condition and illumination. Ground truth observations collected during cropping period were used to derive the paddy signature from the processed satellite images. The dB values extracted as signature then subjected to the multi-temporal feature extraction method for delineating the paddy growing areas. Around 99, 00, 700 ha was mapped as the paddy growing area in Nagapattinam district. Accuracy assessment was done with 40 percent of the ground truth data. The overall classification accuracy was 94 percent with a kappa score of 0.88.

MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery

The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change studies. Satellite-based Synthetic Aperture Radar (SAR) systems have shown advantages of retrieving this information. Operational ice mapping relies on visual analysis of SAR images along with ancillary data. However, these maps estimate ice types and concentrations within large-size polygons of a few tens or hundreds of kilometers, which are subjectively identified and selected by analysts. This study aims at developing an automated algorithm to identify individual MYI floes from SAR images then classify the rest of the image as FYI and other ice types. The algorithm identifies the MYI floes using extended-maximum operator, morphological image processing, and a few geometrical features. Classifying the rest of the image uses texture and neural network model. The input data is a set of Sentinel-1 A/B Extended Wide (EW) mode images, acquired between September and March 2016–2019. Although the overall accuracy (for all type classification) from the new method scored 93.26%, the accuracy from using the texture classifier only was 75.81%. The kappa coefficient from the former was higher than the latter by 0.25. Compared with the operational ice charts from the Canadian Ice Service, ice type maps from the new method show better distribution of MYI at the fine scale of individual floes. Comparison against MYI concentration from two automated algorithms that use a combination of coarse-resolution passive and active microwave data also confirms the advantage of resolving MYI floes from the fine-resolution SAR.

Improving the Estimation of Water Level over Freshwater Ice Cover using Altimetry Satellite Active and Passive Observations

Owing to its temporal resolution of 10-day and its polar orbit allowing several crossings over large lakes, the US National Aeronautics and Space Administration (NASA) and the French Centre National d’Etudes Spatiales (CNES) missions including Topex/Poseidon, Jason-1/2/3 demonstrated strong capabilities for the continuous and long-term monitoring (starting in 1992) of large and medium-sized water bodies. However, the presence of heterogeneous targets in the altimeter footprint, such as ice cover in boreal areas, remains a major issue to obtain estimates of water level over subarctic lakes of similar accuracy as over other inland water bodies using satellite altimetry (i.e., R ≥ 0.9 and RMSE ≤ 10 to 20 cm when compared to in-situ water stages). In this study, we aim to automatically identify the Jason-2 altimetry measurements corresponding to open water, ice and transition (water-ice) to improve the estimations of water level during freeze and thaw periods using only the point measurements of open water. Four Canadian lakes were selected to analyze active (waveform parameters) and passive (brightness temperature) microwave data acquired by the Jason-2 radar altimetry mission: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. To determine lake surface states, backscattering coefficient and peakiness at Ku-band derived from the radar altimeter waveform and brightness temperature at 18.7 and 37 GHz measured by the microwave radiometer contained in the geophysical data records (GDR) of Jason-2 were used in two different unsupervised classification techniques to define the thresholds of discrimination between open water and ice measurements. K-means technique provided better results than hierarchical clustering based upon silhouette criteria and the Calinski-Harabz index. Thresholds of discrimination between ice and water were validated with the Normalized Difference Snow Index (NDSI) snow cover products of the MODIS satellite. The use of open water threshold resulted in improved water level estimation compared to in situ water stages, especially in the presence of ice. For the four lakes, the Pearson coefficient (r) increased on average from about 0.8 without the use of the thresholds to more than 0.90. The unbiased RMSE were generally lower than 20 cm when the threshold of open water was used and more than 22 cm over smaller lakes, without using the thresholds.

Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

Towards high resolution flood monitoring: An integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery

Monitoring changes in flood extent is critical for flood control and mitigation purposes in areas where flooding affects many people and dense infrastructure and properties. Remote sensing can be an effective technique to detect changes in surface water extent and its dynamics. Compared to optical remote sensing, microwave information is suitable for working in any weather condition without severe cloud interference. Usually passive microwave data has a high temporal but a rather coarse spatial resolution, whereas for active microwave data this is reversed and only with ideal satellite constellation observations it can reach high sampling rates. To overcome these spatial and temporal restrictions, we proposed an integrated methodology to combine the passive and active microwave remote sensing and thus provide flood information more frequently at a high resolution. In this paper, a demonstration of the methodology is presented for a flood event occurring in Wuhan, Hubei Province of China in July 2016. The major inundation occurred along the Jushui River, part of the Middle Yangtze River basin. Using the brightness temperature data from a special data set (MEaSUREs), a daily passive microwave signal at the resolution of 3.125 km is used as an indicator to monitor flood occurrence and obtain the flood duration over time. Synthetic Aperture Radar (SAR) imagery (12-day revisit, 10-m spatial resolution) from Sentinel-1 was processed to estimate high resolution flood extents within the time span of the flood based on a threshold-based method together with the High Above Nearest Drainage (HAND) index post-processor. Surface water fraction data generated from SAR images presents a strong correlation with the passive microwave signal (R2 = 0.84) when using a quadratic polynomial fit. The average bias between surface water fraction computed by the passive microwave signals and SAR observations for water pixels within the Jushui River basin for the validation dates is 0.34%, indicating that this relationship can be applied to interpolate surface water fraction for each pixel along the river and other permanent water bodies for days when SAR observations are not available. This integrated method takes advantage of both passive and active microwave remote sensing and enriches temporally sparse flood data. Given the global coverage of the datasets used in this study, it can be utilized to estimate flood status for other flood-prone areas and thus contribute towards societal flood preparedness and response.

Arctic Sea Ice Classification Using Microwave Scatterometer and Radiometer Data During 2002–2017

Temporal and spatial variation of sea ice type in the Arctic is an indicator of regional and global change. Arctic sea ice can be classified into two major categories: multiyear ice (MYI) and first-year ice. In this paper, classification method based on machine learning is established and applied to produce daily sea ice classification data set during the winter (November–April) from 2002 to 2017 using active microwave data from QuikSCAT and Advanced Scatterometer as well as passive microwave data from Advanced Microwave Scanning Radiometer for EOS, Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2 radiometer. First, the open water area is flagged out using brightness temperature (Tb) from the passive microwave sensor. Then, K-means algorithm is applied to identify the clusters of the two ice types in the Tb/backscatter parameter space and finally assign pixels to each class. Two optimization methods based on the movement of MYI and marginal ice zone are used to correct the misclassification of MYI. The results have shown a decrease of MYI in winter from 2002 to 2017, especially in 2008 and 2013 with a remarkable recovery in 2014. The classifications are consistent with results by visual interpretation from synthetic aperture radar images in the Canadian Arctic Archipelago with overall classification accuracy over 93%. Comparison with classifications from previous studies and products shows that our method could reflect more differences in MYI declining trend interannually and less anomalous fluctuations in certain years.

Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation.

It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

Melting over the northeast Antarctic Peninsula (1999–2009): evaluation of a high-resolution regional climate model

Abstract. Surface melting over the Antarctic Peninsula (AP) may impact the stability of ice shelves and thus the rate at which grounded ice is discharged into the ocean. Energy and mass balance models are needed to understand how climatic change and atmospheric circulation variability drive current and future melting. In this study, we evaluate the regional climate model MAR over the AP at a 10 km spatial resolution between 1999 and 2009, a period when active microwave data from the QuikSCAT mission is available. This model has been validated extensively over Greenland, has is applied here to the AP at a high resolution and for a relatively long time period (full outputs are available to 2014). We find that melting in the northeastern AP, the focus area of this study, can be initiated both by sporadic westerly föhn flow over the AP mountains and by northerly winds advecting warm air from lower latitudes. A comparison of MAR with satellite and automatic weather station (AWS) data reveals that satellite estimates show greater melt frequency, a larger melt extent, and a quicker expansion to peak melt extent than MAR in the centre and east of the Larsen C ice shelf. These differences are reduced in the north and west of the ice shelf, where the comparison with satellite data suggests that MAR is accurately capturing melt produced by warm westerly winds. MAR shows an overall warm bias and a cool bias at temperatures above 0 ∘C as well as fewer warm, strong westerly winds than reported by AWS stations located on the eastern edge of the Larsen C ice shelf, suggesting that the underestimation of melt in this region may be the product of limited eastward flow. At higher resolutions (5 km), MAR shows a further increase in wind biases and a decrease in meltwater production. We conclude that non-hydrostatic models at spatial resolutions better than 5 km are needed to better-resolve the effects of föhn winds on the eastern edges of the Larsen C ice shelf.

Monitoring of an Indonesian Tropical Wetland by Machine Learning-Based Data Fusion of Passive and Active Microwave Sensors

In this study, a novel data fusion approach was used to monitor the water-body extent in a tropical wetland (Lake Sentarum, Indonesia). Monitoring is required in the region to support the conservation of water resources and biodiversity. The developed approach, random forest database unmixing (RFDBUX), makes use of pixel-based random forest regression to overcome the limitations of the existing lookup-table-based approach (DBUX). The RFDBUX approach with passive microwave data (AMSR2) and active microwave data (PALSAR-2) was used from 2012 to 2017 in order to obtain PALSAR-2-like images with a 100 m spatial resolution and three-day temporal resolution. In addition, a thresholding approach for the obtained PALSAR-2-like backscatter coefficient images provided water body extent maps. The validation revealed that the spatial patterns of the images predicted by RFDBUX are consistent with the original PALSAR-2 backscatter coefficient images (r = 0.94, RMSE = 1.04 in average), and that the temporal pattern of the predicted water body extent can track the wetland dynamics. The PALSAR-2-like images should be a useful basis for further investigation of the hydrological/climatological features of the site, and the proposed approach appears to have the potential for application in other tropical regions worldwide.

Bayesian Sea Ice Detection With the ERS Scatterometer and Sea Ice Backscatter Model at C-Band

This paper describes the adaptation of a Bayesian sea ice detection algorithm for the scatterometer on-board the European Remote Sensing (ERS) satellites (ERS-1 and ERS-2). The algorithm is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs) and its performance is validated against coincident active and passive microwave data. We furthermore propose a new model for sea ice backscatter at the C-band in vertical polarization based on the sea ice GMFs derived from ERS and advanced scatterometer data. The model characterizes the dependence of sea ice backscatter on the incidence angle and the sea ice type, allowing a more precise incidence angle correction than afforded by the usual linear transformation. The resulting agreement between the ERS, QuikSCAT, and special sensor microwave imager sea ice extents during the year 2000 is high during the fall and winter seasons, with an estimated ice edge accuracy of about 20 km, but shows persistent biases between scatterometer and radiometer extents during the melting period, with scatterometers being more sensitive to summer (lower concentration and rotten) sea ice types.

Snowmelt Pattern Over High-Mountain Asia Detected From Active and Passive Microwave Remote Sensing

The snow in high-mountain Asia (HMA) is of great importance, as it is very sensitive to the climate change. Air temperature and precipitation shifts/increases will be reflected in the timing of snowmelt onset. In this letter, a new algorithm is proposed to determine the snowmelt onset date from active and passive microwave remote sensing data, and the spatial and temporal pattern of snowmelt onset in HMA is studied using active and passive microwave remote sensing for the first time. Over 35 years of passive microwave data and ten years of active microwave data are used to derive the melt onset date in HMA. The active microwave data has 4.5-km resolution so that more detailed spatial pattern of snowmelt onset date can be derived compared to the 25-km resolution passive microwave data. Under climate change background, time series analyses of the snowmelt onset date in HMA are conducted to study the snowmelt onset time changes in recent 35 years. This letter provides an objective evidence of climate change impact on the cryospheric system. Time series analysis shows that the snowmelt onset date is becoming earlier in HMA region during 1988–2015, except the Karakorum Mountains and part of the western Kunlun Mountains. Mean air temperature is compared with the time series snowmelt onset date and the results show that there is strong correlation between mean air temperature and average snowmelt onset date. A 4.5 days/degree rate of snowmelt onset date advancing is found.

Consistency of vegetation index seasonality across the Amazon rainforest

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.