Assessing the impact of hurricanes on the carbon cycle using SMAP satellite and in-situ observations: Role of land cover and precipitation

A possible analysis for failure of U.S. SMAP satellite

Abstract. Very high-resolution (∼10–100 m) surface soil moisture (SM) observations are important for applications in agriculture, among other purposes. This is the original goal of the S2MP (Sentinel-1/Sentinel-2-Derived Soil Moisture Product) algorithm, which was designed to retrieve surface SM at the agricultural plot scale by simultaneously using Sentinel-1 (S1) backscatter coefficients and Sentinel-2 (S2) NDVI (Normalized Difference Vegetation Index) as inputs to a neural network trained with Water Cloud Model simulations. However, for many applications, including hydrology and climate impact assessment at regional level, large maps with a high resolution (HR) of around 1 km are already a significant improvement with respect to most of the publicly available SM datasets, which have resolutions of about 25 km. In this study, the S2MP algorithm was adapted to work at 1 km resolution and extended from croplands to herbaceous vegetation types. A target resolution of 1 km also allows the evaluation of the interest in using NDVI derived from Sentinel-3 (S3) instead of S2. Two sets of SM maps at 1 km resolution were produced with S2MP over six regions of ∼104 km2 in Spain, Tunisia, North America, Australia, and the southwest and southeast regions of France for the whole year of 2019. The first set was derived from the combination of S1 and S2 data (S1 + S2 maps), while the second one was derived from the combination of S1 and S3 (S1 + S3 maps). S1 + S2 and S1 + S3 SM maps were compared to each other, to those of the 1 km resolution Copernicus Global Land Service (CGLS) SM and Soil Water Index (SWI) datasets, and to those of the Soil Moisture Active Passive (SMAP) + S1 product. The S2MP S1 + S2 and S1 + S3 SM maps are in very good agreement in terms of correlation (R≥0.9), bias (≤0.04 m3 m−3), and standard deviation of the difference (SDD≤0.03 m3 m−3) over the six domains investigated in this study. In a second step, the S1 + S3 S2MP maps were compared to the other HR maps. S1 + S3 SM maps are well correlated to the CGLS SM maps (R∼0.7–0.8), but the correlations with respect to the other HR maps (CGLS SWI and SMAP + S1) drop significantly over many areas of the six domains investigated in this study. The highest correlations between the HR maps were found over croplands and when the 1 km pixels have a very homogeneous land cover. The bias among the different maps was found to be significant over some areas of the six domains, reaching values of ±0.1 m3 m−3. The S1 + S3 maps show a lower SDD with respect to CGLS maps (≤0.06 m3 m−3) than with respect to the SMAP + S1 maps (≤0.1 m3 m−3) for all the six domains. Finally, all the HR datasets (S1 + S2, S1 + S3, CGLS, and SMAP + S1) were also compared to in situ measurements from five networks across five countries, along with coarse-resolution (CR) SM products from SMAP, SMOS, and the European Space Agency Climate Change Initiative (CCI). While all the CR and HR products show different bias and SDD, the HR products show lower correlations than the CR ones with respect to in situ measurements. The discrepancies in between the different HR datasets, except for the more simple land cover conditions (homogeneous pixels with croplands) and the lower performances with respect to in situ measurement than coarse-resolution datasets, show the remaining challenges for large-scale HR SM mapping.

Core Ideas Upscaling methods compared in situ measures with soil moisture from the SMAP satellite. The accuracy of SMAP soil moisture products in annual cropland was assessed. The spatial representativeness of sparse in situ networks was determined. In 2015, NASA launched the Soil Moisture Active Passive (SMAP) satellite. Data from this satellite are being exploited to improve forecasting of extreme weather events and delivery of disaster response. International core validation sites (CVSs) have been contributing in situ soil moisture data to validate and calibrate SMAP soil moisture products. Overall the soil moisture retrieval errors have exceeded SMAP's mission requirement (errors below 0.04 m3 m−3), with the exception of some sites of annual cropland as present at the Carman (Canada) CVS. In 2016, a SMAP validation experiment was conducted at the Canadian site in Manitoba (SMAPVEX16‐MB) in an attempt to understand the differences between the SMAP soil moisture retrievals and the permanent in situ network observations. The research presented here analyzed the performance of this network in representing soil moisture within a SMAP pixel and tested five upscaling approaches. Comparisons between the permanent network and SMAPVEX16‐MB measurements (from temporary stations and field measures) confirmed agreement among these three sources of soil moisture measures. The SMAP soil moisture values were compared with in situ soil moisture upscaled from the four tested approaches as well as soil moisture estimated by the NOAH Land Surface Model (LSM). There were similar discrepancies when analyzing all methods (RMSE 0.072–0.074 m3 m−3 for the four upscaling methods; 0.076 m3 m−3 for the LSM approach), yielding no reduction in the soil moisture RMSE for this site. The SMAP team will continue to investigate other factors that may be contributing to errors above 0.04 m3 m−3 at these annually cropped CVSs.

The Soil Moisture Active Passive (SMAP) satellite has been developed by NASA to make global soil moisture measurements on the Earth's land surface. It can also distinguish frozen from thawed land surfaces. The SMAP satellite was launched on January 31, 2015, and the science data production began on March 31, 2015. It has both L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna. The SMAP radar failed in July 2015, while its radiometer continues nominal operations. In this paper, the approximately two months of SMAP synthetic aperture radar (SAR) data has been revisited and scrubbed. The SAR bad data (aka outlier) are detected and removed by statistically investigating the time series difference between scatterometer and linearly averaged SAR measurements within the SMAP antenna footprint (∼38 km). It is performed orbit by orbit. The outlier or bad orbits were identified when a data point is more than three scaled median absolute deviations (MAD) away from the median. On average only about 10% or less of all SAR orbits (more than 700), in each polarization, are classified as outliers.

Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.

The advent of satellite-derived sea surface salinity (SSS) measurements has boosted scientific study in less-sampled ocean regions such as the northwestern Gulf of Guinea (NWGoG). In this study, we examine the seasonal variability of SSS in the NWGoG from the Soil Moisture Active Passive (SMAP) satellite and show that it is well-suited for such regional studies as it is able to reproduce the observed SSS features in the study region. SMAP SSS bias, relative to in-situ data comparisons, reflects the differences between skin layer measurements and bulk surface measurements that have been reported by previous studies. The study results reveal three broad anomalous SSS features: a basin-wide salinification during boreal summer, a basin-wide freshening during winter, and a meridionally oriented frontal system during other seasons. A salt budget estimation suggests that the seasonal SSS variability is dominated by changes in freshwater flux, zonal circulation, and upwelling. Freshwater flux, primarily driven by the seasonally varying Intertropical Convergence Zone, is a dominant contributor to salt budget in all seasons except during fall. Regionally, SSS is most variable off southwestern Nigeria and controlled primarily by westward extensions of the Niger River. Anomalous salty SSS off the coasts of Cote d’Ivoire and Ghana especially during summer are driven mainly by coastal upwelling and horizontal advection.

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations.

Typhoon Chaba impacted the freshwater-enriched ocean in the East China Sea bringing strong wind in October 4–5, 2016. The heavy storm vertically mixed the coastal water around the Jeju Island. The signature of vertical mixing around Jeju Island was indicated by the elevation of sea surface salinity(SSS) observed by NASA SMAP(Soil Moisture Active Passive) Satellite. Moreover, the spatial size of the freshwater plume(<30psu) near the mouth of the Changjiang river diminished. Using the SMAP satellite which collects the SSS under the cloudy condition, in the future, the role of the Typhoon relevant with the ecosystem in the ECS needs to be studied.

We introduce a new version of the multi-mission sea surface salinity (SSS) optimum interpolation analysis (OISSS) which combines observations from NASA&amp;#8217;s AQUARIUS/SAC-D and SMAP (Soil Moisture Active-Passive) satellite missions into continuous and consistent SSS data record. The dataset covers the period from September 2011 to present. Measurements from ESA&amp;#8217;s SMOS (Soil Moisture and Ocean Salinity) satellite are used to fill gaps in SMAP observations during June-July 2019 and August-September 2022, when the SMAP satellite was in a safe mode and did not deliver scientific data. The analysis is based on Optimum Interpolation (OI), utilizes Level-2 (swath) data, and uses satellite-specific bias-correction algorithms to correct the satellite retrievals for large-scale biases. &amp;#160;The dataset includes uncertainty estimates, both formal and empirical. We use this dataset as an example to discuss requirements for the multi-mission SSS data products.To demonstrate its utility, the new dataset is used to characterize spatial patterns of SSS variability in the global ocean and on different time scales. The spatial pattern of the regional SSS trends show that the subtropical North Pacific is becoming fresher while the subtropical South Indian Ocean is becoming saltier. This is seemingly a part of a longer term oscillation as the trends are reversed compared to the preceding decade (2005-2015) estimated from Argo data. In particular, abrupt changes occurred during 2015, related, presumably, to a strong El Nino event of 2015-2016. The annual cycle is a dominant signal globally and can nicely be described by two leading empirical orthogonal functions (EOFs) explaining more than 35% of the total SSS variance. Except for the Indian Ocean, the oscillations are out of phase in the Northern and Southern Hemispheres and describe poleward propagation away from the Equator driven, presumably, by Ekman dynamics. The intra-seasonal signal is strongest in the tropics, particularly in the quasi-zonal bands associated with the Inter-tropical convergence zone (ITCZ) and South Pacific convergence zone (SPCZ), but also near outflows of major rivers, including the Amazon, Congo, Mississippi, Plata, Ganges and Brahmaputra. &amp;#160;Another region of interest is the northern North Atlantic, where satellite observations during the last decade have provided an unprecedented resource to study the spatial distribution and temporal evolution of SSS, allowing to observe areas typically not available by in-situ components of the ocean observing system. Here, the multi-mission SSS dataset is examined in its accuracy and appropriateness for studying SSS variability in high latitudes and marginal seas.&amp;#160;

Most climate change impacts are linked to terrestrial vegetation productivity, carbon stocks and land use change. Changes in land use and climate drive the dynamics of terrestrial carbon cycle. These carbon cycle dynamics operate at different spatial and temporal scales. Quantification of the spatial and temporal variability of carbon flux has been challenging because land-atmosphere-carbon exchange is influenced by many factors, including but not limited to, land use change and climate change and variability. The study of terrestrial carbon cycle, mainly gross primary product (GPP), net ecosystem exchange (NEE), soil organic carbon (SOC) and ecosystem respiration (Re) and their interactions with land use and climate change, are critical to understanding the terrestrial ecosystem. The main objective of this study was to examine the interactions among land use, climate change and terrestrial carbon cycling in the state of Texas using satellite measurements. We studied GPP, NEE, Re and SOC distributions for five selected major land covers and all ten climate zones in Texas using Soil Moisture Active Passive (SMAP) carbon products. SMAP Carbon products (Res=9 km) were compared with observed CO2 flux data measured at EC flux site on Prairie View A&M University Research Farm. Results showed the same land cover in different climate zones has significantly different carbon sequestration potentials. For example, cropland of the humid climate zone has higher (-228 g C/m2) carbon sequestration potentials than the semiarid climate zone (-36 g C/m2). Also, shrub land in the humid zone and in the semiarid zone showed high (-120 g C/m2) and low (-36 g C/m2) potentials of carbon sequestration, respectively, in the state. Overall, the analyses indicate CO2 storage and exchange respond differently to various land covers, and environments due to differences in water availability, root distribution and soil properties.

Large rivers are key components of the land-ocean branch of the global water and biogeochemical cycles. River discharges can have important influences on physical, biological, optical, and chemical processes in coastal oceans. It is, therefore, of importance to routinely monitor the time-varying dispersal patterns of river plumes. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the NASA Soil Moisture Active Passive (SMAP) satellites provide Sea Surface Salinity (SSS) observations capable of characterizing the spatial and temporal variability of major river plumes. The main objective of this study is to examine the consistency of SSS products, from these two missions, and two in-situ gridded salinity products in depicting SSS variations on seasonal to interannual time scales within a few hundred kilometers of major river mouths. We show that SSS from SMOS and SMAP satellites have good consistency in depicting seasonal and interannual SSS variations near major river mouths. The two gridded in-situ products underestimate these variations substantially. This underestimation, most notably associated with the low SSS season following the high-discharge season, is attributable to the limited in-situ sampling of the river plumes when they are the most active. This work underscores the importance of using satellite SSS to study river plumes, as well as to evaluate and constrain models.

Surface soil moisture (SSM) is an important parameter for many applications. Soil Moisture Active Passive (SMAP) satellite mission provides an SSM map at global scale. But its spatial resolution (36 km) is a big restriction for agricultural and hydrological studies at the catchment scale. Therefore, the present study was conducted to disaggregate the passive SMAP soil moisture data using the retrieved Soil Evaporative Efficiency (SEE) at 1-km spatial and daily temporal resolution from Moderate Resolution Imaging Spectroradiometer (MODIS) data and to assess the effectiveness of the method for generating data for different land covers. For this purpose, SMAP data were disaggregated using the SEE retrieved from daily MODIS data located at the southwest part of the United States. The accuracy of spatial and temporal variability of the disaggregated SMAP data was evaluated against the recorded in-situ soil moisture data in 202 stations of the Soil Climate Analysis Network (SCAN) for a period of 1 year. Results indicate that the disaggregated SMAP data have a moderate correlation with in-situ soil moisture data, but it is strongly affected by land cover. The highest accuracy was observed in the pasture/hay land cover class with Correlation Coefficient (R) value of 0.683 and 0.632, Mean Difference (MD) of −0.004 and −0.001, Root-Mean Square Error (RMSE) of 0.049 and 0.056, and unbiased Root-Mean Square Error (ubRMSE) of 0.039 and 0.045 for the disaggregated and original SSM data with the unit of , respectively. The lowest accuracy was found in the barren land (rock/sand/clay) for the disaggregated and original SSM data with R of 0.0278 and 0.155, MD of −0:081 and −0.052, RMSE of 0.134 and 0.116, and ubRMSE of 0.106 and 0.103, respectively. Results indicate that in overall disaggregation of SMAP data using Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and MODIS products has a good potential for generating high spatial and temporal resolution of SSM at the catchment scale. But it is strongly affected by the land cover class type, because the calculation of the SEE is based on the Normalized Difference Vegetation Index (NDVI). Therefore, it can be recommended to retrieve the SEE with the attention to land cover class type and employ the other vegetation indices or methods.

Land degradation (LD) is a critical environmental challenge caused by human activities and climate change. Reversing degraded land requires effective LD monitoring. The UN Sustainable Development Goal (SDG) indicator 15.3.1, "Proportion of land that is degraded over total land area," was established to assess and report LD status at regional and global levels. However, SDG indicator 15.3.1 requires comprehensive, consistent, easily accessible data and would induce large uncertainty, especially in mountainous regions. This study assesses LD in Southern China's mountainous regions by integrating national and global land cover (LC) datasets with a customized LC transition matrix to improve the effectiveness of UN SDG indicator 15.3.1 for LD assessment. The national LC transition matrices were tailored to align with the specific context of the study region and the country's ecological restoration policies and ecosystem services. The results of LD by national LC datasets were compared with the global (default) datasets provided by the Trend.Earth plugin in QGIS. Both sets of results were then compared with the validated LD findings. Using default LC datasets, 20.58% of land areas were classified as degraded, compared to 12.74% with national LC datasets. Land improvement assessed by national data was 7.58% higher than the default datasets. The LD results by national datasets and customized LC transition were closest to the validated LD data, with 94% overall accuracy. Therefore, incorporating national LC datasets and a customized LC transition matrix into UN SDG indicator 15.3.1 could enhance the effectiveness of assessing LD in mountainous regions.

Validation of the SMAP freeze/thaw product using categorical triple collocation

&amp;lt;p&amp;gt;Soil moisture (SM) products derived from the passive satellite missions have been extensively used in various hydrological and environmental processes. However, validation of the satellite derived product is crucial for its reliability in several applications. In this study, we present a comprehensive validation of the descending SM product from Soil Moisture Active Passive (SMAP) Enhanced Level-3 (L3) radiometer (SMAP L3-Version 3) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) Level-3 (Version 1), over the newly established Critical Zone Observatory (CZO) within the Ganga basin, North India. The AMSR2 soil moisture product used here, has been derived using the Land Parameter Retrieval Model (LPRM) algorithm. Four SM derived products from SMAP (L-band) and AMSR2 (C1- and C2- and X-band) are validated against the in-situ observations collected from 21 SM monitoring locations distributed over the CZO within a period from September 2017 to December 2019, for a total of 62 days. Since the remotely sensed SM product has a coarser spatial resolution (here 9 km for SMAP and 10 km for AMSR2), the assessment has been carried out for the temporal variation of the measured values. Four statistical metrics such as bias, root mean square error (RMSE), unbiased root-mean-square error (ubRMSE) and the correlation coefficient (R) have been used here for the evaluation. The SMAP Level-3 products are found to show a satisfactory correlation (R&amp;gt;0.6) compared to the other three SM product. Both the SMAP L3 and the AMSR2 C2 SM shows a negative bias, -0.05 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;/m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; and -0.04 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;/m&amp;lt;sup&amp;gt;3 &amp;lt;/sup&amp;gt;respectively whereas these values are found to be 0.04 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;/m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; and 0.06 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;/m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; for C1 and X bands of AMSR2, respectively. Furthermore, the RMSE between the SMAP L3 and in-situ data is 0.07 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;/m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;, which is slightly underperformed when considering the required accuracy of SMAP. This is possibly due to variation in the sampling depth along with the sampling day distribution over CZO. The AMSR2 SM products (C1-, C2- and X-bands) are found to have a higher RMSE than SMAP L3, ranging from 0.08-0.1 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;/m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;. In addition, the ubRMSE for all remotely sensed soil moisture product range from 0.06-0.08 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;/m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; with the lowest value for the SMAP L3 and AMSR2 C1. The results in this study can be used further for relevant hydrological modelling along with evaluating various downscaling strategies towards improving the coarser resolution satellite soil moisture.&amp;lt;/p&amp;gt;