SMOS Optical Thickness Changes in Response to the Growth and Development of Crops, Crop Management, and Weather

The Soil Moisture and Ocean Salinity (SMOS) remote sensing satellite was launched by the European Space Agency in 2009. The L-band brightness temperature observed by SMOS has been used to produce estimates of both soil moisture and τ, the optical thickness of the land surface. Although τ should theoretically be proportional to the amount of vegetation present within a SMOS pixel, several initial investigations have not been able to confirm this expected behavior. However, when the noise in the SMOS τ product is removed, τ in the U.S. Corn Belt, a region of extensive row-crop agriculture, has a distinct shape that mirrors the growth and development of crops. We find that the peak value of SMOS τ occurs at approximately 1000 °C day (base 10 °C) growing degree days after the mean planting date of maize (corn). We can explain this finding in the following way: τ is directly proportional to the water column density of vegetation; maize contributes the most to growing season changes in τ in the Corn Belt; and maize reaches its maximum water column density at its third reproductive stage of development, at about 1000 °C day growing degree days. Consequently, SMOS τ could be used to monitor the phenology of crops in the Corn Belt at a spatial resolution similar to a U.S. county and a temporal frequency on the order of days. We also examined the magnitude of the change in SMOS τ over the growing season and hypothesized it would be related to the amount of accumulated solar radiation, but found this not to be the case. On the other hand, the change in magnitude was smallest for the year in which the most precipitation fell. These findings are rational since SMOS τ at the satellite scale is in fact a function of both vegetation and soil surface roughness, and soil surface roughness is reduced by precipitation. To fully explain changes in SMOS τ in the Corn Belt it appears that it will be necessary to use in situ and remotely-sensed observations along with agro-ecosystem models to account for land management decisions made by farmers that affect changes in soil surface roughness and all of the relevant biophysical processes that affect the growth and development of crops

Comparison of SMOS vegetation optical thickness data with the proposed SMAP algorithm

Soil moisture is important to agriculture, weather, and climate. Current soil moisture networks measure at single points, while large spatial averages are needed for some crop, weather, and climate models. Large spatial average soil moisture can be measured by microwave satellites. Two missions, the European Space Agency\u27s Soil Moisture Ocean Salinity mission (SMOS) and NASA\u27s Soil Moisture Active Passive mission (SMAP), can or will measure L-band microwave radiation, which can see through denser vegetation and deeper in to the soil than previous missions that used X-band or C-band measurements. Both SMOS and SMAP require knowledge of vegetation optical thickness (τ) to retrieve soil moisture. SMOS is able to measure τ directly through multi-angular measurements. SMAP, which will measure at a single incidence angle, requires an outside source of τ data. The current SMAP baseline algorithm will use a climatology of optical vegetation measurements, the normalized difference vegetation index (NDVI), to estimate τ. SMAP will convert the NDVI climatology to vegetation water content (VWC), then convert VWC to τ through the b parameter. This dissertation aimed to validate SMOS τ using county crop yield estimates in Iowa. SMOS τ was found to be noisy while still having a clear response to vegetation. Counties with higher yields had higher increases in $tau; over growing seasons, so it appears that SMOS τ is valid during the growing season. However, SMOS τ had odd behavior outside of growing seasons which can be attributed to soil tillage and residue management. Next, this dissertation attempted to estimate values of the b parameter at the satellite scale using SMOS τ data, county crop yields, and allometric relationships, such as harvest index. A new allometric relationship was defined, theta_gv_max, which is the ratio of maximum VWC to maximum dry biomass. While uncertainty in the estimated values of b was large, the values were close in magnitude to those found in literature for field-based studies. Finally, this dissertation compared SMOS τ to τ from SMAP\u27s NDVI-based algorithm. At the peak of the growing season, SMAP τ was similar in timing to SMOS τ, while SMAP τ was larger in magnitude than SMOS τ. The larger SMAP τ could be attributed to SMAP\u27s handling of vegetation scattering in its soil moisture retrieval algorithm. For one example case, the difference between SMAP τ and SMOS τ at the peak of the growing season did not appear to cause a large difference in retrieved soil moisture

Quantitative Assessment of Satellite L-Band Vegetation Optical Depth in the U.S. Corn Belt

Satellite L-band vegetation optical depth (L-VOD) contains new information about terrestrial ecosystems. However, it has not been evaluated against the geophysical variable that it represents, plant water, the mass of liquid water contained within vegetation tissue per ground area. We quantitatively assess the seasonal variation of three L-VOD products at the South Fork Core Validation Site in the Corn Belt state of Iowa where L-VOD is directly proportional to crop plant water. We use three satellite-scale crop plant water estimates: in situ measurements; a normalized difference water index (NDWI) calibrated with in situ measurements; and a crop model. We find that overall the L-VOD satellite products are 0.02-0.09 Np (0.4-1.7 kg · m⁻²) lower than the three estimates. We show that overestimation of L-VOD can be attributed to dynamic soil surface roughness, and hypothesize that crop plant water observations will require the incorporation of this effect into retrieval algorithms

A new approach for retrieving soil moisture from SMAP over the Corn Belt

NASA’s Soil Moisture Active Passive (SMAP) satellite utilizes passive observations of L–band (f = 1.41 GHz, λ = 21 cm) brightness temperature to estimate surface soil moisture at a spatial scale of 33 km approximately once per day in the U. S. Corn Belt. These observations have the potential to improve weather forecasting models, increase agricultural productivity, and provide decision support for flood and drought management. However, SMAP Level 2 Soil Moisture (L2SM) performs poorly in croplands validation sites such as the South Fork Iowa River (located in central Iowa); we hypothesize that this is due to the use of generic croplands parameterizations during SMAP L2SM retrieval. We analyzed the ancillary inputs to the τ − ω retrieval model to determine if they could cause the observed seasonal component to SMAP L2SM bias and unbiased RMSE. After implementing a modified surface temperature, in which the SMAP–reported value is divided by the bias correction factor K = 1.02 to be more realistic for the South Fork, we identified roughness and vegetation to be the most likely sources of error in soil moisture retrieval. At L–band, changes in soil surface roughness and vegetation produce the same effect on emissivity, leading to an inability to disentangle roughness–vegetation effects within L2SM retrievals. We utilize our conceptual knowledge of roughness–vegetation patterns, combined with South Fork in situ observations of soil moisture and temperature, to produce the first temporally–dynamic retrievals of HR (model roughness parameter) at satellite–scale. These are consistent with both the wide range of literature values and sampling of physical roughness conducted during the SMAPVEX16–IA campaign. However, when this roughness–vegetation concept is applied to retrieving L2SM the previously observed errors are not mitigated as initially hypothesized. We suggest that the next step towards improving SMAP L2SM in the U. S. Corn Belt includes adopting a first–order radiative transfer model to capture scattering that is currently considered to be negligible

Using SMAP and SMOS vegetation optical depth to measure crop water in vegetation

NASA\u27s Soil Moisture Active Passive (SMAP) and European Space Agency\u27s (ESA) Soil Moisture Ocean Salinity (SMOS) are two microwave remote sensing satellites. They were originally designed to measure soil moisture, but with an algorithm that already retrieves vegetation optical depth (VOD), they could also be used for vegetation measurements. VOD is the degree to which vegetation attenuates microwave radiation from the soil and may be an important product to quantify vegetation changes. SMAP and SMOS have some advantages to measure vegetation compared to existing practices. They can view the entire crop canopy as opposed to just the top layer, due to their ability to monitor soil moisture which is below the crop canopy. SMAP and SMOS also have on average a daily revisit time in the mid–latitudes. Knowing the location and amount of water in a crop canopy could be beneficial for remote sensing because as the crops grow and water becomes allocated differently, SMAP and SMOS are seeing water from many different sources(stems, leaves, ears, soil, etc.). These different sources of water will scatter radiation differently due to their varying sizes and shapes and accounting for water correctly could improve measurements of soil moisture and VOD. A challenge of using SMAP and SMOS is the need to know crop water on the ground for comparison to VOD from the satellites.Data from multiple field experiments were collected and analyzed to show where crop water is in different crop components at varying development stages. New empirical models that relate crop water to crop dry mass were also created with these in situ measurements. We will use this model to hopefully overcome the challenge of comparing satellite VOD to crop water. However, we need to verify that the model is accurate and actually telling us about crop water.To check accuracy of our new empirical model, SMAP and SMOS VOD were compared to crop water estimates from the Agricultural Integrated BIosphere Simulator (Agro-IBIS) at the South Fork SMAP Core Validation Site in Iowa. A crop model was used because it can obtain dry mass for multiple fields in the study area. This dry mass can then be converted to a crop water using our empirical model for comparison to SMAP and SMOS VOD. We find that SMAP and SMOS VOD are directly proportional to crop water. We also found the value of the proportionality constant (or b-parameter ) relating VOD to crop water at the satellite scale is about half as large as previous estimates. After finding that SMAP and SMOS VOD are directly proportional to crop water we wanted to validate SMAP and SMOS VOD with in situ data from the field campaign SMAP Validation Experiment 2016. We found that SMAPv2 VOD had the highest R2 value. The b–parameter was also shown to change over time and that other sources of water in the SMAP and SMOS pixel may need to be taken into account when calculating ab–parameter. Because L-band VOD is directly proportional to crop water at the satellite scale, and because we understand the relationship between crop water and crop dry mass, SMAP and SMOS have the potential to evaluate the large-scale performance of crop models in the Corn Belt on a near daily basis

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable

Leaf wetness: implications for agriculture and remote sensing

Information regarding leaf wetness duration (LWD) has been used in disease management schemes for decades by researchers in the plant disease and agricultural meteorology communities. LWD is currently measured predominantly by electronic leaf wetness sensors or through the use of a model that represents latent heat transfer. Studies have been conducted that examine the placement, orientation and treatment of leaf wetness sensors. Some studies have compared empirical and physical models to LWD measurements obtained from leaf wetness sensors. However, an article that summarizes all aspects of leaf wetness sensors and models, addressing the benefits and disadvantages, has not been provided to extension personnel that need to provide accurate information to growers regarding disease risk associated with LWD. It is recommended that LWD should be estimated using a relative humidity greater than or equal to 90% for operational use. The vertical variability of dew has been examined for a variety of crops. Studies regarding the horizontal spatial variability of dew amount and duration has been limited to small areas, on the order of a few meters. Traditionally, information regarding LWD for disease warning systems has been obtained from a single sensor at a single point in a field. We sought to examine whether or not this provided accurate information regarding LWD, but also sought to determine if dew amount varies within a field. Our study examined how the spatial variability of both dew amount and duration differ within a field by examining locations that were hundreds of meters apart. Dew amount was measured manually, and simultaneously, at three locations within the field on seven mornings. The three sampling locations were chosen based on changes in topography and soil textures. Information regarding LWD was obtained by leaf wetness sensors placed at each of the three locations. It was hypothesized that there would be a significant difference in both dew amount and dew duration between the sites due to changes in the distillation contribution to the overall dew amount. The study found that there was high leaf-to-leaf variability regarding dew amount, and no variability between sites was seen. It was found that there was no significant difference in dew duration at the three locations. The Soil Moisture Ocean Salinity (SMOS) satellite provides the first global estimates of soil moisture using microwave radiometry. This satellite makes passes at 6 pm and 6 am local solar time. The remote-sensing community have indicated that data from the 6 am pass time should be preferred over the 6 pm pass time for a variety of reasons, however land-based studies of soil moisture using microwave radiometry have indicated that the presence of free water on canopy can cause errors in the estimations of soil moisture. Evaluation of the influence of dew on vegetative canopies for satellite measurements has not previously been possible. Our study examined a region in north-central Iowa, where the land-cover is uniform consisting of row crops. We hypothesized that there would be no significant difference in brightness temperature or soil moisture between evening and morning SMOS passes. We examined the soil moisture product and found that there was a significant difference in soil moisture between evening and morning SMOS passes for days when precipitation had not occurred after noon prior to the evening pass, nor during the time period between the evening and morning pass time. The soil moisture product is obtained from measurements of brightness temperature, however no significant difference in brightness temperature was seen between evening and morning passes. We indicate that there may be issues with retrieved values of optical depth during the SMOS processing phase that is resulting in errors in soil moisture measurements. We also highlight the possibility that decreases in the polarization index (a normalization of brightness temperature) could falsely indicate a decrease in soil moisture when it maybe the result of an increase either of the volumetric water content of a vegetative canopy or the presence of free water on the canopy surface

Assessment of Drought in Grasslands: Spatio – Temporal Analyses of Soil Moisture and Extreme Climate Effects in Southwestern Mongolia

Soil moisture plays an essential key role in the assessment of hydrological and meteorological droughts that may affect a wide area of the natural grassland and the groundwater resource. The surface soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological, and agricultural applications, especially in water-limited or drought-prone regions. However, gauging soil moisture is challenging because of its high variability. While point-scale in-situ measurements are scarce, the remote sensing tools remain the only practical means to obtain regional and global-scale soil moisture estimates. A Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to gauge the Earth’s surface soil moisture (SM) at the near-daily time scales. This work aims to evaluate the spatial and temporal patterns of SMOS soil moisture, determine the effect of the climate extremes on the vegetation growth cycle, and demonstrate the feasibility of using our drought model (GDI) the Gobi Drought Index. The GDI is based on the combination of SMOS soil moisture and several products from the MODIS satellite. We used this index for hydro-meteorological drought monitoring in Southwestern Mongolia. Firstly, we validated bias-corrected SMOS soil moisture for Mongolia by the in-situ soil moisture observations 2000 to 2015. Validation shows satisfactory results for assessing drought and water-stress conditions in the grasslands of Mongolia. The correlation analysis between SMOS and Normalized Difference Vegetation Index (NDVI) index in the various ecosystems shows a high correlation between the bias-corrected, monthly-averaged SMOS and NDVI data (R2 > 0.81). Further analysis of the SMOS and in situ SM data revealed a good match between spatial SM distribution and the rainfall events over Southwestern Mongolia. For example, during dry 2015, SM was decreased by approximately 30% across the forest-steppe and steppe areas. We also notice that both NDVI and rainfall can be used as indicators for grassland monitoring in Mongolia. The second part of this research, analyzed several dzud (specific type of climate winter disaster) events (2000, 2001, 2002, and 2010) related to drought, to comprehend the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. We determined how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), NDVI, and livestock mortality data. The NDVI is used as an indicator of vegetation activity and growth. Its spatial and temporal pattern is expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in arid areas. We found that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The snowy winters can cause a 10 to 20-day early peak in NDVI and the following increase in vegetation growth. However, during a year with dry winter conditions, the vegetation growth phase begins later due to water deficiency and the entire year has a weaker vegetation growth. Generally, livestock loss and the reduction of grazing pressure was played a crucial role in vegetation recovery after extreme climatic events in Mongolia. At the last stage of our study, we develop an integrated Gobi drought index (GDI), derived from SMOS and LST, PET, and NDVI MODIS products. GDI can incorporate both, the meteorological and soil moisture drought patterns and sufficiently well represent overall drought conditions in the arid lands. Specifically, the monthly GDI and 1-month standardized precipitation index SPI showed significant correlations. Both of them are useful for drought monitoring in semi-arid lands. But, the SPI requires in situ data that are sparse, while the GDI is free from the meteorological network restriction. Consequently, we compared the GDI with other drought indices (VSWI, NDDI, NDWI, and in-situ SM). Comparison of these drought indices with the GDI allowed assessing the droughts’ behavior from different angles and quantified better their intensity. The GDI maps at fine-scale (< 1km) permit extending the applicability of our drought model to regional and local studies. These maps were generated from 2000 to 2018 across Southwestern Mongolia. Fine-scale GDI drought maps are currently limited to the whole territory for Mongolia but the algorithm is dynamic and can be transported to any region. The GDI drought index can be served as a useful tool for prevention services to detect extremely dry soil and vegetation conditions posing a risk of drought and groundwater resource depletion. It was able to detect the drought events that were underestimated by the National Drought Watch System in Mongolia. In summary, with the help of satellite, climatological, and geophysical data, the integrated GDI can be beneficial for vegetation drought stress characterization and can be a useful tool to monitor the effectiveness of pasture land restoration management practices for Mongolian livelihoods. The future application of the GDI can be extended to monitor potential impacts on water resources and agriculture in Mongolia, which have been impacted by long periods of drought