Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.     

Sub-pixel evaluation of sea ice roughness using AMSR-E data

The rapidly changing sea ice regime in the Arctic has necessitated an evaluation of sea ice roughness at smaller scales than those provided by satellites. In this article, we evaluate sub-pixel (<5.4 km) sea ice roughness using AMSR-E brightness temperature (T_b) 89 GHz data and in situ physical roughness data acquired using a helicopter-based laser system in the southern Beaufort Sea during April–June of 2008. The analysis shows a statistically significant correlation (r² = 0.61, P-value < 0.05, regression line slope = –79.93) of T_b at horizontal polarization (H-pol) decreasing with increasing root mean square (RMS) heights. These results suggest that 89 H-pol is more sensitive (than vertical polarization (V-pol)) to the changes in physical roughness. The temporal evolution in AMSR-E T_b values at 89 H-pol and 89 V-pol shows a decrease from April to June. We conclude that solely the AMSR-E T_b at 5.4 km is insufficient to fully account for the changes occurring in the dielectric properties and surface roughness of sea ice at sub-pixel level of 1–4 km during April–June.     

Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006

AMSR-E has been extensively evaluated under a wide range of ground and climate conditions using in situ and aircraft data, where the latter were primarily used for assessing the T_B calibration accuracy. However, none of the previous work evaluates AMSR-E performance under the conditions of flood irrigation or other forms of standing water. Also, it should be mentioned that global soil moisture retrievals from AMSR-E typically utilize X-band data. Here, C-band based AMSR-E soil moisture estimates are evaluated using 1 km resolution retrievals derived from L-band aircraft data collected during the National Airborne Field Experiment (NAFE'06) field campaign in November 2006. NAFE'06 was conducted in the Murrumbidgee catchment area in southeastern Australia, which offers diverse ground conditions, including extensive areas with dryland, irrigation, and rice fields. The data allowed us to examine the impact of irrigation and standing water on the accuracy of satellite-derived soil moisture estimates from AMSR-E using passive microwave remote sensing. It was expected that in fields with standing water, the satellite estimates would have a lower accuracy as compared to soil moisture values over the rest of the domain. Results showed sensitivity of the AMSR-E to changes in soil moisture caused by both precipitation and irrigation, as well as good spatial (average R = 0.92 and RMSD = 0.049 m³/m³) and temporal (R = 0.94 and RMSD = 0.04 m³/m³) agreement between the satellite and aircraft soil moisture retrievals; however, under the NAFE'06 ground conditions, the satellite retrievals consistently overestimated the soil moisture conditions compared to the aircraft.     

Derivation of melt pond coverage on Arctic sea ice using MODIS observations

The formation of meltponds on the surface of sea ice during summer is one of the main factors affecting variability in surface albedo over the ice cover. However, observations of the spatial extent of ponding are rare. To address this, a MODIS surface reflectance product is used to derive the daily melt pond cover over sea ice in the Beaufort/Chukchi Sea region through the summer of 2004. For this region, the estimated pond cover increased rapidly during the first 20 days of melt from 10% to 40%. Fluctuations in pond cover occurred through summer, followed by a more gradual decrease through late August to 10%. The rapid initial increase in pond cover occurred later as latitude increased and melt progressed northward.

A surface campaign at Barrow in June 2004 provided pond and ice spectral reflectance needed by the MODIS algorithm to deduce pond coverage. Although individual pond and ice reflectance varies within the comparatively small region of measurement, the mean values used within the algorithm ensured that relevant values (i.e. concurrent with satellite observations) were being applied.

Aerosonde unpiloted aerial vehicles (UAVs) were deployed in June 2004 from Barrow, Alaska, to photograph the sea ice so melt pond cover could be estimated. Although the agreement between derived pond cover from UAV photos and estimates from MODIS varies, the mean estimates and distribution of pond coverages are similar, suggesting that the MODIS technique is useful for estimating pond coverage throughout the region. It is recommended that this technique be applied to the entire Arctic through the melt season.     

Mapping cotton fields using data mining and MODIS time-series

ABSTRACT

Cotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012–2013 to 2015–2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and used to build classification rules for mapping cotton fields. Our results demonstrate that the proposed method to map cotton fields achieve high accuracy when field data and visual interpretation of NDVI temporal profiles were used for validation (accuracy higher than 95% and 93%, respectively). Comparisons with the official statistics indicated an optimal fit, with linear correlation (r) and coefficient of determination (R²) above 0.93. Therefore, the proposed method was efficient to distinguish cotton fields from other crop types with similar spectral behaviour. In addition, this method can also be applied to other cotton-producing regions and other production seasons, by reusing the models generated through machine learning approaches.     

Determination of ice cloud models using MODIS and MISR data

Representation of ice clouds in radiative transfer simulations is subject to uncertainties associated with the shapes and sizes of ice crystals within cirrus clouds. In this study, we examined several ice cloud models consisting of smooth, roughened, homogeneous and inhomogeneous hexagonal ice crystals with various aspect ratios. The sensitivity of the bulk scattering properties and solar reflectances of cirrus clouds to specific ice cloud models is investigated using the improved geometric optics method (IGOM) and the discrete ordinates radiative transfer (DISORT) model. The ice crystal habit fractions in the ice cloud model may significantly affect the simulations of cloud reflectances. A new algorithm was developed to help determine an appropriate ice cloud model for application to the satellite-based retrieval of ice cloud properties. The ice cloud particle size retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data, collocated with Multi-angle Imaging Spectroradiometer (MISR) observations, is used to infer the optical thicknesses of ice clouds for nine MISR viewing angles. The relative differences between view-dependent cloud optical thickness and the averaged value over the nine MISR viewing angles can vary from??0.5 to 0.5 and are used to evaluate the ice cloud models. In the case for 2 July 2009, the ice cloud model with mixed ice crystal habits is the best fit to the observations (the root mean square (RMS) error of cloud optical thickness reaches 0.365). This ice cloud model also produces consistent cloud property retrievals for the nine MISR viewing configurations within the measurement uncertainties.     

Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements

Taking three snow seasons from November 1 to March 31 of year 2002 to 2005 in northern Xinjiang, China as an example, this study develops a new daily snow cover product (500 m) through combining MODIS daily snow cover data and AMSR-E daily snow water equivalent (SWE) data. By taking advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data, the new daily snow cover product greatly complements the deficiency of MODIS product when cloud cover is present especially for snow cover product on a daily basis and effectively improves daily snow detection accuracy. In our example, the daily snow agreement of the new product with the in situ measurements at 20 stations is 75.4%, which is much higher than the 33.7% of the MODIS daily product in all weather conditions, even a little higher than the 71% of the MODIS 8-day product (cloud cover of ~ 5%). Our results also indicate that i) AMSR-E daily SWE imagery generally agrees with MOD10A1 data in detecting snow cover, with overall agreement of 93.4% and snow agreement of 96.6% in the study area; ii) AMSR-E daily SWE imagery underestimates the snow covered area (SCA) due to its coarse spatial resolution; iii) The new snow cover product can better and effectively capture daily SCA dynamics during the snow seasons, which plays a significant role in reduction, mitigation, and prevention of snow-caused disasters in pastoral areas.     

Analysis of sea ice motion and deformation using AMSR-E data from 2005 to 2007

Dynamics of Arctic sea ice, including motion and deformation, are studied utilizing data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (EOS) (AMSR-E) during 2005 and 2007. We first derive sea ice motion maps from the satellite data in a grid of 100 km?×?100 km using a two-dimensional wavelet method. These sea ice motion results are compared with those derived from buoy data from the International Arctic Buoy Programme. Secondly, it is well known that sea ice deformation can be characterized by a strain-rate tensor calculated from the ice velocity field. Two components of the strain-rate tensor quantify the divergence and the shearing of the ice field, respectively. Daily maps for both sea ice motion and strain-rate tensor, as well as monthly averages and spatial sums, are computed and analysed. Comparison of the monthly ice motion maps for May 2005 and May 2007 indicates that the anti-cyclonic Beaufort Gyre and Transpolar Drift Stream in the western Arctic are relatively stronger during 2007 than 2005. Different patterns in the spring months' sea ice deformation rates as characterized by the absolute values of the strain-rate tensor are observed when we compare the data of 2007 with those of 2005 and 2006. The sea ice deformation activities in the spring of 2007 happen earlier and are relatively stronger than that of 2005 and 2006. These results might help to explain why the sea ice extent in the summer of 2007 is unprecedentedly low.     

Estimation of vegetation clumping index using MODIS BRDF data

The foliage clumping index quantifies the degree of the deviation of leaf spatial distribution in the canopy from the random case. It is of comparable importance for ecological models as the leaf area index for quantifying radiation interception and distribution in plant canopies. Previously, an improved angular index named normalized difference between hotspot and darkspot was proposed for retrieving the clumping index using multi-angle remote sensing data. Global maps of clumping index have been derived successfully from multi-angular Polarization and Directionality of Earth Reflectance (POLDER) data at ～6 km resolution. In this article, we investigate whether it is feasible to derive the clumping index at 500 m resolution with the 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function model parameters product. The results are compared with an assembled set of field measurements from 63 different sites, covering five continents and diverse biomes.     

A continuous global record of near-surface soil freeze/thaw status from AMSR-E and AMSR2 data

Long term and consistent records of near-surface soil freeze/thaw (F/T) status are required for understanding hydrological, ecological, and biogeochemical responses of land surface to global warming. To create such a record, we compiled and inter-calibrated satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its successor, AMSR2, using linear regression models, and then applied a discriminant algorithm to the calibrated observations to map global F/T status from 2002 to 2018. The new global F/T dataset was rigorously assessed using in situ air and surface temperatures, and modelled soil temperature. Results show that agreement between remotely sensed F/T status and that determined by in situ or modelled temperature exceeds 85% and 79% for ascending and descending orbits, respectively. Moreover, consistency between the F/T datasets derived from two sensors is around 0.8 after calibration, in nonoverlapping time frames. With such an accuracy and consistency, we calculated frost days and frost trends using the F/T dataset. The mean annual number of frost days of high northern latitudes (>45° N) is 279.2 ± 44.1 days. Based on Mann-Kendall’s tau-b test, 7.7% of global lands show a significant warming trend, and most of which are concentrated in the Western United States, Northern and Eastern Canada, Northern Europe and Western China. Such a spatial distribution was found to be consistent with the global land surface temperature anomalies trend from 2002 to 2018. Both the results of applications and favourable accuracy indicate the potential of this long, consistent F/T record to track global temperature change.     

Spatiotemporal variability of snow cover on Tibet,China using MODIS remote-sensing data

ABSTRACT

Snow cover is an important component of the cryosphere, and the study on spatial and temporal variations of snow cover is essential for understanding the consequences and impacts of climate change and water resources management. In this study, the temporal variation of snow-covered area (SCA) and spatial variability of snow-cover frequency (SCF) on Tibet is analysed based on the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra snow cover product (MOD10A2) from 2000 to 2015, and relationships with main climate variables are investigated. Results are as follows: (1) there is a very weak decreasing trend in annual mean SCA, and a slight increasing trend in autumn and winter and a slight decreasing trend in spring and more robust decreasing trend in summer for SCA are found. (2) The temporal variation of SCA is negatively correlated with temperature, whereas it is little correlated with corresponding precipitation. (3) The general trend of spatial SCF variation on Tibet, predominated by snow-cover variations in spring and autumn, tends to decrease in spring while it tends to increase in autumn. (4) The spatial variability of SCF is attributed to snow-cover variations in autumn and spring, which is more obvious in higher latitudes in autumn while it is more noticeable in lower-latitude southeastern plateau in spring. (5) The regions with higher variability of snow cover are main pastoral land and more prone to snow-related disaster in Tibet, becoming key zone of snow-cover monitoring and disaster prevention and mitigation.     

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote-sensing methods have the spatial and temporal resolution required to map hazard increases. Here, a dynamic physically-based slope stability model that requires soil moisture is applied using remote-sensing products from multiple Earth observing platforms. The resulting landslide susceptibility maps using the advanced microwave scanning radiometer (AMSR-E) surface soil moisture are compared to those created using variable infiltration capacity (VIC-3L) modeled soil moisture at Cleveland Corral landslide area in California, US. Despite snow cover influences on AMSR-E surface soil moisture estimates, a good relationship between the downscaled AMSR-E's surface soil moisture and the VIC-3L modeled soil moisture is evident. The AMSR-E soil moisture mean (0.17 cm³/cm³) and standard deviation (0.02 cm³/cm³) are very close to the mean (0.21 cm³/cm³) and standard deviation (0.09 cm³/cm³) estimated by VIC-3L model. Qualitative results show that the location and extent of landslide prone regions are quite similar. Under the maximum saturation scenario, 0.42% and 0.49% of the study area were highly susceptible using AMSR-E and VIC-3L model soil moisture, respectively.     

Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency

A deterministic approach for downscaling ～ 40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km). Four different analytic downscaling relationships are derived from MODIS and physically-based model predictions of soil evaporative efficiency. The four downscaling algorithms differ with regards to i) the assumed relationship (linear or nonlinear) between soil evaporative efficiency and near-surface soil moisture, and ii) the scale at which soil parameters are available (40 km or 10 km). The 1 km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40 km by 60 km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square difference between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the downscaling algorithm used, with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS.     

Quality assessment of Landsat surface reflectance products using MODIS data

Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat-MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions.     

Evaluating drought response of Southern Cape Indigenous Forests,South Africa,using MODIS data

Indigenous forests in South Africa cover less than 0.5% of the total land area but are a valued resource under threat from fragmentation, fires, exploitation, and climate change. The largest indigenous forest complex is located along the southern coast of the Western Cape Province. This complex is made up of sub-forests distinguished by different structural and edaphoclimatic attributes. It has been hypothesized that these sub-forests exhibit different resistance to stressors, such as drought. A time series of MODIS 250 m enhanced vegetation index (EVI) data were used to characterize the foliage condition of the three distinctive sub-forests before, during, and after a severe drought in 2009. The goal was to determine how these sub-forests responded to this disturbance. EVI anomalies for the drought and post-drought periods were calculated using annual median EVI values, since removal of outliers based on quality control flags that accompany the MODIS products or noise-filtering techniques proved to be ineffective. Results of the study indicated that pre-drought foliage density EVI was not controlled by differences in water availability, but may have been due to other edaphoclimatic or structural attributes. Maximum foliage loss occurred one year after the driest year, indicating the cumulative effects of drought stress on forest production and retention of foliage. The hypothesized stress resistance capacity of the three sub-forests was found to correspond to their rate of post-drought recovery. There is a need to tie these satellite observations of forest drought response to ground observations of forest condition, growth, and specific site attributes.     

Optimal interpolation analysis of leaf area index using MODIS data

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σ_o and σ_c. The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002-2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.     

Impacts of hydrologic soil properties on drought detection with MODIS thermal data

Remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), a climatic water budget model, and the STATSGO database were used within a GIS environment to determine the influences of hydrologic soil properties on soil moisture and thermal emission in western-central Kansas for a dry year, 2000. Two important variables, water-holding capacity (WHC) and hydrologic soil group (HSG), were controlled in our water budget experiment to evaluate their impacts on soil moisture content (SMC) changes throughout the period. Results showed that HSG affected drought detection and occurrence very little, but WHC variations explained most local variations of soil moisture content. As a strong indicator of relative soil moisture deficit, the Standardized Thermal Index (STI) patterns were also influenced by WHC. Generally, the earlier the soil moisture content drops below 40%, the earlier the STI reaches a threshold value of 0.2 or higher. Vegetation responses to thermal detection lagged behind the STI by up to 8 weeks, which was computed by comparing the STI and Normalized Difference Vegetation Index (NDVI) deviation from a 10-year mean. The spatial pattern of lag-times was not apparent, but lag-times were correlated with a WHC component.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Estimation of winter wheat phenology under the influence of cumulative temperature and soil salinity in the Yellow River Delta,China, using MODIS time-series data

Understanding the impact of environmental factors on crop phenology is significant in predicting crop growth stages, agricultural decision-making, and yield estimation. Here, using Moderate Resolution Imaging Spectroradiometer time-series data, we present phenological detection mechanisms and an explanation for the phenological variability linked to environmental drivers, such as cumulative temperature and soil salinity, for winter wheat (Triticum aestivum L.) in the Yellow River Delta in 2013. The 8-day normalized difference vegetation index was fitted to a double Gaussian function. Phenological phases, such as the green-up and heading phases, were extracted using maximum curvature approaches. The spatial characteristics of the phenological patterns were investigated. The relationships between the phenological phases and cumulative temperature were explored. Then, the relationships between the phenological phases and soil salinity were evaluated by selecting sites with similar soil fertility and temperature forcing. This study concluded that the regional average green-up date occurred on 5 March, and the regional average heading date occurred on 9 May. The spatial distributions of the green-up and heading phases showed a gradual delay from the southwest to the northeast and from the south to the north. The green-up phase lagged 4–5 days for every 10 degree days that the cumulative temperature decreased. The heading phase lagged 1–2 days for every 10 degree days that the cumulative temperature decreased. The green-up phase in a non-salinization region might be approximately 5–9 days earlier than that in a severe or moderate salinization region. The heading phase in a severe region might occur approximately 1–8 days earlier than that in a non-salinization or moderate salinization region. The method proposed in this article may be useful for understanding the impact of temperature and soil salinity on phenology and could be used to better manage winter wheat in coastal salinization areas.     

Land surface response to precipitation events using MODIS and NEXRAD data

We have developed a wavelet‐based information theoretic approach to examine the interaction between precipitation (PPT) forcing events and the land surface response. Combining Next Generation Weather Radar (NEXRAD) PPT with Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation (NDVI) and surface temperature (T _s) data over the Missouri Basin in the north‐central USA, we are able to address the spatial and temporal fluctuations surrounding the hydrometeorology of grassland ecosystems. Information theory metrics of entropy and mutual information content are combined with a wavelet multi‐resolution analysis to examine to what extent the observed PPT signal directly determines the spatial distribution of the land surface temperature and vegetation and how this relationship varies with spatial scale. Results indicate that (1) there is a reduction in the temporal variance of the wavelet coefficients as the signal is transferred from the PPT into the surface temperature and finally the vegetation signal, (2) there are significant correlations as a function of spatial resolution between PPT–NDVI and PPT–T _s signals which generally increase with spatial resolution, while there is little correlation between the NDVI and T _s signals as a function of resolution, and (3) the scale‐wise entropy and the mutual information content of the signals increase for all fields as the spatial resolution increases. This provides a methodology for determining the relative impact of regional climatology and local land–atmosphere interactions as a function of spatial scale.