Passive microwave soil moisture datasets can be used as an input to provide an integrated assessment of climate variability as it relates to agricultural production. The objective of this research was to examine three passive microwave derived soil moisture datasets over multiple growing seasons in contrasting Canadian agricultural environments. Absolute and relative soil moisture was evaluated from two globally available datasets from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) sensor using different retrieval algorithms, as well as relative soil wetness at a weekly scale from the Special Sensor Microwave/Imager (SSM/I) sensor. At a daily scale, the Land Parameter Retrieval Model (LPRM) provides a better estimate of surface soil moisture conditions than the National Snow and Ice Data Center (NSIDC) dataset, with root mean squared errors ranging from 5 to 10% for LPRM and 12 to 18% for NSIDC soil moisture when a temporal smoothing is applied to the dataset. Both datasets provided better estimates of soil moisture over the temperate site near Elora, Ontario than the prairie site near Davidson, Saskatchewan. The LPRM dataset tends to overestimate soil moisture conditions at both sites, where the NSIDC dataset tends to underestimate absolute soil moisture. These differences in retrieval methods were independent of radiometric frequency used. At weekly scales, the LPRM dataset provides a better relative estimate of wetness conditions when compared to the NSIDC and the Basist Wetness Index (BWI) from SSM/I data, but the SSM/I dataset did provide a reasonably good relative indicator of moisture conditions. The high variability in accuracy of soil moisture estimation related to retrieval algorithms indicates that consistency is needed in these datasets if they are to be integrated in long term studies for yield estimation or data assimilation.

Soil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO₂ in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R²) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R² of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features).

Cropland productivity is impacted by climate. Knowledge on spatial-temporal patterns of the impacts at the regional scale is extremely important for improving crop management under limiting climatic factors. The aim of this study was to investigate the effects of climate variability on cropland productivity in the Canadian Prairies between 2000 and 2013 based on time series of MODIS (Moderate Resolution Imaging Spectroradiometer) FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) product. Key phenological metrics, including the start (SOS) and end of growing season (EOS), and the cumulative FAPAR (CFAPAR) during the growing season (between SOS and EOS), were extracted and calculated from the FAPAR time series with the Parametric Double Hyperbolic Tangent (PDHT) method. The Mann-Kendall test was employed to assess the trends of cropland productivity and climatic variables, and partial correlation analysis was conducted to explore the potential links between climate variability and cropland productivity. An assessment using crop yield statistical data showed that CFAPAR can be taken as a surrogate of cropland productivity in the Canadian Prairies. Cropland productivity showed an increasing trend in most areas of Canadian Prairies, in general, during the period from 2000 to 2013. Interannual variability in cropland productivity on the Canadian Prairies was influenced positively by rainfall variation and negatively by mean air temperature.

Satellite soil moisture is a critical variable for identifying susceptibility to hydroclimatic risks such as drought, dryness, and excess moisture. Satellite soil moisture data from the Soil Moisture Active/Passive (SMAP) mission was used to evaluate the sensitivity to hydroclimatic risk events in Canada. The SMAP soil moisture data sets in general capture relative moisture trends with the best estimates from the passive-only derived soil moisture and little difference between the data at different spatial resolutions. In general, SMAP data sets overestimated the magnitude of moisture at the wet extremes of wetting events. A soil moisture difference from average (SMDA) was calculated from SMAP and historical Soil Moisture and Ocean Salinity (SMOS) data showed a relatively good delineation of hydroclimatic risk events, although caution must be taken due to the large variability in the data within risk categories. Satellite soil moisture data sets are more sensitive to short term water shortages than longer term water deficits. This was not improved by adding “memory” to satellite soil moisture indices to improve the sensitivity of the data to drought, and there is a large variability in satellite soil moisture values with the same drought severity rating.