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Abstract: Surface water inundation in the Arctic–Boreal region is dynamic and strongly influences land-atmosphere water, energy and carbon (CO2, CH4) fluxes, and potential feedbacks to climate change. Here we report on recent (2003–2010) surface inundation patterns across the Arctic–Boreal region (≥50°N) and within major permafrost (PF) zones detected using satellite passive microwave remote sensing retrievals of daily fractional open water (Fw) cover from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E). The AMSR-E Fw (25-km resolution) maps reflect strong microwave sensitivity to sub-grid scale open water variability and compare favorably (0.71≤ R 2 ≤0.84) with alternative, static Fw maps derived from finer scale (30-m to 250-m resolution) Landsat, MODIS and SRTM radar (MOD44W) data. The AMSR-E retrievals show dynamic seasonal and annual variability in surface inundation that is unresolved in the static Fw maps. The AMSR-E Fw record also corresponds strongly (0.71≤ R ≤0.87) with regional wet/dry cycles inferred from basin discharge records. An AMSR-E algorithm sensitivity analysis shows a conservative estimate of Fw retrieval uncertainty (RMSE) within ±4.1% for effective resolution of regional inundation patterns and seasonal to annual variability. A regional trend analysis of the 8-year AMSR-E record shows no significant Arctic–Boreal region wide Fw trend for the period, and instead reveals contrasting inundation changes within different PF zones. Widespread Fw wetting is detected within continuous (92% of grid cells with significant trend; p <0.1) and discontinuous (82%) PF zones, while sporadic/isolated PF areas show widespread (71%) Fw drying trends. These results are consistent with previous studies showing evidence of contrasting regional inundation patterns linked to PF degradation and associated changes to surface hydrology under recent climate warming. [Copyright &y& Elsevier]

Abstract: Conservation tillage management has been advocated for carbon sequestration and soil quality preservation purposes. Past satellite image analyses have had difficulty in differentiating between no-till (NT) and minimal tillage (MT) conservation classes due to similarities in surface residues, and may have been restricted by the availability of cloud-free satellite imagery. This study hypothesized that the inclusion of high temporal data into the classification process would increase conservation tillage accuracy due to the added likelihood of capturing spectral changes in MT fields following a tillage disturbance. Classification accuracies were evaluated for Random Forest models based on 250-m and 500-m MODIS, 30-m Landsat, and 30-m synthetic reflectance values. Synthetic (30-m) data derived from the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) were evaluated because high frequency Landsat image sets are often unavailable within a cropping season due to cloud issues. Classification results from a five-date Landsat model were substantially better than those reported by previous classification tillage studies, with 94% total and ≥88% class producer''s accuracies. Landsat-derived models based on individual image scenes (May through August) yielded poor MT classifications, but a monthly increase in accuracy illustrated the importance of temporal sampling for capturing regional tillage disturbance signatures. MODIS-based model accuracies (90% total; ≥82% class) were lower than in the five-date Landsat model, but were higher than previous image-based and survey-based tillage classification results. Almost all the STARFM prediction-based models had classification accuracies higher than, or comparable to, the MODIS-based results (>90% total; ≥84% class) but the resulting model accuracies were dependent on the MODIS/Landsat base pairs used to generate the STARFM predictions. Also evident within the STARFM prediction-based models was the ability for high frequency data series to compensate for degraded synthetic spectral values when classifying field-based tillage. The decision to use MODIS or STARFM-based data within conservation tillage analysis is likely situation dependent. A MODIS-based approach requires little data processing and could be more efficient for large-area mapping; however a STARFM-based analysis might be more appropriate in mixed-pixel situations that could potentially compromise classification accuracy. [Copyright &y& Elsevier]

Abstract: We used an object-oriented approach in conjunction with the Random Forest algorithm to classify agricultural practices, including tillage (till or no-till (NT)), crop intensity, and grassland-based conservation reserve (CR). The object-oriented approach allowed for per-field classifications and the incorporation of contextual elements in addition to spectral features. Random Forest is a classification tree-based advanced classifier that avoids data over-fitting associated with many tree-based models and incorporates an unbiased internal classification accuracy assessment. Landsat satellite imagery was chosen for its continuous coverage, cost effectiveness, and image accessibility. Classification results for 2007 included producer''s accuracies of 91% for NT and 31% for tillage when applying Random Forest to image objects generated from a May Landsat image. Low classification accuracies likely were attributed to the misclassification of conservation-based tillage practices as NT. Results showed that the binary separation of tillage from NT management is likely not appropriate due to surface spectral and textural similarities between NT and conservation-type tillage practices. Crop and CR lands resulted in producer''s accuracies of 100% and 90%, respectively. Crop and fallow producer''s accuracies were 95% and 82% in the 2007 classification, despite post-senesced vegetation; misclassification within the fallow class was attributed to pixel-mixing problems in areas of narrow (<100 m) strip management. A between-date normalized difference vegetation index approach was successfully used to detect areas having “changed” in vegetation status between the 2007 and prior image dates; classified “changed” objects were then merged with “unchanged” objects to produce crop status maps. Field crop intensity was then determined from the multi-year analysis of generated crop status maps. [Copyright &y& Elsevier]

A method to assess global land surface water ( fw ) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw ( fw LBand ) retrievals were derived using SMAP H-polarization brightness temperature ( T b ) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency T b observations from AMSR2. The resulting fw LBand global record has high temporal sampling (1–3 days) and 36-km spatial resolution. The fw LBand annual averages corresponded favorably ( R  = 0.85, p - value  < 0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly fw LBand averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable fw LBand performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) fw LBand results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m fw LBand retrievals showed favorable spatial accuracy for water (commission error 31.46%, omission error 30.20%) and land (commission error 0.87%, omission error 0.96%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new fw LBand algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk. [ABSTRACT FROM AUTHOR]