Assessing the influence of vegetation cover on soil-moisture signatures in fire-disturbed boreal forests in interior Alaska: Modelled results

Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1 km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1 km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014–2018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014–2018). The field was seeded for the 2014–2015 (S1), 2016–2017 (S2) and 2017–2018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015–2016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated αPT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved αPT remains at a mostly constant value (∼ 0.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181 W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62 W/m2 for S1, S2, S3 and B1 respectively.

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Towards an integrated soil moisture drought monitor for East Africa

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-4587-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 4587-4631 ◽

Cited By ~ 6

Author(s):

W. B. Anderson ◽

B. F. Zaitchik ◽

C. R. Hain ◽

M. C. Anderson ◽

M. T. Yilmaz ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

East Africa ◽

Remote Monitoring ◽

Vegetation Cover ◽

Land Surface ◽

Microwave Remote Sensing ◽

Surface Model ◽

Monitoring Methods ◽

Data Scarcity

Abstract. Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA) to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, and physically-based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the consistency of anomaly estimates across independent methods. This approach holds potential as a remote soil moisture-based drought monitoring system that is robust across the diverse climatic and ecological zones of East Africa.

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Land-Cover Dependent Relationships between Fire and Soil Moisture

Fire ◽

10.3390/fire2040055 ◽

2019 ◽

Vol 2 (4) ◽

pp. 55 ◽

Cited By ~ 1

Author(s):

Alexander J. Schaefer ◽

Brian I. Magi

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Cover ◽

Remote Sensing Data ◽

Sensing Data ◽

Fire Activity ◽

Steep Decline ◽

Human Use ◽

Moisture Conditions ◽

In Fire

For this study, we characterized the dependence of fire counts (FCs) on soil moisture (SM) at global and sub-global scales using 15 years of remote sensing data. We argue that this mathematical relationship serves as an effective way to predict fire because it is a proxy for the semi-quantitative fire–productivity relationship that describes the tradeoff between fuel availability and climate as constraints on fire activity. We partitioned the globe into land-use and land-cover (LULC) categories of forest, grass, cropland, and pasture to investigate how the fire–soil moisture (fire–SM) behavior varies as a function of LULC. We also partitioned the globe into four broadly defined biomes (Boreal, Grassland-Savanna, Temperate, and Tropical) to study the dependence of fire–SM behavior on LULC across those biomes. The forest and grass LULC fire–SM curves are qualitatively similar to the fire–productivity relationship with a peak in fire activity at intermediate SM, a steep decline in fire activity at low SM (productivity constraint), and gradual decline as SM increases (climate constraint), but our analysis highlights how forests and grasses differ across biomes as well. Pasture and cropland LULC are a distinctly human use of the landscape, and fires detected on those LULC types include intentional fires. Cropland fire–SM curves are similar to those for grass LULC, but pasture fires are evident at higher SM values than other LULC. This suggests a departure from the expected climate constraint when burning is happening at non-optimal flammability conditions. Using over a decade of remote sensing data, our results show that quantifying fires relative to a single physical climate variable (soil moisture) is possible on both cultivated and uncultivated landscapes. Linking fire to observable soil moisture conditions for different land-cover types has important applications in fire management and fire modeling.

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Splitting in Fire-Killed Trees in the Boreal Forest of Alberta

Northern Journal of Applied Forestry ◽

10.1093/njaf/20.4.167 ◽

2003 ◽

Vol 20 (4) ◽

pp. 167-174

Author(s):

Nobutaka Nakamura ◽

Paul M. Woodard ◽

Lars Bach

Keyword(s):

Boreal Forest ◽

Boreal Forests ◽

Black Spruce ◽

Lodgepole Pine ◽

White Spruce ◽

Balsam Fir ◽

Crown Fire ◽

In Fire ◽

Solar Angle

Abstract Tree boles in the boreal forests of Alberta, Canada will split once killed by a stand-replacing crown fire. A total of 1,485 fire-killed trees were sampled, 1 yr after burning, in 23 plots in 14 widely separated stands within a 370,000 ha fire. Sampling occurred in the Upper and Lower Foothills natural subregions. The frequency of splitting varied by species but averaged 41% for all species. The order in the frequency of splitting was balsam fir, black spruce, white spruce and lodgepole pine. The type of splitting (straight, spiral, or multiple) varied by species, as did the position of the split on the tree bole. Aspect or solar angle was not statistically related to the type or occurrence of splitting.

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An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data

Remote Sensing ◽

10.3390/rs11232736 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2736 ◽

Cited By ~ 5

Author(s):

Jueying Bai ◽

Qian Cui ◽

Wen Zhang ◽

Lingkui Meng

Keyword(s):

Soil Moisture ◽

Vegetation Cover ◽

Land Surface ◽

Feature Space ◽

Entire Study ◽

Vegetation Indexes ◽

Input Variables ◽

Entire Study Area ◽

Infrared Data

A method is proposed for the production of downscaled soil moisture active passive (SMAP) soil moisture (SM) data by combining optical/infrared data with synthetic aperture radar (SAR) data based on the random forest (RF) model. The method leverages the sensitivity of active microwaves to surface SM and the triangle/trapezium feature space among vegetation indexes (VIs), land surface temperature (LST), and SM. First, five RF architectures (RF1–RF5) were trained and tested at 9 km. Second, a comparison was performed for RF1–RF5, and were evaluated against in situ SM measurements. Third, two SMAP-Sentinel active–passive SM products were compared at 3 km and 1 km using in situ SM measurements. Fourth, the RF5 model simulations were compared with the SMAP L2_SM_SP product based on the optional algorithm at 3 km and 1 km resolutions. The results showed that the downscaled SM based on the synergistic use of optical/infrared data and the backscatter at vertical–vertical (VV) polarization was feasible in semi-arid areas with relatively low vegetation cover. The RF5 model with backscatter and more parameters from optical/infrared data performed best among the five RF models and was satisfactory at both 3 km and 1 km. Compared with L2_SM_SP, RF5 was more superior at 1 km. The input variables in decreasing order of importance were backscatter, LST, VIs, and topographic factors over the entire study area. The low vegetation cover conditions probably amplified the importance of the backscatter and LST. A sufficient number of VIs can enhance the adaptability of RF models to different vegetation conditions.

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Resilience of the boreal forest in response to Holocene fire-frequency changes assessed by pollen diversity and population dynamics

International Journal of Wildland Fire ◽

10.1071/wf09097 ◽

2010 ◽

Vol 19 (8) ◽

pp. 1026 ◽

Cited By ~ 36

Author(s):

Christopher Carcaillet ◽

Pierre J. H. Richard ◽

Yves Bergeron ◽

Bianca Fréchette ◽

Adam A. Ali

Keyword(s):

Boreal Forest ◽

Boreal Forests ◽

Frequency Control ◽

Regional Scale ◽

Fire Regimes ◽

Fire Frequency ◽

Frequency Changes ◽

Fire Intervals ◽

In Fire

The hypothesis that changes in fire frequency control the long-term dynamics of boreal forests is tested on the basis of paleodata. Sites with different wildfire histories at the regional scale should exhibit different vegetation trajectories. Mean fire intervals and vegetation reconstructions are based respectively on sedimentary charcoal and pollen from two small lakes, one in the Mixedwood boreal forests and the second in the Coniferous boreal forests. The pollen-inferred vegetation exhibits different trajectories of boreal forest dynamics after afforestation, whereas mean fire intervals have no significant or a delayed impact on the pollen data, either in terms of diversity or trajectories. These boreal forests appear resilient to changes in fire regimes, although subtle modifications can be highlighted. Vegetation compositions have converged during the last 1200 years with the decrease in mean fire intervals, owing to an increasing abundance of boreal species at the southern site (Mixedwood), whereas changes are less pronounced at the northern site (Coniferous). Although wildfire is a natural property of boreal ecosystems, this study does not support the hypothesis that changes in mean fire intervals are the key process controlling long-term vegetation transformation. Fluctuations in mean fire intervals alone do not explain the historical and current distribution of vegetation, but they may have accelerated the climatic process of borealisation, likely resulting from orbital forcing.

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