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Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0–5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016–2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) andin situweather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.

 

The changing thermal state of permafrost is an important indicator of climate change in northern high latitude ecosystems. The seasonally thawed soil active layer thickness (ALT) overlying permafrost may be deepening as a consequence of enhanced polar warming and widespread permafrost thaw in northern permafrost regions (NPRs). The associated increase in ALT may have cascading effects on ecological and hydrological processes that impact climate feedback. However, past NPR studies have only provided a limited understanding of the spatially continuous patterns and trends of ALT due to a lack of long-term high spatial resolution ALT data across the NPR. Using a suite of observational biophysical variables and machine learning (ML) techniques trained with availablein situALT network measurements (n= 2966 site-years), we produced annual estimates of ALT at 1 km resolution over the NPR from 2003 to 2020. Our ML-derived ALT dataset showed high accuracy (R²= 0.97) and low bias when compared within situALT observations. We found the ALT distribution to be most strongly affected by local soil properties, followed by topographic elevation and land surface temperatures. Pair-wise site-level evaluation between our data-driven ALT with Circumpolar Active Layer Monitoring data indicated that about 80% of sites had a deepening ALT trend from 2003 to 2020. Based on our long-term gridded ALT data, about 65% of the NPR showed a deepening ALT trend, while the entire NPR showed a mean deepening trend of 0.11 ± 0.35 cm yr⁻¹[25%–75% quantile: (−0.035, 0.204) cm yr⁻¹]. The estimated ALT trends were also sensitive to fire disturbance. Our new gridded ALT product provides an observationally constrained, updated understanding of the progression of thawing and the thermal state of permafrost in the NPR, as well as the underlying environmental drivers of these trends.

 

Increasing climate aridity and drought, exacerbated by global warming, are increasing risks for western United States of America (U.S.A.) rainfed farming, and challenging producers’ capacity to maintain production and profitability. With agricultural water demand in the region exceeding limited supplies and fewer opportunities to develop new water sources, rainfed agriculture is under increasing pressure to meet the nation’s growing food demands. This study examines three major western U.S.A. rainfed crops: barley, spring wheat, and winter wheat. We analyzed the relationship between crop repurposing (the ratio of acres harvested for grain to the total planted acres) to seasonal climatic water deficit (CWD). To isolate the climate signal from economic factors, our analysis accounted for the influence of crop prices on grain harvest. We used historical climate and agricultural data between 1958 and 2020 to model crop repurposing (e.g. forage) across the observed CWD record using a fixed effect model. Our methodology is applicable for any region and incorporates regional differences in farming and economic drivers. Our results indicate that farmers are less likely to harvest barley and spring wheat for grain when the spring CWD is above average. Of the major winter wheat growing regions, only the Northern High Plains in Texas showed a trend of decreasing grain harvest during high CWD. For the majority of major crop growing regions, grain prices increased with lower levels of grain harvest. Interestingly, winter wheat repurposing is significantly higher in the southern Great Plains (∼50% harvested for grain) compared to the rest of the West (∼90%). Our results highlight that the major barley and spring wheat regions’ grain harvests are vulnerable to high spring CWD and low summer CWD, while winter wheat grain harvest is unaffected by variable CWD in most of the West.

 

The Western United States (U.S.) relies heavily on scarce water resources for both ecological services and irrigation. However, the response of irrigation water use during drought is not well documented. Irrigation decision‐making is complex and influenced by human and environmental factors such as water deliveries, crop yields, equipment, labor, crop prices, and climate variability. While few irrigation districts have plans to curtail water deliveries during droughts, water rights, fallowing patterns, crop rotations, and profit expectations also influence irrigation management at the farm scale. This study uses high‐resolution satellite data to examine the response of irrigators to drought by using a novel measure of irrigation management, the Standardized Irrigation Management Index. We assess the state of drought at the field and basin scales in terms of climate and streamflow and analyze the importance of variations in crop price and drought status on decision‐making and water use. We show significant variability in field‐scale response to drought and that crop type, irrigation type, and federal management explain regional and field‐scale differences. The relative influence of climate and prices on crop transitions indicate prices more strongly drive crop planting decisions. The study provides insights into irrigation management during drought, which is crucial for sustainable water supply in the face of the ongoing water supply crisis in the U.S. Southwest.

 

The Yukon River basin encompasses over 832,000 km2 of boreal Arctic Alaska and northwest Canada, providing a major transportation corridor and multiple natural resources to regional communities. The river seasonal hydrology is defined by a long winter frozen season and a snowmelt-driven spring flood pulse. Capabilities for accurate monitoring and forecasting of the annual spring freshet and river ice breakup (RIB) in the Yukon and other northern rivers is limited, but critical for understanding hydrologic processes related to snow, and for assessing flood-related risks to regional communities. We developed a regional snow phenology record using satellite passive microwave remote sensing to elucidate interactions between the timing of upland snowmelt and the downstream spring flood pulse and RIB in the Yukon. The seasonal snow metrics included annual Main Melt Onset Date (MMOD), Snowoff (SO) and Snowmelt Duration (SMD) derived from multifrequency (18.7 and 36.5 GHz) daily brightness temperatures and a physically-based Gradient Ratio Polarization (GRP) retrieval algorithm. The resulting snow phenology record extends over a 29-year period (1988–2016) with 6.25 km grid resolution. The MMOD retrievals showed good agreement with similar snow metrics derived from in situ weather station measurements of snowpack water equivalence (r = 0.48, bias = −3.63 days) and surface air temperatures (r = 0.69, bias = 1 day). The MMOD and SO impact on the spring freshet was investigated by comparing areal quantiles of the remotely sensed snow metrics with measured streamflow quantiles over selected sub-basins. The SO 50% quantile showed the strongest (p < 0.1) correspondence with the measured spring flood pulse at Stevens Village (r = 0.71) and Pilot (r = 0.63) river gaging stations, representing two major Yukon sub-basins. MMOD quantiles indicating 20% and 50% of a catchment under active snowmelt corresponded favorably with downstream RIB (r = 0.61) from 19 river observation stations spanning a range of Yukon sub-basins; these results also revealed a 14–27 day lag between MMOD and subsequent RIB. Together, the satellite based MMOD and SO metrics show potential value for regional monitoring and forecasting of the spring flood pulse and RIB timing in the Yukon and other boreal Arctic basins.