A lack of liquid water limits life on glaciers worldwide but specialized microbes still colonize these environments. These microbes reduce surface albedo, which, in turn, could lead to warming and enhanced glacier melt. Here we present results from a replicated, controlled field experiment to quantify the impact of microbes on snowmelt in red-snow communities. Addition of nitrogen-phosphorous-potassium fertilizer increased alga cell counts nearly fourfold, to levels similar to nitrogen-phosphorusenriched lakes; water alone increased counts by half. The manipulated alga abundance explained a third of the observed variability in snowmelt. Using a normalized-di erence spectral index we estimated alga abundance from satellite imagery and calculated microbial contribution to snowmelt on an icefield of 1,900 km 2 . The red-snow area extended over about 700 km 2 , and in this area we determined that microbial communities were responsible for 17% of the total snowmelt there. Our results support hypotheses that snow-dwelling microbes increase glacier melt directly in a bio-geophysical feedback by lowering albedo and indirectly by exposing low-albedo glacier ice. Radiative forcing due to perennial populations of microbes may match that of non-living particulates at high latitudes. Their contribution to climate warming is likely to grow with increased melt and nutrient input.G lacier ablation is sensitive to changes in albedo 1 , with atmospheric 2,3 , hydrological 4 and ecological 5,6 consequences. Fresh snow reflects >90% of visible radiation, but during melt its grain size and water content increase, reducing albedo and further increasing snowmelt 1 . Impurities, including black carbon 3 , dust 4 , and resident microbes [7][8][9][10][11][12][13][14][15][16][17][18][19] , also lower albedo; however, microbes differ from non-living particulates in several critical ways. Perennial populations of photosynthetic microbes actively resurface following overwinter burial by snow 20 , and depend on liquid water and nutrients for survival and reproduction 13,14,[20][21][22] . This requirement for liquid water in a frozen environment imposes a selective force favouring a physiology that increases melt proximal to cell walls. The generation of meltwater through microbes' albedoreducing properties motivates an hypothesis of bio-geophysical feedback on glacial landscapes 13,16 , such as the Greenland ice sheet. This feedback hypothesis, whereby microbes increase because they produce needed meltwater, is an active research area [13][14][15][16][17][18][19] , yet field experiments testing its assumptions are absent.Glacier microbiomes are water-limited 21,22 , because ice is generally not metabolically available, and oligotrophic, because their nutrient content equals that of precipitation plus deposition by airborne dust, pollen, and so on, with only limited N-fixation by local cyanobacteria 21,22 . Moreover, rapidly percolating water through large-grained snow may exacerbate both water-and nutrient limitation for algae in supraglacial...

A lack of liquid water limits life on glaciers worldwide but specialized microbes still colonize these environments. These microbes reduce surface albedo, which, in turn, could lead to warming and enhanced glacier melt. Here we present results from a replicated, controlled field experiment to quantify the impact of microbes on snowmelt in red-snow communities. Addition ofnitrogen–phosphorous–potassium fertilizer increased alga cell counts nearly fourfold, to levels similar to nitrogen–phosphorusenriched lakes; water alone increased counts by half. The manipulated alga abundance explained a third of the observed variability in snowmelt. Using a normalized-dierence spectral index we estimated alga abundance from satellite imagery and calculated microbial contribution to snowmelt on an icefield of 1,900 km2. The red-snow area extended over about 700 km2, and in this area we determined that microbial communities were responsible for 17% of the total snowmelt there. Our results support hypotheses that snow-dwelling microbes increase glacier melt directly in a bio-geophysical feedback by lowering albedo and indirectly by exposing low-albedo glacier ice. Radiative forcing due to perennial populations of microbes maymatch that of non-living particulates at high latitudes. Their contribution to climate warming is likely to grow with increased melt and nutrient input.

Recently, agricultural remote sensing community has endeavored to utilize the power of artificial intelligence (AI). One important topic is using AI to make the mapping of crops more accurate, automatic, and rapid. This article proposed a classification workflow using deep neural network (DNN) to produce high-quality in-season crop maps from Landsat imageries for North Dakota. We use historical crop maps from the agricultural department and North Dakota ground measurements as training datasets. Processing workflows are created to automate the tedious preprocessing, training, testing, and postprocessing workflows. We tested this hybrid solution on new images and received accurate results on major crops such as corn, soybean, barley, spring wheat, dry beans, sugar beets, and alfalfa. The pixelwise overall accuracy in all three test regions is over 82% for all land types (including noncrop land), which is the same level of accuracy as the U.S. Department of Agriculture Cropland Data Layer. The texture of DNN maps is more consistent with fewer noises, which is more comfortable to read. We find DNN is better on recognizing big farmlands than recognizing the scattered wetlands and suburban regions in North Dakota. The model trained on multiple scenes of multiple years and months yields higher accuracy than any of the models trained only on a single scene, a single month, or a single year. These results reflect that DNN can produce reliable in-season maps for major crops in North Dakota big farms and could provide a relatively accurate reference for the minor crops in scattered wetland fields.

AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research.

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