Journal Paper Digests

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Journal Paper Digests 2021 #13

  • Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future
  • Estimation of soil water retention in conservation agriculture using published and new pedotransfer functions
  • On the interpretation of surprisingly high variation of soil map diversity in country-wide study of flood-affected agroecosystems using the legacy data in the Czech Republic
  • Building a resilient and sustainable food system in a changing world -& nbsp;A case for climate-smart and nutrient dense crops

Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future

Satellite based remote sensing offers one of the few approaches able to monitor the spatial and temporal development of regional to continental scale droughts. A unique element of remote sensing platforms is their multi-sensor capability, which enhances the capacity for characterizing drought from a variety of perspectives. Such aspects include monitoring drought influences on vegetation and hydrological responses, as well as assessing sectoral impacts (e.g., agriculture). With advances in remote sensing systems along with an increasing range of platforms available for analysis, this contribution provides a timely and systematic review of multi sensor remote sensing drought studies, with a particular focus on drought related datasets, drought related phenomena and mechanisms, and drought modeling. To explore this topic, we first present a comprehensive summary of large-scale remote sensing datasets that can be used for multi-sensor drought studies. We then review the role of multi-sensor remote sensing for exploring key drought related phenomena and mechanisms, including vegetation responses to drought, land-atmospheric feedbacks during drought, drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, as well as drought trends under climate change. A summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices is also provided. We conclude that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts. We specifically highlight that more mechanism-oriented drought studies that leverage a combination of sensors and techniques (e.g., optical, microwave, hyperspectral, LiDAR, and constellations) across a range of spatiotemporal scales are needed in order to progress and advance our understanding, characterization and description of drought in the future.

Estimation of soil water retention in conservation agriculture using published and new pedotransfer functions

Conservation agriculture has been developed as a means to improve the sustainability of agricultural systems and reduce drawbacks of conventional agricultural practices. Cropping practices can have a large influence on soil properties such as water retention. Proper tools are needed to assess and model effects of conservation agriculture on soil properties. As measuring soil water retention is expensive and time consuming, pedotransfer functions (PTFs) are now commonly used to predict them. The objectives of this study were to (i) present a new dataset of conservation agriculture data, (ii) assess performances of existing PTFs in predicting soil water retention of soils under conservation agriculture and (iii) develop new specific PTFs to predict water retention in conservation agriculture more accurately. We used data collected only in fields under conservation agriculture in France to evaluate several published PTFs with three evaluation criteria (RMSE, prediction bias (ME) and Nash-Sutcliffe Efficiency (EF)). We then developed new PTFs using three methods - multiple linear regression, regression tree and random forest - to predict soil water content at matric heads of -100 (theta(100), field capacity for sandy soils), -330 (theta(330), field capacity for other soils) and -15 000 cm (theta(15) 000, wilting point). Soil tillage, presence of a cover crop, rotation length and previous reduced/no tillage were used as predictors in addition to basic soil properties for regression trees and random forests. The quality of prediction (RMSE, ME and EF) was calculated for each new PTF using a cross-validation procedure. Generally, predictions of wilting point had lower absolute error than those of sandy-soil field capacity (RMSE = 0.044 and 0.066 cm(3)/cm(3), respectively). EF was usually negative for all water contents. The cross-validation performance of the new PTFs was similar for multiple linear regression (RMSE: 0.028, ME: 0.000, EF: 0.34 for theta(100)) and random forest (RMSE: 0.027, ME: 0.000, EF: 0.36 for theta(100)), and generally worse for regression tree (especially EF). Multiple linear regression that did not consider cropping practices performed as well as random forest and thus did not identify any major influence of agricultural management on predicted water content. Future research on developing PTFs should focus on identifying more relevant predictors.

On the interpretation of surprisingly high variation of soil map diversity in country-wide study of flood-affected agroecosystems using the legacy data in the Czech Republic

Legacy soil data have been produced worldwide to provide maps of soil classes or properties. In this study, we joined the legacy results of conventional soil mapping with the application of the pedodiversity concept and compositional data analysis in a comprehensive spatial analysis of the mapped soil diversity within flood-prone agroecosystems in the Czech Republic. After the statistical differences in Shannon’s entropy (conventional and adjusted with taxonomic distances) over different regional inundation areas had been statistically examined, their interpretation was based on Aitchison’s geometry of simplex, rendering an appropriate statistical framework to avoid methodological bias through the use of raw proportions in soil taxa compositions. Working with tailored orthonormal coordinates enabled us to verify the dependency of the soil taxa composition on the multicategory factors of agroecosystem type and flood-periodicity through a conventional linear modelling. The supervised compositional approach improved interpretability of soil information within the Czech legacy soil maps, and hence general patterns of pedodiversity in floodplains might be effectively described in an informative and quantitative manner. The most obvious differences of the soil map diversity proved the regional inundation areas within the catchments with the enhanced complexity of drainage network that might arise from multiple sources (substrate diversity, climatic zonality, man-induced landscape alteration).

Building a resilient and sustainable food system in a changing world -& nbsp;A case for climate-smart and nutrient dense crops

Current food production and consumption practices have had negative impacts on the environment and are central to global health concerns. Using a mixed-methods review, we examined the nutritional and environmental impacts of our global food systems and addressed the apparent decrease in food sources and crop diversity, and its implication on sustainable and healthy diets. Moreover, we explored the merits of weighing the use of natural capital and agricultural inputs against the output generated in terms of nutrient density. Transforming our food systems to safeguard planetary health will require a shift towards sufficient production of nutrient dense crops that are environmentally sustainable. Such a transformation largely depends on valuing crops for their natural nutrient density and matching them to suitable environments.

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