Remote Sensing of Plant-Climate Interactions

Dear Colleagues,

The remote sensing dynamic processes related to the plant–climate interactions, such as evapotranspiration (ET) and studies on the interface of plants and the atmosphere, including climate change impacts on plant physiological processes (heat, water stress, plant biogenic volatile emissions), are key to understanding the water balance, energy budget, feedback mechanisms, and productivity of the vegetation land surface moving forward. Improving accuracy in assessing these dynamic processes has implications for water management in precision agriculture practices, impact mitigation of extreme events, agricultural management practices, as well as larger-scale modeling of the Earth’s climate and weather owing to its impact on land surface–atmosphere interactions and soil water content. For example, remote sensing has enabled estimation of ET on a global scale at high space–time resolution. Various ET estimation techniques have also been developed for remote sensing products, but the accuracy of these algorithms varies with land cover, hydroclimate, terrain, seasonality, and the space and time scale of remote sensing data. Additionally, calibration and validation of these algorithms remains a challenge because of (1) the limited availability of ground-based calibration and validation data, (2) space–time scale discrepancy between footprints of available ground-based measurements techniques like eddy covariance towers, lysimeter methods, scintillometer methods, and remote sensing data, and (3) scale discrepancy between various inputs of ET and water and heat stress estimation algorithms and remote sensing data.

This Special Issue invites studies focused on improving estimates of all plant–climate interface interactions, including but not limited to evapotranspiration and other assessments of water and heat stress from remote sensing platforms (satellite, UAV, or proximal sensing) under varying hydroclimates and land-cover conditions. We especially encourage studies that (1) provide guidelines for improved parameterizations of different existing algorithms for remote sensing data under different hydroclimates, seasons, and land cover conditions, (2) use advanced techniques like machine learning, data assimilation, data fusion for utilizing various sources of ground-based data or multiple remote sensing platforms for estimating, calibrating, and validating remote sensing data and (3) assess the accuracy of different plant–climate interface algorithms with varying space–time scale remote sensing inputs. Multimodel comparisons and feedback mechanisms under different land-use land cover, seasons, and hydroclimates are also encouraged.

• Studies exploring the efficacy of different remote sensing-based products to compute water use efficiency and their comparison with water use efficiency products from platforms like ECOSTRESS are particularly invited;
• Monitoring and modeling agroclimatic interactions with cultivars at different scales, including applications in high throughput plant phenotyping and precision agriculture;
• Novel developments in software, sensors, platforms or methods towards assessing climate change impacts using different remote sensing approaches;
• Advances in image segmentation and classification for the study of specific traits related to plant–climate interactions and the soil–plant–atmosphere continuum;
• Integration of different scales of remote sensing measurements (e.g. satellite, UAV, ground-based measurements) towards further understanding scaling dynamics related to plant–climate interactions;
• Comparisons of long-term vegetation activity based on remote sensing data and field-based measurements (permanent plots, eddy covariance, tree-ring parameters);
• Remote sensing based smart apps for irrigation scheduling, phenotyping, and plant stress monitoring and their evolving role in a scenario of increased frequency of extreme events like flash droughts and floods.

Dr. Shawn C. Kefauver
Dr. Nandita Gaur
Guest Editors

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