Drought and salinity stress are considered to be the two main factors limiting crop productivity. With climate change, these stresses are projected to increase, further exacerbating the risks to global food security. Consequently, to tackle this problem, better agricultural management is required on the basis of improved drought and salinity stress monitoring capabilities. Remote sensing makes it possible to monitor crop health at various spatiotemporal scales and extents. However, remote sensing has not yet been used to monitor both drought and salinity stresses simultaneously. The aim of this paper is to review the current ability of remote sensing to detect the impact of these stresses on vegetation indices (VIs) and crop trait responses. We found that VIs are insufficiently accurate (0.02 ≤ R² ≤ 0.80) to characterize the crop health under drought and salinity stress. In contrast, we found that plant functional traits have a high potential to monitor the impacts of such stresses on crop health, as they are more in line with the vegetation processes. However, we also found that further investigations are needed to achieve this potential. Specifically, we found that the spectral signals concerning drought and salinity stress were inconsistent for the various crop traits. This inconsistency was present (a) between studies utilizing similar crops and (b) between investigations studying different crops. Moreover, the response signals for joint drought and salinity stress overlapped spectrally, thereby significantly limiting the application of remote sensing to monitor these separately. Therefore, to consistently monitor crop responses to drought and salinity, we need to resolve the current indeterminacy of the relationships between crop traits and spectrum and evaluate multiple traits simultaneously. Using radiative transfer models (RTMs) and multi-sensor frameworks allow monitoring multiple crop traits and may constitute a way forward toward evaluating drought and salinity impacts.

Drought impact monitoring is of crucial importance in light of climate change. However, we lack an understanding of the concomitant responses of ecosystems to a variety of drought characteristics and the links between drought and ecosystem anomaly characteristics for a comprehensive set of vegetation types to provide needed information for water management. In response, this study presents a new framework that allows us to explore the relationship between drought and its impact on ecosystems in greater detail. Specifically, our framework focuses on estimating jointly the hydrological and ecosystem temporal evolution and anomalies around a drought event using four pairs of metrics: onset-onset, duration-duration, intensity-intensity, and severity-severity of drought and vegetation damage. Additionally, we incorporated a metric on vegetation vulnerability based on changes in damage severity along a gradient of increasing drought severity. Based on this framework, we evaluated drought vulnerability patterns of various vegetation types across the Netherlands and Belgium in 2018 at high spatiotemporal resolution. Our results reveal a differential vulnerability of vegetation between ecosystems with increasing drought severity, which could aid future drought impact predictions. In particular, mosaic grasslands and tree/shrub croplands are highly sensitive to increasing drought severity. Individual characteristics (onset, duration, intensity and severity) of drought and vegetation damage behave differently in various vegetation types. For instance, broadleaved forests respond faster than other forests, while mixed forests suffer less damage than other types. The early warning threshold to drought for most vegetation types is around a Standardized Precipitation Evapotranspiration Index (SPEI) value of -1. The characterization of a suite of drought response characteristics through our impact analysis framework can be used in a wide variety of regions to understand current and possible future responses to drought.

It is difficult to accurately identify and extract bodies of water and underwater vegetation from satellite images using conventional vegetation indices, as the strong absorption of water weakens the spectral feature of high near-infrared (NIR) reflected by underwater vegetation in shallow lakes. This study used the shallow Lake Ulansuhai in the semi-arid region of China as a research site, and proposes a new concave–convex decision function to detect submerged aquatic vegetation (SAV) and identify bodies of water using Gao Fen 1 (GF-1) multi-spectral satellite images with a resolution of 16 meters acquired in July and August 2015. At the same time, emergent vegetation, “Huangtai algae bloom”, and SAV were classified simultaneously by a decision tree method. Through investigation and verification by field samples, classification accuracy in July and August was 92.17% and 91.79%, respectively, demonstrating that GF-1 data with four-day short revisit period and high spatial resolution can meet the standards of accuracy required by aquatic vegetation extraction. The results indicated that the concave–convex decision function is superior to traditional classification methods in distinguishing water and SAV, thus significantly improving SAV classification accuracy. The concave–convex decision function can be applied to waters with SAV coverage greater than 40% above 0.3 m and SAV coverage 40% above 0.1 m under 1.5 m transparency, which can provide new methods for the accurate extraction of SAV in other regions.

Ecosystems are threatened by increasing droughts under climate change. A multitude of plant physiological regulation processes determine the overall drought resistance of ecosystems. So far, these physiological strategies to resist drought are poorly understood at large scales across different ecosystem types because the detection of these physiological regulation processes is mostly limited to in situ measurements on individual plants. In this study, by using high-resolution remote sensing data, we evaluated drought strategies of different ecosystem types throughout Europe by evaluating three key physiological regulation aspects (evapotranspiration, water content, and carbon regulation) based on their associated vegetation attributes. We found that different ecosystem types show divergent responses in these physiological attributes, suggesting different optimization strategies with respect to water saving versus spending, water content stabilizing versus fluctuating, and leaf conserving versus shedding strategies facing drought. These drought strategies from remote sensing provide timely ecosystem response information, facilitating earth system model predictions and aiding the protection against future droughts at large scales.