The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5-7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images.

By effectively observing the land surface and obtaining farmland conditions, satellite remote sensing has played an essential role in agricultural drought monitoring over past decades. Among all remote sensing techniques, optical and thermal remote sensing have the most extended history of being utilized in drought monitoring. The primary goal of this paper is to illustrate how optical and thermal remote sensing have been and will be applied in the monitoring, assessment, and prediction of agricultural drought. We group the methods into four categories: optical, thermal, optical and thermal, and multi-source. For each category, a concise explanation is given to show the inherent mechanisms. We pay special attention to solar-induced chlorophyll fluorescence, which has great potential in early drought detection. Finally, we look at the future directions of agricultural drought monitoring, including (1) early detection; (2) spatio-temporal resolution; (3) organic combination of multi-source data; and (4) smart prediction and assessment based on deep learning and cloud computing.

Leaf area index (LAI), an important parameter describing a crop canopy structure and its growth status, can be estimated from remote sensing data by statistical methods involving vegetation indices (VIs). This letter reports the development of a new VI, the inverted difference vegetation index (IDVI), for crop LAI retrieval. The IDVI can overcome the saturation issue of the normalized difference vegetation index (NDVI) at high LAI values and exhibits robust insensitivity to crop leaf water and chlorophyll content. By combining the IDVI and NDVI with a scaling factor, we constructed a novel statistical regression model with parameters that can be calibrated to a specific region to estimate the LAI. Validations on simulated data and in situ observations show that the proposed retrieval method with the IDVI is stable for low and high LAIs and obtains better results than the empirical method involving the NDVI at the regional scale. Findings in this letter will benefit future agricultural applications.

Crop growth in different periods influences the final yield. This study started from the agronomic mechanism of yield formation and aimed to extract useful spectral characteristics in different phenological phases, which could directly describe the final yield and dynamic contributions of different phases to the yield formation. Hyperspectral information of the winter wheat canopy was acquired during three important phases (jointing stage, heading stage, and grain-filling stage). An enhanced 2D correlation spectral analysis method modified by mutual information was proposed to identify the sensitive wavebands. The selected wavebands performed well with good mechanism interpretation and close correlation with important crop growth parameters and main physiological activities related to yield formation. The quantitative contribution proportions of plant growth in three phases to the final yield were estimated by determining the coefficients of partial least square models based on full spectral information. They were then used as single-phase weight factors to merge the selected wavebands. The support vector machine model based on the weighted spectral dataset performed well in yield prediction with satisfactory accuracy and robustness. This result would provide rapid and accurate guidance for agricultural production and would be valuable for the processing of hyperspectral remote sensing data.

The interaction of human immunodeficiency virus type 1 (HIV-1) with CD4 and one of a cadre of chemokine receptors triggers conformational changes in the HIV-1 envelope (Env) glycoprotein that lead to membrane fusion. The coreceptor activity of the second extracellular loop of CXCR4, which is restricted to dual tropic and T-tropic strains, was insensitive to the removal of charged residues either singly or in combinations by alanine scanning mutagenesis or to the conversion of acidic residues to lysine. Conversion of Asp-187 to a neutral residue exclusively unmasked activity with M-tropic Env in fusion and infection experiments. Insertion of the D187V mutation into chimeras containing extracellular loop 2 of CXCR4 in a CXCR2 framework also resulted in the acquisition of M-tropic coreceptor activity. The independence of CXCR4 coreceptor activity from charged residues and the extension of its repertoire by removing Asp-187 suggest that this interaction is not electrostatic and that coreceptors have the potential to be utilized by a spectrum of Env, which may be masked by charged amino acids in extracellular domains. These findings indicate that the primary structural determinants of coreceptors that program reactivity with M-, dual, and T-tropic Env are surprisingly subtle and that relatively insignificant changes in CXCR4 can dramatically alter utilization by Env of varying tropism.

This study analysed the changing pattern of the spectral features of copper-stressed leaves for several vegetation types, and explored the mechanism of the Copper Stress Vegetation Index (CSVI). First, the change of seven key spectral features (Green Peak, Red Valley, Red Shoulder, NIR (Near Infrared) Reflectance Platform, Blue-Edge, Yellow-Edge, and Red-Edge) with copper stress level from low to high, were presented and analysed. Second, the chlorophyll contents in leaves were investigated to explain the spectral characteristics at the visible band. Third, the leaf structure and absorption related to copper were analysed to explore the reason of changing pattern at NIR band. The results showed that there are significant changing trends at Blue-Edge, Green Peak, and Red-Edge while the changing pattern at NIR band depends on the vegetation type. The analysis on chlorophyll content, leaf structure, and absorption related to copper, gave an overall mechanism explanation for the spectral characteristics of copper-stressed vegetation and the wavelengths used in CSVI. The results and conclusions in this paper, contribute new knowledge of copper-stressed vegetation reflectance and the CSVI, and provide mechanism basement for the remote sensing of copper-stressed vegetation.