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Many regions of the world have experienced dramatic changes in occurrence of heat waves. Investigating changes in their spatial patterns is of great importance for understanding the causes of heat waves and their related processes. This paper, based on observation data collected from 611 meteorological stations, used the weighted mean center tool in ArcGIS to calculate China’s heat wave center of gravity and to trace its migration over the past five decades. The results show an association between its north-south movements and the change of Arctic Oscillation Index, especially for the period after the mid-1980s, during which substantial north-south displacements took place. This study also revealed a close relationship between its west-east movements and the change in annual number of heat wave days before the 1990s, after which it was replaced by a time-dependent relationship that showed increasing heat wave trend only. [ABSTRACT FROM AUTHOR]

In 2012, the USA Corn Belt, an intensive agricultural region of the USA, was hit by a widespread severe drought, affecting states such as Illinois, Iowa, Nebraska, and Indiana. In this study, time series (2000–2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were investigated to assess the 2012 drought conditions during the corn-growing season. Seven MODIS indices generated based on eight day MODIS reflectance and land surface temperature (LST) products were examined with standardized precipitation index (SPI) and Palmer-Zacross the Corn Belt to evaluate the relative performance of each MODIS index to detect agricultural drought. The normalized difference infrared index (NDII6) anomaly shows the highest correlation coefficient (r) with SPI at three time scales and correlates best with Palmer-Z, which suggests good sensitivity of the NDII6 anomaly to precipitation and moisture deficiency in agricultural areas. The temporal and spatial features of drought provided by MODIS indices were compared with maps of the USA Drought Monitor (USDM), the current advanced tool for drought monitoring. The rapid intensification of drought across the Corn Belt in 2012 summer captured by MODIS index anomalies agreed with the changes of USDM maps quite well, especially in August and September when extreme drought occurred. Through comparison with the USDM drought map, the NDII6 anomaly demonstrated an advantage in monitoring drought condition over irrigated land and showed the potential to advance fine-scale agricultural drought monitoring by providing more detailed spatial characterization. [ABSTRACT FROM PUBLISHER]

The detection and mapping of burned areas from wildland fires is one of the most important approaches for evaluating the impacts of fire events. In this study, a novel burned area detection algorithm for rapid response applications using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data was developed. Spectra from bands 5 and 6, the composite indices of the Normalized Burn Ratio, and the Normalized Difference Vegetation Index were employed as indicators to discover burned pixels. Historical statistical data were used to provide pre-fire baseline information. Differences in the current (post-fire) and historical (pre-fire) data were input into a support vector machine classifier, and the fire-affected pixels were detected and mapped by the support vector machine classification process. Compared with the existing MODIS level 3 monthly burned area product MCD45, the new algorithm is able to generate burned area maps on a daily basis when new data become available, which is more applicable to rapid response scenarios when major fire incidents occur. The algorithm was tested in three mega-fire cases that occurred in the continental USA. The experimental results were validated against the fire perimeter database generated by the Geospatial Multi-Agency Coordination Group and were compared with the MCD45 product. The validation results indicated that the algorithm was effective in detecting burned areas caused by mega-fires. [ABSTRACT FROM AUTHOR]

A new approach is developed for quick detection of sand and dust storms (SDSs) over arid and semi-arid regions of the northwestern part of China, where the bright-reflecting source areas of Asian dust outbreaks are located. The Asian dust particles, once with proper conditions, can even transport across the Pacific Ocean and reach the USA and Canada. Remote-sensing data products of mineral dust near its source are deficient because of the radiance contributions of the bright surface. In this article, based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, consecutive separation of dust cloud from bright underlying surface and water/ice cloud is completed by utilizing a refined cloud mask algorithm and the normalized difference dust index (NDDI). Thresholds are determined through statistical analysis of MODIS measurements over the Taklimakan and Gobi deserts. Validations with ground observations over the sites in Inner Mongolia and Xinjiang in China demonstrated good performance of the proposed method in separating SDS from bright surface and cloud. [ABSTRACT FROM AUTHOR]

Efficient and accurate detection of the temporal dynamics and spatial variations of leaf dry matter content would help monitor key properties and processes in vegetation and the wider ecosystem. However, leaf water content strongly absorbs at shortwave infrared wavelengths, reducing the signal from dry matter. The major objective of this study was to examine relationship between spectral reflectance of fresh leaves and the ratio of leaf dry mass to leaf area, across a wide range of species at the leaf scale. A narrow-band, normalized index combining two distinct wavebands centred at 1649 and 1722 nm achieved the highest overall performance and discriminatory power compared to either single band or first derivatives. The normalized index was evaluated using the PROSPECT (leaf optical properties spectra) simulated reflectance spectra and field measurements from the Leaf Optical Properties Experiment (LOPEX) data set. Both evaluations show that leaf dry matter contents were retrievable with R 2 of 0.845 and 0.681 and regression slopes of 0.903 and 0.886. This study suggests that spectral reflectance measurements hold promise for the assessment of dry matter content for green leaves. Further investigation needs to be conducted to evaluate the effectiveness of this normalized index at canopy scales. [ABSTRACT FROM AUTHOR]

The effects of soil moisture and leaf water content on canopy reflectance of MODIS shortwave infrared (SWIR) bands 5, 6, and 7 and water-related indices are studied quantitatively using the coupled soil-leaf-canopy reflectance model. Canopy spectra simulations under various input conditions show that soil moisture has a large effect on each SWIR reflectance at low leaf area index (LAI) values, among which band 5 is the most sensitive to soil moisture variations, while band 7 responds strongest to dry soil conditions. Band 5 is also better suited to measure leaf water content change, since it obtains a higher variation when leaf water content changes from dry to wet. In general, each SWIR band responds to soil moisture and leaf water content differently. By using the normalized calculation between the water absorption-sensitive band and insensitive band, the Normalized Difference Water Index shows the most capability to remove the soil background effect and enhance the sensitivity to leaf water content. These two moisture variables may be separated by combining multiple rather than one SWIR band with a near-infrared band considering that each SWIR band has a different response to soil moisture and leaf water content. [ABSTRACT FROM AUTHOR]