https://uwe-repository.worktribe.com/preview/1492008/Remote%20Sensing%20of%20Drought%20-%20Review%20Paper%20%28Rev%202%29%20Clean.pdf

Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities

This article reviews recent literature on drought of the last millennium, followed by an update on global aridity changes from 1950 to 2008. Projected future aridity is presented based on recent studies and our analysis of model simulations. Dry periods lasting for years to decades have occurred many times during the last millennium over, for example, North America, West Africa, and East Asia. These droughts were likely triggered by anomalous tropical sea surface temperatures (SSTs), with La Ni˜ na-like SST anomalies leading to drought in North America, and El-Ni˜ no-like SSTs causing drought in East China. Over Africa, the southward shift of the warmest SSTs in the Atlantic and warming in the Indian Ocean are responsible for the recent Sahel droughts. Local feedbacks may enhance and prolong drought. Global aridity has increased substantially since the 1970s due to recent drying over Africa, southern Europe, East and South Asia, and eastern Australia. Although El Ni˜ no-Southern Oscillation (ENSO), tropical Atlantic SSTs, and Asian monsoons have played a large role in the recent drying, recent warming has increased atmospheric moisture demand and likely altered atmospheric circulation patterns, both contributing to the drying. Climate models project increased aridity in the 21 st century over most of Africa, southern Europe and the Middle East, most of the Americas, Australia, and Southeast Asia. Regions like the United States have avoided prolonged droughts during the last 50 years due to natural climate variations, but might see persistent droughts in the next 20–50 years. Future efforts to predict drought will depend on models’ ability to predict tropical SSTs. 2010 JohnWiley &Sons,Ltd.WIREs Clim Change2010 DOI:10.1002/wcc.81

Drought under Global Warming: A Review

The Standardized Precipitation Evapotranspiration Index (SPEI) was computed based on the monthly precipitation and air temperature values at 609 locations over China during the period 1951–2010.Various characteristics of drought across China were examined including: long-term trends, percentage of area affected, intensity, duration, and drought frequency. The results revealed that severe and extreme droughts have become more serious since late 1990s for all of China (with dry area increasing by ∼3.72% per decade); and persistent multi-year severe droughts were more frequent in North China, Northeast China, and western Northwest China; significant drying trends occurred over North China, the southwest region of Northeast China, central and eastern regions of Northwest China, the central and southwestern parts of Southwest China and southwestern and northeastern parts of western Northwest mainly due to a decrease in precipitation coupled with a general increase in temperature. In addition, North China, the western Northwest China, and the Southwest China had their longest drought durations during the 1990s and 2000s. Droughts also affected western Northwest, eastern Northwest, North, and Northeast regions of China more frequently during the recent three decades. The results of this article could provide certain references and triggers for establishing a drought early warning system in China. © 2013 Royal Meteorological Society

Are droughts becoming more frequent or severe in China

Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities

Flash droughts characterization over China: From a perspective of the rapid intensification rate.

This study analyzes the changes in drought patterns in China’s major crop-growing areas over the past 60 years. The analysis was done using both weather station data and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rainfall data to calculate the Standardized Precipitation Index (SPI). The results showed that the occurrences of extreme drought were the most serious in recent years in the Southwest China and Sichuan crop-growing areas. The Yangtze River (MLRY) and South China crop-growing areas experienced extreme droughts during 1960–1980, whereas the Northeast China and Huang–Huai–Hai crop-growing areas experienced extreme droughts around 2003. The analysis showed that the SPIs calculated by TRMM data at time scales of one, three, and six months were reliable for monitoring drought in the study regions, but for 12 months, the SPIs calculated by gauge and TRMM data showed less consistency. The analysis of the spatial distribution of droughts over the past 15 years using TMI rainfall data revealed that more than 60% of the area experienced extreme drought in 2011 over the MLRY region and in 1998 over the Huang–Huai–Hai region. The frequency of different intensity droughts presented significant spatial heterogeneity in each crop-growing region.

https://www.mdpi.com/2072-4292/10/2/171/pdf

SPI-Based Analyses of Drought Changes over the Past 60 Years in China’s Major Crop-Growing Areas

Performance comparison of the MODIS and the VIIRS 1.38 μm cirrus cloud channels using libRadtran and CALIOP data

A practical algorithm was proposed to retrieve land surface temperature (LST) from Visible Infrared Imager Radiometer Suite (VIIRS) data in mid-latitude regions. The key parameter transmittance is generally computed from water vapor content, while water vapor channel is absent in VIIRS data. In order to overcome this shortcoming, the water vapor content was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The analyses on the estimation errors of vapor content and emissivity indicate that when the water vapor errors are within the range of ±0.5 g/cm ଶ , the mean retrieval error of the present algorithm is 0.634 K; while the land surface emissivity errors range from −0.005 to +0.005, the mean retrieval error is less than 1.0 K. Validation with the standard atmospheric simulation shows the average LST retrieval error for the twenty-three land types is 0.734 K, with a standard deviation value of 0.575 K. The comparison between the ground station LST data indicates the retrieval mean accuracy is −0.395 K, and the standard deviation value is 1.490 K in the regions with vegetation and water cover. Besides, the retrieval results of the test data have also been compared with the

https://www.mdpi.com/1424-8220/14/11/21385/pdf

An Algorithm for Retrieving Land Surface Temperatures Using VIIRS Data in Combination with Multi-Sensors

The invention relates to an automatic power saving method for reducing the consumption of the electric quantity of a battery by optimizing system parameters within relatively stable non-operation time by obtaining a user operation rule on the basis of learning the operation behavior habit of a user. The operation habit of the user within a period of time is mainly learned to obtain the usability in different time periods, and then the usability is subjected to comparative analysis with a judgment threshold value so as to determine whether to enter the power saving mode or not. In the stabilizing stage, the rule which is previously learned is mainly updated so as to be optimized.

Automatic power saving method based on learning operation habit of user

Rice is the most important food crop in the developing world, and more than half of the global population consumes it as a staple food. Mapping the area of rice cultivation in a timely and accurate manner is essential to ensure food safety and evaluate its environmental impact. Deep learning performs very well in high-resolution remote sensing classification; however, due to lack of high-quality training datasets and spatial semantic information of Landsat data, paddy rice mapping based on deep learning and Landsat data has received less attention. In this study, we constructed the first large-scale training dataset and a deep learning network, named full resolution network (FR-Net), for mapping paddy rice based on Landsat 8 OLI data. The pixel-wise annotated dataset is composed of 64 Landsat 8 OLI scenes covering the main areas producing rice in northeast China. To overcome the coarse segmentation borders resulting from other deep learning models, and especially with the low-resolution Landsat data, a new multi-resolution fusion unit (MRFU) was proposed to fuse different resolution streams and maintain the high-resolution streams of the model. In comparison with other models, the FR-Net acquired the highest accuracy, with MCC of 0.893 and F1 score of 0.898, and in particular, the results of different band combinations showed that the FR-Net perform better for feature extraction than other models. Without using the sensitive bands, i.e., short-wave infrared 1 and 2, the MCC of FR-Net was slightly decreased by 4.14%, smaller than other models, e.g., 8.51% for U-Net and 11.76% for RS-Net etc. The comparison showed the proposed FR-Net presented highest spatial precision among all these models, by which a fine boundary could be obtained for Landsat data to achieve high quality mapping result. The analysis of the performance in different temporal showed that FR-Net performed better at the beginning or the ending of growth stages, and the omission or false alarm main occurred in small size patches or the boundaries. In all, low-resolution characteristics of Landsat data should be more extensively investigated when developing and using deep learning in classification. 

Fen Zhao

Papers

SPI-Based Analyses of Drought Changes over the Past 60 Years in China’s Major Crop-Growing Areas

Performance comparison of the MODIS and the VIIRS 1.38 μm cirrus cloud channels using libRadtran and CALIOP data

An Algorithm for Retrieving Land Surface Temperatures Using VIIRS Data in Combination with Multi-Sensors

Automatic power saving method based on learning operation habit of user

A full resolution deep learning network for paddy rice mapping using Landsat data