基于多源遥感数据的农业干旱监测模型构建及应用

Determining variable weights for an Optimal Scaled Drought Condition Index (OSDCI): Evaluation in Central Asia

Validation of water vapor retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region

Evaluation and modification of the Drought Severity Index (DSI) in East Asia

The moderate resolution imaging spectroradiometer (MODIS) is one of the widely used sensors to address environmental and climate change subjects with a daily global coverage. MODIS Collection 6 aerosol products at 10-km resolution are used in this study to monitor aerosol variability and assess its uncertainty using ground-based measurements. The aerosol optical depth (AOD) is retrieved by different algorithms based on the pixel surface, determining between land and ocean. Using data collected from Sidi Salem Aerosol Robotic Network (AERONET) station, we computed the accuracy for aerosol optical depth (AOD) retrieved from MODIS aboard the AQUA satellite using two validation methods. The results show a good agreement between MODIS and AERONET data for the study period using both the algorithms. We obtained high values of the correlation coefficient. These findings indicate that MODIS data perform well over Ben Salem AERONET station and are recommended for air quality monitoring over Tunisia. The conducted validation throughout the AERONET leads to a degree of confidence that allows a deep investigation of the AOD spatial variability over Tunisia. Then, MODIS data shows high performance with good certainty to identify the principal dust sources and typical transport paths occurring on the study region.

The objective of this research is into detecting sea ice by using PALSAR (Phased-Array type L-band SAR) polarimetric data. It is generally difficult to detect thin sea ice area by using the methods based on the backscattering coefficient. We propose a new method utilizing scattering entropy to detect sea ice. This paper shows the results of extracting sea ice area from PALSAR fully polarimetric data acquired from 2009 to 2010. The MODIS (Moderate Resolution Imaging Spectroradiometer) data are used as sea ice reference. The threshold of PALSAR scattering entropy to discriminate sea ice from open water is determined from the distribution of scattering entropy for both sea ice and open water. We compared PALSAR derived sea ice area with MODIS derived area. Since most of PALSAR detected sea ice area is also detected by MODIS data, we can conclude that our proposed method is reliable to detect thin sea ice area in the Sea of Okhotsk. The high resolution backscattering and scattering entropy images give us an idea that there are some difficulties in detecting thin sea ice only by backscattering coefficient.

Estimation of surface upward longwave radiation from MODIS and VIIRS clear-sky data in the Tibetan Plateau

We address a need for hydrometeorological early warning and information systems (EWIS) in Southern Africa. In the project 'Co-Design of Hydrometeorological Information system for Sustainable Water Resource Management in Southern Africa' (Co-HYDIM-SA) we want to enhance water security in the two transboundary regions: Cuvelai-Cunene and Notwane (Namibia and Angola; Botswana and South Africa.The Global Precipitation Climatology Centre (GPCC) has many years of experience in hosting an operational and publicly available global drought monitoring service, by combining the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). For the SPI (SPEI) the 'gamma' ('log-logistic') distribution is fitted to the cumulative distribution function of the precipitation (climatic water balance) data. The main challenge of drought monitoring in the focus region is data scarcity. Therefore, we opted for the well-known and widely used SPI and SPEI, because these rely solely on precipitation and temperature data when calculating the potential evapotranspiration following Thornthwaite (1948). Nevertheless, observation data is difficult to acquire. Only few parameters are available and gaps in time series from stations are often present. That’s why, we work on a flexible data input in the operational system, where we can decide which data source should be used. For precipitation we mainly rely on the gridded GPCC dataset based on station data, whereas for temperature the gridded dataset from the Climate Prediction Center (CPC) is used. Furthermore, we plan to include satellite products (GIRAFE, CHIRPS, GPCP) and reanalysis (ERA5-Land) datasets. For the data acquisition and the implementation of the product, the collaboration with stakeholders in the focus region is essential. Therefore, they are included in the decision making and informed about our progress. The ‘co-design’ approach is an essential part of the project and is achieved by a close partnership with local Universities and a regular contact to the stakeholders.At the EGU26 I want to present the Co-HYDIM-SA project, my findings and challenges we have encountered. Until today, we have calculated time series for the two Drought Indices (SPI, SPEI) and compared them with specific drought events. In general, the indices are consistent with the described droughts. One disadvantage of the SPI is that it has limitations during the dry season, especially for short term data aggregation. Whereas, the SPEI is characterized by its all-year round usability, due to the integration of potential evapotranspiration in addition to the precipitation data. As a next step, we will compare the grid data to station time series and evaluate the results by calculating skill scores.

In the summer and autumn of 2019, an extreme drought lasting 0.5 year in the middle and lower reaches of the Yangtze River had a major impact on local agricultural production and the ecological environment. Traditionally, drought severity assessment relies on drought-related parameters observed by in situ measurements, such as precipitation, evapotranspiration, runoff, and soil moisture. In the past two decades, GRACE/GRACE-FO gravity satellite data have been widely used to estimate global and regional terrestrial water storage anomalies (TWSA), but there are still limited studies on the quantitative assessment of the severity of drought using GRACE/GRACE-FO data. The objective of this study was to analyse the drought severity in the middle and lower reaches of the Yangtze River in the summer and autumn of 2019 using GRACE/GRACE-FO data and to compare our estimates with the traditional drought index. The GRACE-based drought severity index (GRACE-DSI) is a monthly global drought severity index based on the TWSA from the GRACE/GRACE-FO (GRACE-TWSA). In this study, we used spherical harmonic (SH) products from the GRACE/GRACE-FO to obtain the TWSA. GRACE data processing includes replacing the degree-1 and C 20 , removing the 2004–2009 mean SH, filtering to reduce correlated errors, Gaussian smoothing, calculating the equivalent water layer thickness on 1° ´ 1° grids, and correcting leakage errors with scale factors. We then calculated the GRACE-DSI based on the GRACE-TWSA with the trend and seasonal cycles removed. To ensure consistency, we processed the standardised precipitation evapotranspiration index (SPEI) similar to the GRACE data and standardised the new SPEI as the SPEI-Z. Our results indicated that the GRACE-DSI can successfully monitor the occurrence and development of drought in the middle and lower reaches of the Yangtze River in spatiotemporal domains. The GRACE/GRACE-FO successfully detected the extreme drought event that occurred in the east of Hubei, north of Jiangxi, and south of Anhui during the summer and autumn of 2019. In July, the arid region was located in the north of the Yangtze River basin, including Henan and Shandong provinces. In August, the drought intensified and moved south to the middle and lower reaches of the Yangtze River, reaching a maximum in September, especially in Hubei, Jiangxi, and Anhui provinces. The drought was weakened in October, but intensified again in November and December and expanded through the middle and lower reaches of the Yangtze River to the coastal regions, such as Fujian and Guangzhou provinces. The mean time series of the GRACE-DSI over Hubei, Jiangxi, Anhui, and Hunan provinces showed good agreement with the SPEI-Z in both amplitude and phase on the six month time scale (SPEI06-Z) with a correlation coefficient of 0.84. The time series of the GRACE-DSI and SPEI06-Z both clearly showed the extreme drought events that occurred in Hubei, Jiangxi, Anhui, and Hunan provinces during the spring and summer of 2011 and the summer and autumn of 2019. Our results also indicate that the GRACE-FO has the same drought monitoring capability as the GRACE.

Remote sensing has been widely used in vegetation-dynamics monitoring. Many studies have used data acquired by multispectral sensors, such as the Landsat TM sensor, due to their high spatial resolution (30 m). However, during the growing season, the temporal resolution (16 day) cannot capture rapid changes of vegetation. Meanwhile, coarse-spectral-resolution sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), have high-frequency temporal information that can catch the details of landscape changes. In this research, we proposed a data-fusion approach that lead us to merge the MODIS and Landsat TM data to create a dataset of vegetation dynamics with both a high spatial resolution and a fine temporal resolution. The Comanche and Faith Ranches, located in west Texas, were chosen for this study. The MODIS product was used as a regionally consistent reference dataset to correct the Landsat imagery. Based on this new dataset, NDVI time-series curves from 2004 to 2011 were calculated with the MODIS 13 Vegetation Dataset. One random sample of red-band images was tested and compared with MODIS data. A high correlation coefficient 0.907 and RMSE 0.0245 was found.

Dinocysts as proxy of primary productivity in mid–high latitudes of the Northern Hemisphere

Bi-scale analysis of multitemporal land cover fractions for wetland vegetation mapping

A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data

Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model

Clouds play a major role in controlling Earth's climate, and cloud detection is a crucial step in the numerical weather prediction, and global climate models. Multi-angle imaging spectroradiometer (MLSR) and moderate resolution imaging spectroradiometer (MODIS) were launched in 1999 by clouds. However, cloud detection algorithms using either MISR or MODIS data separately do not take full advantage of the data collected by both sensors. In this paper, we propose and test two schemes to combine MISR and MODIS data for cloud detection in polar regions, Both schemes are follow-ups of a two-step polar cloud detection algorithm using MISR data: enhanced linear correlation matching classification followed by quadratic discriminate analysis (ELMS-QDA) (T, Shi et al.). The first scheme is mapping the MODIS cloud detection results to the MISR grid based on a nearest neighbor method, then only reporting the agreed pixels of the ELCMC-QDA results (from MISR) and MODIS operational results. This scheme improves the classification accuracy, but reduces the coverage of the results. Instead of combining the MISR and MODIS results directly, the second scheme uses the agreed pixels of ELCMC results and MODIS operational results as the training data for the QDA on MISR features, and output the results from the QDA. Both schemes are tested over a region where expert labels show that both MISR and MODIS operational algorithms do not work well (according to expert labels, 53% and 12.72% misclassification rates for MISR and MODIS operational algorithms respectively). The first scheme only makes 0.72% of errors, but leaves 68.72% of pixels unclassified. The second scheme reaches a 2.93% of misclassification rate, which is smaller than a 4.09% rate from ELCMC-QDA, and it provides a full coverage. Hence we propose using QDA on ELCMC and MODIS agreed pixels as an algorithm to fuse the MISR and MODIS information for the polar cloud detection.

Drought causes insufficient soil moisture and crop water balance damage. One of the most commonly used indicators in drought monitoring is the Standardized Precipitation Evapotranspiration Index (SPEI). In this study, the performance of the SPEI at 1-, 3-, 6-, 9-, and 12-month timescales was compared and analyzed from temporal and spatial variations at 12 meteorological stations in the Barmer arid region from 1979 to 2013. To determine the significance of drought characteristic trends, the modified Mann-Kendall (M-K) test is used. The results revealed that as the timescale increased, the temporal variations in the SPEI became more consistent. The M-K test revealed that the SPEI showed decreasing trends at 1 and 3 months, but increasing trends at 6, 9, and 12 months. The findings of this study are instructive and practical for drought assessment, risk management, and decision-making.