Cropland retirement is a widespread phenomenon across the world. The conversion of inefficient cropland to forest or grassland is a policy aimed at restoring ecology, improving the environment, and promoting economic development. However, in most developing countries, the results of cropland retirement and land restoration are characterized by spatial fragmentation, and there are significant temporal differences as a result of poor agricultural intensification, human interference, and regional environmental differences. This substantially increases the difficulty of information extraction and reduces the extraction accuracy of remote sensing methods. In this paper, we developed a new phenology-based cropland retirement remote sensing (PCRRS) model to detect the extent and timing of cropland retirement. Considering the characteristic growth of crops, the normalized difference vegetation index (NDVI), at the start, middle, and end of the growth cycle, is the phenological metric to distinguish cropland from other vegetation types. In addition, the interannual variation of phenological metrics are significant after cropland retirement, which is the key to effectively identify retired cropland. High-resolution Google Earth images were used to verify the accuracy of the algorithm. The results suggested that the overall accuracy of our algorithm exceeded 85%, and was more suitable for sloping cropland. In comparison with other cropland retirement extraction methods, the PCRRS model had high sensitivity and stability. We found it was common for sloping cropland to be retired earlier, and we also identified the existing inter-planting phenomena between crops and shrubs in areas with gentle slopes. Overall, this study provided a basis for understanding the drivers of cropland retirement and evaluating their environmental effects.

Coverage, resolution, and accuracy in the spatial and temporal estimates of remotely sensed precipitation from space satellites, along with the number of instruments deployed to deliver these observations, are increasing. Of key interest in this study is the unsurpassed opportunity offered by their broad and continuous coverage to complement sparse, but more accurate, in situ rain gauge measurements for building climate resilience at the local, regional, and global scales. For many parts of the globe, this opportunity remains untapped. Australia is no exception and provides a unique challenge, given the small fraction of the continent that has rain gauges, the highly diverse climate due to its large size, and the apparent worsening of extreme weather events in both frequency and intensity. Notwithstanding this great impetus, a continent-wide record of multi-satellite-gauge fused precipitation data for Australia remains lacking. This missing data asset is a prerequisite for understanding the emerging spatiotemporal dynamics of precipitation, without which reliable forecasts would be difficult if not impossible. This study seeks to address this need. Here, we develop a method which can synergistically fuse precipitation data from different sources. More specifically, the aim of this study is to develop Precipitation Profiler-Observation Fusion and Estimation (PPrOFusE), a tool to deliver high-quality gauge and multi-satellite fused precipitation data. We test and apply this tool for Australia, but it is by no means limited in scope to this region. By design, PPrOFusE has a built-in capability to assess the strengths and weaknesses of each platform. In this case study, we fused data for a period of 22 years (2000-2022) using the rain gauge network data from the Australian Bureau of Meteorology (BOM) together with satellite data from the Japan Aerospace Exploration Agency's (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Center Morphing technique (CMORPH). Our proposed precipitation data fusion method consists of two steps. Step 1, the relationship among the three sources of data is modeled by multiple linear regression at each rain gauge location, returning the least squares estimates for the associated regression coefficient vector. Step 2, such regression coefficient vector estimates for all rain gauge locations are fitted by a spatial autoregression model, whereafter the multiple linear regression coefficient vectors for those locations void of rain gauges are predicted by spatial interpolation. Key findings are twofold. First, CMORPH is more accurate for most regions of Australia than GSMaP. Second, a clustering analysis of the fused precipitation over the last 20 years suggests two key trends on Australia's changing climate, relative to BOM's six major climate zones, from the previous century: (a) increased spatial variability to the north, consistent with meteorological expectations, amid a southwards expansion of the wet summer dominant zones across the continent; (b) the edge of the arid region shifts southwards and pushes out Mediterranean climate and winter dominant rainfall zones across southern Australia.

The Aral Sea, once the fourth largest freshwater lake on Earth, has lost circa 90% of its original water surface in 1960. Maps of different land cover categories provide a suitable baseline to plan and implement effective measures to combat ongoing desertification, such as reforestation of dried out Aral Sea soils. In this study, we used satellite-based remote sensing data and applied a machine learning method (Random Forest) to map land cover in the Aralkum in 2020. We tested different satellite data from optical (Landsat-8, Sentinel-2) and Radar instruments (Sentinel-1) and trained a random forest model for classifying different combinations of these data sets into ten distinct land cover classes. We further calculated per-pixel uncertainty based on posterior classification probability scores. An accuracy assessment, based on in-situ data, revealed that the average overall accuracy of land cover maps was 86.8%. Fusing optical and radar instruments achieved the highest overall accuracy (88.8%, with lower/higher 95% confidence interval values of 87.6%/89.9%, and a Kappa value of 0.865. Classification uncertainty was lower in more homogeneous landscapes (i.e. large expanses of a single land cover class like water or shrubland). Only around 9% of the study area was still water in 2020, while 32% was covered by saline soils with high erosion risk. Several potential applications of this land cover map in the Aralkum exist - spanning many areas of environmental impact assessment, policy, and planning and management or afforestation. This methodological framework can similarly provide a useful template for more broadly assessing large-scale, land dynamics at high-resolution in the entire Aralkum and surrounding areas.

Flood irrigation after crop harvest, e.g. autumn irrigation (AI), is a common irrigation practice in arid and semi-arid regions like Hetao Irrigation District (HID) in Northwest China to increase soil moisture and leach soil salt. Detailed information about the extent, timing, and amount of AI is imperative for modeling agro-hydrological processes and irrigation management. However, little attention is given to the identification of the above AI factors. There are basically three major difficulties in estimating the annual changes in AI, including a suitable index to identify AI, temporal instability of thresholds, and an effective validation method for irrigation timing. Therefore, this study proposes a simple and effective threshold-based method to extract the extent and timing of AI in the HID using MODIS water indices at a daily timescale. The Multi-Band Water Index (MBWI) time series is first reconstructed using an adaptive weighted Savitzky-Golay filter and then used to identify the AI extent and time. The proposed model has a stronger generalization capability both in time and space due to robust thresholds selected from the Z-score normalized feature variable. The model is validated both at pixels generated by the segmentation of Sentinel-derived MBWI using a threshold-based model and at sampling points from the field survey. Results show that the model performed well with an overall accuracy of more than 90.0% for the irrigation area. The overall accuracies of irrigation timing are 76.4% and 91.7% based on the middle-to-late and whole irrigation periods, respectively. We found a decreasing trend in the AI area and a gradual delay in the starting time of AI in the HID, mainly due to changes in cropping patterns, climate, and irrigation fees. Overall, the model is promising in identifying flood irrigation extent and timing in large irrigation districts and is helpful for irrigation scheduling.

Air pollution is a major factor affecting human life and living quality in arid and semiarid regions. This study was conducted in the Al-Ahsa district in the Eastern part of Saudi Arabia to measure carbon dioxide (CO2) concentration over different land-use types. Initially, the study's land use/land cover (LULC) was classified using the spectral characteristics of Landsat-8 data. Then, sensors were placed in five sites of different LULC types to detect CO2, air temperature, and relative humidity. The Friedman test was used to compare CO2 concentration among the five sites. Five LULC types were identified over the study area: date palm, cropland, bare land, urban land, and water. The results indicated that CO2 concentration showed a maximum mean value of 577 ppm recorded from a site dominated by urban lands. During the peak time of human transportation, a maximum value of 659 ppm was detected. The CO2 concentration mean values detected for the other LULC types showed 535, 515, and 484 ppm for the bare land, cropland, and date palm, respectively. This study's sensors and procedures helped provide information over relatively small areas. However, modelling CO2 fluctuations with time for LULC changes might improve management and sustainability.

Accurate sand and dust storm (SDS) detection is important for assessing SDS disaster risk. Machine learning (ML) based SDS detection approaches have been widely used in recent years due to their higher accuracy and better detection results. However, this approach usually requires manual annotation of numerous training samples that are, in practice, laborious and time-consuming. To overcome this challenge, we propose a novel hybrid SDS detection method that combines the support vector machine (SVM) algorithm implemented on the Google Earth Engine (GEE) cloud computing platform with a spectral index to aid the automatic labelling of training samples. Based on 8 SDS events captured by MODIS over Arid Central Asia (ACA), the effectiveness and accuracy of this method were assessed and compared to traditional approaches. The experimental results indicate that the proposed method can distinguish between mixed pixels (thin cloud and land surface) and SDS pixels and that it minimizes misdetection more effectively. This method achieved more than 98% training accuracy and validation accuracy in SDS detection.

The use of the soil conservation service (SCS) curve number (CN) model for estimation of the rainfall excess and the SCS-unit hydrograph (UH) model are common tools for flood studies in arid regions. In this research, we are investigating the capability of these models to simulate flood events in the arid region under the common parametrization provided by the SCS model (SCS-CN, the initial abstraction ratio, lambda, and UH theory) and the optimum parameterization for best simulating the hydrologic response. A case study is performed in Al-Lith basin in the west of Saudi Arabia (SA). The study simulates measured rainfall-runoff events in the area using seven scenarios (various SCS-CN estimation methods: least-squares method (CNLSM), asymptotic fitting method (CN infinity), SCS-CN tables (CNdesign), and antecedent moisture content CN (CNI, CNII, and CNIII), lambda = 0.2 and 0.01, and SCS-UH and UH derived from streamflow data) and a comparison is made between the observations and model results under the common parameterization of the SCS model and parameterization estimated from the Saudi arid environment. The comparison between simulated and observed peak flow and runoff volume of the studied events shows high scatter which is a common feature in arid regions due to the inherent uncertainties in the hydrological processes which are not yet resolved due to the lack of detailed measurements of the rainfall-runoff processes. Statistical analysis showed that lambda = 0.01 provides a minimum root mean square error (RMSE) in the peak flow (24.8 m(3)/s) and the runoff volume (0.31 million m(3)) with CNLSM obtained by LSM. CN infinity is bad to simulate the hydrologic response. The SCS-CN Tables cannot be used for hydrological simulation. They can rather be used for the design purposes of mitigation structures.

本发明公开了一种干旱区胡杨断根萌蘖的轮灌修复方法，其包括在胡杨母株未萌芽时，获取待修复胡杨林的林地影像，基于林地影像获取待修复胡杨林的待修复区域；在待修复区域距离母株预设距离处挖断根沟；采集灌溉区的地下水埋深，判断地下水埋深是否低于2～4m，若是，对胡杨林进行汊渗轮灌后进入下一步，否则直接进入下一步；在断根沟的两侧铺设有机土壤，并在有机土壤表面放置滴灌带，内部布置湿度传感器；在胡杨断根未萌蘖前，通过滴灌带保持有机土壤的土壤含水率，直至断根萌蘖平均株高达到预设值，回收滴灌带；每隔预设天数采集灌溉区的地下水埋深，当地下水埋深低于2m时，对胡杨林进行汊渗轮灌，直至萌蘖株高大于30cm。

本发明公开了一种干旱区通过人工漂种快速补充退化生态系统种源的方法，包括：采集待恢复区退化植物群落本土优势建群种物种的种子并检测种子活力，选取达标的种子备用；对待恢复区进行漫灌补水，激活土壤种子库，改善优势建群种物种的繁育生境；对待恢复区进行灌溉补水时通过水流顺水飘撒处理得到的待恢复区退化植物群落本土优势建群种物种的种子，令种子随水飘播以实现种源补充。本发明通过科学地选择待恢复区人工漂种补充种源的物种，并借助人工生态输水的契机，结合有控制的人工漫溢和小范围洪泛生境创造，进行人工漂种，可使干旱的待恢复区的草本植物和灌木物种增加，群落盖度和群落密度显著提高，对干旱区生态系统具有良好的改善效果。

Through field investigation, UPLC-MS/MS technology and MaxEnt model were performed to predict the suitable distribution area for red huajiao (Zanthoxylum bungeanum maxim.) in China from 2021 s to 2060 s, and evaluate the effects of climate factors on the quality of red huajiao. The results demonstrated that mean temperature of the coldest quarter and min temperature of the coldest month were the most important environmental variables influencing red huajiao distribution. Suitable habitats for red huajiao were located mainly in dry and hot valley zone in the Qinba Mountains and the semi-humid and semi-arid areas of the Loess Plateau. The amides contents were higher in high suitability areas, while it was decreased in medium and low suitability areas, and temper-ature, wind speed and precipitation played a key role in their accumulation. This investigation was of great significance for the planting area optimization, quality control, benefit improvement and industrial development of red huajiao.