Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

The spatial distribution of sub-pixel components has an impact on retrieval accuracy, and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index (LAI). To investigate this effect, we constructed three realistic scenarios with the same LAI values and other properties, except that the simulated plants had different distributions. We implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor (BRF) datasets based upon these simulated scenes. The inversion was conducted using these data, which showed that spatial distribution affects retrieval accuracy. The inversion was also conducted for LAI based on charge-coupled device (CCD) data from the Environment and Disaster Monitor Satellite (HJ-1), which depicted both forest and drought-resistant crop land cover. This showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI inversion. The spatial distribution of global fractal dimension index, which can be used to describe the area of sub-pixel components and their spatial distribution modes, shows good consistency with the coarse resolution LAI inversion error.     

Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

The spatial distribution of sub-pixel components has an impact on retrieval accuracy, and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index (LAI). To investigate this effect, we constructed three realistic scenarios with the same LAI values and other properties, except that the simulated plants had different distributions. We implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor (BRF) datasets based upon these simulated scenes. The inversion was conducted using these data, which showed that spatial distribution affects retrieval accuracy. The inversion was also conducted for LAI based on charge-coupled device (CCD) data from the Environment and Disaster Monitor Satellite (HJ-1), which depicted both forest and drought-resistant crop land cover. This showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI inversion. The spatial distribution of global fractal dimension index, which can be used to describe the area of sub-pixel components and their spatial distribution modes, shows good consistency with the coarse resolution LAI inversion error.

     

Monitoring of rice crop using ENVISAT ASAR data

Rice is the most important food source for people and is cultivated in most countries, among which China is one of the most productive. Increase of the world population and demands for economic devel-opment lead to the need of an efficient monitoring system for rice cultivation and forecasting of rice yield. Conventional methods for rice monitoring are based on ground-collected statistics, which is time consum-ing, inaccurate and expensive. Since the 1980s, satel-lite remote sensing has been c…     

Determination of regional distribution of crop transpiration and soil water use efficiency using quantitative remote sensing data through inversion

As is widely known, there is a severe shortage of water resources in North China. There have been frequent droughts in recent years. Developing water saving measures, especially in agricul-ture, has become an urgent task. In water-saving agriculture, one …     

机载激光雷达森林叶面积指数反演研究

叶面积指数(LAI)是分析冠层结构最常用的参数之一,它控制着植被的生物物理过程,如光合、呼吸、蒸腾、碳循环和降水截获等,因此快速、可靠和客观地评价LAI非常重要.本文发展了激光穿透指数(LPI)的简化计算方法,首次利用校正后的回波强度计算出LPI,以LPI为变量基于Beer-Lambert定律实现了甘肃大野口研究区森林LAI反演,并且与原始回波强度和回波数反演LAI的精度进行对比,结果发现通过距离和角度校正后的回波强度值能提高LAI反演精度.为了评价模型的可靠性和泛化性能,用留一法交叉验证程序(LOOCV)对最佳反演模型进行了验证,表明该模型没有过度拟合,具有很好的泛化能力.最后,用没有参加建模的16个实测LAI对预测值进行精度验证(R²=0.810,RMSE=0.198),发现校正后的回波强度反演山区森林LAI精度较高.本文还对激光雷达LAI反演结果与传统光学TM影像的反演结果进行了对比分析,结果表明机载激光雷达反演LAI精度(R²=0.825,RMSE=0.165)高于光学TM遥感数据(R²=0.605,RMSE=0.257),因此,可用激光雷达数据实现研究区的高精度LAI反演,为生态环境研究提供可靠的基础数据.     

Calculating actual crop evapotranspiration under soil water stress conditions with appropriate numerical methods and time step

Calculation of actual crop evapotranspiration under soil water stress conditions is crucial for hydrological modeling and irrigation water management. Results of actual evapotranspiration depend on the estimation of water stress coefficient from soil water storage in the root zone, which varies with numerical methods and time step used. During soil water depletion periods without irrigation or precipitation, the actual crop evapotranspiration can be calculated by an analytical method and various numerical methods. We compared the results from several commonly used numerical methods, including the explicit, implicit and modified Euler methods, the midpoint method, and the Heun's third‐order method, with results of the analytical method as the bench mark. Results indicate that relative errors of actual crop evapotranspiration calculated with numerical methods in one time step are independent of the initial soil water storage in the range of soil water stress. Absolute values of relative error decrease with the order of numerical methods. They also decrease with the number of time step, which can ensure the numerical stability of successive simulation of soil water balance. Considering the calculation complexity and calculation errors caused by numerical approximation for different time step and maximum crop evapotranspiration, the explicit Euler method is recommended for the time step of 1 day (d) or 2 d for maximum crop evapotranspiration less than 5 mm/d, the midpoint method or the modified Euler method for the time step of up to one week or 10 d for maximum crop evapotranspiration less than 5 mm/d, and the Heun's third‐order method for the time step of up to 15 d. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth–Wallace?

Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman–Monteith (P–M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO‐56. In this paper the Shuttleworth–Wallace (S–W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere–Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA‐AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S–W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data‐poor or ungauged large basins. A comparison is made between the FAO‐56 method and the S–W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10‐day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S–W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non‐linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S–W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions. Copyright © 2006 John Wiley & Sons, Ltd.     

Estimating leaf area index by inversion of reflectance model for semiarid natural grasslands

The study developed an integrated reflectance model combining radiative transfer and geometric optical properties in order to inverse leaf area index (LAI) of semiarid natural grasslands. In order to better link remote sensing information with land plants, and facilitate regional and global climate change studies, the model introduced a simple but important geometrical similarity parameter related to plant crown shapes. The model revealed the influences of different plant crown shapes (such as spherical, cylindrical/cuboidal and conic crowns) on leaf/branch angle distribution frequencies, shadow ground coverage, shadowed or sunlit background fractions, canopy reflectance, and scene reflectance. The modeled reflectance data agreed with the measured ones in the three Leymus chinensis steppes with different degradation degrees, which validated the reflectance model. The lower the degradation degree was, the better the modeled data agreed with the measured data. After this reflectance model was coupled with the optimization inversion method, LAI over the entire study region was estimated once every eight days using the eight-day products of surface reflectance obtained by multi-spectral Moderate-Resolution Imaging Spectroradiometer (MODIS) during the growing seasons in 2002. The temporal and spatial patterns of inversed LAI for the steppes with different cover degrees, swamps, flood plains, and croplands agreed with the general laws and measurements very well. But for unused land cover types (sands, saline, and barren lands) and forestlands, totally accounting for about 10% of the study region, the reasonable LAI values were not derived by inversing, requiring further revising of the model or the development of a new model for them. Supported by the National Natural Science Foundation of China (Grant No. 30500076), the National Basic Research Program of China (Grant No. 2007CB714407), and the President Foundation of Graduate University of Chinese Academy of Sciences (Grant No. YZJJ200205)     

Grain yield reliability analysis with crop water demand uncertainty

A new method of reliability analysis for crop water production function is presented considering crop water demand uncertainty. The procedure uses an advanced first-order second moment (AFOSM) method in evaluating the crop yield failure probability. To determine the variance and the mean of actual evapotranspiration as the component of interest for AFOSM analysis, an explicit stochastic optimization model for optimal irrigation scheduling is developed based on the first and second-order moment analysis of the soil moisture state variables. As a result of the study, the violation probabilities of crop yield at different levels were computed from AFOSM method. Also using the optimization results and the double bounded density function estimation methodology, the weekly soil moisture density function is derived which can be used as a short term reliability index. The proposed approach does not involve any discretization of system variables. The results of reliability analysis and optimization model compare favorably with those obtained from simulation.     

Estimation of winter leaf area index and sky view fraction for snow modelling in boreal coniferous forests: consequences on snow mass and energy balance

Leaf area index (LAI) and canopy coverage are important parameters when modelling snow process in coniferous forests, controlling interception and transmitting radiation. Estimates of LAI and sky view factor show large variability depending on the estimation method used, and it is not clear how this is reflected in the calculated snow processes beneath the canopy. In this study, the winter LAI and sky view fraction were estimated using different optical and biomass‐based approximations in several boreal coniferous forest stands in Fennoscandia with different stand density, age and site latitude. The biomass‐based estimate of LAI derived from forest inventory data was close to the values derived from the optical measurements at most sites, suggesting that forest inventory data can be used as input to snow hydrological modelling. Heterogeneity of tree species and site fertility, as well as edge effects between different forest compartments, caused differences in the LAI estimates at some sites. A snow energy and mass balance model (SNOWPACK) was applied to detect how the differences in the estimated values of the winter LAI and sky view fraction were reflected in simulated snow processes. In the simulations, an increase in LAI and a decrease in sky view fraction changed the snow surface energy balance by decreasing shortwave radiation input and increasing longwave radiation input. Changes in LAI and sky view fraction affected directly snow accumulation through altered throughfall fraction and indirectly snowmelt through the changed surface energy balance. Changes in LAI and sky view fraction had a greater impact on mean incoming radiation beneath the canopy than on other energy fluxes. Snowmelt was affected more than snow accumulation. The effect of canopy parameters on evaporation loss from intercepted snow was comparable with the effect of variation in governing meteorological variables such as precipitation intensity and air temperature. Copyright © 2012 John Wiley & Sons, Ltd.     

Simulation of crop growth and energy and carbon dioxide fluxes at different time steps from hourly to daily

Understanding the exchange processes of energy and carbon dioxide (CO₂) in the soil–vegetation–atmosphere system is important for assessing the role of the terrestrial ecosystem in the global water and carbon cycle and in climate change. We present a soil–vegetation–atmosphere integrated model (ChinaAgrosys) for simulating energy, water and CO₂ fluxes, crop growth and development, with ample supply of nutrients and in the absence of pests, diseases and weed damage. Furthermore, we test the hypotheses of whether there is any significant difference between simulations over different time steps. CO₂, water and heat fluxes were estimated by the improving parameterization method of the coupled photosynthesis–stomatal conductance–transpiration model. Soil water evaporation and plant transpiration were calculated using a multilayer water and heat‐transfer model. Field experiments were conducted in the Yucheng Integrated Agricultural Experimental Station on the North China Plain. Daily weather and crop growth variables were observed during 1998–2001, and hourly weather variables and water and heat fluxes were measured using the eddy covariance method during 2002–2003. The results showed that the model could effectively simulate diurnal and seasonal changes of net radiation, sensible and latent heat flux, soil heat flux and CO₂ fluxes. The processes of evapotranspiration, soil temperature and leaf area index agree well with the measured values. Midday depression of canopy photosynthesis could be simulated by assessing the diurnal change in canopy water potential. Moreover, the comparisons of simulated daily evapotranspiration and net ecosystem exchange (NEE) under different time steps indicated that time steps used by a model affect the simulated results. There is no significant difference between simulated evapotranspiration using the model under different time steps. However, simulated NEE produces large differences in the response to different time steps. Therefore, the accurate calculation of average absorbed photosynthetic active radiation is important for the scaling of the model from hourly steps to daily steps in simulating energy and CO₂ flux exchanges between winter wheat and the atmosphere. Copyright © 2007 John Wiley & Sons, Ltd.     

4D active time constrained resistivity inversion

Techniques that incorporate regularization in space and time have been proposed to reduce inversion artifacts that may lead a misinterpretation of geophysical monitoring data. Applying this time regularization, however, may result in a model too smoothly carrying in the time domain. To alleviate this problem, we propose an algorithm for inverting time-lapse resistivity monitoring data.Here the time regularization is not considered to be constant between different time steps but is now allowed to vary depending on the degree of spatial resistivity changes occurring between different monitoring stages. Two methods are proposed to assign different time Lagrangian values, one based on a pre-estimation during execution time, and one using a-priori information. Both methods require a threshold to characterize the significance of the observed resistivity changes with time. We performed numerous numerical experiments using synthetic data to provide reasonable threshold values. Synthetic data tests illustrate that the new algorithm, named 4D Active Time Constrained (4D-ATC), produces in most cases improved time-lapse images when compared with existing techniques. Further the applicability of the new scheme is demonstrated with real data. Overall, the new algorithm is shown to be a useful tool for processing time-lapse resistivity data, which can be used with minor modifications to other types of time-lapse geophysical data.     

A hybrid inversion method of damped least squares with simulated annealing used for Rayleigh wave dispersion curve inversion

Surface wave methods are becoming increasingly popular in many geotechnical applications and in earthquake seismology due to their noninvasive characteristics.Inverse surface wave dispersion curves are a crucial step in most surface wave methods.Many inversion methods have been applied to surface wave dispersion curve inversion,including linearized inversion and nonlinearized inversion methods.In this study,a hybrid inversion method of Damped Least Squares(DLS) with Very Fast Simulated Annealing(VFSA) is developed for multi-mode Rayleigh wave dispersion curve inversion.Both synthetic and in situ fi eld data were used to verify the validity of the proposed method.The results show that the proposed method is superior to the conventional VFSA method in aiming at global minimum,especially when parameter searching space is adjacent to real values of the parameters.The advantage of the new method is that it retains both the merits of VFSA for global search and DLS for local search.At high temperatures,the global search dominates the runs,while at a low temperatures,the local search dominates the runs.Thus,at low temperatures,the proposed method can almost directly approach the actual model.     

Evidence of large rainfall partitioning patterns by banana and impact on surface runoff generation

In this article the effect of redistribution of rainfall by banana on local water fluxes and the possible impact of these fluxes on surface runoff has been studied. First the water redistribution by a banana canopy at three development stages (vegetative, flowering, and bunch stage) was measured. The results showed a considerable stemflow, proportional to the leaf area index (LAI), which represented 18 to 26% of the incident rainfall volume according to the age of the crop. Consequently, the rainfall rate was 28‐fold higher at the plant collar for a fully developed banana canopy. For the throughfall, on average, the higher the LAI, the lower the mean throughfall. In addition, the spatial distribution of the throughfall varied according to the distance from the pseudostem. Notably, for the earlier stages, the area between the pseudostem and 0·5 m from it received weak throughfall. Secondly, simulations were carried out with a simple two‐compartment model simulating the total surface runoff volume. The simulations showed stemflow combined with the agronomical practice of furrowing has an effect on runoff compared to bare soil. A relative increase in surface runoff volume of three‐fold was encountered on a plot with a fully developed banana and a infiltration rate of 60 mm h^?1. However, the absolute increase was only a few percentage of the incident rainfall volume, although it represented large water volumes given the tropical rains. These features must be taken into account for hydrological management of such systems. Copyright © 2007 John Wiley & Sons, Ltd.     

Assessing crop yield and crop water productivity and optimizing irrigation scheduling of winter wheat and summer maize in the Haihe plain using SWAT model

Haihe plain is an important food production area in China, facing an increasing water shortage. The water used for agriculture accounts for about 70% of total water resources. Thus, it is critical to optimize the irrigation scheduling for saving water and increasing crop water productivity (CWP). This study first simulated crop yield and CWP for winter wheat and summer maize in historical scenario during 1961–2005 for Haihe plain using previously well‐established Soil and Water Assessment Tool model. Then, scenarios under historical irrigation (scenario 1) and sufficient irrigation (scenario 2) were, respectively, simulated both with sufficient fertilizer. The crop yield in scenario 2 was considered as the potential crop yield. The optimal irrigation scheduling with sufficient fertilizer (scenario 3) was explored by iteratively adjusting irrigation scheduling based on the scenario 1 and previous studies related to water stress on crop growth. Results showed that net irrigation amount was, respectively, reduced 23.1% and 18.8% in scenario 3 for winter wheat and summer maize when compared with scenario 1. The CWP was 12.1% and 8.2% higher with very slight change of crop yield. Using optimal irrigation scheduling could save 8.8 × 10⁸ m³ irrigation water and reduce about 16.3% groundwater over‐exploitation in winter wheat growth period. The corresponding yield was 18.5% and 12.9% less than potential yield for winter wheat and summer maize but using less irrigation water. Therefore, it could be considered that the optimal irrigation was reasonable, which provided beneficial suggestions for increasing efficiency of agricultural water use with sustainable crop yield in Haihe plain. Copyright © 2013 John Wiley & Sons, Ltd.     

A lateral model parameter correlation procedure for one-dimensional inverse modelling

We present a new, fast and versatile method, the lateral parameter correlation method, of invoking lateral smoothness in model sections of one-dimensional (1D) models. Modern, continuous electrical and electromagnetic methods are capable of recording very large data sets and except for a few cases, standard inversion methodology still relies on 1D models. In environments where the lateral rate of change of resistivity is small, 1D inversion can be justified but model sections of concatenated 1D models do not necessarily display the expected lateral smoothness.
The lateral parameter correlation method has three steps. First, all sounding data are inverted individually. Next, a laterally smooth version of each model parameter, one at a time, is found by solving a simple constrained inversion problem. Identity is postulated between the uncorrelated and correlated parameters and the equations are solved including a model covariance matrix. As a last step, all sounding data are inverted again to produce models that better fit the data, now subject to constraints by including the correlated parameter values as a priori values. Because the method separates the inversion from the correlation it is much faster than methods where the inversion and correlation are solved simultaneously, typically with a factor of 200–500.
Theoretical examples show that the method produces laterally smooth model sections where the main influence comes from the well-determined parameters in such a way that problems with equivalence and poor resolution are alleviated. A field example is presented, demonstrating the improved resolution obtained with the lateral parameter correlation method. The method is very flexible and is capable of coupling models from inversion of different data types and information from boreholes.     

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter(En KF) and the unscented Kalman filter(UKF) algorithms were used to assimilate remotely sensed leaf area index(LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site(EMS) and the Dinghushan site. After MODIS LAI data from 2000–2004 were assimilated into the improved Biome-BGC model using the En KF algorithm at the Harvard Forest site, the R2 between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error(SAE) and the root mean square error(RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R2 values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach.     

Drip irrigation enhances shallow groundwater contribution to crop water consumption in an arid area

Shallow groundwater plays a key role in agro‐hydrological processes of arid areas. Groundwater often supplies a necessary part of the water requirement of crops and surrounding native vegetation, such as groundwater‐dependent ecosystems. However, the impact of water‐saving irrigation on cropland water balance, such as the contribution of shallow groundwater to field evapotranspiration, requires further investigation. Increased understanding of quantitative evaluation of field‐scale water productivity under different irrigation methods aids policy and decision‐making. In this study, high‐resolution water table depth and soil water content in field maize were monitored under conditions of flood irrigation (FI) and drip irrigation (DI), respectively. Groundwater evapotranspiration (ET_g) was estimated by the combination of the water table fluctuation method and an empirical groundwater–soil–atmosphere continuum model. The results indicate that daily ET_g at different growth stages varies under the two irrigation methods. Between two consecutive irrigation events of the FI site, daily ET_g rate increases from zero to greater than that of the DI site. Maize under DI steadily consumes more groundwater than FI, accounting for 16.4% and 14.5% of ET_a, respectively. Overall, FI recharges groundwater, whereas DI extracts water from shallow groundwater. The yield under DI increases compared with that under FI, with less ET_a (526 mm) compared with FI (578 mm), and irrigation water productivity improves from 3.51 kg m^?3 (FI) to 4.58 kg m^?3 (DI) through reducing deep drainage and soil evaporation by DI. These results highlight the critical role of irrigation method and groundwater on crop water consumption and productivity. This study provides important information to aid the development of agricultural irrigation schemes in arid areas with shallow groundwater.     

基于交叉梯度约束的时间域激发极化法二维同步反演

时间域激发极化法(Time-domain induced polarization method,简称为TDIP)已有的反演算法采用的是分步反演的思路,即先由视电阻率资料反演电阻率,固定电阻率再由视极化率资料反演极化率,这样就存在极化率结果严重依赖于电阻率反演结果的问题.为了有效解决这一问题,本文实现了TDIP二维数据空间分步反演算法,提出了基于交叉梯度约束的TDIP二维同步反演策略,实现了交叉梯度约束的电阻率和极化率二维同步反演算法.分别用电阻率和极化率结构一致和不一致的二维模型合成数据进行了分步和同步反演试算,对不同模型试算结果进行了对比分析.结果表明:对于电阻率和极化率结构一致和不一致模型,同步反演结果比分步反演结果能更好地确定异常体的空间分布范围,反演得到的电阻率和极化率值更接近真值.理论模型算例表明本文提出的同步反演算法有效解决了分步反演的问题,优于分步反演算法,具有更好的实用性.     

A drought index for Rainfed agriculture: The Standardized Precipitation Crop Evapotranspiration Index (SPCEI)

A drought index is one of the main methods used for measuring drought and represents the basis of drought monitoring, early warning, and classification. On the basis of an analysis of the advantages and limitations of the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Precipitation Crop Evapotranspiration Index (SPCEI), which is a drought index of rainfed agriculture, was constructed in this study. The applicable conditions of the SPCEI were then investigated, and the results showed that the SPCEI was suitable for dryland crops under non‐irrigated conditions in arid and semi‐arid areas. The difference between the SPEI and SPCEI is analysed. Compared with the SPEI, the SPCEI considers crop evapotranspiration and the crop growth stage and was found to be more suitable for monitoring agricultural drought. Qigihar, which is located in a semi‐arid area in western Heilongjiang Province, China, was then analysed as an example. The characteristics of the spatial and temporal variability of regional agricultural drought were analysed based on maize and soybean in dryland areas. The results for the different growth stages of maize and soybean showed that drought intensity is more serious in the initial stage in the middle part. In crop development, mid‐season and late season stage, the drought conditions gradually increased from north to south. The drought degree of the two crops at the initial stage gradually increased, and the drought degree at the crop development stage gradually decreased. The main reason is that precipitation gradually increases during the crop development stage.