Moisture Data Research Articles

Better localized predictions with Out-of-Scope information and Explainable AI: One-Shot SAR backscatter nowcast framework with data from neighboring region

Synthetic Aperture Radar (SAR) provides 10-m weather-independent global Earth surface observations for various tasks such as land cover land use mapping, water body delineation, and vegetation change monitoring. However, the application of SAR imagery has been limited to retrospective tasks by a “first event then observation” rule. Recent studies have proven the feasibility of a one-shot nowcast of backscatters of SAR imagery using meteorological driving forces, soil moisture, geomorphic factors, and previous SAR images collected for the target area. Although the approach is promising, spatial connectivity, more specifically, the influence of the status of surrounding areas of the target location has yet to be considered. To fill that gap, this study proposed two nowcasting frameworks that can integrate precipitation and soil moisture data collected from surrounding areas through spatial aggregation (SA) and by processing spatial series (SS), respectively. The catastrophic 2019 Central US Flooding was used as a case study with the goal of predicting backscatters of SAR imagery captured during the event. The results from SA, SS, and a framework that only considers localized input (S0) are compared against each other as well as with the benchmark performance created with persistence assumption. Results show that S0, SA, and SS outperform the benchmark. More importantly, considering data from upstream neighboring areas further improves prediction accuracy compared to S0 where only inputs from the target region are considered. The gradients of results considering/not considering neighboring data indicates that neighboring data can alter the model’s attention on each feature of the localized input matrix. The difference in gradients between SA and SS indicates the way the neighboring information is integrated also matters. The methodology proposed by this study can serve as a building block for more active usage of SAR imagery in forward-looking tasks such as early flood warning and response.

Evaluation of satellite-based soil moisture for agricultural drought monitoring in the Brazilian semiarid region

The Northeast region of Brazil, particularly the Brazilian semiarid region (BSR), is frequently affected by drought events, such as the great drought that started in 2012 and is considered one of the most severe droughts in recent decades. Monitoring and quantifying this type of event is essential for decision-making and the adoption of impact mitigation strategies. In this study, remotely sensed soil moisture (SM) data from three products derived from Soil Moisture and Ocean Salinity (SMOS-CATDS and SMOS-IC) and Soil Moisture Active Passive (SMAP) were validated in the BSR using a dense network of in situ stations (N = 120) from 2015 to 2020. Subsequently, the best product was selected for the evaluation of agricultural drought, based on the Soil Water Deficit Index (SWDI), which was compared with the Atmospheric Water Deficit (AWD) obtained from in situ data (N = 50). After validation, SMAP was selected as the best product for estimating SM in the BSR, and SWDI-SMAP showed a strong temporal correlation with AWD. Based on these two indices, the results indicate the severity of drought in the BSR during the evaluated period, with significant social and economic impacts, highlighting the need for continuous monitoring, especially to support family farming, which is predominant in the region.

On the Sources of Water Supply Forecast Error in Western Colorado

Abstract Annual spring and summer runoff from western Colorado is relied upon by 40 million people, six states, and two countries. Cool season precipitation and snowpack have historically been robust predictors of seasonal runoff in western Colorado. Forecasts made with this information allow water managers to plan for the season ahead. Antecedent hydrological conditions, such as root zone soil moisture and groundwater storage, and weather conditions following peak snowpack also impact seasonal runoff. The roles of such factors were scrutinized in 2020 and 2021: seasonal runoff was much lower than expectations based on snowpack values alone. We investigate the relative importance of meteorological and hydrological conditions occurring before and after the snowpack season in predicting seasonal runoff in western Colorado. This question is critical because the most effective investment strategy for improving forecasts depends on if errors arise before or after the snowpack season. This study is conducted using observations from the Snow Telemetry Network, root zone soil moisture and groundwater data from the Western Land Data Assimilation Systems, and a random forest–based statistical forecasting framework. We find that on average, antecedent root zone soil moisture and groundwater storage values do not add significant skill to seasonal water supply forecasts in western Colorado. In contrast, using precipitation and temperature data after the time of peak snowpack improves water supply forecasts significantly. The 2020 and 2021 runoffs were hampered by dry conditions both before and after the snowpack season. Both antecedent soil moisture and spring/summer precipitation data improved water supply forecast accuracy in these years. Significance Statement Seasonal water supply forecasts in western Colorado are highly valuable because spring and summer runoff from this region helps support the water supply of 40 million people. Accurate forecasts improve the management of the region’s water. Heavy investments have been made in improving our ability to monitor antecedent hydrological conditions in western Colorado, such as root zone soil moisture and groundwater. However, results from this study indicate that the largest source of uncertainty in western Colorado runoff forecasts is future weather. Therefore, improved subseasonal-to-seasonal weather forecasts for western Colorado are what is most needed to improve regional water supply forecasts, and the ability to properly manage western Colorado water.

IoT-based smart irrigation management system using real-time data

<p>An adequate water supply is essential for the growth and development of crops. When rainfall is insufficient, irrigation is necessary to meet crop water needs. It is a crucial and strategic aspect of economic and social development. To combat climate change, there is a need to adopt irrigation management techniques that increase and stabilize agricultural production while saving water, using intelligent agricultural water technologies. Internet of things (IoT) based technologies can achieve optimal use of water resources. This article introduces a smart realtime irrigation management system based on the internet of things. It provides optimal management of irrigation decisions using real-time weather and soil moisture data, as well as data from precipitation forecasts. The proposed algorithm is developed in real-time based on the IoT, enabling us to guide irrigation and control the amount of water in agricultural applications. The system uses real-time data analysis of climate, soil, and crop data to provide flexible planning of the irrigation system’s use. A case study from the Fez-Meknes region in Morocco is presented to demonstrate the proposed system’s effectiveness</p>

Higher soil moisture increases microclimate temperature buffering in temperate broadleaf forests

Forest canopies can buffer the understory against temperature extremes, often creating cooler microclimates during warm summer days compared to temperatures outside the forest. The buffering of maximum temperatures in the understory results from a combination of canopy shading and air cooling through soil water evaporation and plant transpiration. Therefore, buffering capacity of forests depends on canopy cover and soil moisture content, which are increasingly affected by more frequent and severe canopy disturbances and soil droughts. The extent to which this buffering will be maintained in future conditions is unclear due to the lack of understanding about the relationship between soil moisture and air temperature buffering in interaction with canopy cover and topographic settings. We explored how soil moisture variability affects temperature offsets between outside and inside the forest on a daily basis, using temperature and soil moisture data from 54 sites in temperate broadleaf forests in Central Europe over four climatically different summer seasons. Daily maximum temperatures in forest understories were on average 2 °C cooler than outside temperatures. The buffering of understory temperatures was more effective when soil moisture was higher, and the offsets were more sensitive to soil moisture on sites with drier soils and on sun-exposed slopes with high topographic heat load. Based on these results, the soil–water limitation to forest temperature buffering will become more prevalent under future warmer conditions and will likely lead to changes in understory communities. Thus, our results highlight the urgent need to include soil moisture in models and predictions of forest microclimate, understory biodiversity and tree regeneration, to provide a more precise estimate of the effects of climate change.

An Investigation of Non‐Spherical Smoke Particles Using CATS Lidar

AbstractSmoke particles originating from biomass burning events are typically assumed to be spherical, yet non‐spherical smoke particles are also reported from in situ observations. The spatial and temporal distributions of non‐spherical smoke particles, which could have impacts on passive‐ and active‐based satellite aerosol retrievals, are not yet well understood. In this analysis, using NASA's Cloud Aerosol Transport System (CATS) lidar data during the biomass burning season over Africa and South America from 2015 to 2017, we studied the frequency and distribution of non‐spherical smoke particles. A supplemental smoke aerosol typing algorithm was developed to identify aerosol layers containing non‐spherical smoke particles which could otherwise be misclassified as dust or dust mixture using the CATS standard aerosol typing algorithm. Approximately 30% of smoke layers over Africa and South America are non‐spherical (depolarization ratio >0.1) and align with dry biomes of low soil moisture values. Conversely, spherical smoke layers (depolarization ratio <0.1) are in moist regions. The modified algorithm with improved discrimination of non‐spherical smoke detection using CATS depolarization ratio was further verified with the National Oceanic and Atmospheric Administration Hybrid Single‐Particle Lagrangian Integrated Trajectory model, Aerosol Robotic Network Ångström exponent retrievals, and National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis soil moisture data. This study highlights the limitations of current aerosol typing algorithms and the potential of algorithms employing ancillary data to improve aerosol typing such as multi‐wavelength volume depolarization ratio measurements or synergy with passive sensors to further discriminate between aerosol types from spaceborne elastic backscatter lidar.

Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model

Root-zone soil moisture (RZSM) plays a key role in the hydrologic cycle and regulates water–heat exchange. Although site observations can provide soil profile moisture measurements, they have a restricted representation. Satellites can determine soil moisture on a large scale, yet the depth of detection is limited. RZSM can be estimated on a large scale using the soil moisture analytical relationship (SMAR) and surface soil moisture (SSM). However, the applicability of the SMAR to different deep-root zones and covariate sources is unclear. This paper investigates the applicability of the SMAR in the Shandian River Basin, upstream of the Luan River in China, by combining site and regional soil moisture, soil properties, and meteorological data. In particular, we first compared the estimation results of the SMAR at different depths (10–20 cm; 10–50 cm) and using covariates from different sources (dataset, SMAR-P1; literature, SMAR-P2) at the site in order to generate SMAR calibration parameters. The parameters were then regionalized based on multiple linear regression by combining the SMAR-P1, SMAR-P2, and SMAR calibration parameters in the 10–50 cm root zone. Finally, the Shandian River RZSM was estimated using regional surface soil moisture and the aforementioned regionalized parameters. At the site scale, diffusion coefficient b obtained in the 10–20 cm root zone at the same depth as the surface layer exceeded the upper limit of the SMAR by one. This is not fit an environment within the site context, and thus the SMAR is not applicable at this particular depth. The opposite is observed for the 10–50 cm root zone. In addition, SMAR-P1 (RMSE = 0.02) outperformed SMAR-P2 (RMSE = 0.04) in the estimation of the RZSM at 10–50 cm. Parameter regionalization analysis revealed the failure of SMAR-P2 to pass the significance test (p > 0.05) for building a multivariate linear model, while SMAR-P1 successfully passed the significance test (p < 0.05) and finished the parameter regionalization process. The median RMSE and median R2adj of the regional RZSM results were determined as 0.12 and 0.3, respectively. The regional RZSM agrees with the spatial trend of the Shandian River. This study examines the suitability of the SMAR model in varying deep-root zones and with diverse covariate sources. The results provide a crucial basis for future utilization of the SMAR.

Soil moisture-based global liquefaction model (SMGLM) using soil moisture active passive (SMAP) satellite data

The role of soil saturation condition on the liquefaction occurrence highlights the need for a tool to track the ground-truth soil moisture content involved in this seismic phenomenon. Soil Moisture Active Passive (SMAP) satellite estimates near-real time surface and root zone soil moisture measurements with global coverage. Typical proxies for soil saturation in liquefaction analysis include average water table depth patterns, mean annual precipitation measurements, and topographic conditions. As an alternative to these proxies, this paper incorporates satellite-based soil moisture data to enhance the understanding of the interrelation between saturation conditions and liquefaction events. Proposing a new approach for sampling non-liquefaction cases, a liquefaction/non-liquefaction database was developed in this paper based on reconnaissance reports of eleven target earthquakes. Well-known geospatial explanatory variables as well as new SMAP-based soil moisture parameters, affecting soil liquefaction, are used to develop a new soil moisture-based global liquefaction model (SMGLM), which is compared with an existing global liquefaction model. Considering the ongoing advancement of earth observing satellites, the results of this paper can build a basis for developing fully satellite-based models that could identify liquefied sites using high resolution near-real time soil moisture data.

Simulation of soil water, heat, and salt adsorptive transport under film mulched drip irrigation in an arid saline-alkali area using HYDRUS-2D

The film mulched drip irrigation (FMDI) in severe drought and salinity areas makes the transport of water, heat, and salt in the soil more complicated. In this paper, two-year field experiments were carried out in southern Xinjiang from 2020 to 2021 (Salt levels: 0.2%, 0.4%, and 0.6%; Irrigation levels: 30 mm and 45 mm (2020). 22.5 mm, 30 mm, 37.5 mm, and 45 mm (2021)). Using the measured data of soil moisture, heat, and salinity, the HYDRUS-2D model was calibrated and validated, and the effects of soil adsorption on the simulation results were compared. Meanwhile, six irrigation scenarios were designed (The three levels of irrigation times were 8 times, 10 times, and 12 times. The total irrigation amounts were 360 mm and 450 mm). The results indicate that the simulated results of salt transport under the condition of kinetic adsorption matched accurately with the observed data (the maximum of RMSE was 0.293 ds m−1, and the minimum values of R2 and d were 0.923 and 0.934, respectively); similarly, the simulation results of soil water and heat transport were acceptable (the maximum values of RMSE were 0.033 cm cm−1 and 1.897 °C, and the minimum values of R2 and d were 0.877, 0.99 and 0.915, 0.807, respectively). The scenario simulation indicate that salt leaching mainly occurred under plastic film, the desalination in the 0–40 cm soil layer was obvious (The rate range of salt leaching is 29.8∼58.8%), and salt accumulation appeared in the 40–100 cm layer under plastic film in the specific scenarios. Considering the optimal desalination in the cotton root zone, the irrigation mode with 8 irrigation times (The irrigation interval of 12 d during the budding period, 2 times; the irrigation interval of 8 d, 6 times) with a total irrigation amount of 450 mm was recommended, which provides a reference for the regulation of water, heat, and salt in severe drought and salinity areas.

Evapotranspiration of Palmer amaranth (Amaranthus palmeri) in maize, soybean, and fallow under subsurface drip and center-pivot irrigation systems

AbstractPalmer amaranth (Amaranthus palmeri S. Watson) is a major biotic constraint in agronomic cropping systems in the United States. While crop–weed competition models offer a beneficial tool for understanding and predicting crop yield losses, within these models, certain weed biological characteristics and their responses to the environment are unknown. This limits understanding of weed growth in competition with crops under different irrigation methods and how competition for soil moisture affects crop growth parameters. This research measured the effect of center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) on the actual evapotranspiration (ETa) of A. palmeri grown in maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and fallow subplots. Twelve A. palmeri plants were alternately transplanted 1 m apart in the middle two rows of maize, soybean, and fallow subplots under CPI and SDI in 2019 and 2020 in south-central Nebraska. Maize, soybean, and fallow subplots without A. palmeri were included for comparison. Soil-moisture sensors were installed at 0-0.30, 0.30-0.60, and 0.60-0.90-m soil depths next to or between three A. palmeri and crop plants in each subplot. Soil-moisture data were recorded hourly from the time of A. palmeri transplanting to crop harvest. The results indicate differences in A. palmeri ETa between time of season (early, mid-, and late season) and crop type across 2019 and 2020. Although irrigation type did not affect subplot data, the presence of A. palmeri had an impact on subplot ETa across both years, which can be attributed to the variable relationship between volumetric soil water content (VWC) and ETa throughout the growing season due to advancing phenological stages and management practices. This study provides important and first-established baseline data and information about A. palmeri evapotranspiration and its relation to morphological features for future use in mechanistic crop–weed competition models.

Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data

This paper compares predictive algorithms for smart agriculture sensor data in Internet of Things (IoT) applications. The main objective of IoT in agriculture is to improve productivity and reduce production costs using advanced technology and artificial intelligence. In this study, we compared various predictive algorithms for analyzing IoT smart agriculture sensor data. Specifically, we evaluated the performance of NeuralProphet, Random Forest Regression, SARIMA, and Artificial Neural Networks (ANN) by KERAS algorithms on a dataset containing temperature, humidity, and soil moisture data. The dataset was collected using IoT sensors in a smart agriculture system. The results showed that Random Forest Regression, Seasonal ARIMA, and Artificial Neural Networks by KERAS algorithms outperformed NeuralProphet algorithm in terms of accuracy and computational efficiency.

Can We Predict Available Water Capacity from Long‐Term Soil Moisture Data?

Can We Predict Available Water Capacity from Long‐Term Soil Moisture Data?

Identification of the Representative Point for Soil Moisture Storage Using a Precipitation History Model

Soil water storage is an essential variable in hydrological processes at the hillslope scale. This study proposed models for predicting soil water based on the precipitation history. According to the mathematical analysis of soil water storage on the hillslope scale, hydrological fluxes can be effectively expressed in terms of the weighted time series of precipitation and evapotranspiration. Moreover, the impact of evapotranspiration on soil water storage was incorporated into the model structure as an autoregressive process. A new soil water prediction model was developed through the integration of the soil moisture stochastic process into the structure of a precipitation-based model for the hillslope scale. Intensive soil moisture and rainfall data collected over two years were then used to test the performance of the developed models for two different hillslopes. The proposed model exhibited a better ability to find representative points for soil water storage than either existing precipitation-based models or the temporal stability method.

Sequestering carbon in the subsoil benefits crop transpiration at the onset of drought

Abstract. Increasing soil organic carbon is promoted as a negative emission technology for the agricultural sector with a potential co-benefit for climate adaptation due to increased soil water retention. Field-scale hydrological models are powerful tools for evaluating how the agricultural systems would respond to the changing climate in upcoming years and decades, for predicting impacts, and for looking for measures that would help decrease drought-driven crop stress under current and future climatic conditions. We quantified how different levels of soil organic carbon (SOC) additions at varied soil depths are expected to influence drought-induced transpiration reduction (Treddry) in maize cultivated in Switzerland. Parameterization of the model based on a pedotransfer function (PTF) was validated against soil moisture data from a long-term lysimeter experiment with a typical Swiss soil, and the model was subsequently applied under climate forcing between 1981 until 2099, representative of three distinct climatic sites of Switzerland. We used the same PTF to indirectly assess the effects of SOC additions at different depths on soil hydraulic properties. We found a threshold in both the added amount of SOC (2 % added) and the depth of sequestering that SOC (top 65 cm), beyond which any additional benefit appears to be substantially reduced. However, adding at least 2 % SOC down to at least 65 cm depth can reduce Treddry in maize, i.e. increase transpiration annually but mostly at the onset of summer drought, by almost 40 mm. We argue that SOC increases in subsoils can play a supporting role in mitigating drought impacts in rain-fed cropping in Switzerland.

Modelling in-ground wood decay using time-series retrievals from the 5th European climate reanalysis (ERA5-Land)

ABSTRACT This article presents models to predict the time until mechanical failure of in-ground wooden test specimens resulting from fungal decay. Historical records of decay ratings were modelled by remotely sensed data from ERA5-Land. In total, 2,570 test specimens of 16 different wood species were exposed at 21 different test sites, representing three continents and climatic conditions from sub-polar to tropical, spanning a period from 1980 until 2022. To obtain specimen decay ratings over their exposure time, inspections were conducted in mostly annual and sometimes bi-annual intervals. For each specimen’s exposure period, a laboratory developed dose–response model was populated using remotely sensed soil moisture and temperature data retrieved from ERA5-Land. Wood specimens were grouped according to natural durability rankings to reduce the variability of in-ground wood decay rate between wood species. Non-linear, sigmoid-shaped models were then constructed to describe wood decay progression as a function of daily accumulated exposure to soil moisture and temperature conditions (dose). Dose, a mechanistic weighting of daily exposure conditions over time, generally performed better than exposure time alone as a predictor of in-ground wood decay progression. The open-access availability of remotely sensed soil-state data in combination with wood specimen data proved promising for in-ground wood decay predictions.

Assessing the interannual and subseasonal variabilities in water storage using multi-source soil moisture products and GRACE/GRACE-FO satellites and its applications

Since 2002, the terrestrial water storage anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-on (GRACE-FO), have been widely used in water storage studies. Interannual and subseasonal variabilities in terrestrial water storage are critical for understanding the change of water cycle and interpretation of GRACE error. In this study, we evaluate the interannual plus subseasonal terms of global TWSA from GRACE satellite data and soil moisture storage estimated from multi-source soil moisture data. Results show that soil moisture change dominated total water storage change in most regions, especially in relatively arid regions. We further reconstruct the continuous TWSA by combing the trend and seasonal terms from the GRACE(-FO) TWSA data with the interannual and subseasonal variabilities of multi-source soil moisture products on a global scale from April 2002 to December 2021. Our reconstruction result is further compared with the TWSA from GRACE(-FO) and two recent reconstruction products. The comparisons show that the reconstruction result has great consistency with GRACE(-FO), with 82% of the grids showing Nash-Sutcliffe efficiency coefficient values greater than 0.6 based on JPL Mascon solution. This study emphasizes that the information from soil moisture can provide an efficient approach for tracking the interannual and subseasonal variabilities of long-term terrestrial water storage.

Extreme Compound and Seesaw Hydrometeorological Events in New Zealand: An Initial Assessment

AbstractAttention is increasingly being turned toward land atmosphere interactions within the wider hydrological cycle when investigating extreme hydrometeorological events. This is particularly the case with the identification of compound and seesaw events. To do so, accurate soil moisture data are essential. Here, soil moisture from three reanalysis products (ERA5‐Land, BARRA and ERA5) is compared to station observations from 12 sites across New Zealand, for an average timespan of 18 years. Soil moisture data from all three reanalyses are used to investigate land‐atmosphere coupling with gridded (observational) precipitation and temperature. This enables compound (co‐occurrence of hot and dry) and seesaw (rapid transitions from dry to wet) events to be identified and examined. No best performing reanalysis data set for soil moisture is evident (median Pearson's r range: 0.78–0.81). All reanalyses successfully capture the seasonal and residual component of soil moisture, but not the observed soil moisture trends at each location. Strong coupling between soil moisture and temperature occurs in all three reanalyses across the predominately energy‐limited regions of the lower North Island and entire South Island. Consequently, these regions reveal a high frequency of compound event occurrence and potential shifts in land states to a water limited phase during compound months. A series of seesaw events is also detected for the first time in New Zealand (terminating approximately one‐fifth of drought events), with a high frequency of seesaw event occurrence detected in previously identified areas of atmospheric river (AR) activity.

Designing an Optimized RLC Network for Efficient Soil Moisture Data Logger System Using IoT

Designing an Optimized RLC Network for Efficient Soil Moisture Data Logger System Using IoT

Modeling of Soil Moisture Data by ARMA Time Series

The use of known data to predict future environmental parameters plays a crucial role in agriculture. In this paper, we propose a novel time series prediction method that combines the Auto-Regressive Moving Average Model (ARMA) and Gradient Boost Regression Tree(GBR) to forecast future soil moisture values. Firstly, the optimal number of decomposition modes for AMRA is determined by using Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF) plots. Secondly, according to statistics of XIlin Gol grassland offered by the Huawei Cup Mathematical Modeling Contest in 2022, the data including soil evaporation, precipitation, and soil moisture in the past ten years, are used as input parameters of ARMA to predict the precipitation and soil evaporation from 2022 to 2023. Then, the superiority of GBR was verified by comparing algorithms such as Support Vector Regression (SVR) and Random Forest(RF). Finally, GBR was used to realize the prediction for different soil moisture values from 2022 to 2023.

Spatio-Temporal Analysis and Contribution of Agricultural Drought in Daling River Basin: A VIC Model-Based Soil Moisture Simulation and SMAPI Evaluation

Soil moisture is a crucial factor that directly influences agricultural drought. As such, investigating drought-monitoring methods utilizing soil moisture data is of significant importance for accurately evaluating and predicting agricultural drought. However, the current soil moisture data for the Daling River Basin is insufficient. Therefore, the variable infiltration capacity (VIC) hydrological model was utilized to simulate soil moisture in the Daling River Basin. The simulated data were then analyzed in conjunction with the standardized moisture anomaly index (SMAPI) to analyze and evaluate the spatio-temporal characteristics of agricultural drought in the Darling River Basin. The results indicate that the frequency of drought occurrence in the basin follows a seasonal pattern of winter > spring > autumn > summer. Between 1981 and 2019, 24 out of 39 years experienced slight or greater drought, 15 years experienced moderate or more severe drought, and 4 years experienced severe drought. Drought conditions have become exceptionally severe in the 21st century. Specifically, the frequency of drought occurrence from 2001 to 2019 was nearly 10 times higher compared to the period from 1981 to 2000. The droughts were most severe in the southeast and southwest of the Daling River Basin, while the northeast and northwest experienced relatively mild drought. Agricultural drought is influenced by numerous complex factors. The contribution of climate change (CC) and other factors (OF) to agricultural drought was quantified by using a partial derivative under six different scenarios. Results showed that SMAPI was positively correlated with precipitation and solar radiation, while negatively correlated with temperature. From 1981 to 2000, SMAPI exhibited an increasing trend that accounted for 61.66% of variability, while a decreasing trend accounted for 38.34%. From 2001 to 2019, SMAPI exhibited a significant decreasing trend that accounted for 93.53% of the variability, while the increasing trend only accounted for 6.47%. CC was the dominant factor in most of the areas with increased SMAPI. OF was the main controlling factor for areas with decreased SMAPI.