宁夏各地区农田土壤水分蒸发量预测数据

通过全自动小型气象站对宁夏各地区农田实时监测环境温度、环境湿度、风速、土壤含水率、本周降水量、植被指数、太阳日照时间等数据并每日上传即时数据，根据上述7个数据预测出农田土壤水分蒸发量。该预测数量可为农田水分盈亏分析提供数据支撑，指导农田科学灌溉和排水，从而提高作物的产量和质量。另外可结合地理信息系统（GIS）技术，将各地点的农田地理数据和土壤水分蒸发量信息进行深度整合和分析，绘制地理位置-土壤水分蒸发量地图，以直观的可视化形式呈现给用户，增强地理位置与土壤水分蒸发量关系的理解。每天早上通过全自动小型气象站对宁夏各地区不同编号的农田实时监测，采集环境温度、环境湿度、风速、土壤含水率、本周降水量、植被指数、太阳日照时间等数据并每日上传即时数据。 通过广义回归神经网络(GRNN)方法对土壤水分蒸发量进行预测，利用主成分分析法提取影响土壤水分蒸发量的7个因子（环境温度、环境湿度、风速、土壤含水率、本周降水量、植被指数、太阳日照时间），将上述7个因子作为GRNN模型的输入量，土壤水分蒸发量作为输出量从而预测出农田土壤水分蒸发量。GRNN模型预测值与实测值拟合程度较高，模型模拟精度较高，可用于宁夏各地区农田土壤水分蒸发量预测。

Fully automatic small meteorological stations are employed to conduct real-time monitoring of farmland environmental temperature, ambient humidity, wind speed, soil moisture content, weekly precipitation, vegetation index, sunshine duration and other relevant data across various regions of Ningxia, with real-time data uploaded daily. The farmland soil water evaporation can be predicted based on the aforementioned seven datasets. These prediction results can provide data support for farmland water balance analysis, guide scientific irrigation and drainage practices in farmlands, and thus improve crop yield and quality. Additionally, by integrating Geographic Information System (GIS) technology, in-depth integration and analysis can be performed on farmland geographic data and soil water evaporation information of each location, and geographic location-soil water evaporation maps can be generated. These maps are presented to users in an intuitive visual format to enhance the understanding of the relationship between geographic location and soil water evaporation.

Every morning, fully automatic small meteorological stations conduct real-time monitoring on farmlands with unique IDs in various regions of Ningxia, collecting the above-mentioned environmental parameters including temperature, humidity, wind speed, soil moisture content, weekly precipitation, vegetation index and sunshine duration, and upload real-time data on a daily basis.

Soil water evaporation prediction is implemented using the Generalized Regression Neural Network (GRNN) method. Seven influencing factors (environmental temperature, ambient humidity, wind speed, soil moisture content, weekly precipitation, vegetation index, sunshine duration) that affect soil water evaporation are extracted via Principal Component Analysis (PCA). The seven factors are taken as the input variables of the GRNN model, while soil water evaporation serves as the output variable, enabling the prediction of farmland soil water evaporation. The GRNN model shows a high fitting degree between predicted and measured values, with high simulation accuracy, and can be applied to predict farmland soil water evaporation in various regions of Ningxia.

搜集汇总

数据集介绍

特点

该数据集包含宁夏各地区农田的土壤水分蒸发量预测数据，每日更新，数据规模为11473条。通过环境温度、湿度等7个因子预测蒸发量，应用于农田科学灌溉和GIS技术整合分析。