河南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河南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河南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，可用于河南各地区农田土壤水分蒸发量预测。

Real-time monitoring of environmental temperature, ambient humidity, wind speed, soil moisture content, weekly precipitation, vegetation index, solar sunshine duration and other related data on farmlands across Henan Province is conducted via fully automatic mini meteorological stations, with real-time data uploaded daily. The farmland soil water evaporation is predicted based on the aforementioned seven types of data. This predicted evaporation data can provide data support for farmland water surplus and deficit analysis, guide scientific irrigation and drainage of farmlands, and thereby improve crop yield and quality. In addition, combined with Geographic Information System (GIS) technology, in-depth integration and analysis can be carried out on farmland geographic data and soil water evaporation information of various locations, and a geographic location-soil water evaporation map can be drawn, which is presented to users in an intuitive visual form to enhance the understanding of the relationship between geographic location and soil water evaporation. Every morning, fully automatic mini meteorological stations are used to conduct real-time monitoring on farmlands with different serial numbers across Henan Province, collecting environmental temperature, ambient humidity, wind speed, soil moisture content, weekly precipitation, vegetation index, solar sunshine duration and other related data, and uploading real-time data on a daily basis. Soil water evaporation is predicted using the Generalized Regression Neural Network (GRNN) method. Principal Component Analysis (PCA) is employed to extract seven factors affecting soil water evaporation: environmental temperature, ambient humidity, wind speed, soil moisture content, weekly precipitation, vegetation index, and solar sunshine duration. These seven factors are taken as the input variables of the GRNN model, with soil water evaporation as the output variable to achieve the prediction of farmland soil water evaporation. The predicted values from the GRNN model have a high degree of fitting with the measured values, and the model has high simulation accuracy, which can be used for the prediction of farmland soil water evaporation across Henan Province.

搜集汇总

数据集介绍

特点

该数据集包含河南各地区农田土壤水分蒸发量的预测数据，每日更新，规模为11501条。数据通过全自动小型气象站实时监测，并利用广义回归神经网络(GRNN)方法进行预测，旨在为农田水分盈亏分析和科学灌溉提供数据支撑。