浙江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浙江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浙江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，可用于浙江各地区农田土壤水分蒸发量预测。

A fully automatic small-scale weather station is employed to perform real-time monitoring of farmlands across different regions of Zhejiang Province, collecting real-time data such as ambient temperature, ambient humidity, wind speed, soil moisture content, weekly precipitation, vegetation index, and sunshine duration. The collected real-time data is uploaded daily. Based on these seven datasets, the farmland soil water evaporation can be predicted. The predicted evaporation values can provide data support for farmland water surplus and deficit analysis, guide scientific irrigation and drainage operations on farmlands, and thus improve crop yield and quality. Additionally, by integrating Geographic Information System (GIS) technology, the farmland geographic data and soil water evaporation information of each location can be deeply integrated and analyzed, and a geographic location-soil water evaporation map can be plotted. This map is presented to users in an intuitive visual format, enhancing the understanding of the relationship between geographic location and soil water evaporation. Real-time monitoring is conducted every morning on farmlands with unique identification numbers across Zhejiang Province using the fully automatic small-scale weather stations, with the aforementioned seven types of data collected and real-time data uploaded daily. The soil water evaporation is predicted via the Generalized Regression Neural Network (GRNN) method. Specifically, Principal Component Analysis (PCA) is utilized to extract the seven impact factors affecting soil water evaporation (ambient temperature, ambient humidity, wind speed, soil moisture content, weekly precipitation, vegetation index, and sunshine duration), which are then taken as the input variables of the GRNN model, while the farmland soil water evaporation is set as the output variable to realize the prediction. The GRNN model shows a high fitting degree between predicted and measured values, with high simulation accuracy, and can be used to predict the farmland soil water evaporation across various regions of Zhejiang Province.

搜集汇总

数据集介绍

特点

该数据集包含浙江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11604条。通过全自动小型气象站实时监测环境温度、湿度等7个因子，并利用GRNN模型预测土壤水分蒸发量，为农田水分盈亏分析和科学灌溉提供数据支撑。