Soil and groundwater remediation act has been enacted and executed since year 2000 in Taiwan.  It has been ten good years till today where lots of remediation techniques progressively employed to improve Taiwan soil and groundwater resource quality.  Regulatory agencies, academia, remediation consulting firms, on-site professional engineers all have contribute the proud ten years in terms of soil and groundwater clean-up contribution.  However, some of technologies were un-environmental friendly even detrimental and damage to Taiwan precious soil and groundwater resources.  In Article one of the current Taiwan soil and groundwater Act, it clearly stated that soil is a precious nature resources.  Soil definitely is not a waste, shame on us most of current most commonly employed remediation are unlawful and merely aiming to save time and money consideration without any care to our land.  Dig-and-dump and soil acid washing are damaged employed in almost every single local environment agency soil clean-up project.  Lot of money, effort and time has been spent during past ten years.  Most of the spending is not improving soil quality using Green approach.

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.

Soil and groundwater remediation Act was enacted in year 2000. More than ten years has already passed,  Monitoring project has been completed,pollution status has been defined,contaminated sites depollution have been launched,a great progress has been made. This paper majorly to depict the extensive farmland soil qauality monittoring which is unpredent in Taiwan and believe has never been done worldwide.

This project was initiated from February 8^th, 2002 to August 8^th, 2002. The project tasks including digitalization of cadastre, farmland listing, basic information collecting, field investigation, sampling & analysis planning, field sampling, soil sample analysis, data evaluation, suggestion of contaminated farmland control, and analysis of potential pollution sources and transfer routes.

2,251 soil samples,had been sampled from Chang-Hwa County, Yun-Lin County, Nan-Tao County, and Chia-Yi City, and been analyzed in this project. 44% of these samples concentration exceed the soil pollution control standard (Table 1), including 492 farmlands (125.65 ha registered) with total contaminated farming area of 108.38 ha in Chang-Hwa, and 6 farmlands (0.39 ha registered) with total contaminated farming area of 0.39 ha in Nan-Tao County. However, the concentration of samples from Ynu-Lin County and Chia-Yi City do not exceed the soil pollution control standard.

To coordinate with the investigation results of the relative project regarding to water and sediment quality of irrigation channels in Chang-Hwa area, the pollution sources are preliminary concluded to be the irrigation channels surrounding the farmlands in Chang-Hwa area. As to the Nan-Tao County, the abandoned brick furnace plants neighboring the farmland are suspected to be the pollution sources.

The results show that the soil of the investigation area in Chang-Hwa County is the most polluted. Base on the Geostatistics study and the distribution of the irrigation channels; the area neighboring the investigated farmland in this project is suspected being polluted. For the farmlands exceeding soil control standard, Geostatistics method is suggested to coordinate with the information of the irrigation system to clarify the contaminated area so as to be the basis of land control and remediation work. As to the farmlands, not being investigated in this project but with high pollution potential according to the Geostatistics study, detail investigations are suggested. Regarding to soil pollution remediation, it is suggested to coordinate with the effluent control and irrigation channel remediation to achieve an all-out success.

In order to optimize the environmental factors for cucumber growth, a fertilizer and water control system was designed based on the Internet of Things (IoT) system. The IoT system monitors environmental factors such as temperature, light and soil Ec value, and uses image processing to obtain four growth indicators such as cucumber stem height, stem diameter size, number of leaves and number of fruit set to establish a single growth indicator model for temperature, light, soil Ec value and growth stage, and the four growth indicators were fused to obtain the comprehensive growth indicator I_c for cucumber, and calculates its deviation to determine the cucumber growth status. Based on the integrated growth index I_c of cucumber, a soil Ec control model was established to provide the optimal environment and fertilizer ration for cucumber at different growth stages to achieve stable and high yield of cucumber.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.