. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements ( Dorigo et al. ,  2011 b , a ) . The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal ( https://ismn.earth/en/ , last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

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The International Soil Moisture Network: Serving Earth system science for over a decade

The Internet of Things (IoT) is converting the agriculture industry and solving the immense problems or the major challenges faced by the farmers todays in the field. India is one of the 13th countries in the world having scarcity of water resources. Due to ever increasing of world population, we are facing difficulties in the shortage of water resources, limited availability of land, difficult to manage the costs while meeting the demands of increasing consumption needs of a global population that is expected to grow by 70% by the year 2050. The influence of population growth on agriculture leads to a miserable impact on the farmers livelihood. To overcome the problems we design a low cost system for monitoring the agriculture farm which continuously measure the level of soil moisture of the plants and alert the farmers if the moisture content of particular plants is low via sms or an email. This system uses an esp8266 microcontroller and a moisture sensor using Losant platform. Losant is a simple and most powerful IoT cloud platform for the development of coming generation. It offers the real time data visualization of sensors data which can be operate from any part of the world irrespective of the position of field.

An IoT based soil moisture monitoring on Losant platform

Agricultural and hydrological applications could greatly benefit from soil moisture (SM) information at sub-field resolution and (sub-) daily revisit time. However, current operational satellite missions provide soil moisture information at either lower spatial or temporal resolution. Here, we downscale coarse resolution (25–36 km) satellite SM products with quasi-daily resolution to the field scale (30 m) using the random forest (RF) machine learning algorithm. RF models are trained with remotely sensed SM and ancillary variables on soil texture, topography, and vegetation cover against SM measured in the field. The approach is developed and tested in an agricultural catchment equipped with a high-density network of low-cost SM sensors. Our results show a strong consistency between the downscaled and observed SM spatio-temporal patterns. We found that topography has higher predictive power for downscaling than soil texture, due to the hilly landscape of the study area. Furthermore, including a proxy of vegetation cover results in considerable improvements of the performance. Increasing the training set size leads to significant gain in the model skill and expanding the training set is likely to further enhance the accuracy. When only limited in-situ measurements are available as training data, increasing the number of sensor locations should be favored over expanding the duration of the measurements for improved downscaling performance. In this regard, we show the potential of low-cost sensors as a practical and cost-effective solution for gathering the necessary observations. Overall, our findings highlight the suitability of using ground measurements in conjunction with machine learning to derive high spatially resolved SM maps from coarse-scale satellite products.

Deriving Field Scale Soil Moisture from Satellite Observations and Ground Measurements in a Hilly Agricultural Region

Agriculture functions as an indispensable act in the world by meeting one of the basic needs of an individual named food, in spite of the fact that the assets are being reduced day by day and also various other problems arise irrespective of biases. In this scenario, unlike many technologies, when every other way fails to sense the routine of a crop, automation takes place by connecting to the invincible storages like cloud and streamlining the process by figuring its hardware and implementing user-friendly internet platform. IoT has set a benchmark in the technologies and has become a backbone to agriculture. This advancement in technology helps in farming automation, which helps in shaping a farmer’s workspace, ensuring them with device management, connectivity management, and productivity as a result along with remote management. This paper gives an insight on introduction to IoT, agriculture IoT, emerging wireless technologies of IoT, architectures and applications of IoT.

IoT Technologies in Agricultural Environment: A Survey

In this study, prior to the growth of ZnO nanorods (NRs), a 100 nm-thick ZnO seed layer was deposited by RF magnetron sputtering on a glass substrate. Subsequently, ZnO NRs were grown on ZnO seed layers via the hydrothermal method at 90°C for 6 h and annealing at 400°C. By using a Pt electrode as reference electrode, the potentiometric method was used to measure the potential difference between the two ends, and an operation amplifier (OPA) was employed as the readout circuit. The sensor and reference electrode were placed in a buffer solution of different pH values (4, 6, 8, and 10) for potentiometric analysis. Results showed that the average sensitivity of the ZnO NR sensor was ~4.4 × 10−2 V/pH, and linearity was ~0.98. The Arduino Uno was used to collect data, and the XBee module was utilized for wireless transmission. The computer side used the C# program to present the user interface, display the measurement results, and realize a wireless sensing network.

https://iopscience.iop.org/article/10.1149/2.0071909jes/pdf

Wireless Zinc Oxide Based pH Sensor System

Persistent increase in population of world is demanding more and more supply of food. Hence there is a significant need of advancement in cultivation to meet up the future food needs. It is important to know moisture levels in soil to maximize the output. But most of farmers cannot afford high cost devices to measure soil moisture. Our research work in this paper focuses on home-made low cost moisture sensor with accuracy. In this paper we present a method to manufacture soil moisture sensor to estimate moisture content in soil hence by providing information about required water supply for good cultivation. This sensor is tested with several samples of soil and able to meet considerable accuracy. Measuring soil moisture is an effective way to determine condition of soil and get information about the quantity of water that need to be supplied for cultivation. Two separate methods are illustrated in this paper to determine soil moisture over an area and along the depth.

Matti Satish Kumar

Papers

Monitoring moisture of soil using low cost homemade Soil moisture sensor and Arduino UNO