Earth Observation Technologies for Land and Agricultural Development - EOT

11/09/2025

The recent paper has been published in the Remote Sensing Journal.
The title is "Integrating Multi-Temporal Satellite Data and Machine Learning Approaches for Crop Rotation Pattern Mapping in Thailand"

The gaps remain in accurately mapping season-specific crop types in mixed cropping systems, smallholder farms, and regions with cloudy conditions. Additionally, there is a lack of seasonal reference data and limited comprehensive methods for measuring optimal machine learning approaches, as seen in Thailand.
In particular, season-specific crop mapping based on several machine learning models, combined with a large number of reference datasets across crop-growing seasons in Southeast Asian countries, has yet to be rigorously tested under conditions of high crop diversity and the complexity of cropping systems with mixed crops in small fields. To address these gaps, this study concentrates on mapping crop types and cropping patterns in Thailand.

Finding more information, here is a link: https://www.mdpi.com/2072-4292/17/18/3156
Remote Sensing Journal: ISI-Q1 (IF=4.1), SCOPUS Q1

22/08/2025

หน่วยวิจัยเทคโนโลยีการสำรวจโลกเพื่อการพัฒนาที่ดินและการเกษตร
(Earth observation technologies (EOT) for land and agricultural development) ร่วมนำเสนอผลงานวิจัยและนวัตกรรมในงานมหกรรม MSU Innovation Expo 2025 ครั้งที่ 1 เมื่อวันที่ 22 สิงหาคม 2568 ณ ศูนย์ประชุมเฉลิมพระเกียรติฯ มหาวิทยาลัยมหาสารคาม มหกรรม MSU Innovation Expo 2025 จัดขึ้นเพื่อเป็นเวทีเผยแพร่ผลงานวิจัยและนวัตกรรม ตลอดจนสร้างเครือข่ายความร่วมมือระหว่างมหาวิทยาลัย ภาคธุรกิจ และสังคม Savittri Ratanopad SuwanleeJaturong Som-ardKemin Kasa@Surasak KeawsomseeAkkaneerut Watwiangkam

15/08/2025

30-m spatial resolution global forest canopy height map was developed through the integration of the Global Ecosystem Dynamics Investigation (GEDI) lidar forest structure measurements and Landsat analysis-ready data time-series (Landsat ARD).

19/06/2025

Our recent paper, "Evaluating and mapping spatial drought in northeast Thailand: utilizing analytic hierarchy process and random forest algorithms", has been published.
This work develops a comprehensive drought vulnerability map in Northeast (NE) Thailand using a multi-criteria geo-spatial mapping and RF algorithms.

Please visit the link: https://doi.org/10.1080/10106049.2025.2518413
Geocarto International
ISI Q2 IF 3.3; SCOPUS Q1 Tier 1

14/06/2025

Our recent paper, "Synergistic use of multi-sensor satellite data for mapping crop types and land cover dynamics from 2021 to 2023 in Northeast Thailand" has been published.
This work develops a new workflow classification well generates a historic and reliable land cover map in Northeast (NE) Thailand using Sentinel-1, Sentinel-2, and the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) datasets.

Please visit the link: https://www.sciencedirect.com/science/article/pii/S1569843225003206

International Journal of Applied Earth Observation and Geoinformation
ISI Q1 IF 7.6; SCOPUS Q1 Tier 1

08/06/2025

Data collection for monitoring w**d and white leaf disease in sugarcane field.
08.06.2025

growth and health monitoring

28/05/2025

Remote sensing in agriculture involves using satellite or drone-based sensors to collect data about crops, soil, and environmental conditions. Here are the main applications:

1. Crop Monitoring
Remote sensing helps track crop health, growth stages, and detect issues like disease, pests, or water stress. This allows farmers to take timely action.

2. Soil Mapping
Multispectral and hyperspectral sensors identify soil properties such as moisture content, texture, organic matter, and nutrient levels, aiding in site-specific soil management.

3. Yield Estimation
By analyzing plant vigor and biomass, remote sensing enables early prediction of crop yields, helping in logistics, marketing, and resource planning.

4. Field Management
Variable-rate application of fertilizers, pesticides, and irrigation is guided by remotely sensed data, increasing efficiency and reducing environmental impact.

5. Disaster Assessment
It helps assess damage from droughts, floods, or storms, and aids in insurance claims or recovery planning.

6. Land Use and Crop Type Mapping
Remote sensing allows classification and mapping of different crops and land uses over large areas, essential for agricultural planning and policy-making.

These applications enhance decision-making, increase productivity, and support sustainable agriculture.

~ NGA Geography

04/05/2025

Our recent paper, "Integrating PRISMA hyperspectral data with Sentinel-1, Sentinel-2 and Landsat data for mapping crop types and land cover in Northeast Thailand," has been published. This work tried using hyperspectral data with multi-temporal in 2022 S1, S2, and Landsat data to map crop types over a small field size region in Thailand.

Please visit the link: https://www.sciencedirect.com/science/article/pii/S1110982325000195

ISI Q2 IF 3.7; SCOPUS Q1

24/04/2025

Lidar data observation for rubber tree plantation (24 April 2025) in Kalasin province

Lidar data observation for rubber tree plantation (24 April 2025) in Kalasin province.
We will also try to observe the sugarcane plantations.
Lab

02/03/2025

Fieldwork from 27-28 Feb. 2025: We have reached four weather stations over the study regions to collect weather data during sugarcane crop growth. With this, the EOT team will integrate the big earth observation data to monitor sugarcane crop water stress.

15/02/2025

Developing prompt sugarcane classification in northeast, Thailand.

Thanks the funding department from Mahasarakham University.

IF: 4.2, ISI Q1

The effectiveness of machine learning algorithms and the limited reference data introduce uncertainness for sugarcane classification. To address these problems, our study classified sugarcane plant...

15/02/2025

One of our paper has been published now. This work develops the workflow for prompt sugarcane classification over the large scale.

Here is full article: Mapping sugarcane plantations in Northeast Thailand using multi-temporal data from multi-sensors and machine-learning algorithms

The effectiveness of machine learning algorithms and the limited reference data introduce uncertainness for sugarcane classification. To address these problems, our study classified sugarcane plant...