Soil Moisture Retrieval from MODIS and AMSRE Satellite Data A Case Study of Sindh Province, Pakistan
Bibliographic record
Abstract
Sindh province has diverse agro-climatological regions ranging from irrigated agricultural belt in the middle and desert to the east and bare hilly ranges on the west. Climate of the province is semi-arid with low annual precipitation of around 200mm. Agriculture and agribusiness is the main source of livelihood for majority in the province. Soil moisture study is an important parameter in agriculture, hydrology and hydrometeorology for studies related to sustainable development of agriculture and agribusiness in the province. In agriculture, soil moisture is used to study evapotranspiration, droughts, irrigation scheduling, and crop yield forecasting. It is also important for the environmental studies like subsequent precipitation patterns, temperature change and water quality. Soil moisture plays an important role in hydrology e.g., flood control, soil erosion and slope failure, reservoir management, geotechnical engineering and runoff generation. Due to synoptic coverage and high temporal resolution satellite remote sensing is ideal for instantaneous measurement of soil moisture content and its spatial and temporal behavior. In this study soil moisture at province level has been mapped through Advanced Microwave Scanning Radiometer (AMSR-E) and Moderate Resolution Imaging Spector Radiometer (MODIS) for the years 2007 and 2010. As 2007 was as normal year while 2010 was a wet year due to heavy rainfall and flood in the province, both the years have been selected to study soil moisture anomalies in normal and wet seasons. The results of MODIS derived soil moisture is in moderate agreement with AMSR-E soil moisture product proving the effectiveness of high resolution products in optical range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".