Soil moisture mapping using machine learning technique
DOI:
https://doi.org/10.5564/mjgg.v60i44.3062Keywords:
Soil moisture, machine learning, SMAPAbstract
Soil moisture is an essential component in the energy cycle, water resource, hydrological regime, and processes of the land surface. Mapping and monitoring of soil moisture are crucial for the prevention of flood and drought, estimation of evapotranspiration, and water resource management. Using remote sensing to create soil moisture mapping at large scale has become one of the most energy and time-efficient methods in soil study. Thus, we aimed to map the soil moisture for Mongolia based on downscaled Soil Moisture Active Passive (SMAP) data by combining it with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Temperature (LST) of Moderate Resolution Imaging Spectroradiometer (MODIS) data using the Machine Learning-based Random Forest (RF) approach. The SMAP was positively correlated with NDVI (r=0.72, p<0.01) and EVI (r=0.73, p<0.01) but it was negatively correlated with LST (r= -0.66, p<0.05). The performance of the RF was high, and the correlation was r2=0.7. Therefore, our study suggests that the Machine Learning-based RF approach can be used to model soil moisture on a large scale.
Машин сургалтын аргаар хөрсний чийгийг зураглах арга зүй
Хөрсний чийг нь усны эргэлт, энергийн урсгалд чухал нөлөө үзүүлдгээс гадна, газрын гадаргын нөхцөл болон гадаргын усанд маш чухал нөлөөтэй. Иймд, хөрсний чийгийн зураглал болон мониторингийн судалгаа нь ган, зудын мониторинг, үерийн урьдчилсан сэрэмжлүүлэг болон усны нөөцийн менежментэд чухал үүрэг гүйцэтгэдэг судалгааны нэг юм. Сүүлийн үед, өргөн уудам газар нутагт хөрсний чийгийг зураглахын тулд зайнаас тандан судлалын аргыг ашиглах нь эдийн засаг болон цаг хугацааны хувьд үр ашигтай аргуудын нэг болоод байна. Иймд Монгол орны хэмжээнд хөрсний чийгийг зураглахдаа Soil Moisture Active Passive (SMAP) хиймэл дагуулын бүтээгдэхүүнийг ашиглан машин сургалтын санамсаргүй ой (RF)-н аргаар мэдээний орон зайн шийдийг сайжруулан зураглалаа. Ингэхдээ Moderate Resolution Imaging Spectroradiometer (MODIS) хиймэл дагуулын бүтээгдэхүүнүүдэд (ургамлын нормчилсон ялгаврын индекс (NDVI), ургамлын сайжруулсан индекс (EVI), газрын гадаргын температур (LST) тулгуурлан SMAP хиймэл дагуулын бүтээгдэхүүний орон зайн шийдийг сайжруулан өөрчилсөн, хамаарлыг тооцсон. Ингэхэд NDVI (r=0.72, p<0.01) болон EVI (r=0.73, p<0.01) нь SMAP-тай эерэг хамааралтай байсан бол LST (r= -0.66, p<0.05)-тай урвуу хамааралтай байсан. RF-н алгоритмаар машин сургалтын аргыг ашиглан Монгол орны хэмжээнд хөрсний чийгийг зураглахад загварын үр дүн гүйцэтгэл сайтай буюу хамаарал нь r2=0.7 гарсан. Иймд машин сургалтын санамсаргүй ойн алгоритмаар том хэмжээний газар нутгийг хамруулан хөрсний чийгийг загварчлах боломжтой болох нь судалгааны үр дүнгээс харагдаж байна.
Түлхүүр үгс: Хөрсний чийг, машин сургалт, SMAP
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