Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring
Abstract
:1. Introduction
2. Materials and Methods
2.1. In Situ SM Dataset
2.2. Optical Remote Sensing Dataset
2.3. Soil Properties and Meteorological Dataset
2.4. Soil Moisture Products
2.5. Methods
2.5.1. Surface Soil Moisture Derived from a Downscaling Framework (RF-SM)
2.5.2. Soil Water Deficit Index (SWDI)
2.5.3. Vegetation Health Index (VHI)
2.5.4. Evaluation Methods
- (1)
- Model Evaluation Methods
- (2)
- Evaluation of the Drought Indices SWDI and VHI
3. Results
3.1. Evaluation of the RF-SM Dataset
3.2. Comparison between the RF-SM-SWDI and VHI
3.3. Spatiotemporal Drought Monitoring at the Field Scale
4. Discussion
4.1. Utility of Surface SM Metrics for Monitoring Drought
4.2. Performance of the RF-SM-SWDI
4.2.1. Comparison between the RF-SM-SWDI and VHI
4.2.2. Spatiotemporal Consistency of Drought Monitoring
4.3. Issues of Drought Monitoring with High Spatial Resolution
5. Conclusions
- (1)
- The RF-SM dataset yielded better performance results than the four single SM products when compared with the observed SM at in situ stations, whether based on different land cover types or ISMN networks. When the utility of surface SM in agricultural drought monitoring is recognized, RF-SM data exhibit significant value in capturing drought conditions driven by SM because of their high spatial resolution.
- (2)
- The SWDI relies on SM estimation and corresponding soil hydraulic parameters, which effectively overcomes the limitations of drought research over certain time periods. The RF-SM-SWDI exhibited a favorable correlation with the VHI at approximately 70% of the stations. Further comparison of the substudy area showed that the coupling of the LST and SM produced a strong correlation between the TCI and SWDI, while the changes in vegetation conditions caused a significantly different spatial patterns for the VCI and SWDI, which ultimately affected the difference in the distribution and temporal variations of the RF-SM-SWDI and VHI related to the land cover types.
- (3)
- The RF-SM-SWDI provided data for drought conditions which included more detailed spatial information and demonstrated the seasonal evolution and patterns of the different land cover types. Compared with the results of the STDB, the RF-SM-SWDI recognized Mediterranean climate characteristics in the substudy area, accurately monitoring summer drought events and drought mitigation in winter, which made it possible to achieve real-time monitoring of short-term and flash droughts at the field scale.
- (4)
- Due to the limitations of the available datasets, the application of RF-SM-SWDI exhibits some difficulties in long-term agricultural drought monitoring on a larger geographical scale. Therefore, advanced data fusion and data assimilation technology, along with more drought-related surface information, will help to accurately analyze drought distribution and track drought evolution.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Sheffield, J.; Andreadis, K.; Wood, E.F.; Lettenmaier, D. Global and continental drought in the second half of the twentieth century: Severity–area–duration analysis and temporal variability of large-scale events. J. Clim. 2009, 22, 1962–1981. [Google Scholar] [CrossRef]
- Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. 2010, 391, 202–216. [Google Scholar] [CrossRef]
- Dai, A. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008. J. Geophys. Res. Atmos. 2011, 116, 1–26. [Google Scholar] [CrossRef]
- Damberg, L.; AghaKouchak, A. Global trends and patterns of drought from space. Theor. Appl. Climatol. 2014, 117, 441–448. [Google Scholar] [CrossRef]
- Song, X.; Song, Y.; Chen, Y. Secular trend of global drought since 1950. Environ. Res. Lett. 2020, 15, 094073. [Google Scholar] [CrossRef]
- Krishnamurthy, R.P.K.; Fisher, J.B.; Choularton, R.J.; Kareiva, P.M. Anticipating drought-related food security changes. Nat. Sustain. 2022, 5, 956–964. [Google Scholar] [CrossRef]
- Krishnamurthy, R.P.K.; Fisher, J.B.; Schimel, D.S.; Kareiva, P.M. Applying tipping point theory to remote sensing science to improve early warning drought signals for food security. Earth’s Future 2020, 8, e2019EF001456. [Google Scholar] [CrossRef]
- Lottering, S.; Mafongoya, P.; Lottering, R. Drought and its impacts on small-scale farmers in sub-Saharan Africa: A review. S. Afr. Geogr. J. 2021, 103, 319–341. [Google Scholar] [CrossRef]
- Sweet, S.K.; Wolfe, D.W.; DeGaetano, A.; Benner, R. Anatomy of the 2016 drought in the Northeastern United States: Implications for agriculture and water resources in humid climates. Agric. For. Meteorol. 2017, 247, 571–581. [Google Scholar] [CrossRef]
- Hazaymeh, K.; Hassan, Q.K. Remote sensing of agricultural drought monitoring: A state of art review. AIMS Environ. Sci. 2016, 3, 604–630. [Google Scholar] [CrossRef]
- Liu, X.; Zhu, X.; Pan, Y.; Li, S.; Liu, Y.; Ma, Y. Agricultural drought monitoring: Progress, challenges, and prospects. J. Geogr. Sci. 2016, 26, 750–767. [Google Scholar] [CrossRef]
- Watson, A.; Miller, J.; Künne, A.; Kralisch, S. Using soil-moisture drought indices to evaluate key indicators of agricultural drought in semi-arid Mediterranean Southern Africa. Sci. Total Environ. 2022, 812, 152464. [Google Scholar] [CrossRef] [PubMed]
- Blumenstock, G. Drought in the United States Analyzed by Means of the Theory of Probability; United States Department of Agriculture: Washington, DC, USA, 1942.
- McQuigg, J. A simple index of drought conditions. Weatherwise 1954, 7, 64–67. [Google Scholar] [CrossRef]
- Munger, T.T. Graphic method of representing and comparing drought intensities. Mon. Weather Rev. 1916, 44, 642–643. [Google Scholar] [CrossRef]
- Palmer, W.C. Meteorological Drought; US Department of Commerce, Weather Bureau: Washington, DC, USA, 1965.
- Barichivich, J.; Osborn, T.; Harris, I.; van der Schrier, G.; Jones, P. Drought: Monitoring global drought using the self-calibrating Palmer Drought Severity Index. Bull. Am. Meteorol. Soc. 2019, 100, S39–S40. [Google Scholar]
- Yan, H.; Wang, S.Q.; Wang, J.B.; Lu, H.Q.; Guo, A.H.; Zhu, Z.C.; Myneni, R.B.; Shugart, H.H. Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982–2011 by using PDSI indices and agriculture drought survey data. J. Geophys. Res. Atmos. 2016, 121, 2283–2298. [Google Scholar] [CrossRef]
- Zoljoodi, M.; Didevarasl, A. Evaluation of spatial-temporal variability of drought events in Iran using palmer drought severity index and its principal factors (through 1951–2005). Atmos. Clim. Sci. 2013, 3, 30772. [Google Scholar] [CrossRef]
- Ellis, S.L.; Lohman, M.G.; Sedinger, J.S.; Williams, P.J.; Riecke, T.V. Long-term trends and drought: Spatiotemporal variation in juvenile sex ratios of North American ducks. Ecol. Evol. 2022, 12, e9099. [Google Scholar] [CrossRef]
- Nie, N.; Zhang, W.; Chen, H.; Guo, H. A global hydrological drought index dataset based on gravity recovery and climate experiment (GRACE) data. Water Resour. Manag. 2018, 32, 1275–1290. [Google Scholar] [CrossRef]
- Shin, J.Y.; Chen, S.; Lee, J.-H.; Kim, T.-W. Investigation of drought propagation in South Korea using drought index and conditional probability. Terr. Atmos. Ocean. Sci. 2018, 29, 231–241. [Google Scholar] [CrossRef]
- Gilbert, P.-L.; Boyer, M. Palmer’s Z-Index as an Underlying Index for a Weather-based Crop Insurance Policy: Saskatchewan Study Case. Ph.D. Thesis, HEC Montréal, Montréal, BC, Canada, 2021. [Google Scholar]
- Ngarukiyimana, J.; Tan, G.; Ongoma, V.; Ogwang, B.; Vuguziga, F. Application of Z-Index in analysis of recent March–May Drought over Rwanda. Rom. J. Meteorol. 2016, 13, 1–16. [Google Scholar]
- Trnka, M.; Hlavinka, P.; Semerádová, D.; Dubrovsky, M.; Zalud, Z.; Mozny, M. Agricultural drought and spring barley yields in the Czech Republic. Plant Soil Environ. 2007, 53, 306. [Google Scholar] [CrossRef]
- Heim, R.R., Jr. A review of twentieth-century drought indices used in the United States. Bull. Am. Meteorol. Soc. 2002, 83, 1149–1166. [Google Scholar] [CrossRef]
- Choi, M.; Jacobs, J.M.; Anderson, M.C.; Bosch, D.D. Evaluation of drought indices via remotely sensed data with hydrological variables. J. Hydrol. 2013, 476, 265–273. [Google Scholar] [CrossRef]
- West, H.; Quinn, N.; Horswell, M. Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities. Remote Sens. Environ. 2019, 232, 111291. [Google Scholar]
- Zhang, L.; Jiao, W.; Zhang, H.; Huang, C.; Tong, Q. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sens. Environ. 2017, 190, 96–106. [Google Scholar] [CrossRef]
- Yao, Y.; Liang, S.; Qin, Q.; Wang, K. Monitoring drought over the conterminous United States using MODIS and NCEP Reanalysis-2 data. J. Appl. Meteorol. Climatol. 2010, 49, 1665–1680. [Google Scholar] [CrossRef]
- Aulia, M.R.; Setiawan, Y.; Fatikhunnada, A. Drought detection of west java’s paddy field using MODIS EVI satellite images (case study: Rancaekek and rancaekek wetan). Procedia Environ. Sci. 2016, 33, 646–653. [Google Scholar] [CrossRef]
- Nanzad, L.; Zhang, J.; Tuvdendorj, B.; Nabil, M.; Zhang, S.; Bai, Y. NDVI anomaly for drought monitoring and its correlation with climate factors over Mongolia from 2000 to 2016. J. Arid. Environ. 2019, 164, 69–77. [Google Scholar] [CrossRef]
- Wagle, P.; Xiao, X.; Torn, M.S.; Cook, D.R.; Matamala, R.; Fischer, M.L.; Jin, C.; Dong, J.; Biradar, C. Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought. Remote Sens. Environ. 2014, 152, 1–14. [Google Scholar] [CrossRef]
- AghaKouchak, A. A baseline probabilistic drought forecasting framework using standardized soil moisture index: Application to the 2012 United States drought. Hydrol. Earth Syst. Sci. 2014, 18, 2485–2492. [Google Scholar] [CrossRef]
- Martínez-Fernández, J.; González-Zamora, A.; Sánchez, N.; Gumuzzio, A.; Herrero-Jiménez, C. Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index. Remote Sens. Environ. 2016, 177, 277–286. [Google Scholar] [CrossRef]
- Zhu, Y.; Liu, Y.; Wang, W.; Singh, V.P.; Ren, L. A global perspective on the probability of propagation of drought: From meteorological to soil moisture. J. Hydrol. 2021, 603, 126907. [Google Scholar] [CrossRef]
- Amalo, L.F.; Hidayat, R.; Sulma, S. Analysis of agricultural drought in east java using vegetation health index. AGRIVITA J. Agric. Sci. 2017, 40, 63–73. [Google Scholar] [CrossRef]
- Jiang, R.; Liang, J.; Zhao, Y.; Wang, H.; Xie, J.; Lu, X.; Li, F. Assessment of vegetation growth and drought conditions using satellite-based vegetation health indices in Jing-Jin-Ji region of China. Sci. Rep. 2021, 11, 13775. [Google Scholar] [CrossRef]
- Kloos, S.; Yuan, Y.; Castelli, M.; Menzel, A. Agricultural drought detection with MODIS based vegetation health indices in southeast Germany. Remote Sens. 2021, 13, 3907. [Google Scholar] [CrossRef]
- Berg, A.; Sheffield, J. Climate change and drought: The soil moisture perspective. Curr. Clim. Chang. Rep. 2018, 4, 180–191. [Google Scholar] [CrossRef]
- Wagner, W.; Hahn, S.; Kidd, R.; Melzer, T.; Bartalis, Z.; Hasenauer, S.; Figa, J.; De Rosnay, P.; Jann, A.; Schneider, S. The ASCAT soil moisture product: A review of its. Meteorol. Z. 2013, 22, 1–29. [Google Scholar] [CrossRef]
- Entekhabi, D.; Njoku, E.G.; O’neill, P.E.; Kellogg, K.H.; Crow, W.T.; Edelstein, W.N.; Entin, J.K.; Goodman, S.D.; Jackson, T.J.; Johnson, J. The soil moisture active passive (SMAP) mission. Proc. IEEE 2010, 98, 704–716. [Google Scholar] [CrossRef]
- Kerr, Y.H.; Waldteufel, P.; Wigneron, J.-P.; Martinuzzi, J.; Font, J.; Berger, M. Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens. 2001, 39, 1729–1735. [Google Scholar] [CrossRef]
- Dorigo, W.; Wagner, W.; Albergel, C.; Albrecht, F.; Balsamo, G.; Brocca, L.; Chung, D.; Ertl, M.; Forkel, M.; Gruber, A. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ. 2017, 203, 185–215. [Google Scholar] [CrossRef]
- Rodell, M.; Houser, P.; Jambor, U.; Gottschalck, J.; Mitchell, K.; Meng, C.-J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.; Bosilovich, M. The global land data assimilation system. Bull. Am. Meteorol. Soc. 2004, 85, 381–394. [Google Scholar] [CrossRef]
- De Rosnay, P.; Drusch, M.; Vasiljevic, D.; Balsamo, G.; Albergel, C.; Isaksen, L. A simplified extended Kalman filter for the global operational soil moisture analysis at ECMWF. Q. J. R. Meteorol. Soc. 2013, 139, 1199–1213. [Google Scholar] [CrossRef]
- Beck, H.E.; Pan, M.; Miralles, D.G.; Reichle, R.H.; Dorigo, W.A.; Hahn, S.; Sheffield, J.; Karthikeyan, L.; Balsamo, G.; Parinussa, R.M. Evaluation of 18 satellite-and model-based soil moisture products using in situ measurements from 826 sensors. Hydrol. Earth Syst. Sci. 2021, 25, 17–40. [Google Scholar] [CrossRef]
- Chen, Y.; Yang, K.; Qin, J.; Zhao, L.; Tang, W.; Han, M. Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. J. Geophys. Res. Atmos. 2013, 118, 4466–4475. [Google Scholar] [CrossRef]
- Sehgal, V.; Gaur, N.; Mohanty, B.P. Global flash drought monitoring using surface soil moisture. Water Resour. Res. 2021, 57, e2021WR029901. [Google Scholar] [CrossRef]
- Sheffield, J.; Wood, E.F. Characteristics of global and regional drought 1950–2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. J. Geophys. Res. Atmos. 2007, 112. [Google Scholar] [CrossRef]
- Qiu, J.; Crow, W.T.; Nearing, G.S.; Mo, X.; Liu, S. The impact of vertical measurement depth on the information content of soil moisture times series data. Geophys. Res. Lett. 2014, 41, 4997–5004. [Google Scholar] [CrossRef]
- Tebbs, E.; Gerard, F.; Petrie, A.; De Witte, E. Emerging and potential future applications of satellite-based soil moisture products. In Satellite Soil Moisture Retrieval; Elsevier: Amsterdam, The Netherlands, 2016; pp. 379–400. [Google Scholar]
- Wu, Z.; Qiu, J.; Liu, S. Advances in agricultural drought monitoring based on soil moisture. Prog. Geogr. 2020, 39, 1758–1769. [Google Scholar] [CrossRef]
- Zhou, H.; Wu, J.; Li, X.; Liu, L.; Yang, J.; Han, X. Suitability of assimilated data-based standardized soil moisture index for agricultural drought monitoring. Acta Ecol. Sin 2019, 39, 2191–2202. [Google Scholar]
- Champagne, C.; McNairn, H.; Berg, A. Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing. Remote Sens. Environ. 2011, 115, 2434–2444. [Google Scholar] [CrossRef]
- Sheffield, J.; Goteti, G.; Wen, F.; Wood, E.F. A simulated soil moisture based drought analysis for the United States. J. Geophys. Res. Atmos. 2004, 109, 1–19. [Google Scholar] [CrossRef]
- Narasimhan, B.; Srinivasan, R. Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agric. For. Meteorol. 2005, 133, 69–88. [Google Scholar] [CrossRef]
- Zeri, M.; Williams, K.; Cunha, A.P.M.; Cunha-Zeri, G.; Vianna, M.S.; Blyth, E.M.; Marthews, T.R.; Hayman, G.D.; Costa, J.M.; Marengo, J.A. Importance of including soil moisture in drought monitoring over the Brazilian semiarid region: An evaluation using the JULES model, in situ observations, and remote sensing. Clim. Resil. Sustain. 2022, 1, e7. [Google Scholar] [CrossRef]
- Wambua, R.M. Spatio-temporal characterization of agricultural drought using soil moisture deficit index (SMDI) in the upper Tana River basin, Kenya. Int. J. Eng. Res. Adv. Technol. 2019, 5, 93–106. [Google Scholar] [CrossRef]
- Zhang, G.; Su, X.; Ayantobo, O.O.; Feng, K. Drought monitoring and evaluation using ESA CCI and GLDAS-Noah soil moisture datasets across China. Theor. Appl. Climatol. 2021, 144, 1407–1418. [Google Scholar] [CrossRef]
- Torres, G.M.; Lollato, R.P.; Ochsner, T.E. Comparison of drought probability assessments based on atmospheric water deficit and soil water deficit. Agron. J. 2013, 105, 428–436. [Google Scholar] [CrossRef]
- Kędzior, M.; Zawadzki, J. SMOS data as a source of the agricultural drought information: Case study of the Vistula catchment, Poland. Geoderma 2017, 306, 167–182. [Google Scholar] [CrossRef]
- Bai, J.; Cui, Q.; Chen, D.; Yu, H.; Mao, X.; Meng, L.; Cai, Y. Assessment of the SMAP-derived soil water deficit index (SWDI-SMAP) as an agricultural drought index in China. Remote Sens. 2018, 10, 1302. [Google Scholar] [CrossRef]
- Martínez-Fernández, J.; González-Zamora, A.; Sánchez, N.; Gumuzzio, A. A soil water based index as a suitable agricultural drought indicator. J. Hydrol. 2015, 522, 265–273. [Google Scholar] [CrossRef]
- Mishra, A.; Vu, T.; Veettil, A.V.; Entekhabi, D. Drought monitoring with soil moisture active passive (SMAP) measurements. J. Hydrol. 2017, 552, 620–632. [Google Scholar] [CrossRef]
- Park, S.; Im, J.; Park, S.; Rhee, J. Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agric. For. Meteorol. 2017, 237, 257–269. [Google Scholar] [CrossRef]
- Zhu, Q.; Luo, Y.; Xu, Y.-P.; Tian, Y.; Yang, T. Satellite soil moisture for agricultural drought monitoring: Assessment of SMAP-derived soil water deficit index in Xiang River Basin, China. Remote Sens. 2019, 11, 362. [Google Scholar] [CrossRef]
- Mullapudi, A.; Vibhute, A.D.; Mali, S.; Patil, C.H. A review of agricultural drought assessment with remote sensing data: Methods, issues, challenges and opportunities. Appl. Geomat. 2023, 15, 1–13. [Google Scholar] [CrossRef]
- Peng, J.; Loew, A.; Merlin, O.; Verhoest, N.E. A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys. 2017, 55, 341–366. [Google Scholar] [CrossRef]
- Peng, J.; Albergel, C.; Balenzano, A.; Brocca, L.; Cartus, O.; Cosh, M.H.; Crow, W.T.; Dabrowska-Zielinska, K.; Dadson, S.; Davidson, M.W. A roadmap for high-resolution satellite soil moisture applications–confronting product characteristics with user requirements. Remote Sens. Environ. 2021, 252, 112162. [Google Scholar] [CrossRef]
- Fang, B.; Kansara, P.; Dandridge, C.; Lakshmi, V. Drought monitoring using high spatial resolution soil moisture data over Australia in 2015–2019. J. Hydrol. 2021, 594, 125960. [Google Scholar] [CrossRef]
- Ganguli, P.; Raut, A.; Purushothaman, N.K.; Reddy, N.N.; Das, B.S. Climate-Catchment-Soil-and Land Use and Land Cover Controls on Streamflow Droughts in Tropical Catchments of Peninsular India. In Proceedings of the AGU Fall Meeting Abstracts, Chicago, IL, USA, 12–16 December 2022; p. H32E-06. [Google Scholar]
- Colliander, A.; Fisher, J.B.; Halverson, G.; Merlin, O.; Misra, S.; Bindlish, R.; Jackson, T.J.; Yueh, S. Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15. IEEE Geosci. Remote Sens. Lett. 2017, 14, 2107–2111. [Google Scholar] [CrossRef]
- Tomer, S.K.; Al Bitar, A.; Sekhar, M.; Zribi, M.; Bandyopadhyay, S.; Kerr, Y. MAPSM: A spatio-temporal algorithm for merging soil moisture from active and passive microwave remote sensing. Remote Sens. 2016, 8, 990. [Google Scholar] [CrossRef]
- Guevara, M.; Vargas, R. Downscaling satellite soil moisture using geomorphometry and machine learning. PLoS ONE 2019, 14, e0219639. [Google Scholar] [CrossRef]
- Sahoo, A.K.; De Lannoy, G.J.; Reichle, R.H.; Houser, P.R. Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA. Adv. Water Resour. 2013, 52, 19–33. [Google Scholar] [CrossRef]
- Zhang, Y.; Liang, S.; Zhu, Z.; Ma, H.; He, T. Soil moisture content retrieval from Landsat 8 data using ensemble learning. ISPRS J. Photogramm. Remote Sens. 2022, 185, 32–47. [Google Scholar] [CrossRef]
- Abowarda, A.S.; Bai, L.; Zhang, C.; Long, D.; Li, X.; Huang, Q.; Sun, Z. Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale. Remote Sens. Environ. 2021, 255, 112301. [Google Scholar] [CrossRef]
- Dorigo, W.; Van Oevelen, P.; Wagner, W.; Drusch, M.; Mecklenburg, S.; Robock, A.; Jackson, T. A new international network for in situ soil moisture data. Eos Trans. Am. Geophys. Union 2011, 92, 141–142. [Google Scholar] [CrossRef]
- Dorigo, W.; Wagner, W.; Hohensinn, R.; Hahn, S.; Paulik, C.; Xaver, A.; Gruber, A.; Drusch, M.; Mecklenburg, S.; van Oevelen, P. The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci. 2011, 15, 1675–1698. [Google Scholar] [CrossRef]
- Hengl, T.; Mendes de Jesus, J.; Heuvelink, G.B.; Ruiperez Gonzalez, M.; Kilibarda, M.; Blagotić, A.; Shangguan, W.; Wright, M.N.; Geng, X.; Bauer-Marschallinger, B. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 2017, 12, e0169748. [Google Scholar] [CrossRef]
- Poggio, L.; De Sousa, L.M.; Batjes, N.H.; Heuvelink, G.; Kempen, B.; Ribeiro, E.; Rossiter, D. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil 2021, 7, 217–240. [Google Scholar] [CrossRef]
- Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
- O’Neill, P.; Entekhabi, D.; Njoku, E.; Kellogg, K. The NASA soil moisture active passive (SMAP) mission: Overview. In Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010; pp. 3236–3239. [Google Scholar]
- O’Neill, P.; Bindlish, R.; Chan, S.; Njoku, E.; Jackson, T. Algorithm Theoretical Basis Document Level 2 & 3 Soil Moisture (Passive) Data Products; California Institute of Technology: Pasadena, CA, USA, 2018. [Google Scholar]
- Kerr, Y.H.; Waldteufel, P.; Wigneron, J.-P.; Delwart, S.; Cabot, F.; Boutin, J.; Escorihuela, M.-J.; Font, J.; Reul, N.; Gruhier, C. The SMOS mission: New tool for monitoring key elements ofthe global water cycle. Proc. IEEE 2010, 98, 666–687. [Google Scholar] [CrossRef]
- Wigneron, J.-P.; Mialon, A.; De Lannoy, G.; Fernández-Morán, R.; Al-Yaari, A.; Ebrahimi, M.; Rodriguez-Fernandez, N.; Kerr, Y.; Quets, J.; Pellarin, T. SMOS-IC: Current status and overview of soil moisture and VOD applications. In Proceedings of the IGARSS 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 1451–1453. [Google Scholar]
- Kumar, S.V.; Jasinski, M.; Mocko, D.M.; Rodell, M.; Borak, J.; Li, B.; Beaudoing, H.K.; Peters-Lidard, C.D. NCA-LDAS land analysis: Development and performance of a multisensor, multivariate land data assimilation system for the National Climate Assessment. J. Hydrometeorol. 2019, 20, 1571–1593. [Google Scholar] [CrossRef]
- Jasinski, M.F.; Borak, J.S.; Kumar, S.V.; Mocko, D.M.; Peters-Lidard, C.D.; Rodell, M.; Rui, H.; Beaudoing, H.K.; Vollmer, B.E.; Arsenault, K.R. NCA-LDAS: Overview and analysis of hydrologic trends for the national climate assessment. J. Hydrometeorol. 2019, 20, 1595–1617. [Google Scholar] [CrossRef] [PubMed]
- Gruber, A.; Scanlon, T.; van der Schalie, R.; Wagner, W.; Dorigo, W. Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology. Earth Syst. Sci. Data 2019, 11, 717–739. [Google Scholar] [CrossRef]
- Martinez-Fernandez, J.; Ceballos, A.; Casado, S.; Moran, C.; Hernandez, V. Runoff and soil moisture relationships in a small forested basin in the Sistema Central ranges (Spain). IAHS Publ. 2005, 299, 31. [Google Scholar]
- Hogg, E.; Barr, A.; Black, T. A simple soil moisture index for representing multi-year drought impacts on aspen productivity in the western Canadian interior. Agric. For. Meteorol. 2013, 178, 173–182. [Google Scholar] [CrossRef]
- Hunt, E.D.; Hubbard, K.G.; Wilhite, D.A.; Arkebauer, T.J.; Dutcher, A.L. The development and evaluation of a soil moisture index. Int. J. Climatol. A J. R. Meteorol. Soc. 2009, 29, 747–759. [Google Scholar] [CrossRef]
- Bouma, J. Transfer functions and threshold values: From soil characteristics to land qualities. In Quantified Land Evaluation; Proc. ISSS/SSSA Workshop; ITC Publication: Washington, DC, USA, 1987. [Google Scholar]
- Wösten, J.; Pachepsky, Y.A.; Rawls, W. Pedotransfer functions: Bridging the gap between available basic soil data and missing soil hydraulic characteristics. J. Hydrol. 2001, 251, 123–150. [Google Scholar] [CrossRef]
- Saxton, K.E.; Rawls, W.J. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 2006, 70, 1569–1578. [Google Scholar] [CrossRef]
- Kogan, F.N. Global drought watch from space. Bull. Am. Meteorol. Soc. 1997, 78, 621–636. [Google Scholar] [CrossRef]
- Kogan, F. Early drought detection, monitoring, and assessment of crop losses from space: Global approach. In Disaster Forewarning Diagnostic Methods and Management; SPIE: Cergy-Pontoise, France, 2006; pp. 56–60. [Google Scholar]
- Kogan, F.N. Operational space technology for global vegetation assessment. Bull. Am. Meteorol. Soc. 2001, 82, 1949–1964. [Google Scholar] [CrossRef]
- Gupta, H.V.; Kling, H.; Yilmaz, K.K.; Martinez, G.F. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol. 2009, 377, 80–91. [Google Scholar] [CrossRef]
- Savage, M.J.; Ritchie, J.T.; Bland, W.L.; Dugas, W.A. Lower limit of soil water availability. Agron. J. 1996, 88, 644–651. [Google Scholar] [CrossRef]
- Kogan, F. World droughts in the new millennium from AVHRR-based vegetation health indices. Eos Trans. Am. Geophys. Union 2002, 83, 557–563. [Google Scholar] [CrossRef]
- Gleick, P.H. Water in Crisis: A Guide to the World’s Fresh Water Resources; Oxford University Press: Oxford, UK, 1993; pp. 1051–1761. [Google Scholar]
- Robinson, D.A.; Campbell, C.S.; Hopmans, J.W.; Hornbuckle, B.K.; Jones, S.B.; Knight, R.; Ogden, F.; Selker, J.; Wendroth, O. Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review. Vadose Zone J. 2008, 7, 358–389. [Google Scholar] [CrossRef]
- Seneviratne, S.I.; Corti, T.; Davin, E.L.; Hirschi, M.; Jaeger, E.B.; Lehner, I.; Orlowsky, B.; Teuling, A.J. Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Sci. Rev. 2010, 99, 125–161. [Google Scholar] [CrossRef]
- Purdy, A.J.; Fisher, J.B.; Goulden, M.L.; Colliander, A.; Halverson, G.; Tu, K.; Famiglietti, J.S. SMAP soil moisture improves global evapotranspiration. Remote Sens. Environ. 2018, 219, 1–14. [Google Scholar] [CrossRef]
- Sadeghi, M.; Ebtehaj, A.; Crow, W.T.; Gao, L.; Purdy, A.J.; Fisher, J.B.; Jones, S.B.; Babaeian, E.; Tuller, M. Global estimates of land surface water fluxes from SMOS and SMAP satellite soil moisture data. J. Hydrometeorol. 2020, 21, 241–253. [Google Scholar] [CrossRef]
- Yao, Y.; Zhang, Y.; Liu, Q.; Liu, S.; Jia, K.; Zhang, X.; Xu, Z.; Xu, T.; Chen, J.; Fisher, J.B. Evaluation of a satellite-derived model parameterized by three soil moisture constraints to estimate terrestrial latent heat flux in the Heihe River basin of Northwest China. Sci. Total Environ. 2019, 695, 133787. [Google Scholar] [CrossRef]
- Galbally, I.E.; Kirstine, W.V.; Meyer, C.; Wang, Y.P. Soil–atmosphere trace gas exchange in semiarid and arid zones. J. Environ. Qual. 2008, 37, 599–607. [Google Scholar] [CrossRef]
- Kolus, H.R.; Huntzinger, D.N.; Schwalm, C.R.; Fisher, J.B.; McKay, N.; Fang, Y.; Michalak, A.M.; Schaefer, K.; Wei, Y.; Poulter, B. Land carbon models underestimate the severity and duration of drought’s impact on plant productivity. Sci. Rep. 2019, 9, 2758. [Google Scholar] [CrossRef]
- Miller, D.L.; Wolf, S.; Fisher, J.B.; Zaitchik, B.F.; Xiao, J.; Keenan, T.F. Increased photosynthesis during spring drought in energy-limited ecosystems. Nat. Commun. 2023, 14, 7828. [Google Scholar] [CrossRef]
- Qiu, B.; Xue, Y.; Fisher, J.B.; Guo, W.; Berry, J.A.; Zhang, Y. Satellite chlorophyll fluorescence and soil moisture observations lead to advances in the predictive understanding of global terrestrial coupled carbon-water cycles. Glob. Biogeochem. Cycles 2018, 32, 360–375. [Google Scholar] [CrossRef]
- Sehgal, V.; Sridhar, V.; Tyagi, A. Stratified drought analysis using a stochastic ensemble of simulated and in-situ soil moisture observations. J. Hydrol. 2017, 545, 226–250. [Google Scholar] [CrossRef]
- Baldocchi, D.D.; Keeney, N.; Rey-Sanchez, C.; Fisher, J.B. Atmospheric humidity deficits tell us how soil moisture deficits down-regulate ecosystem evaporation. Adv. Water Resour. 2022, 159, 104100. [Google Scholar] [CrossRef]
- Pollacco, J.A.P.; Mohanty, B.P. Uncertainties of Water Fluxes in Soil-Vegetation-Atmosphere Transfer Models: Inverting Surface Soil Moisture and Evapotranspiration Retrieved from Remote Sensing. Vadose Zone J. 2012, 11, 1–21. [Google Scholar] [CrossRef]
- Sehgal, V.; Sridhar, V. Watershed-scale retrospective drought analysis and seasonal forecasting using multi-layer, high-resolution simulated soil moisture for Southeastern US. Weather Clim. Extrem. 2019, 23, 100191. [Google Scholar] [CrossRef]
- Velpuri, N.M.; Senay, G.B.; Morisette, J.T. Evaluating new SMAP soil moisture for drought monitoring in the rangelands of the US high plains. Rangelands 2016, 38, 183–190. [Google Scholar] [CrossRef]
- Alessi, M.J.; Herrera, D.A.; Evans, C.P.; DeGaetano, A.T.; Ault, T.R. Soil Moisture Conditions Determine Land-Atmosphere Coupling and Drought Risk in the Northeastern United States. J. Geophys. Res. Atmos. 2022, 127, e2021JD034740. [Google Scholar] [CrossRef]
- Zhou, S.; Williams, A.P.; Berg, A.M.; Cook, B.I.; Zhang, Y.; Hagemann, S.; Lorenz, R.; Seneviratne, S.I.; Gentine, P. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl. Acad. Sci. USA 2019, 116, 18848–18853. [Google Scholar] [CrossRef]
- McColl, K.A.; Alemohammad, S.H.; Akbar, R.; Konings, A.G.; Yueh, S.; Entekhabi, D. The global distribution and dynamics of surface soil moisture. Nat. Geosci. 2017, 10, 100–104. [Google Scholar] [CrossRef]
- Qiu, S.; Williams, I.N. Observational evidence of state-dependent positive and negative land surface feedback on afternoon deep convection over the Southern Great Plains. Geophys. Res. Lett. 2020, 47, e2019GL086622. [Google Scholar] [CrossRef]
- Tuttle, S.; Salvucci, G. Empirical evidence of contrasting soil moisture–precipitation feedbacks across the United States. Science 2016, 352, 825–828. [Google Scholar] [CrossRef] [PubMed]
- Baik, J.; Zohaib, M.; Kim, U.; Aadil, M.; Choi, M. Agricultural drought assessment based on multiple soil moisture products. J. Arid Environ. 2019, 167, 43–55. [Google Scholar] [CrossRef]
- Cui, C.; Xu, J.; Zeng, J.; Chen, K.-S.; Bai, X.; Lu, H.; Chen, Q.; Zhao, T. Soil moisture mapping from satellites: An intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales. Remote Sens. 2017, 10, 33. [Google Scholar] [CrossRef]
- Ma, H.; Zeng, J.; Chen, N.; Zhang, X.; Cosh, M.H.; Wang, W. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations. Remote Sens. Environ. 2019, 231, 111215. [Google Scholar] [CrossRef]
- Yue, J.; Tian, J.; Tian, Q.; Xu, K.; Xu, N. Development of soil moisture indices from differences in water absorption between shortwave-infrared bands. ISPRS J. Photogramm. Remote Sens. 2019, 154, 216–230. [Google Scholar] [CrossRef]
- Chaney, N.W.; Roundy, J.K.; Herrera-Estrada, J.E.; Wood, E.F. High-resolution modeling of the spatial heterogeneity of soil moisture: Applications in network design. Water Resour. Res. 2015, 51, 619–638. [Google Scholar] [CrossRef]
- Baroni, G.; Ortuani, B.; Facchi, A.; Gandolfi, C. The role of vegetation and soil properties on the spatio-temporal variability of the surface soil moisture in a maize-cropped field. J. Hydrol. 2013, 489, 148–159. [Google Scholar] [CrossRef]
- Entekhabi, D.; Rodriguez-Iturbe, I.; Castelli, F. Mutual interaction of soil moisture state and atmospheric processes. J. Hydrol. 1996, 184, 3–17. [Google Scholar] [CrossRef]
- Cai, Y.; Zheng, W.; Zhang, X.; Zhangzhong, L.; Xue, X. Research on soil moisture prediction model based on deep learning. PLoS ONE 2019, 14, e0214508. [Google Scholar] [CrossRef]
- Al-Yaari, A.; Wigneron, J.-P.; Dorigo, W.; Colliander, A.; Pellarin, T.; Hahn, S.; Mialon, A.; Richaume, P.; Fernandez-Moran, R.; Fan, L. Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements. Remote Sens. Environ. 2019, 224, 289–303. [Google Scholar] [CrossRef]
- Patzold, S.; Mertens, F.M.; Bornemann, L.; Koleczek, B.; Franke, J.; Feilhauer, H.; Welp, G. Soil heterogeneity at the field scale: A challenge for precision crop protection. Precis. Agric. 2008, 9, 367–390. [Google Scholar] [CrossRef]
- Cassel, D.; Nielsen, D. Field capacity and available water capacity. In Methods of Soil Analysis: Part 1 Physical and Mineralogical Methods; American Society of Agronomy, Inc.: Madison, WI, USA; Soil Science Society of America, Inc.: Madison, WI, USA, 1986; Volume 5, pp. 901–926. [Google Scholar]
- Kogan, F. Vegetation health method. In Remote Sensing for Food Security; Springer: Berlin/Heidelberg, Germany, 2019; pp. 51–73. [Google Scholar]
- Mu, L.L.; Wu, B.F.; Yan, N.N. Validation of Agricultural Drought Indices and Their Uncertainty Analysis. Bull. Soil Water Conserv. 2007, 27, 119–122. [Google Scholar]
- Lakhankar, T.; Krakauer, N.; Khanbilvardi, R. Applications of microwave remote sensing of soil moisture for agricultural applications. Int. J. Terraspace Sci. Eng. 2009, 2, 81–91. [Google Scholar]
- Colliander, A.; Cosh, M.H.; Kelly, V.R.; Kraatz, S.; Bourgeau-Chavez, L.; Siqueira, P.; Roy, A.; Konings, A.G.; Holtzman, N.; Misra, S. SMAP detects soil moisture under temperate forest canopies. Geophys. Res. Lett. 2020, 47, e2020GL089697. [Google Scholar] [CrossRef]
- Ning, J.; Yao, Y.; Tang, Q.; Li, Y.; Fisher, J.B.; Zhang, X.; Jia, K.; Xu, J.; Shang, K.; Yang, J. Soil moisture at 30 m from multiple satellite datasets fused by random forest. J. Hydrol. 2023, 625, 130010. [Google Scholar] [CrossRef]
- Qin, Q.; Wu, Z.; Zhang, T.; Sagan, V.; Zhang, Z.; Zhang, Y.; Zhang, C.; Ren, H.; Sun, Y.; Xu, W. Optical and thermal remote sensing for monitoring agricultural drought. Remote Sens. 2021, 13, 5092. [Google Scholar] [CrossRef]
- Karnieli, A.; Agam, N.; Pinker, R.T.; Anderson, M.; Imhoff, M.L.; Gutman, G.G.; Panov, N.; Goldberg, A. Use of NDVI and land surface temperature for drought assessment: Merits and limitations. J. Clim. 2010, 23, 618–633. [Google Scholar] [CrossRef]
- Sun, H.; Zhou, B.; Liu, H. Spatial evaluation of soil moisture (SM), land surface temperature (LST), and LST-derived SM indexes dynamics during SMAPVEX12. Sensors 2019, 19, 1247. [Google Scholar] [CrossRef]
- Tian, J.; Lu, H.; Yang, K.; Qin, J.; Zhao, L.; Jiang, Y.; Shi, P.; Ma, X.; Zhou, J. Improving Surface Soil Moisture Estimation through Assimilating Satellite Land Surface Temperature with a Linear SM-LST Relationship. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023, 16, 7777–7790. [Google Scholar] [CrossRef]
- Wei, W.; Pang, S.; Wang, X.; Zhou, L.; Xie, B.; Zhou, J.; Li, C. Temperature vegetation precipitation dryness index (TVPDI)-based dryness-wetness monitoring in China. Remote Sens. Environ. 2020, 248, 111957. [Google Scholar] [CrossRef]
- Sivakumar, M.; Stone, R.; Sentelhas, P.C.; Svoboda, M.; Omondi, P.; Sarkar, J.; Wardlow, B. Agricultural drought indices: Summary and recommendations. In Proceedings of the An Expert Meeting, Murcia, Spain, 2–4 June 2010; Agricultural Drought Indices. World Meteorological Organization: Geneva, Switzerland, 2011; pp. 2–4. [Google Scholar]
- Svoboda, M.; LeComte, D.; Hayes, M.; Heim, R.; Gleason, K.; Angel, J.; Rippey, B.; Tinker, R.; Palecki, M.; Stooksbury, D. The drought monitor. Bull. Am. Meteorol. Soc. 2002, 83, 1181–1190. [Google Scholar] [CrossRef]
- Abatzoglou, J.T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 2013, 33, 121–131. [Google Scholar] [CrossRef]
- Purdy, A.J.; Kawata, J.; Fisher, J.B.; Reynolds, M.; Om, G.; Ali, Z.; Babikian, J.; Roman, C.; Mann, L. Designing drought indicators. Bull. Am. Meteorol. Soc. 2019, 100, 2327–2341. [Google Scholar] [CrossRef]
- Deitch, M.J.; Sapundjieff, M.J.; Feirer, S.T. Characterizing precipitation variability and trends in the world’s Mediterranean-climate areas. Water 2017, 9, 259. [Google Scholar] [CrossRef]
- Lund, J.; Medellin-Azuara, J.; Durand, J.; Stone, K. Lessons from California’s 2012–2016 drought. J. Water Resour. Plan. Manag. 2018, 144, 04018067. [Google Scholar] [CrossRef]
- Abatzoglou, J.T.; McEvoy, D.J.; Redmond, K.T. The west wide drought tracker: Drought monitoring at fine spatial scales. Bull. Am. Meteorol. Soc. 2017, 98, 1815–1820. [Google Scholar] [CrossRef]
- Anderson, M.C.; Zolin, C.A.; Sentelhas, P.C.; Hain, C.R.; Semmens, K.; Yilmaz, M.T.; Gao, F.; Otkin, J.A.; Tetrault, R. The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sens. Environ. 2016, 174, 82–99. [Google Scholar] [CrossRef]
- Duncan, L.L.; Perrone, D.; Jacobi, J.H.; Hornberger, G.M. Drought planning and management: Using high spatial resolution as part of the solution. Environ. Sci. Technol. 2015, 49, 2639–2647. [Google Scholar] [CrossRef]
- Long, D.; Bai, L.; Yan, L.; Zhang, C.; Yang, W.; Lei, H.; Quan, J.; Meng, X.; Shi, C. Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution. Remote Sens. Environ. 2019, 233, 111364. [Google Scholar] [CrossRef]
- Xiao, Z.; Jiang, L.; Zhu, Z.; Wang, J.; Du, J. Spatially and temporally complete satellite soil moisture data based on a data assimilation method. Remote Sens. 2016, 8, 49. [Google Scholar] [CrossRef]
- Mardian, J. The role of spatial scale in drought monitoring and early warning systems: A review. Environ. Rev. 2002, 30, 438–459. [Google Scholar] [CrossRef]
- Sun, L.; Mitchell, S.W.; Davidson, A. Multiple drought indices for agricultural drought risk assessment on the Canadian prairies. Int. J. Climatol. 2012, 32, 1628–1639. [Google Scholar] [CrossRef]
- Hazaymeh, K.; Hassan, Q. A remote sensing-based agricultural drought indicator and its implementation over a semi-arid region, Jordan. J. Arid Land 2017, 9, 319–330. [Google Scholar] [CrossRef]
Datasets | Details | Description | Variables | Spatial Resolution | Temporal Resolution |
---|---|---|---|---|---|
In situ dataset | ISMN | In situ SM | SM | Point | Hourly |
Optical remote sensing dataset | Landsat 8 surface reflectance | Red band | SR_b4 | 30 m | 16-day |
NIR band | SR_b5 | ||||
SWIR band | SR_b6 | ||||
SWIR band | SR_b7 | ||||
ST band | ST_b10 | ||||
Soil properties dataset | SoilGrids V.2.0 | Clay content | Clay | 250 m | Static |
Sand content | Sand | ||||
Silt content | Silt | ||||
Meteorological dataset | ERA 5 | Relative humidity | RH | 0.25° | 3 h |
Atmospheric temperature | T2m | ||||
Wind | Wind | ||||
Precipitation | Precipitation | ||||
Soil moisture products | SMAP | Satellite-derived | SM | 36 km | Daily |
SMOS-IC | Satellite-derived | SM | 25 km | Daily | |
NCA-LDAS | Model-derived | SM | 0.125° | Daily | |
ESA CCI | Satellite-derived | SM | 0.25° | Daily |
SWDI Value | VHI Value | Drought Severity |
---|---|---|
≤−10 | 0 to 10 | Extreme drought |
−10 to −5 | 10 to 20 | Severe drought |
−5 to −2 | 20 to 40 | Moderate drought |
−2 to 0 | 40 to 60 | Mild drought |
≥0 | 60 to 100 | No drought |
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Ning, J.; Yao, Y.; Fisher, J.B.; Li, Y.; Zhang, X.; Jiang, B.; Xu, J.; Yu, R.; Liu, L.; Zhang, X.; et al. Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring. Remote Sens. 2024, 16, 3372. https://doi.org/10.3390/rs16183372
Ning J, Yao Y, Fisher JB, Li Y, Zhang X, Jiang B, Xu J, Yu R, Liu L, Zhang X, et al. Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring. Remote Sensing. 2024; 16(18):3372. https://doi.org/10.3390/rs16183372
Chicago/Turabian StyleNing, Jing, Yunjun Yao, Joshua B. Fisher, Yufu Li, Xiaotong Zhang, Bo Jiang, Jia Xu, Ruiyang Yu, Lu Liu, Xueyi Zhang, and et al. 2024. "Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring" Remote Sensing 16, no. 18: 3372. https://doi.org/10.3390/rs16183372